CN117308925B - Navigation method, device, equipment and medium for spectral map inertial navigation combination - Google Patents
Navigation method, device, equipment and medium for spectral map inertial navigation combination Download PDFInfo
- Publication number
- CN117308925B CN117308925B CN202311610655.6A CN202311610655A CN117308925B CN 117308925 B CN117308925 B CN 117308925B CN 202311610655 A CN202311610655 A CN 202311610655A CN 117308925 B CN117308925 B CN 117308925B
- Authority
- CN
- China
- Prior art keywords
- particle
- value
- inertial navigation
- mobile carrier
- speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 230000003595 spectral effect Effects 0.000 title claims description 10
- 239000002245 particle Substances 0.000 claims abstract description 191
- 238000001228 spectrum Methods 0.000 claims abstract description 100
- 238000001914 filtration Methods 0.000 claims abstract description 78
- 238000005259 measurement Methods 0.000 claims abstract description 70
- 230000005855 radiation Effects 0.000 claims abstract description 50
- 238000012937 correction Methods 0.000 claims abstract description 39
- 239000013598 vector Substances 0.000 claims description 76
- 239000011159 matrix material Substances 0.000 claims description 21
- 238000006073 displacement reaction Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 14
- 238000009825 accumulation Methods 0.000 claims description 13
- 238000012544 monitoring process Methods 0.000 claims description 13
- 238000010276 construction Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 3
- 238000012952 Resampling Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 5
- 230000004927 fusion Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0252—Radio frequency fingerprinting
- G01S5/02521—Radio frequency fingerprinting using a radio-map
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0257—Hybrid positioning
- G01S5/0268—Hybrid positioning by deriving positions from different combinations of signals or of estimated positions in a single positioning system
- G01S5/02685—Hybrid positioning by deriving positions from different combinations of signals or of estimated positions in a single positioning system involving dead reckoning based on radio wave measurements
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Navigation (AREA)
Abstract
The application relates to a navigation method, a device, equipment and a medium for a spectrum map inertial navigation combination. The method comprises the following steps: the method comprises the steps of constructing a radio spectrum scene through a spectrum map, scattering particles in the scene, combining directly acquired radiation field intensity measurement values, obtaining a mobile carrier position estimation value and a mobile carrier speed estimation value through a particle filtering algorithm, calculating an error state according to inertial navigation measurement data through a Kalman filtering algorithm, performing closed-loop correction on errors of inertial navigation position information and speed information, and performing closed-loop correction on the spectrum map according to the mobile carrier position estimation value and the radiation field intensity measurement values. The invention realizes the estimation of the position and the speed by fusing the radiation field intensity measurement value, the inertial navigation measurement value and the frequency spectrum map, solves the problems of space ambiguity and frequency spectrum map error caused by the radiation field intensity measurement value, and improves the frequency spectrum map precision in surrounding scenes.
Description
Technical Field
The present application relates to the navigation field, the Radio-SLAM navigation field, and the inertial navigation (IMU) field in an unknown environment, and in particular, to a navigation method, apparatus, computer device, and storage medium for a spectral map inertial navigation combination.
Background
In the conventional Radio navigation method, navigation methods based on directly measured Radio signal strength RSS (Received signal strength) values and position tags can navigate based on a spectrum map (Radio map), such as an approximation matching method and fingerprint matching. If the spectrum map is applied to Radio real-time positioning and map construction (Radio-SLAM), the user can acquire surrounding spectrum scenes according to the real-time position and the spectrum map, and navigation and motion state estimation can be performed through the RSS information measured by the user and the RSS information and position tags contained in the spectrum scenes. This has the advantage that the influence of the difficult estimation of the radiation source position can be reduced. The spectrum map may be generated by a typical architecture consisting of a mobile user with a data fusion unit and receiver and a plurality of monitoring stations configured with the receiver, the data fusion unit may generate a Radio spectrum map (Radio map) using RSS data collected by the monitoring stations. However, due to the influence of a computing mode and environment, the problem that the error of the spectrum map generated by the framework is large and the space construction is insufficient still exists, and the problem of space ambiguity is caused by the RSS value influenced by the environment and multipath. Spatial blurring means that the RSS difference between two spatially distinct points may not be significant, resulting in matching difficulties. Therefore, the prior art has the problem of poor effect.
Disclosure of Invention
In view of the foregoing, there is a need for a navigation method, apparatus, computer device, and storage medium that can effectively implement a spectrum map/inertial navigation combination of Radio-SLAM navigation based on opportunistic Radio signals in an unknown environment.
A navigation method of a spectral map inertial navigation combination, the method comprising:
acquiring inertial navigation measurement data, radiation field strength measurement values and a frequency spectrum map constructed by a monitoring station of a mobile carrier, and constructing a radio spectrum scene around the mobile carrier; the inertial navigation measurement data comprises inertial navigation position information, speed information and attitude information;
generating a particle swarm in the radio spectrum scene, and obtaining a particle field intensity predicted value; the particle states in the particle swarm further comprise a position state and an observed noise variance;
obtaining observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, updating a particle weight according to the observed quantity, and obtaining a filtered particle position state vector and a weight thereof through a particle filtering algorithm;
obtaining a mobile carrier position estimated value according to the filtered particle position state vector and the weight thereof, and obtaining a mobile carrier speed estimated value according to the mobile carrier position estimated value through time accumulation;
calculating an error state through a Kalman filtering algorithm according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value, and performing closed-loop correction on errors of the inertial navigation position information and the speed information according to the error state;
and carrying out closed-loop correction on the frequency spectrum map according to the position estimated value of the mobile carrier and the radiation field intensity measured value.
In one embodiment, the method further comprises: obtaining an observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, wherein the observed quantity is as follows:
；
wherein,for the radiation field intensity measurement, +.>For the predicted value of the field strength of the particle, +.>For generating a number of particles in said radio spectrum scene, < > or->Representing the current time;
updating the particle weight according to the observed quantity as follows:
；
wherein,is particle group radius>For measuring the variance matrix of the noise +.>Representing an exponential function>Representing the matrix transpose.
In one embodiment, the method further comprises: and (3) screening particles according to the particle weight, and resampling by changing a particle distribution space if the screened effective particle number is smaller than a preset threshold value until the effective particle number is larger than the threshold value, and determining a particle position state vector and the weight thereof after particle filtration.
In one embodiment, the method further comprises: and obtaining a mobile carrier position estimation value according to the filtered particle position state vector and the weight thereof, wherein the mobile carrier position estimation value is as follows:
；
wherein,for the number of particles after particle filtration, +.>Is the position information of the filtered particles.
In one embodiment, the method further comprises: determining a moving load in a speed estimation stage by accumulating time and continuously and effectively filtering at intervals of 1s for 10 timesThe displacement distance of the effective filtering of the body isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein the vector comprises->Components in three directions, subscript ++>The value of the north-east coordinate system of the plane is only taken in the north-east coordinate system, the earth direction is ignored, and the subscript is +.>Representing the effective filtering moment +.>Representing the actual time;
in the speed estimation stage, the displacement increment obtained by the actual speed of the moving carrier is obtainedThe method comprises the steps of carrying out a first treatment on the surface of the Wherein->，/>Representing the speed of inertial navigation solution;
estimating the speed of the moving carrier according to the displacement distance of the effective filtering of the moving carrier and the displacement increment obtained by the actual speed of the moving carrier, and constraining the speed direction of the moving carrier by using a yaw angle:
；
；
wherein the subscriptRepresenting the estimated value before particle filtration, subscript +.>Representing the new estimate after the particle filtering,representing the yaw angle.
In one embodiment, the method further comprises: selecting an error state vector; the error state elements in the error state vector include: a ground position error vector, a ground speed error vector, a misalignment angle vector, a gyro zero offset and a plus-meter zero offset; the opposite position error vector is obtained by differencing inertial navigation position information in the inertial navigation measurement data and the position estimated value of the mobile carrier; the earth velocity error vector is obtained by differencing inertial navigation velocity information in the inertial navigation measurement data and the velocity estimated value of the mobile carrier;
constructing a differential equation of the error state vector;
constructing a Kalman filtering observation equation;
performing Kalman filtering according to the differential equation of the error state vector and the Kalman filtering observation equation to obtain an optimal estimated value of the error state vector;
and carrying out closed loop correction on the errors of the inertial navigation position information and the speed information according to the optimal estimated value of the error state vector.
In one embodiment, the method further comprises: the Kalman filtering observed quantity is selected as follows:
the subscript IMU represents a state value of inertial navigation output, the subscript PF represents a state value of particle filter output, and an observation equation is established:
；
wherein,for the error state vector,/or->For observing the noise matrix>In order to observe the matrix,。
a navigation device of a spectral map inertial navigation assembly, the device comprising:
the radio spectrum scene construction module is used for acquiring inertial navigation measurement data, radiation field strength measurement values of the mobile carrier, and a spectrum map constructed by the monitoring station, and constructing a radio spectrum scene around the mobile carrier; the inertial navigation measurement data comprises inertial navigation position information, speed information and attitude information;
the particle group generation module is used for generating a particle group in the radio spectrum scene and obtaining a particle field intensity predicted value; the particle states in the particle swarm further comprise a position state and an observed noise variance;
the particle filtering module is used for obtaining observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, updating the particle weight according to the observed quantity, and obtaining a filtered particle position state vector and the weight thereof through a particle filtering algorithm;
the navigation information estimation module is used for obtaining a mobile carrier position estimation value according to the filtered particle position state vector and the weight thereof, and obtaining a mobile carrier speed estimation value according to the mobile carrier position estimation value through time accumulation;
the inertial navigation error closed-loop correction module is used for calculating an error state through a Kalman filtering algorithm according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value, and carrying out closed-loop correction on the errors of the inertial navigation position information and the speed information according to the error state;
and the spectrum map closed-loop correction module is used for carrying out closed-loop correction on the spectrum map according to the mobile carrier position estimation value and the radiation field intensity measurement value.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring inertial navigation measurement data, radiation field strength measurement values and a frequency spectrum map constructed by a monitoring station of a mobile carrier, and constructing a radio spectrum scene around the mobile carrier; the inertial navigation measurement data comprises inertial navigation position information, speed information and attitude information;
generating a particle swarm in the radio spectrum scene, and obtaining a particle field intensity predicted value; the particle states in the particle swarm further comprise a position state and an observed noise variance;
obtaining observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, updating a particle weight according to the observed quantity, and obtaining a filtered particle position state vector and a weight thereof through a particle filtering algorithm;
obtaining a mobile carrier position estimated value according to the filtered particle position state vector and the weight thereof, and obtaining a mobile carrier speed estimated value according to the mobile carrier position estimated value through time accumulation;
calculating an error state through a Kalman filtering algorithm according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value, and performing closed-loop correction on errors of the inertial navigation position information and the speed information according to the error state;
and carrying out closed-loop correction on the frequency spectrum map according to the position estimated value of the mobile carrier and the radiation field intensity measured value.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring inertial navigation measurement data, radiation field strength measurement values and a frequency spectrum map constructed by a monitoring station of a mobile carrier, and constructing a radio spectrum scene around the mobile carrier; the inertial navigation measurement data comprises inertial navigation position information, speed information and attitude information;
generating a particle swarm in the radio spectrum scene, and obtaining a particle field intensity predicted value; the particle states in the particle swarm further comprise a position state and an observed noise variance;
obtaining observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, updating a particle weight according to the observed quantity, and obtaining a filtered particle position state vector and a weight thereof through a particle filtering algorithm;
obtaining a mobile carrier position estimated value according to the filtered particle position state vector and the weight thereof, and obtaining a mobile carrier speed estimated value according to the mobile carrier position estimated value through time accumulation;
calculating an error state through a Kalman filtering algorithm according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value, and performing closed-loop correction on errors of the inertial navigation position information and the speed information according to the error state;
and carrying out closed-loop correction on the frequency spectrum map according to the position estimated value of the mobile carrier and the radiation field intensity measured value.
According to the navigation method, the navigation device, the computer equipment and the storage medium of the spectrum map/inertial navigation combination, a radio spectrum scene is constructed through the spectrum map, particles are scattered in the radio spectrum scene, a mobile carrier position estimated value and a mobile carrier speed estimated value are obtained through a particle filtering algorithm in combination with a directly acquired radiation field intensity measured value, an error state is calculated through a Kalman filtering algorithm according to inertial navigation measurement data, closed-loop correction is carried out on errors of inertial navigation position information and speed information, and closed-loop correction is carried out on the spectrum map according to the mobile carrier position estimated value and the radiation field intensity measured value. The invention realizes the estimation of the position and the speed by fusing the radiation field intensity measurement value, the inertial navigation measurement value and the frequency spectrum map, solves the problems of space ambiguity and frequency spectrum map error caused by the radiation field intensity measurement value, improves the frequency spectrum map precision in surrounding scenes, and provides a new solving way for the navigation problem in unknown environments.
Drawings
FIG. 1 is a flow diagram of a navigation method of a combined inertial navigation of a map of a spectrum in one embodiment;
FIG. 2 is a schematic diagram of particle filtering in one embodiment;
FIG. 3 is a flow chart of a navigation method of the combined inertial navigation of a map of a spectrum in another embodiment;
FIG. 4 is a block diagram of a navigation device of a spectrum map inertial navigation assembly in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a navigation method of a spectrum map inertial navigation combination, including the steps of:
step 102, acquiring inertial navigation measurement data, radiation field intensity measurement values and a frequency spectrum map constructed by a monitoring station of the mobile carrier, and constructing a radio frequency spectrum scene around the mobile carrier.
The invention provides a navigation method combining a frequency spectrum map and an inertial navigation (IMU) system.
The inertial navigation system is an autonomous navigation system which does not depend on external information and radiates energy to the outside, and obtains information such as speed, yaw angle, position and the like in a navigation coordinate system by measuring acceleration of a carrier in an inertial reference system, integrating the acceleration with time, and transforming the acceleration into the navigation coordinate system. The inertial navigation measurement data in this embodiment includes inertial navigation position information, velocity information, and attitude information.
The radiation field strength measurement, i.e. the radio signal strength RSS value, can be measured directly.
The spectrum map may be generated by a typical architecture consisting of a mobile user with a data fusion unit and receiver and a plurality of monitoring stations configured with the receiver, the data fusion unit may generate a radio spectrum map using RSS data collected by the monitoring stations. If the spectrum map is applied to the Radio-SLAM, the surrounding spectrum scene can be obtained according to the real-time position and the spectrum map of the navigation user in the prior art.
Step 104, generating particle swarm in the radio spectrum scene, and obtaining the predicted value of the particle field intensity.
Generating particle swarms in a radio spectrum scene, resulting in a particle state is prior art. The present step spreads the particles in the radio spectrum scene to obtain three state values of the particles,respectively a predicted value of the field intensity of the particles, position information of the particles and a corresponding noise variance of the particles.
And 106, obtaining an observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, updating the particle weight according to the observed quantity, and obtaining a filtered particle position state vector and the weight thereof through a particle filtering algorithm.
The particle filtering algorithm uses broadcast particles (sampling) distributed in a map as a guess of the pose of the mobile carrier, then the radiation field intensity measured value is compared with the surrounding environment of each particle, the higher the similarity is, the higher the confidence (weight) of the particle approaching the real pose of the mobile carrier is, and the process of calculating the particle weight can cause sample degradation, namely the larger the weight variance of the particle swarm is. The method is to screen the particles according to the weight, thus obtaining the particles closest to the real pose of the mobile carrier, and the average pose (i.e. the result obtained by filtering) of the particles is used as an estimation of the positioning of the mobile carrier after screening, so that the particle swarm always converges to the vicinity of the real pose of the mobile carrier.
Fig. 2 is a schematic flow chart of a particle filtering algorithm adopted in the present invention.
First go through the shapeInitializing a state, and then acquiring an inertial navigation state estimation value; generating random particle groups, each particle comprising a position state and a velocity state, generating a matching map from a spectrum map、/>，/>Representing a predicted value of the field strength of the particle>Representing the particle-to-noise variance; combining radiation field intensity measurement data->Calculate->、/>、/>，/>Indicate the observed quantity +.>Representing particle weight, +.>A variance matrix representing measurement noise; if the weight is degraded, the effective particle number is smaller than the set threshold, i.eThe particle filter algorithm is resampled by changing the particle distribution space until the effective particle number is larger than the set threshold, i.e +.>Giving a new particle position state vector and a weight thereof>For performing a state estimation of the moving carrier, the state estimation comprising a position estimation and a velocity estimation.
Step 108, obtaining a mobile carrier position estimated value according to the filtered particle position state vector and the weight thereof, and obtaining a mobile carrier speed estimated value according to the mobile carrier position estimated value through time accumulation.
The position estimated value and the speed estimated value of the mobile carrier are the results which are required to be output by the integrated navigation, and in addition, the posture information output by the integrated navigation is still the posture value output by the inertial navigation system.
Step 110, calculating an error state through a Kalman filtering algorithm according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value, and performing closed-loop correction on errors of the inertial navigation position information and the speed information according to the error state.
The invention creatively selects 15-dimensional error state vectors based on a field map/inertia/magnetometer integrated navigation system as follows:
；
the meaning of each error status element is as follows:
-to the position error vector, i.e. the difference between the predicted position vector value and the true position vector value is +.>Projection in the system is respectively north position errorm) Error of eastern positionm) Error of the position of the earthm) The method comprises the steps of carrying out a first treatment on the surface of the The invention uses the estimated value of the position of the obtained mobile carrier as the predicted value, the inertial navigation position information as the true value, and the opposite position error vector is obtained by the difference between the two.
-a ground speed error vector, i.e. the difference between the predicted value and the true value of the ground speed vector is +.>Projection in the system (North east)m/s) The method comprises the steps of carrying out a first treatment on the surface of the The invention takes the estimated value of the speed of the mobile carrier as the predicted value, takes the inertial navigation speed information as the true value, and obtains the error vector of the earth speed according to the difference between the estimated value and the true value.
Misalignment angle vector (true local north east coordinate system +.>Rotation->After the angle, the local north east coordinate system, which becomes misaligned +.>) The elements are->、/>、/>（rad）；
He Huo Zao (zero offset of Gyroscope)rad/s) Values in the body coordinate system b;
-add the table zero offset (++)>) Values in the body coordinate system b;
all the error states (true values) correspond to IMU sampling moments。
The kalman filter algorithm is generally divided into two stages: prediction and updating. In the prediction stage, predicting a state value at the current moment by using the system model and a state estimation value at the last moment; in the updating stage, the predicted value is compared with the observed value to obtain a residual error, and the predicted value is adjusted according to the residual error size to obtain a more accurate state estimation value. In the invention, when an observation equation is constructed, kalman filtering observance is selected as follows:
；
the subscript IMU represents the state value of the inertial navigation output, and the subscript PF represents the state value of the particle filter output.
The method corrects inertial navigation errors based on Kalman filtering, and estimates zero offset errors of a gyroscope and an accelerometer.
And step 112, performing closed loop correction on the frequency spectrum map according to the mobile carrier position estimation value and the radiation field intensity measurement value.
The method uses the position estimation value and the measured radiation field intensity RSS value to carry out SLAM closed loop correction on the frequency spectrum map, and improves the accuracy of the frequency spectrum map.
In the navigation method of the spectrum map/inertial navigation combination, a radio spectrum scene is constructed through a spectrum map, particles are scattered in the radio spectrum scene, a mobile carrier position estimated value and a mobile carrier speed estimated value are obtained through a particle filtering algorithm by combining directly acquired radiation field intensity measured values, an error state is calculated according to inertial navigation measured data through a Kalman filtering algorithm, closed-loop correction is carried out on errors of inertial navigation position information and speed information, and closed-loop correction is carried out on the spectrum map according to the mobile carrier position estimated value and the radiation field intensity measured values. The invention realizes the estimation of the position and the speed by fusing the radiation field intensity measurement value, the inertial navigation measurement value and the frequency spectrum map, solves the problems of space ambiguity and frequency spectrum map error caused by the radiation field intensity measurement value, improves the frequency spectrum map precision in surrounding scenes, and provides a new solving way for the navigation problem in unknown environments.
In another embodiment, as shown in fig. 3, a navigation method of a spectrum map inertial navigation combination is provided, including:
step 1, extracting opportunistic radio signal strength measurement values read by a system of a mobile carrierI.e. RSS measurements, and assuming that the spectrum map constructed by the monitoring station and the inertial navigation initial position, velocity and attitude are known, it is noted thatThe measurement values of the gyroscope and the accelerometer of the inertial navigation are +.>。
Step 2, constructing a surrounding radio spectrum scene according to the position of the user, the RSS measured value and the frequency spectrum map, and scattering particles in the scene to obtain。
Step 3, obtaining a particle field intensity predicted value by using the step 2And true measured value->Difference between them constructs observational quantity->Updating particle weight->；
；
And 4, judging the effective particle number.
If the weight is degraded, the effective particle number is smaller than the set threshold valueThe particle filtering algorithm is resampled by changing the particle distribution space until the effective particle number is greater than the set threshold +.>Giving a new particle position error state vector and a weight thereof>。
Step 5, estimating the system position。
Step 6, estimating the system position by the step 5Estimating the system speed by time accumulation>。
Step 7, willAnd substituting the calculated error state into a Kalman filtering formula, and correcting the inertial navigation position and speed error in a closed loop.
And 8, finishing correction of the SLAM closed-loop spectrum scene map by using the estimated value and the radiation field intensity measured value.
The specific implementation process of the step 6 is as follows:
continuous effective filtering at intervals of 1s for 10 times by time accumulation to obtain a speed estimation stage with displacement increment of. Taking into account thatSince the filtering failure occurs, the 10 effective filtering is not continuous, and the displacement estimated value of the moving carrier is +.>. Wherein the vector comprises->Components in three directions, subscript ++>The value of the north-east coordinate system of the plane is only taken in the north-east coordinate system, the earth direction is ignored, and the subscript is +.>Representing the effective filtering moment +.>Indicating the actual time.
In the speed estimation stage, the displacement increment obtained by the actual speed of the moving carrier is obtainedThe method comprises the steps of carrying out a first treatment on the surface of the Wherein->，/>Representing the speed of inertial navigation solution;
estimating the speed of the moving carrier according to the effective filtered displacement distance and the displacement distance calculated by the speed, and constraining the speed direction of the moving carrier by using the yaw angle:
；/>；
wherein the subscriptRepresenting the estimated value before particle filtration, subscript +.>Representing the new estimate after the particle filtering,representing the yaw angle.
The specific implementation process of the step 7 is as follows:
step 7.1, selecting a 15-dimensional error state vector based on a field map/inertia/magnetic force Ji Song integrated navigation system as follows:
；
step 7.2, error State differential equation, includingThe systematic differential equations of position, speed and attitude errors and the first-order Gaussian Markov model for expressing the gyroscopes and the plus-sheet zero-bias error differential are combined and written into a matrix form, and can be recorded as follows:
；
in the above-mentioned method, the step of,error state transition matrix representing a 15-dimensional system, < >>Noise transfer matrix representing a 15-dimensional system, +.>The continuous random process of white noise for a 15-dimensional system is +.>Time to->Average value of time of day。
Step 7.3, an EKF observation equation is established as follows:
selecting observables
；
Wherein the IMU represents the state value of the inertial navigation output and the subscript PF represents the state value of the particle filter output. Then there is the following observation equation:
；
wherein the method comprises the steps ofTo observe noise arrays
；
Step 7.4, EKF filtering
The discrete form Kalman filtering process is divided into two processes of prediction (time update) and update (measurement update), wherein the prediction process further comprises state one-step prediction and variance one-step prediction. Error state forecast value and variance matrix thereof are
；/>；
In the method, in the process of the invention,for the optimal estimation of the error state at the last moment, < >>Is the corresponding variance matrix. />For system noise->Is a variance matrix of (a).
The updating process calculates a filter gain coefficient matrixOptimal estimate +.>And an optimal estimated value variance matrixThe specific formula is shown as follows;
；
；
；
given an initial valueAnd initial variance matrix->According to->Measurement of time of day->Can be recursively obtainedkAnd estimating the state of the moment. In the filter design herein, the +_ is due to the initial alignment>Zero and/or head-on>Values are given empirically.
In order to realize the step 4, the method uses the inertial navigation speed estimated value to assist the state transfer of the particles, so that the problem of particle decay is avoided. In order to realize the step 7, the method corrects inertial navigation errors based on Kalman filtering, and estimates zero offset errors of the gyroscope and the accelerometer. In order to enable the spectrum map to be higher in accuracy, the method uses the position estimation value and the measured radiation field intensity RSS value to carry out SLAM closed loop correction on the spectrum map, and improves the spectrum map accuracy.
It should be understood that, although the steps in the flowcharts of fig. 1 and 2 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1, 2 may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed sequentially, but may be performed alternately or alternately with at least a portion of the other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 4, there is provided a navigation device of a spectrum map/inertial navigation combination, comprising: a radio spectrum scene building module 402, a particle swarm generation module 404, a particle filtering module 406, a navigation information estimation module 408, an inertial navigation error closed loop correction module 410, and a spectral map closed loop correction module 412, wherein:
the radio spectrum scene construction module 402 is configured to acquire inertial navigation measurement data, radiation field intensity measurement values of the mobile carrier, and a spectrum map constructed by the monitoring station, and construct a radio spectrum scene around the mobile carrier; the inertial navigation measurement data comprises inertial navigation position information, speed information and attitude information;
the particle swarm generation module 404 is configured to generate a particle swarm in a radio spectrum scene, and obtain a predicted value of a particle field intensity; the particle states in the particle swarm also comprise a position state and an observed noise variance;
the particle filtering module 406 is configured to obtain an observed quantity according to a difference value between the radiation field intensity measurement value and the particle field intensity prediction value, update a particle weight value according to the observed quantity, and obtain a filtered particle position state vector and a weight thereof through a particle filtering algorithm;
a navigation information estimation module 408, configured to obtain a mobile carrier position estimation value according to the filtered particle position state vector and the weight thereof, and obtain a mobile carrier speed estimation value according to time accumulation of the mobile carrier position estimation value;
the inertial navigation error closed-loop correction module 410 is configured to calculate an error state according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value through a kalman filter algorithm, and perform closed-loop correction on errors of the inertial navigation position information and the speed information according to the error state;
the spectrum map closed-loop correction module 412 is configured to perform closed-loop correction on the spectrum map according to the mobile carrier position estimation value and the radiation field intensity measurement value.
The particle filter module 406 is further configured to obtain an observed quantity according to a difference between the radiation field intensity measurement value and the particle field intensity prediction value, where the observed quantity is:
；
wherein,for radiation field intensity measurement, +.>For the predicted value of the particle field intensity +.>For the number of particles generated in a radio spectrum scene, < >>Representing the current time;
updating the particle weight according to the observed quantity as follows:
；
wherein,is particle group radius>For measuring the variance matrix of the noise +.>Representing an exponential function>Representing the matrix transpose.
The particle filtering module 406 is further configured to perform particle screening according to the particle weight, and resample by changing the particle distribution space if the effective particle number after screening is smaller than a preset threshold value until the effective particle number is greater than the threshold value, and determine a particle position state vector and a weight thereof after particle filtering.
The navigation information estimation module 408 is further configured to obtain a mobile carrier position estimation value according to the filtered particle position state vector and the weight thereof, where the mobile carrier position estimation value is:
；
wherein,for the number of particles after particle filtration, +.>Is the position information of the filtered particles.
The navigation information estimation module 408 is further configured to determine, by time accumulation, a displacement distance of the effective filtering of the moving carrier in the speed estimation stage as a speed estimation stage by performing the effective filtering continuously at intervals of 1s for 10 timesThe method comprises the steps of carrying out a first treatment on the surface of the Wherein the vector comprises->Components in three directions, subscript ++>The value of the north-east coordinate system of the plane is only taken in the north-east coordinate system, the earth direction is ignored, and the subscript is +.>Representing the effective filtering moment +.>Representing the actual time;
in the speed estimation stage, the displacement increment obtained by the actual speed of the moving carrier is obtainedThe method comprises the steps of carrying out a first treatment on the surface of the Wherein->，/>Representing the speed of inertial navigation solution;
estimating the speed of the moving carrier according to the effective filtered displacement distance of the moving carrier and the displacement increment obtained by the actual speed of the moving carrier, and constraining the speed direction of the moving carrier by using a yaw angle:
；
；
wherein the subscriptRepresenting the estimated value before particle filtration, subscript +.>Representing the new estimate after the particle filtering,representing the yaw angle.
The inertial navigation error closed loop correction module 410 is further configured to select an error state vector; the error state elements in the error state vector include: a ground position error vector, a ground speed error vector, a misalignment angle vector, a gyro zero offset and a plus-meter zero offset; the position error vector is obtained by differencing inertial navigation position information in inertial navigation measurement data and a position estimated value of the mobile carrier; the earth velocity error vector is obtained by differencing inertial navigation velocity information and a moving carrier velocity estimated value in inertial navigation measurement data; constructing a differential equation of an error state vector; constructing a Kalman filtering observation equation; performing Kalman filtering according to a differential equation and a Kalman filtering observation equation of the error state vector to obtain an optimal estimated value of the error state vector; and carrying out closed loop correction on the errors of the inertial navigation position information and the speed information according to the optimal estimated value of the error state vector.
The inertial navigation error closed loop correction module 410 is further configured to select the kalman filter observables as:
；
the subscript IMU represents a state value of inertial navigation output, the subscript PF represents a state value of particle filter output, and an observation equation is established:
；
wherein,is an error state vector, +.>For observing the noise matrix>In order to observe the matrix,。
for specific limitations of the navigation device of the spectrum map/inertial navigation combination, reference may be made to the above limitation of the navigation method of the spectrum map/inertial navigation combination, and no further description is given here. The various modules in the above-described combined spectral map/inertial navigation device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a navigation method of a spectral map inertial navigation combination. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment a computer device is provided comprising a memory storing a computer program and a processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A navigation method of a spectral map inertial navigation combination, the method comprising:
acquiring inertial navigation measurement data, radiation field strength measurement values and a frequency spectrum map constructed by a monitoring station of a mobile carrier, and constructing a radio spectrum scene around the mobile carrier; the inertial navigation measurement data comprises inertial navigation position information, speed information and attitude information;
generating a particle swarm in the radio spectrum scene, and obtaining a particle field intensity predicted value; the particle states in the particle swarm further comprise a position state and an observed noise variance;
obtaining observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, updating a particle weight according to the observed quantity, and obtaining a filtered particle position state vector and a weight thereof through a particle filtering algorithm;
obtaining a mobile carrier position estimated value according to the filtered particle position state vector and the weight thereof, and obtaining a mobile carrier speed estimated value according to the mobile carrier position estimated value through time accumulation;
calculating an error state through a Kalman filtering algorithm according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value, and performing closed-loop correction on errors of the inertial navigation position information and the speed information according to the error state;
and carrying out closed-loop correction on the frequency spectrum map according to the position estimated value of the mobile carrier and the radiation field intensity measured value.
2. The method of claim 1, wherein deriving an observed quantity from a difference between the radiation field strength measurement and the particle field strength prediction value, and updating the particle weight based on the observed quantity, comprises:
obtaining an observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, wherein the observed quantity is as follows:
；
wherein,for the radiation field intensity measurement, +.>For the predicted value of the field strength of the particle, +.>For generating a number of particles in said radio spectrum scene, < > or->Representing the current time;
updating the particle weight according to the observed quantity as follows:
；
wherein,is particle group radius>For measuring the variance matrix of the noise +.>Representing an exponential function>Representing the matrix transpose.
3. The method of claim 1, wherein deriving the filtered particle location state vector and its weights by a particle filtering algorithm comprises:
and (3) screening particles according to the particle weight, and resampling by changing a particle distribution space if the screened effective particle number is smaller than a preset threshold value until the effective particle number is larger than the threshold value, and determining a particle position state vector and the weight thereof after particle filtration.
4. The method of claim 2, wherein deriving a mobile carrier position estimate from the filtered particle position state vector and its weights comprises:
and obtaining a mobile carrier position estimation value according to the filtered particle position state vector and the weight thereof, wherein the mobile carrier position estimation value is as follows:
；
wherein,for the number of particles after particle filtration, +.>Is the position information of the filtered particles.
5. The method of claim 1, wherein deriving a mobile carrier velocity estimate from the mobile carrier position estimate by time accumulation comprises:
determining said velocity by time accumulation, continuously effective filtering at 10 1s intervals as a velocity estimation phaseThe displacement distance of effective filtering of the moving carrier in the estimation stage isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein the vector comprises->Components in three directions, subscript ++>The value of the north-east coordinate system of the plane is only taken in the north-east coordinate system, the earth direction is ignored, and the subscript is +.>Representing the effective filtering moment +.>Representing the actual time;
in the speed estimation stage, the displacement increment obtained by the actual speed of the moving carrier is obtainedThe method comprises the steps of carrying out a first treatment on the surface of the Wherein->，/>Representing the speed of inertial navigation solution;
estimating the speed of the moving carrier according to the displacement distance of the effective filtering of the moving carrier and the displacement increment obtained by the actual speed of the moving carrier, and constraining the speed direction of the moving carrier by using a yaw angle:
；
；
wherein the subscriptRepresenting the estimated value before particle filtration, subscript +.>Representing the new estimate after particle filtering, +.>Representing the yaw angle.
6. The method of claim 1, wherein calculating an error state from the inertial measurement data, the mobile carrier position estimate, and the mobile carrier velocity estimate by a kalman filter algorithm, and performing closed loop correction on errors of the inertial position information and the velocity information from the error state, comprises:
selecting an error state vector; the error state elements in the error state vector include: a ground position error vector, a ground speed error vector, a misalignment angle vector, a gyro zero offset and a plus-meter zero offset; the opposite position error vector is obtained by differencing inertial navigation position information in the inertial navigation measurement data and the position estimated value of the mobile carrier; the earth velocity error vector is obtained by differencing inertial navigation velocity information in the inertial navigation measurement data and the velocity estimated value of the mobile carrier;
constructing a differential equation of the error state vector;
constructing a Kalman filtering observation equation;
performing Kalman filtering according to the differential equation of the error state vector and the Kalman filtering observation equation to obtain an optimal estimated value of the error state vector;
and carrying out closed loop correction on the errors of the inertial navigation position information and the speed information according to the optimal estimated value of the error state vector.
7. The method of claim 6, wherein constructing a kalman filter observation equation comprises:
the Kalman filtering observed quantity is selected as follows:
；
the subscript IMU represents a state value of inertial navigation output, the subscript PF represents a state value of particle filter output, and an observation equation is established:
；
wherein,for the error state vector,/or->For observing the noise matrix>In order to observe the matrix,。
8. a navigation device of a spectral map inertial navigation assembly, the device comprising:
the radio spectrum scene construction module is used for acquiring inertial navigation measurement data, radiation field strength measurement values of the mobile carrier, and a spectrum map constructed by the monitoring station, and constructing a radio spectrum scene around the mobile carrier; the inertial navigation measurement data comprises inertial navigation position information, speed information and attitude information;
the particle group generation module is used for generating a particle group in the radio spectrum scene and obtaining a particle field intensity predicted value; the particle states in the particle swarm further comprise a position state and an observed noise variance;
the particle filtering module is used for obtaining observed quantity according to the difference value of the radiation field intensity measured value and the particle field intensity predicted value, updating the particle weight according to the observed quantity, and obtaining a filtered particle position state vector and the weight thereof through a particle filtering algorithm;
the navigation information estimation module is used for obtaining a mobile carrier position estimation value according to the filtered particle position state vector and the weight thereof, and obtaining a mobile carrier speed estimation value according to the mobile carrier position estimation value through time accumulation;
the inertial navigation error closed-loop correction module is used for calculating an error state through a Kalman filtering algorithm according to the inertial navigation measurement data, the mobile carrier position estimation value and the mobile carrier speed estimation value, and carrying out closed-loop correction on the errors of the inertial navigation position information and the speed information according to the error state;
and the spectrum map closed-loop correction module is used for carrying out closed-loop correction on the spectrum map according to the mobile carrier position estimation value and the radiation field intensity measurement value.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311610655.6A CN117308925B (en) | 2023-11-29 | 2023-11-29 | Navigation method, device, equipment and medium for spectral map inertial navigation combination |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311610655.6A CN117308925B (en) | 2023-11-29 | 2023-11-29 | Navigation method, device, equipment and medium for spectral map inertial navigation combination |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117308925A CN117308925A (en) | 2023-12-29 |
| CN117308925B true CN117308925B (en) | 2024-02-09 |
Family
ID=89281535
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311610655.6A Active CN117308925B (en) | 2023-11-29 | 2023-11-29 | Navigation method, device, equipment and medium for spectral map inertial navigation combination |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117308925B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333479A (en) * | 2019-07-09 | 2019-10-15 | 东华大学 | It is a kind of based on the wireless location method for improving particle filter under complex indoor environment |
| CN111044050A (en) * | 2019-12-30 | 2020-04-21 | 中电海康集团有限公司 | Bluetooth positioning method based on particle filtering and Kalman filtering |
| EP3764058A1 (en) * | 2019-07-10 | 2021-01-13 | HERE Global B.V. | Indoor optimized offline radio map |
| CN112729301A (en) * | 2020-12-10 | 2021-04-30 | 深圳大学 | Indoor positioning method based on multi-source data fusion |
| CN115406435A (en) * | 2022-08-24 | 2022-11-29 | 同济大学 | Indoor electronic map construction method and device based on WLAN and MEMS and storage medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103228040A (en) * | 2012-01-31 | 2013-07-31 | 国际商业机器公司 | Indoor electronic map generating method and system, and indoor target positioning method and system |
| US10190881B2 (en) * | 2015-01-08 | 2019-01-29 | Profound Positioning Inc. | Method and apparatus for enhanced pedestrian navigation based on WLAN and MEMS sensors |
| WO2018139773A1 (en) * | 2017-01-25 | 2018-08-02 | 한국과학기술연구원 | Slam method and device robust to changes in wireless environment |
-
2023
- 2023-11-29 CN CN202311610655.6A patent/CN117308925B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333479A (en) * | 2019-07-09 | 2019-10-15 | 东华大学 | It is a kind of based on the wireless location method for improving particle filter under complex indoor environment |
| EP3764058A1 (en) * | 2019-07-10 | 2021-01-13 | HERE Global B.V. | Indoor optimized offline radio map |
| CN111044050A (en) * | 2019-12-30 | 2020-04-21 | 中电海康集团有限公司 | Bluetooth positioning method based on particle filtering and Kalman filtering |
| CN112729301A (en) * | 2020-12-10 | 2021-04-30 | 深圳大学 | Indoor positioning method based on multi-source data fusion |
| CN115406435A (en) * | 2022-08-24 | 2022-11-29 | 同济大学 | Indoor electronic map construction method and device based on WLAN and MEMS and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements;Brida, Peter et al.;《SENSORS》;第21卷(第7期);1-18 * |
| 机会信号导航综述;黄高明等;《控制与决策》;第34卷(第6期);1121-1131 * |
| 采用EKF与PF的室内融合定位技术;张雨婷;陈璟;;传感技术学报(第02期);89-95 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117308925A (en) | 2023-12-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7302345B2 (en) | Method and apparatus for generating magnetic field map and method and apparatus for checking pose of mobile body using the magnetic field map | |
| CN109991636A (en) | Map constructing method and system based on GPS, IMU and binocular vision | |
| CN107884800B (en) | Combined navigation data resolving method and device for observation time-lag system and navigation equipment | |
| CN107255795A (en) | Localization Approach for Indoor Mobile and device based on EKF/EFIR mixed filterings | |
| KR101985344B1 (en) | Sliding windows based structure-less localization method using inertial and single optical sensor, recording medium and device for performing the method | |
| CN115143954B (en) | Unmanned vehicle navigation method based on multi-source information fusion | |
| CN112964291B (en) | Sensor calibration method, device, computer storage medium and terminal | |
| US20220057517A1 (en) | Method for constructing point cloud map, computer device, and storage medium | |
| CN114022561A (en) | Urban area monocular mapping method and system based on GPS constraint and dynamic correction | |
| CN110779514B (en) | Hierarchical Kalman fusion method and device for auxiliary attitude determination of bionic polarization navigation | |
| CN116448111A (en) | Pedestrian indoor navigation method, device and medium based on multi-source information fusion | |
| CN111678513A (en) | Ultra-wideband/inertial navigation tight coupling indoor positioning device and system | |
| CN113218389B (en) | Vehicle positioning method, device, storage medium and computer program product | |
| CN110375740B (en) | Vehicle navigation method, device, equipment and storage medium | |
| CN117308925B (en) | Navigation method, device, equipment and medium for spectral map inertial navigation combination | |
| CN114897942B (en) | Point cloud map generation method and device and related storage medium | |
| CN114001730B (en) | Fusion positioning method, fusion positioning device, computer equipment and storage medium | |
| CN116972834A (en) | Multi-sensor fusion positioning method, device, system and storage medium | |
| US20240134033A1 (en) | Method for determining a movement state of a rigid body | |
| CN116929343A (en) | Pose estimation method, related equipment and storage medium | |
| CN117367458A (en) | Map accuracy verification method and device, terminal equipment and medium | |
| CN113034538B (en) | Pose tracking method and device of visual inertial navigation equipment and visual inertial navigation equipment | |
| Wang | Research on Adaptive Filtering Collaborative Graph Optimization Navigation Method | |
| CN113701749A (en) | Vehicle positioning and navigation method and system based on stereoscopic vision inertial odometer | |
| Indelman et al. | Real-time mosaic-aided aerial navigation: II. Sensor fusion |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |