CN114353787B - Multisource fusion positioning method - Google Patents
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Abstract
The invention discloses a multisource fusion positioning method, which comprises the following steps: acquiring positioning information to be corrected through a micro-electromechanical system sensor group in a user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through a mobile hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention combines the positioning results output by the two positioning sources of the action hot spot and the micro-electromechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
Description
Technical Field
The invention relates to the field of intelligent terminals, in particular to a multi-source fusion positioning method.
Background
Wi-Fi based positioning is considered to be an effective way to achieve ubiquitous and high-precision indoor navigation, especially with support of the Wi-Fi Fine Time Measurement (FTM) protocol for next generation wireless access points. Microelectromechanical Systems (MEMS) sensors can provide accurate short-term navigation solutions while providing a viable solution for building Wi-Fi fingerprint-based navigation libraries by collecting and mining user's daily spatiotemporal trajectory information and along-road acquired signals of opportunity. Currently, the positioning method in the intelligent terminal is generally used for positioning by adopting a single indoor positioning source, such as Wi-Fi positioning or MEMS sensor positioning. Due to the complexity and diversity of indoor scenes, it is difficult to obtain accurate positioning results using a single indoor positioning source.
Accordingly, there is a need for improvement and development in the art.
Disclosure of Invention
The invention aims to solve the technical problems that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
The technical scheme adopted by the invention for solving the problems is as follows:
In a first aspect, an embodiment of the present invention provides a multi-source fusion positioning method, where the method includes:
acquiring positioning information to be corrected through a micro-electromechanical system sensor group in a user terminal;
determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through a mobile hotspot;
and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal.
In one embodiment, the mems sensor group includes a magnetometer, a gyroscope, an accelerometer, and a barometer, and the acquiring, by the mems sensor group in the user terminal, positioning information to be corrected includes:
acquiring angular velocity data output by the gyroscope;
Acquiring acceleration data output by the accelerometer;
acquiring magnetic field data output by the magnetometer;
Acquiring air pressure data output by the air pressure gauge, and determining height data corresponding to the user terminal according to the air pressure data;
And determining the positioning information to be corrected according to the angular speed data, the acceleration data, the magnetic field data and the altitude data.
In one embodiment, the determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data, and the altitude data includes:
Inputting the angular velocity data and the acceleration data into an inertial navigation positioning model to obtain inertial navigation positioning information;
inputting the acceleration data into a pedestrian navigation positioning model to obtain pedestrian navigation positioning information;
and inputting the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information into a preset filtering algorithm to obtain the positioning information to be corrected.
In one embodiment, when the target positioning algorithm is an action hotspot ranging algorithm and an action hotspot fingerprint algorithm, the determining the target positioning algorithm corresponding to the user terminal, and determining the reference positioning information according to the target positioning algorithm includes:
determining first reference positioning information corresponding to the user terminal through the action hot spot ranging algorithm;
determining second reference positioning information corresponding to the user terminal through the action hot spot fingerprint algorithm;
And taking the first reference positioning information and the second reference positioning information as the reference positioning information.
In one embodiment, the determining, by the mobile hotspot ranging algorithm, the first reference positioning information corresponding to the ue includes:
Acquiring channel state data between a plurality of first access points and the user terminal respectively;
inputting the channel state data corresponding to the first access points into the action hot spot ranging algorithm to obtain distance data between the first access points and the user terminal;
And determining the first reference positioning information according to the distance data respectively corresponding to the first access points.
In one embodiment, the determining, by the action hotspot fingerprint algorithm, the second reference positioning information corresponding to the user terminal includes:
acquiring signal intensity data between a plurality of second access points and the user terminal respectively;
and inputting the signal intensity data corresponding to the second access points into the action hot spot fingerprint algorithm to obtain the second reference positioning information.
In one embodiment, the correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal includes:
determining first error data corresponding to the MEMS sensor group according to the first reference positioning information;
Correcting the positioning information to be corrected according to the first error data to obtain corrected positioning information;
Determining second error data corresponding to the MEMS sensor group according to the second reference positioning information;
And correcting the corrected positioning information according to the second error data to obtain the target positioning information.
In one embodiment, when the target positioning algorithm is a mobile hotspot fingerprint algorithm, the determining the target positioning algorithm corresponding to the user terminal, and determining the reference positioning information according to the target positioning algorithm, includes:
and determining the reference positioning information through the action hot spot fingerprint algorithm.
In one embodiment, the correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal includes:
determining error data corresponding to the MEMS sensor group according to the reference positioning information;
and correcting the positioning information to be corrected according to the error data to obtain the target positioning information.
In a second aspect, an embodiment of the present invention further provides a computer readable storage medium having stored thereon a plurality of instructions, where the instructions are adapted to be loaded and executed by a processor to implement the steps of any of the above-described multisource fusion positioning methods.
The invention has the beneficial effects that: the embodiment of the invention acquires positioning information to be corrected through a micro-electromechanical system sensor group in the user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through a mobile hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention combines the positioning results output by the two positioning sources of the action hot spot and the micro-electromechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
Fig. 1 is a flow chart of a multi-source fusion positioning method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a closed loop detection algorithm according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of data fusion using unscented kalman filter algorithm according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a trust ellipse provided by an embodiment of the present invention.
FIG. 5 is a schematic diagram showing the comparison of the heading calculation effect according to the embodiment of the present invention.
Fig. 6 is a schematic diagram showing a comparison of coordinate resolving effects according to an embodiment of the present invention.
FIG. 7 is a graph showing a comparison of different combined mold positioning trajectories according to an embodiment of the present invention.
Fig. 8 is a diagram showing comparison of positioning accuracy of different combined die types according to an embodiment of the present invention.
Fig. 9 is a comparison chart of different combined mould positioning tracks in a large-scale indoor scene provided by the embodiment of the invention.
Fig. 10 is a positioning accuracy comparison chart of the algorithm and the similar algorithm in an office scene provided by the embodiment of the invention.
Fig. 11 is a positioning accuracy comparison chart of the algorithm and the similar algorithm in the corridor scene provided by the embodiment of the invention.
Fig. 12 is a schematic diagram of an internal module of a multi-source positioning device according to an embodiment of the present invention.
Fig. 13 is a schematic block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
The invention discloses a multisource fusion positioning method, which aims to make the purposes, technical schemes and effects of the multisource fusion positioning method clearer and more definite, and further details the multisource fusion positioning method by referring 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 scope of the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Wi-Fi based positioning is considered to be an effective way to achieve ubiquitous and high-precision indoor navigation, especially with support of the Wi-Fi Fine Time Measurement (FTM) protocol for next generation wireless access points. Microelectromechanical Systems (MEMS) sensors can provide accurate short-term navigation solutions while providing a viable solution for building Wi-Fi fingerprint-based navigation libraries by collecting and mining user's daily spatiotemporal trajectory information and along-road acquired signals of opportunity. Currently, the positioning method in the intelligent terminal is generally used for positioning by adopting a single indoor positioning source, such as Wi-Fi positioning or MEMS sensor positioning. Due to the complexity and diversity of indoor scenes, it is difficult to obtain accurate positioning results using a single indoor positioning source.
In view of the above-mentioned drawbacks of the prior art, the present invention provides a multi-source fusion positioning method, which includes: acquiring positioning information to be corrected through a micro-electromechanical system sensor group in a user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through a mobile hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention combines the positioning results output by the two positioning sources of the action hot spot and the micro-electromechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
As shown in fig. 1, the method comprises the steps of:
and S100, acquiring positioning information to be corrected through a micro-electromechanical system sensor group in the user terminal.
Specifically, the mems sensor group in this embodiment may be a sensor group for positioning in any user's terminal device. The MEMS sensor group calculates the current position information of the user by acquiring the angle change information and the speed change information when the terminal equipment moves. However, because the mems sensor group has error accumulation and noise signals, there is usually a large gap between the positioning information directly output by the mems sensor group and the real position information of the user, so the positioning information output by the mems sensor group is not directly used in the embodiment, but is used as the positioning information to be corrected.
In one implementation, the mems sensor group includes a magnetometer, a gyroscope, an accelerometer, and a barometer, and the step S100 specifically includes the following steps:
Step S101, obtaining angular velocity data output by the gyroscope;
Step S102, acquiring acceleration data output by the accelerometer;
Step S103, acquiring magnetic field data output by the magnetometer;
Step S104, acquiring air pressure data output by the air pressure gauge, and determining height data corresponding to the user terminal according to the air pressure data;
step S105, determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data, and the altitude data.
Specifically, the mems sensor group in the present embodiment mainly includes a magnetometer, a gyroscope, an accelerometer, and a barometer. The magnetometer can calculate the magnetic field around the user terminal and output magnetic field data; the gyroscope can calculate the angle deflection condition of the user terminal and output angular velocity data; the accelerometer can calculate the speed change condition of the user terminal and output acceleration data; the barometer can calculate air pressure data around the user terminal, so that the height of the user terminal is calculated based on the air pressure data, and the height data is output. And finally, the user terminal can calculate the positioning information to be corrected based on the angular speed data, the acceleration data, the magnetic field data and the height data acquired by the MEMS sensor group.
In one implementation manner, the step S102 specifically includes the following steps:
S1021, inputting the angular velocity data and the acceleration data into an inertial navigation positioning model to obtain inertial navigation positioning information;
Step S1022, inputting the acceleration data into a pedestrian navigation positioning model to obtain pedestrian navigation positioning information;
step S1023, inputting the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information into a preset filtering algorithm to obtain the positioning information to be corrected.
In short, in order to improve the accuracy of positioning the mems sensor, the embodiment inputs the data collected by the mems sensor group into two positioning models respectively, and fuses the positioning information output by the two positioning models into the positioning information to be corrected. Specifically, in this embodiment, angular velocity data and acceleration data are input into an inertial navigation positioning model to obtain inertial navigation positioning information. The basic working principle of inertial navigation is based on Newton's law of mechanics, and information such as speed, yaw angle and position in a navigation coordinate system can be obtained 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. Secondly, the embodiment also inputs the acceleration data into the pedestrian navigation positioning model to obtain pedestrian navigation positioning information. The pedestrian navigation technology is a precise positioning technology capable of providing navigation services such as walking planning for pedestrians, and the like, and outputs positioning information of the pedestrians by measuring step sizes and heading of the pedestrians. In order to fuse the positioning information respectively output by the two positioning models, in the embodiment, magnetic field data, height data, inertial navigation positioning information and pedestrian navigation positioning information are input into a preset filtering algorithm together, and after the input data are fused through the filtering algorithm, the positioning information to be corrected is output.
In one implementation manner, the inertial navigation positioning model includes a gesture updating module, a speed updating module and a position updating module, and the working principles of the three modules are as follows:
And a gesture updating module:
Wherein, For updated gesture quaternion information,As the posture quaternion information of the last moment,Is the variation of the gesture quaternion.
And a speed updating module:
Wherein, For updated velocity vector,For the velocity compensation amount, g n is the gravitational acceleration, and T s is the sampling interval.
And a position updating module:
Wherein, For updated position vector,Is the speed value at the previous time.
In one implementation manner, the pedestrian navigation positioning model comprises a step length calculation module, a two-dimensional coordinate updating module and a three-dimensional height updating module, wherein the working principles of the three modules are as follows:
step length calculating module:
α＝μ·(Amax-Amin)1/4
Where α is the pedestrian step size calculated after gait detection, μ is the step size coefficient, and a max and a min are the peak and valley values of the acceleration module.
Two-dimensional coordinate updating module:
Wherein, (E t-1,Nt-1) and (E t,Nt) are two-dimensional position coordinates of the current moment and the last moment respectively, alpha (t) is a step value of the current moment, and theta (t) is a heading value of the current moment.
Three-dimensional height updating module:
Wherein Δh t is a height update value calculated by air pressure update, p t is an air pressure meter output value, and p 0 is a reference air pressure value.
In one implementation manner, as shown in fig. 3, the filtering algorithm is an unscented kalman filtering algorithm, and the step S1023 specifically includes the following steps:
Inputting the magnetic field data, the altitude data, the inertial navigation positioning information and the pedestrian navigation positioning information into the unscented Kalman filtering algorithm;
And determining speed error data, position error data and magnetic field error data corresponding to the inertial navigation positioning information based on the magnetic field data, the altitude data, the inertial navigation positioning information and the pedestrian navigation positioning information through the unscented Kalman filtering algorithm, correcting the inertial navigation positioning information according to the speed error data, the position error data and the magnetic field error data, and outputting positioning information to be corrected after correction. In other words, in this embodiment, the inertial navigation positioning information and the pedestrian navigation positioning information are fused by the unscented kalman filtering algorithm, the inertial navigation positioning information is actually used as a state quantity by the unscented kalman filtering algorithm, the pedestrian navigation positioning information is used as an observed quantity, an error value of the state quantity is determined based on the observed quantity, and the state quantity is corrected based on the error value, so that the positioning information to be corrected, which is output by the mems sensor group, is obtained after correction.
The principle of operation of the unscented kalman filter algorithm is illustrated, for example, as follows:
firstly, establishing an error model for updating the position speed and the attitude of a sensor:
Wherein, δp n,δvn,φ1×3, Respectively representing position error, speed error, attitude error, gyroscope zero offset and accelerometer zero offset.
Next, a state update model and an equation of the error vector are established, and one-step prediction is carried out on the fifteen-dimensional error:
In the method, in the process of the invention, For acceleration vectors, τ bg and τ ba are measurement error coefficients associated with the event, and w εg and w εa are driving noise.
Finally, an observation equation is established to update the error vector, firstly, the observation of the velocity vector is updated:
Wherein, Is the error vector of the velocity,Speed information provided for pedestrian navigation algorithm,Speed information provided for inertial navigation algorithms.
Then the location update:
Wherein, Is the error vector of the position,Location information provided for pedestrian navigation algorithm,Location information provided for inertial navigation algorithms.
And finally, updating the magnetic field vector, namely firstly extracting the magnetic field vector at the first moment as a reference value:
Wherein, For the calculated magnetic field reference vector,For rotation matrix,Output for the magnetometer.
An observation update model of the magnetic field vector is then established:
Wherein, Is the error vector of the magnetic field,For real-time rotation matrix,The value is output in real time for the magnetometer.
As shown in fig. 1, the method further comprises the steps of:
step 200, determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through a mobile hotspot.
In particular, mobile hotspot (Wi-Fi) is a wireless networking technology, generally applicable to a variety of user terminals. At present, the intelligent terminal comprises one or more positioning algorithms executed based on the action hot spot, and the positioning algorithms related to the action hot spot are generally influenced by signal intensity and channel state, so that compared with a micro-electromechanical system sensor, the intelligent terminal can adopt the action hot spot to perform positioning in a smaller range, but has higher precision. In this embodiment, the positioning algorithm based on the implementation of the action hotspot included in the user terminal is defined as the target positioning algorithm, and since there may be a difference between the target algorithms included in different user terminals, for example, only the action hotspot fingerprint algorithm exists in the terminal of the user a, and not only the action hotspot fingerprint algorithm but also the action hotspot ranging algorithm exists in the user B. It is therefore necessary to first determine what target positioning algorithm is included in the current user terminal. Because the positioning accuracy of the target positioning algorithm is higher than that of the MEMS sensor group, the positioning information output based on the target positioning algorithm is used as reference positioning information for correcting the positioning information output by the MEMS sensor group.
In one implementation manner, when the target positioning algorithm is an action hotspot ranging algorithm and an action hotspot fingerprint algorithm, the step S200 specifically includes the following steps:
Step S201, determining first reference positioning information corresponding to the user terminal through the action hot spot ranging algorithm;
step S202, determining second reference positioning information corresponding to the user terminal through the action hot spot fingerprint algorithm;
Step S203, using the first reference positioning information and the second reference positioning information as the reference positioning information.
Specifically, if the current ue includes both an action hotspot ranging algorithm and an action hotspot fingerprint algorithm, the action hotspot ranging algorithm and the action hotspot fingerprint algorithm are adopted to output positioning information respectively, wherein the positioning information output by the action hotspot ranging algorithm is defined as first positioning information, and the positioning information output by the action hotspot fingerprint algorithm is defined as second positioning information. And then, using both positioning information as reference positioning information for correcting the positioning information to be corrected.
In one implementation, the step S201 specifically includes the following steps:
Step 2011, obtaining channel state data between a plurality of first access points and the user terminal respectively;
Step 2012, inputting the channel state data corresponding to the plurality of first access points into the action hotspot ranging algorithm to obtain distance data between the plurality of first access points and the user terminal;
step S2013, determining the first reference positioning information according to the distance data respectively corresponding to the plurality of first access points.
Specifically, the plurality of first access points may be a plurality of Wi-Fi access points disposed around the user terminal, the distance data between each first access point and the user terminal may be calculated by obtaining channel state data between each first access point and the user terminal, and the position of the user terminal may be calculated based on all obtained distance data, so as to obtain the first reference positioning information.
In one implementation manner, the plurality of first access points include a first access point and a second access point, and the step S2013 specifically includes the following steps:
Acquiring position information corresponding to the first access point and the second access point respectively;
Determining ranging positioning information according to the distance data and the position information respectively corresponding to the first access point and the second access point;
Constructing a vector to be inspected according to the position information and the ranging positioning information respectively corresponding to the first access point and the second access point;
Judging whether the closed loop of the vector to be checked is established, and taking the ranging positioning information as the first reference positioning information when the closed loop of the vector to be checked is established.
The principle of closed loop detection is illustrated as follows:
1. Constructing a ranging model of a mobile hotspot ranging algorithm:
Lobserved＝LFTM+dbias+dN+drandom
wherein, L observed is a ranging distance value obtained by the receiving end, L FTM is a true value, d bias is an initial zero offset value, d N is a non-line-of-sight error value, and d random is a random error value.
2. A closed loop model is built, as shown in fig. 2, wherein the closed loop model in an ideal state is:
Wherein, AndRespectively representing vectors constructed between the three points a, B, C. The closed loop model after adding the error amount is as follows:
The newly added parameter d p represents an uncertainty error caused by the position update in S102.
3. The closed loop model equation is converted from vector form to coordinate form:
Wherein X BC and Y BC each represent a closed-loop vector Coordinate resolution result ofAndRespectively representing the difference in coordinates between the position coordinates based on the one-step prediction to the two Wi-Fi base stations.
4. And constructing a corresponding vector according to the predicted Wi-Fi precise ranging result of the position coordinate pair:
Wherein, AndRepresenting ranging vectors constructed based on updated position,Representing the actual ranging result between the points A and B,The euclidean distance between the predicted position a and the point B corresponding to Wi-Fi AP is represented. Further, closed loop vectors in the form of coordinates can be constructed:
Wherein, For a closed-loop vector, whether the final closed-loop result is true can be expressed by the following equation:
Wherein, For the closed loop vector modulus, D r is the variance of the random error of the ranging, D p is the variance of the uncertainty error of the predicted position, μ is the scale. When the closed loop vector module value is larger than the calculation result on the right side, the closed loop vector can be considered to be interfered by the non-line-of-sight error.
In one implementation, the step S202 specifically includes the following steps:
Step S2021, obtaining signal intensity data between a plurality of second access points and the user terminal respectively;
Step S2022, inputting the signal intensity data corresponding to the plurality of second access points into the action hotspot fingerprint algorithm to obtain the second reference positioning information.
In particular, the plurality of second access points may be Wi-Fi access points around the user terminal, wherein the second access points may have access points coinciding with the first access points. And acquiring signal intensity data between each second access point and the user terminal, wherein the signal intensity data can reflect the distance between each second access point and the user terminal to a certain extent, so that the current position information of the user terminal, namely the second reference positioning information, can be obtained through the signal intensity data of each second access point and the action hot spot fingerprint algorithm.
In one implementation, the step S2022 specifically includes the following steps:
Acquiring a preset action hot spot fingerprint database, wherein the action hot spot fingerprint database comprises a plurality of reference access points, and each reference access point is provided with a signal intensity label and an address label;
Comparing the signal intensity data corresponding to the second access points with the action hot spot fingerprint database to obtain candidate reference access points;
inputting a plurality of reference access points into a nearest neighbor matching algorithm to obtain weight values corresponding to the candidate reference access points respectively;
And determining the second reference positioning information according to the weight values and the address labels which correspond to the candidate reference access points respectively.
For example, the nearest neighbor matching algorithm works as follows:
K value self-adaptive adjustment and error evaluation in nearest neighbor matching method:
In the method, in the process of the invention, For the nearest value of the K nearest neighbor values, dis t,other is the other value, and when gamma is smaller than a set threshold value kappa, K nearest neighbor values meeting the condition are reserved, so that the self-adaptive adjustment of the K value is completed. The calculated weighted positions are as follows:
Wherein, For the weight size of each nearest neighbor position, POS (x i,yi) is the corresponding two-dimensional position coordinate. The weighted position error estimation formula is as follows:
Wherein, P rssi (t) and P rssi (t-1) represent the position coordinates of the previous and the next moments obtained by fingerprint matching, and P MEMS (t) and P MEMS (t-1) represent the position coordinates of the previous and the next moments obtained by using the microsensor method.
In one implementation manner, the embodiment further needs to test the second reference status information to improve the data reliability of the second reference location information, and the test method is as follows:
1. Acquiring preset trust location area information, and comparing the second reference positioning information with the trust location area information;
2. When the position corresponding to the second reference positioning information is located in the region corresponding to the trust position region information, judging that the second reference positioning information is available;
3. and when the position corresponding to the second reference positioning information is located outside the area corresponding to the trust position area information, judging that the second reference positioning information is not available.
In another implementation manner, when the target positioning algorithm is a mobile hotspot fingerprint algorithm, the step S200 specifically includes the following steps:
step S204, determining the reference positioning information through the action hot spot fingerprint algorithm.
Specifically, since the mobile hotspot ranging algorithm is not used for positioning on each terminal, if only the mobile hotspot fingerprint algorithm exists on the current user terminal, the mobile hotspot fingerprint algorithm is the target positioning algorithm, and the positioning information obtained through the mobile hotspot fingerprint algorithm is the reference positioning information.
As shown in fig. 1, the method further comprises the steps of:
And step S300, correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal.
Specifically, because the reference positioning information is the positioning information of the user terminal calculated based on the action hot spot, compared with the positioning information to be corrected obtained based on the micro-electromechanical system sensor group, the error accumulation is less, and the positioning is more accurate. Therefore, the embodiment corrects the positioning information to be corrected by adopting the reference positioning information, and takes the positioning information obtained after correction as the target positioning information finally output by the MEMS sensor group.
In one implementation manner, when the target positioning algorithm is an action hotspot ranging algorithm and an action hotspot fingerprint algorithm, the step S300 specifically includes the following steps:
step 301, determining first error data corresponding to the mems sensor group according to the first reference positioning information;
step S302, correcting the positioning information to be corrected according to the first error data to obtain corrected positioning information;
Step S303, determining second error data corresponding to the MEMS sensor group according to the second reference positioning information;
And step S304, correcting the corrected positioning information according to the second error data to obtain the target positioning information.
In short, when the mobile hotspot ranging algorithm exists in the user terminal, the first reference positioning information obtained based on the mobile hotspot ranging algorithm is preferentially adopted to correct the positioning information to be corrected, and then the second reference positioning information obtained based on the mobile hotspot fingerprint algorithm is adopted to correct the positioning information to be corrected. Specifically, first error data of a micro-electromechanical system sensor group is determined based on first reference positioning information, and after the first error data in the positioning information to be corrected is eliminated, corrected positioning information is obtained. And then determining second error data of the MEMS sensor group based on the second reference positioning information, and eliminating the second error data in the corrected positioning information to obtain final target positioning information output by the MEMS sensor group.
In another implementation manner, when the target positioning algorithm is a mobile hotspot fingerprint algorithm, the step S300 specifically includes the following steps:
Step S305, determining error data corresponding to the MEMS sensor group according to the reference positioning information;
and step S306, correcting the positioning information to be corrected according to the error data to obtain the target positioning information.
In short, when only the action hot spot fingerprint algorithm exists in the user terminal, only the reference positioning information obtained based on the action hot spot fingerprint algorithm is adopted to correct the positioning information to be corrected. Specifically, error data of the MEMS sensor group is determined based on the reference positioning information, and the target positioning information output by the MEMS sensor group is obtained after the error data in the positioning information to be corrected is eliminated.
For example, first, a tightly coupled model based on Wi-Fi precise ranging and microsensors is established, and state quantities are constructed by using sensor errors and Wi-Fi ranging zero offset errors:
Where F is the state matrix, G s is the state noise drive matrix, ε s is the state noise. Wi-Fi precise ranging zero offset error equation is constructed as follows:
Wherein b RTT is Wi-Fi precise ranging zero offset error, For time-dependent coefficients,Is white gaussian noise. The final constructed augmented state equation is:
the observation update equation based on Wi-Fi precise ranging results is as follows:
Where δz d is the difference between the actual Wi-Fi range-finding value and the sensor-position-based range-finding value, d FTM,m and d MEMS,m represent Wi-Fi range-finding value and sensor-position-based range-finding value, respectively.
Then, a loose coupling model based on Wi-Fi fingerprints and a microsensor is established, a state quantity is constructed by using sensor error values, and an observation equation is constructed by using position coordinates provided by the fingerprints as observed quantities:
In the method, in the process of the invention, AndRepresenting position and velocity observations by fingerprintingAndRepresenting the observed quantity of position and velocity obtained by the microsensor method.
Finally, a mixed positioning model is established, as shown in fig. 4, firstly, a trust ellipse is established for removing the observation coarse difference in the final fusion process, wherein the long axis of the trust ellipse is calculated as follows:
Wherein a is the calculated major axis of the ellipse, AndRespectively representing north and east position error covariance parameters in covariance matrix in filter,S e is a scaling factor, which is the northeast position error covariance parameter in the covariance matrix in the filter. The short axis of the trust ellipse is calculated as follows:
the azimuthal angle of the trust ellipse to the north direction is recorded as follows:
In the initial stage of multi-source fusion positioning, when a pedestrian moves from a scene supporting Wi-Fi precise measurement to a scene not supporting Wi-Fi precise measurement, the error variance of positioning initialization is larger due to insufficient iteration times; thus, the value of the parameter Se is adjusted high to avoid removing the position result to which the useful fingerprint matches. Along with the increase of the iteration times, the Se value is gradually reduced, and finally the Se value is kept unchanged, so that the rough difference of Wi-Fi fingerprint results is eliminated.
In order to demonstrate the technical effects of the present invention, the inventors made the following experiments:
By comparing the heading resolving precision based on the microsensor and the heading resolving precision based on the gyroscope and the magnetometer, the embodiment of the invention can be found to obtain a better heading resolving result. Similarly, the position resolving precision based on the microsensor is compared with the position resolving precision of the traditional dead reckoning algorithm, and a better result is obtained. Pairs of heading solutions and positioning results are shown in fig. 5 and 6, for example.
Fig. 7 and 8 further illustrate a comparison of positioning trajectories and corresponding positioning accuracies using several different combining modes proposed by the present invention. As can be seen from fig. 8, the positioning mode using a single micro sensor still has larger accumulated error, the loose coupling model can effectively eliminate the error existing in the positioning mode of the single micro sensor, the original tight coupling model uses Wi-Fi precise ranging and micro sensor positioning, the positioning precision is higher, the precision after the self-calibration algorithm is further improved, and the final hybrid positioning model achieves the highest positioning precision. Fig. 9 compares the positioning effect of the multi-source fusion algorithm provided by the invention with the positioning effect of a single positioning source in a larger-scale indoor office scene, and it can be found that the multi-source fusion indoor positioning fusion frame provided by the embodiment of the invention can achieve the positioning accuracy better than 1.06 m under 75% of conditions in an office scene supported by Wi-Fi precise ranging, and can achieve the positioning accuracy better than 1.65 m under 75% of conditions in a corridor scene not supported by Wi-Fi precise ranging. Compared with the positioning effect realized by the positioning source of the microsensor, the positioning effect is remarkably improved, and the high-precision indoor positioning requirement of the common crowd using the intelligent mobile phone terminal can be effectively met. Fig. 10 and fig. 11 respectively compare the positioning accuracy of the algorithm provided by the invention and the positioning accuracy of two similar algorithms in office and corridor scenes, and can find that the algorithm provided by the invention obtains higher positioning accuracy in two indoor scenes, so that the algorithm has stronger robustness and universality.
Based on the above embodiment, the present invention further provides a multi-source positioning device, as shown in fig. 12, including:
The micro-electromechanical system sensor group 01 is used for acquiring positioning information to be corrected;
The action hotspot module 02 is configured to determine a target positioning algorithm corresponding to the user terminal, and determine reference positioning information according to the target positioning algorithm, where the target positioning algorithm is a positioning algorithm executed by the user terminal through the action hotspot;
And the positioning correction module 03 is configured to correct the positioning information to be corrected according to the reference positioning information, so as to obtain target positioning information corresponding to the user terminal.
Based on the above embodiment, the present invention also provides a terminal, and a functional block diagram thereof may be shown in fig. 13. The terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. Wherein the processor of the terminal is adapted to provide computing and control capabilities. The memory of the terminal includes a nonvolatile 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 terminal is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a multi-source fusion localization method. The display screen of the terminal may be a liquid crystal display screen or an electronic ink display screen.
It will be appreciated by those skilled in the art that the functional block diagram shown in fig. 13 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the terminal to which the present inventive arrangements may be applied, and that a particular terminal may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In one implementation, one or more programs are stored in a memory of the terminal and configured to be executed by one or more processors, the one or more programs including instructions for performing a multi-source fusion positioning method.
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 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 (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In summary, the invention discloses a multisource fusion positioning method, which comprises the following steps: acquiring positioning information to be corrected through a micro-electromechanical system sensor group in a user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through a mobile hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention combines the positioning results output by the two positioning sources of the action hot spot and the micro-electromechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
Claims (7)
1. A method of multi-source fusion localization, the method comprising:
acquiring positioning information to be corrected through a micro-electromechanical system sensor group in a user terminal;
determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through a mobile hotspot;
correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal;
When the target positioning algorithm is a mobile hotspot ranging algorithm and a mobile hotspot fingerprint algorithm, determining the target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm includes:
Acquiring channel state data between a plurality of first access points and the user terminal respectively;
inputting the channel state data corresponding to the first access points into the action hot spot ranging algorithm to obtain distance data between the first access points and the user terminal;
Determining first reference positioning information according to the distance data respectively corresponding to a plurality of first access points;
acquiring signal intensity data between a plurality of second access points and the user terminal respectively;
Inputting the signal intensity data corresponding to the second access points into the action hot spot fingerprint algorithm to obtain second reference positioning information;
taking the first reference positioning information and the second reference positioning information as the reference positioning information;
The plurality of first access points include a first access point and a second access point, and determining the first reference positioning information according to the distance data corresponding to the plurality of first access points respectively includes: acquiring position information corresponding to the first access point and the second access point respectively; determining ranging positioning information according to the distance data and the position information respectively corresponding to the first access point and the second access point; constructing a vector to be inspected according to the position information and the ranging positioning information respectively corresponding to the first access point and the second access point; judging whether the closed loop of the vector to be checked is established, and taking the ranging positioning information as the first reference positioning information when the closed loop of the vector to be checked is established; the principle of closed loop detection is as follows:
constructing a ranging model of a mobile hotspot ranging algorithm:
Lobserved＝LFTM+dbias+dN+drandom
Wherein, L observed is the ranging distance value obtained by the receiving end, L FTM is the true value, d bias is the initial zero offset value, d N is the non-line-of-sight error value, and d random is the random error value;
establishing a closed-loop model, wherein the closed-loop model in an ideal state is as follows:
Wherein, AndRespectively representing vectors constructed between the three points A, B and C; the closed loop model after adding the error amount is as follows:
wherein the newly added parameter d p represents an uncertainty error caused by the position update;
the closed loop model equation is converted from vector form to coordinate form:
Wherein X BC and Y BC each represent a closed-loop vector Coordinate resolution result of AndRespectively representing coordinate differences between the predicted position coordinates and two Wi-Fi base stations;
Constructing a corresponding vector for the mobile hotspot ranging result according to the predicted position coordinates:
Wherein, AndRepresenting ranging vectors constructed based on updated position,Representing the actual ranging result between the points A and B,Representing Euclidean distance between the predicted position A and the point B corresponding to the Wi-Fi access point; constructing a closed loop vector in the form of coordinates:
Wherein, For a closed loop vector, whether the final closed loop result is true is expressed by the following equation:
Wherein, For closed loop vector modulus, D r is the variance of the random error of the range, D p is the variance of the uncertainty error of the predicted position, μ is the scale; when the closed loop vector modulus value is larger than the calculation result on the right side, judging that the closed loop vector is interfered by a non-line-of-sight error;
Inputting the signal intensity data corresponding to the plurality of second access points into the action hot spot fingerprint algorithm to obtain the second reference positioning information, wherein the method comprises the following steps: acquiring a preset action hot spot fingerprint database, wherein the action hot spot fingerprint database comprises a plurality of reference access points, and each reference access point is provided with a signal intensity label and an address label; comparing the signal intensity data corresponding to the second access points with the action hot spot fingerprint database to obtain candidate reference access points; inputting a plurality of reference access points into a nearest neighbor matching algorithm to obtain weight values corresponding to the candidate reference access points respectively; determining the second reference positioning information according to the weight values and the address labels respectively corresponding to the candidate reference access points;
the K value self-adaptive adjustment method in the nearest neighbor matching algorithm comprises the following steps:
In the method, in the process of the invention, Dis t,other is other values for the nearest value in the K nearest neighbor values, and when gamma is smaller than a set threshold value kappa, K nearest neighbor values meeting the conditions are reserved, so that the self-adaptive adjustment of the K values is completed; the calculated weighted positions are as follows:
Wherein, For the weight size of each nearest neighbor position, POS (x i,yi) is the corresponding two-dimensional position coordinate.
2. The method of claim 1, wherein the mems sensor group includes a magnetometer, a gyroscope, an accelerometer, and a barometer, and the acquiring the positioning information to be corrected by the mems sensor group in the user terminal includes:
acquiring angular velocity data output by the gyroscope;
Acquiring acceleration data output by the accelerometer;
acquiring magnetic field data output by the magnetometer;
Acquiring air pressure data output by the air pressure gauge, and determining height data corresponding to the user terminal according to the air pressure data;
And determining the positioning information to be corrected according to the angular speed data, the acceleration data, the magnetic field data and the altitude data.
3. The multi-source fusion positioning method according to claim 2, wherein the determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data, and the altitude data includes:
Inputting the angular velocity data and the acceleration data into an inertial navigation positioning model to obtain inertial navigation positioning information;
inputting the acceleration data into a pedestrian navigation positioning model to obtain pedestrian navigation positioning information;
and inputting the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information into a preset filtering algorithm to obtain the positioning information to be corrected.
4. The multi-source fusion positioning method according to claim 1, wherein the correcting the positioning information to be corrected according to the reference positioning information to obtain the target positioning information corresponding to the user terminal includes:
determining first error data corresponding to the MEMS sensor group according to the first reference positioning information;
Correcting the positioning information to be corrected according to the first error data to obtain corrected positioning information;
Determining second error data corresponding to the MEMS sensor group according to the second reference positioning information;
And correcting the corrected positioning information according to the second error data to obtain the target positioning information.
5. The multi-source fusion positioning method according to claim 1, wherein when the target positioning algorithm is a mobile hotspot fingerprint algorithm, the determining the target positioning algorithm corresponding to the user terminal, and determining the reference positioning information according to the target positioning algorithm, includes:
and determining the reference positioning information through the action hot spot fingerprint algorithm.
6. The multi-source fusion positioning method according to claim 5, wherein the correcting the positioning information to be corrected according to the reference positioning information to obtain the target positioning information corresponding to the user terminal includes:
determining error data corresponding to the MEMS sensor group according to the reference positioning information;
and correcting the positioning information to be corrected according to the error data to obtain the target positioning information.
7. A computer readable storage medium having stored thereon a plurality of instructions adapted to be loaded and executed by a processor to implement the steps of the multisource fusion localization method of any of the preceding claims 1-6.
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