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CN117998289A - Indoor positioning method and device with strong robustness - Google Patents

Indoor positioning method and device with strong robustness Download PDF

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Publication number
CN117998289A
CN117998289A CN202410023759.5A CN202410023759A CN117998289A CN 117998289 A CN117998289 A CN 117998289A CN 202410023759 A CN202410023759 A CN 202410023759A CN 117998289 A CN117998289 A CN 117998289A
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node
value
nodes
preset
rssi
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CN117998289B (en
Inventor
袁雪林
彭丁卫
欧阳明俊
朱祥维
王尚洋
沈丹
何少烽
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Sun Yat Sen University
Sun Yat Sen University Shenzhen Campus
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Sun Yat Sen University
Sun Yat Sen University Shenzhen Campus
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • 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
    • G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
    • 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
    • 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
    • G01S5/02528—Simulating radio frequency fingerprints
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Quality & Reliability (AREA)
  • Electromagnetism (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a strong-robustness indoor positioning method and a device, wherein the method comprises the following steps: acquiring a plurality of AP nodes, wherein each AP node is an AP node with an RSSI value smaller than a preset power threshold value; after calculating the entropy value of each AP node by using a nonlinear function, extracting a preset number of AP nodes as target nodes according to the entropy value; constructing a loss function by using a circle equation with a preset number of target nodes, and solving the minimum value of the loss function to obtain a positioning coordinate, wherein the circle equation is a matrix equation of a circle constructed by taking the target nodes as circle centers. The invention can avoid the situation that the positioning is deviated due to the mismatch between the fingerprint database and the online stage, not only can improve the positioning precision, but also can adapt to the time variation of a channel so as to screen the AP nodes with better RSSI value stability, thereby improving the positioning robustness.

Description

Indoor positioning method and device with strong robustness
Technical Field
The invention relates to the technical field of indoor positioning, in particular to a strong-robustness indoor positioning method and device.
Background
The RSSI (RECEIVED SIGNAL STRENGTH Indicator) is an indication of the strength of the received signal and is implemented after the back-channel baseband receive filter. The RSSI positioning technology is a positioning technology for measuring the distance between a signal point and a receiving point according to the received signal strength and further performing positioning calculation according to corresponding data.
The RSSI positioning technology specifically comprises the following steps: an offline phase and an online phase. In the off-line stage, the hardware equipment collects the signal intensity of each AP (Access Point) node at RP (Reference Point) nodes to form fingerprint vectors (x RP,yRP,rssiAP1,rssiAP2,KK,rssiAPn), and each fingerprint vector forms a certain row of the fingerprint library matrix. And in the online stage, signal intensity vector RSSIs of all APs are collected at points to be detected, and vector similarity measurement (such as Euclidean distance between calculated vectors, larger distance represents less similarity) with fingerprint library is completed by using methods such as WKNN (WEIGHTED K-Nearest Neighbor), KNN (K-Nearest Neighbor), K-means+WKNN/KNN, bayes estimation and the like, and finally positioning is realized according to coordinates of all RP nodes in a fingerprint library matrix in the offline stage.
However, the method has the following technical problems: each of the channel propagation models changes due to time-varying characteristics of the channel itself and changes in the environment itself, such as personnel flow, movement of the AP (Access Point) nodes, etc. Once the propagation model according to the off-line stage and the on-line stage is changed, the fingerprint library constructed in the off-line stage is not matched with the on-line stage, so that deviation occurs in positioning, and the positioning precision is reduced.
Disclosure of Invention
The invention provides a robust indoor positioning method and a robust indoor positioning device, and the method can solve one or more of the technical problems.
A first aspect of an embodiment of the present invention provides a robust indoor positioning method, where the method includes:
Acquiring a plurality of AP nodes, wherein each AP node is an AP node with an RSSI value smaller than a preset power threshold value;
after calculating the entropy of each AP node by using a nonlinear function, extracting a preset number of AP nodes as target nodes according to the entropy;
constructing a loss function by using a preset number of round equations of the target nodes, and solving the minimum value of the loss function to obtain positioning coordinates, wherein the round equations are matrix equations of circles constructed by taking the target nodes as circle centers.
In a possible implementation manner of the first aspect, the acquiring a plurality of AP nodes includes:
Constructing a propagation model of a Gaussian white noise corresponding channel, and acquiring a plurality of node signals on the basis of the propagation model, wherein each node signal is a signal of an AP node;
After determining the RSSI value corresponding to each node signal, screening a plurality of AP nodes with RSSI values smaller than a preset power threshold from the plurality of node signals as target node signals, and determining the AP node of each target node signal to obtain a plurality of AP nodes.
In a possible implementation manner of the first aspect, the propagation model is as follows:
rssiAPi＝PT-Pd0-10nilog10di+X＝α-10nilog10di+N(0,δ2);
In the above equation, P T is the transmission power of the signal, P d0 is the loss at the distance APd 0, X is white gaussian noise, and α is an invariant of the channel parameters of the propagation model.
In a possible implementation manner of the first aspect, the determining the invariant α includes:
After determining any random node in an area to be positioned, acquiring signal intensity values of the random node for a plurality of times at random distances from the random node to obtain a plurality of signal intensity values;
After calculating the average value of the signal intensity values to obtain an intensity average value, constructing an intensity coordinate system by taking the intensity average value as an ordinate and taking the logarithm of the random distance as an abscissa;
and adding the signal intensity values serving as coordinate points to the intensity coordinate system and connecting the intensity coordinate system to form a coordinate curve, and calculating the longitudinal intercept of the coordinate curve based on a linear fitting method of a least square method to obtain an invariant alpha.
In a possible implementation manner of the first aspect, the calculating, using a nonlinear function, an entropy value of each AP node includes:
counting the maximum value and the minimum value of the RSSI value of each AP node, and generating an RSSI vector by using the maximum value and the minimum value;
After normalizing the RSSI vector to obtain a normalized vector, mapping the normalized vector to a y-axis of a preset coordinate system by using a nonlinear function, wherein the preset coordinate system is a coordinate system set for a preset compression coefficient u value;
Counting the number of AP nodes in M sections of cells on a y axis, and calculating the ratio of the number of the AP nodes to the sampling times to obtain sampling frequency;
substituting the sampling frequency into a preset information entropy formula to calculate and obtain the entropy value of the AP node.
In a possible implementation manner of the first aspect, the constructing a loss function using a circle equation of a preset number of the target nodes, and solving a minimum value of the loss function, to obtain positioning coordinates includes:
Constructing a circle corresponding to the target node by using a preset radius value with the target node as a circle center, and acquiring an equation corresponding to the circle to obtain a circle equation, wherein the preset radius value is calculated based on an intensity average value of the target node and a random parameter, and the random parameter is an integer randomly generated according to an indoor environment to be positioned;
subtracting the preset number of the round equations from each other to obtain a plurality of linear equations, and converting the plurality of linear equations into a matrix equation set;
And solving the matrix equation set by using a least square method to obtain a solving coordinate, defining a loss function by using the solving coordinate, and obtaining a positioning coordinate by solving the minimum value of the loss function.
In one possible implementation manner of the first aspect,
Generating the loss function as shown in the following formula:
In the above-mentioned method, the step of, The preset radius value is shown as the following formula:
in the above formula, alpha is an invariant, For intensity average, n i is a random parameter.
A second aspect of an embodiment of the present invention provides a robust indoor positioning apparatus, the apparatus comprising:
The acquisition module is used for acquiring a plurality of AP nodes, wherein each AP node is an AP node with an RSSI value smaller than a preset power threshold value;
the node extraction module is used for extracting a preset number of AP nodes as target nodes according to the entropy value after calculating the entropy value of each AP node by using a nonlinear function;
And the positioning module is used for constructing a loss function by utilizing a preset number of round equations of the target nodes, solving the minimum value of the loss function, and obtaining positioning coordinates, wherein the round equations are matrix equations of circles constructed by taking the target nodes as circle centers.
In a possible implementation manner of the second aspect, the acquiring a plurality of AP nodes includes:
Constructing a propagation model of a Gaussian white noise corresponding channel, and acquiring a plurality of node signals on the basis of the propagation model, wherein each node signal is a signal of an AP node;
After determining the RSSI value corresponding to each node signal, screening a plurality of AP nodes with RSSI values smaller than a preset power threshold from the plurality of node signals as target node signals, and determining the AP node of each target node signal to obtain a plurality of AP nodes.
In a possible implementation manner of the second aspect, the propagation model is as follows:
rssiAPi＝PT-Pd0-10nilog10di+X＝α-10nilog10di+N(0,δ2);
In the above equation, P T is the transmission power of the signal, P d0 is the loss at the distance APd 0, X is white gaussian noise, and α is an invariant of the channel parameters of the propagation model.
In a possible implementation manner of the second aspect, the determining the invariant α includes:
After determining any random node in an area to be positioned, acquiring signal intensity values of the random node for a plurality of times at random distances from the random node to obtain a plurality of signal intensity values;
After calculating the average value of the signal intensity values to obtain an intensity average value, constructing an intensity coordinate system by taking the intensity average value as an ordinate and taking the logarithm of the random distance as an abscissa;
and adding the signal intensity values serving as coordinate points to the intensity coordinate system and connecting the intensity coordinate system to form a coordinate curve, and calculating the longitudinal intercept of the coordinate curve based on a linear fitting method of a least square method to obtain an invariant alpha.
In a possible implementation manner of the second aspect, the calculating, using a nonlinear function, an entropy value of each AP node includes:
counting the maximum value and the minimum value of the RSSI value of each AP node, and generating an RSSI vector by using the maximum value and the minimum value;
After normalizing the RSSI vector to obtain a normalized vector, mapping the normalized vector to a y-axis of a preset coordinate system by using a nonlinear function, wherein the preset coordinate system is a coordinate system set for a preset compression coefficient u value;
Counting the number of AP nodes in M sections of cells on a y axis, and calculating the ratio of the number of the AP nodes to the sampling times to obtain sampling frequency;
substituting the sampling frequency into a preset information entropy formula to calculate and obtain the entropy value of the AP node.
In a possible implementation manner of the second aspect, the constructing a loss function using a circle equation of a preset number of the target nodes, and solving a minimum value of the loss function, to obtain positioning coordinates includes:
Constructing a circle corresponding to the target node by using a preset radius value with the target node as a circle center, and acquiring an equation corresponding to the circle to obtain a circle equation, wherein the preset radius value is calculated based on an intensity average value of the target node and a random parameter, and the random parameter is an integer randomly generated according to an indoor environment to be positioned;
subtracting the preset number of the round equations from each other to obtain a plurality of linear equations, and converting the plurality of linear equations into a matrix equation set;
And solving the matrix equation set by using a least square method to obtain a solving coordinate, defining a loss function by using the solving coordinate, and obtaining a positioning coordinate by solving the minimum value of the loss function.
In one possible implementation manner of the second aspect,
Generating the loss function as shown in the following formula:
In the above-mentioned method, the step of, The preset radius value is shown as the following formula:
in the above formula, alpha is an invariant, For intensity average, n i is a random parameter.
Compared with the prior art, the indoor positioning method and device with strong robustness provided by the embodiment of the invention have the beneficial effects that: the invention can obtain a plurality of AP nodes with RSSI values smaller than a preset power threshold; screening a preset number of AP nodes as target nodes according to the entropy values of the AP nodes; the invention can avoid the situation that the positioning is deviated due to the mismatching of the fingerprint database and the online stage, not only can improve the positioning precision, but also can adapt to the time variation of a channel so as to screen the AP nodes with better stability of the RSSI value, thereby improving the positioning robustness.
Drawings
Fig. 1 is a schematic flow chart of a robust indoor positioning method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the non-linear function for AP entropy calculation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a baseline and intersection of four circles according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating an indoor positioning method with high robustness according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a robust indoor positioning device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the above-mentioned problems, the following detailed description and explanation will be given of a robust indoor positioning method according to the embodiments of the present application.
Referring to fig. 1, a flow chart of a robust indoor positioning method according to an embodiment of the present invention is shown.
Among them, as an example, the robust indoor positioning method may include:
S11, acquiring a plurality of AP nodes, wherein each AP node is an AP node with an RSSI value smaller than a preset power threshold.
In an embodiment, in an indoor environment to be detected, a plurality of AP nodes (Access points) may be provided, and signals of many AP nodes exist. Each AP node has a corresponding signal value.
Because the RSSI value of each AP node is different, after a plurality of AP nodes are determined, one-time screening can be performed, and the AP nodes with the RSSI value smaller than the preset power threshold value are screened to select the AP nodes with small RSSI value fluctuation so as to improve the accuracy of subsequent positioning.
As an example, step S11 may include the following sub-steps:
s111, constructing a propagation model of a Gaussian white noise corresponding channel, and acquiring a plurality of node signals on the basis of the propagation model, wherein each node signal is a signal of an AP node.
In an actual application example, it may be assumed that the noise in the room is white gaussian noise, and a propagation model of a corresponding channel may be pre-constructed, and when the RSSI value of each AP node (Access Point) is sampled several times, the sampling time is short, and parameters of the propagation model of the corresponding channel are not changed. On this basis, node signals of a plurality of AP nodes can be acquired.
In one embodiment, the propagation model is represented by the following formula:
rssiAPi＝PT-Pd0-10nilog10di+X＝α-10nilog10di+N(0,δ2);
In the above equation, P T is the transmission power of the signal, P d0 is the loss at the distance APd 0, X is white gaussian noise, and α is an invariant of the channel parameters of the propagation model.
Wherein, as an example, the operation of determining the invariant α may comprise the sub-steps of:
s21, after any random node is determined in the area to be positioned, the signal intensity values of the random node are acquired for a plurality of times at random distances from the random node, and a plurality of signal intensity values are obtained.
S22, calculating the average value of the signal intensity values to obtain an intensity average value, and then constructing an intensity coordinate system by taking the intensity average value as an ordinate and taking the logarithm of the random distance as an abscissa.
S23, adding the signal intensity values serving as coordinate points to the intensity coordinate system and connecting the intensity coordinate system to form a coordinate curve, and calculating the longitudinal intercept of the coordinate curve based on a linear fitting method of a least square method to obtain an invariant alpha.
In one embodiment, to reduce the number of uncertainty parameters, a unique invariant α may be determined based on the circumstances. A more reasonable value of alpha ensures that the most accurate possible distance estimation and the smallest possible loss function are obtained during the random parameter search.
Specifically, after determining any random node AP i in the area to be located, the signal strength value rsi of the random node AP i may be acquired multiple times at different random distances d i from the random node AP i, to obtain multiple signal strength values.
Calculating the average value of the signal intensity values to obtain the intensity average valueLog 10di of random distance d i from random node AP i is taken as the abscissa, and the intensity average/>An intensity coordinate system is constructed as the ordinate.
The data of a plurality of AP nodes are added in an intensity coordinate system, and the longitudinal intercept alpha of a straight line can be obtained by utilizing a straight line fitting method based on a least square method. Once α is obtained by measurement, it can be treated as an invariant α within a certain scene.
S112, after the RSSI value corresponding to each node signal is determined, a plurality of AP nodes with the RSSI values smaller than a preset power threshold value are screened from the plurality of node signals to be target node signals, and the AP nodes of each target node signal are determined to obtain a plurality of AP nodes.
In order to select an AP node with small fluctuation of RSSI values. In an actual indoor environment, signals of a plurality of AP nodes exist, and the signal intensity and fluctuation of each AP node also differ. The RSSI value corresponding to the node signal may be determined, and then the AP nodes whose RSSI value is less than a preset power threshold (assume-90 dbM) during the sampling process are screened, thereby obtaining a plurality of AP nodes.
S12, after calculating the entropy value of each AP node by using a nonlinear function, extracting a preset number of AP nodes as target nodes according to the entropy value.
In an embodiment, normalization operation may be performed on the AP node, then, the entropy value of the processed AP node is obtained by using a method of weighting and entropy calculation by using a nonlinear function, and then, the AP with high signal stability is selected according to the entropy value.
Optionally, the AP nodes with the maximum entropy are selected according to the preset number, or the AP nodes with the minimum entropy are selected according to the preset number, so as to be used as the target nodes.
In an alternative embodiment, step S12 may comprise the sub-steps of:
s121, counting the maximum value and the minimum value of the RSSI value of each AP node, and generating an RSSI vector by using the maximum value and the minimum value.
S122, after normalization processing is carried out on the RSSI vector to obtain a normalized vector, mapping the normalized vector to a y-axis of a preset coordinate system by using a nonlinear function, wherein the preset coordinate system is a coordinate system set for a preset compression coefficient u value.
S123, counting the number of AP nodes in the M sections of cells on the y axis, and calculating the ratio of the number of the AP nodes to the sampling times to obtain the sampling frequency.
S124, substituting the sampling frequency into a preset information entropy formula to calculate and obtain the entropy value of the AP node.
In one embodiment, assume that the ith AP node AP i is sampled N times, and the maximum max and minimum min of RSSI are counted in the N samples to obtain an RSSI vector, where the RSSI vector is shown in the following formula: RSSI APi＝(rssi1i,rssi2i,L L,rssiNi).
And normalizing the RSSI vector, wherein the normalized vector obtained after normalization is shown in the following formula: RSSI APi'＝(RSSIAPi -min)/(max-min).
The normalized vector RSSI APi' is then mapped to the y-axis of the preset coordinate system using a nonlinear function y=log (1+u ·x). The preset coordinate system is a coordinate system set for a preset compression coefficient u value.
For a specific compression coefficient u value, the y-axis is uniformly divided into M-segment cells, and the range of RSSI APi' corresponding to each cell is calculated and stored in advance. Counting the number of RSSIs APi' in M sections of cells on the y axis, dividing the number by the sampling frequency N to obtain a frequency p i, and calculating the entropy value of the AP node according to the information entropy formula I= -p ilog2pi of shannon. The entropy value is small, and the fluctuation of the RSSI is small.
Referring to fig. 2, a schematic diagram of AP entropy calculation by using a nonlinear function according to an embodiment of the present invention is shown.
Specifically, fig. 2 gives an example of a nonlinear function log (1+5·x) for calculating the AP entropy value. In fig. 2, the y-axis is divided into 9 segments, and the red marked point represents the boundary point of RSSI APi' corresponding to a certain segment of cells. In consideration of the fact that the fluctuation of the RSSI value is larger in hours, the nonlinear function is divided more densely near x=0, namely the RSSI value is divided more densely near the minimum value, and the RSSI value is divided more sparsely near the maximum value, so that the AP with good stability can be screened more. The entropy value of the AP can be calculated according to the information entropy formula i= -p ilog2pi by only counting how many measured values of RSSI are included in a certain cell.
In an embodiment, in order to ensure that the accuracy of subsequent positioning is improved under the condition of limited searching times, the first 4 AP nodes with the smallest entropy values can be selected as signal sources and sequentially used as target nodes.
Optionally, the RSSI value of the AP i node is collected for 20 times to obtain the average value of the signal strength
S13, constructing a loss function by using a preset number of round equations of the target nodes, and solving the minimum value of the loss function to obtain positioning coordinates, wherein the round equations are matrix equations of circles constructed by taking the target nodes as circle centers.
In an embodiment, a circular equation may be constructed by using each objective node, then a loss function may be constructed by using four circular equations, and finally the minimum value of the loss function may be solved, thereby obtaining the positioning coordinates. The above-mentioned round equation is a matrix equation of a circle constructed with the target node as the center of a circle.
In one embodiment, step S13 may include the sub-steps of:
S131, taking the target node as a circle center, constructing the target node by adopting a preset radius value to correspond to the target node to obtain a circle and obtaining an equation corresponding to the circle to obtain a circle equation, wherein the preset radius value is calculated based on an intensity average value of the target node and a random parameter, and the random parameter is an integer randomly generated according to an indoor environment to be positioned.
S132, after subtracting the preset number of the round equations from each other to obtain a plurality of linear equations, converting the plurality of linear equations into a matrix equation set.
S133, solving the matrix equation set by using a least square method to obtain solving coordinates, defining a loss function by using the solving coordinates, and obtaining a minimum value of the loss function to obtain positioning coordinates.
Specifically, assuming that M AP nodes meet the requirement (where M < =4), M equations are obtained, wherein M uses the AP nodes as circle centers, a preset radius value is used as a radius to construct a corresponding circle, and then the circle equation is determined.
Wherein the preset radius value can be a distance estimation valueMay be calculated based on the intensity average of the target node and a random parameter. Wherein, distance estimation value/>The calculation of (2) may be as follows:
the preset radius value may be represented by the following formula:
in the above formula, alpha is an invariant, For intensity average, n i is a random parameter.
The equation for a circle can be shown as follows:
wherein the above-mentioned random parameter is an integer randomly generated according to the indoor environment to be located.
Specifically, the random parameter setting is that the upper and lower bounds of a given n i are [ bottom, up ], and the value of n i is randomly selected. In an actual indoor environment, a bottom=0.1 and up=6.0 may be selected.
To ensure that the generated random n i can traverse uniformly within the range of values, the path loss factor n i can be obtained by multiplying the bottom and the up by 10, returning the integer step length (the default integer step length is 1) within the range of [10 x bottom and 10 x up ] as a random integer with variable size, and dividing the random number by 10.
In one embodiment, at most 4 APs can be screened out, and then a distance estimate can be obtained according to the above formulaThe random parameters are set to solve the optimization equation by a random search method, and the optimal parameter setting is found to minimize the loss function, so that the most accurate user coordinates are obtained.
Referring to fig. 3, a schematic diagram of the baseline and intersection points of four circles according to an embodiment of the present invention is shown.
In one embodiment, after the round equations for each AP node are constructed, the M round equations may be subtracted from each other to obtain M (M-1)/2 straight line equations, each of which is referred to as the base line of two circles. If two circles intersect, the baseline is determined by the common intersection of the two circles. If two circles are tangent, the baseline is the common tangent of the two circles. If the two circles are separated, the point on the base line is equal to the length of the tangent of the two circles. As particularly shown in fig. 3.
Specifically, writing M (M-1)/2 straight lines into a matrix equation set can be shown as follows:
Ax＝b；
Wherein A is M (M-1)/2 rows and2 columns of matrix; b is M (M-1)/2 rows, 1 columns of column vectors; x is the two-dimensional position (x, y) of the user. And obtaining a solving coordinate C of the user position by using a least square method, wherein the solving coordinate can be shown as follows:
C＝(x,y)T＝(ATA)-1ATb；
If the solving coordinate C point is out of the circle, the distance from the solving coordinate C point to the circle center is larger than the radius; if the solving coordinate C point is in the circle, the distance from the solving coordinate C point to the circle center is smaller than the radius; if the solving coordinate C point is on a circle, the distance from the solving coordinate C point to the circle center is equal to the radius. Considering the different radii of the different circles, the defined loss function may be as follows:
In the above-mentioned method, the step of, Is a preset radius value.
As shown in fig. 3, four circles, six baselines are drawn, and the center point represents point C. Due to the path loss factor n i at this time. At this time, the channel parameters are not accurate enough, and the four circles do not intersect at one point, resulting in a larger loss function.
At this time, under the given random search times, the parameter estimation value and the C point coordinate corresponding to the minimum Loss value are reserved, and finally the three parameter values are output. At the minimum of the loss function, M circles tend to intersect at one point, and finally the output C point coordinate is the positioning result.
Referring to fig. 4, an operation flowchart of a robust indoor positioning method according to an embodiment of the present invention is shown.
Specifically, the robust indoor positioning method may include the steps of:
In the first step, each AP samples N times to eliminate AP nodes with RSSI < -90db M.
And secondly, normalizing the RSSI fingerprint.
Thirdly, respectively determining the upper and lower bounds of the loss factor ni; the invariant alpha is determined by a least square method through actual measurement; the nonlinear function weights the RSSI fingerprint, calculates entropy, and then picks the first 4 nodes with the smallest entropy values.
Fourth, random parameter n i is set.
And fifthly, calculating a loss function and judging whether the maximum searching times are reached.
Sixth, if the maximum searching times are not reached, returning to the fourth step; and if the maximum searching times are reached, outputting the minimum loss function, the positioning coordinates and the channel parameters.
Compared with a method for positioning by using a fingerprint library only, the method has better robustness and practicability. The invention does not need to establish a fingerprint library in an off-line stage, and realizes real-time positioning by estimating the channel parameters in real time by an optimization method. Therefore, the invention effectively avoids the problem of low positioning precision caused by channel time variation and environment variation. And secondly, the AP nodes with smaller fluctuation are screened by a nonlinear weighting entropy calculation method before positioning, so that the positioning accuracy is further improved.
This approach is simpler and more efficient than the approach of fusing other sensors. The positioning method integrating the information such as RSSI, inertial sensor, indoor map and the like requires additional inertial sensor equipment and more complex processing algorithms, and also requires the knowledge of prior information such as measurement errors of the inertial sensor equipment. In addition, the acquisition of the indoor map increases the workload of the earlier stage; due to purchase, movement and the like of furniture, the indoor map also needs to be updated in real time, which increases the cost of post maintenance of the positioning facilities. The method of fusing multiple types of data by using the neural network is at risk of unstable generalization capability of the model.
In addition, the invention does not need a fingerprint library, and can estimate the parameters of the channel propagation model in real time according to the RSSI vector acquired in real time, thereby finally realizing the positioning method. The method not only can adapt to time variation of the channel, but also screens AP nodes with better RSSI value stability, and improves the robustness of the system.
In this embodiment, the embodiment of the present invention provides a robust indoor positioning method, which has the following beneficial effects: the invention can obtain a plurality of AP nodes with RSSI values smaller than a preset power threshold; screening a preset number of AP nodes as target nodes according to the entropy values of the AP nodes; the invention can avoid the situation that the positioning is deviated due to the mismatching of the fingerprint database and the online stage, not only can improve the positioning precision, but also can adapt to the time variation of a channel so as to screen the AP nodes with better stability of the RSSI value, thereby improving the positioning robustness.
The embodiment of the invention also provides a strong-robustness indoor positioning device, and referring to fig. 5, a schematic structural diagram of the strong-robustness indoor positioning device is shown.
Among others, the robust indoor positioning device may include, as an example:
An obtaining module 201, configured to obtain a plurality of AP nodes, where each AP node is an AP node whose RSSI value is less than a preset power threshold;
The node extraction module 202 is configured to extract a preset number of AP nodes as target nodes according to the entropy value after calculating the entropy value of each AP node by using a nonlinear function;
and the positioning module 203 is configured to construct a loss function by using a preset number of round equations of the target nodes, and calculate the minimum value of the loss function to obtain positioning coordinates, where the round equations are matrix equations of circles constructed by taking the target nodes as circle centers.
Optionally, the acquiring a plurality of AP nodes includes:
Constructing a propagation model of a Gaussian white noise corresponding channel, and acquiring a plurality of node signals on the basis of the propagation model, wherein each node signal is a signal of an AP node;
After determining the RSSI value corresponding to each node signal, screening a plurality of AP nodes with RSSI values smaller than a preset power threshold from the plurality of node signals as target node signals, and determining the AP node of each target node signal to obtain a plurality of AP nodes.
Optionally, the propagation model is represented by the following formula:
rssiAPi＝PT-Pd0-10nilog10di+X＝α-10nilog10di+N(0,δ2);
In the above equation, P T is the transmission power of the signal, P d0 is the loss at the distance APd 0, X is white gaussian noise, and α is an invariant of the channel parameters of the propagation model.
Optionally, the operation of determining the invariant α includes:
After determining any random node in an area to be positioned, acquiring signal intensity values of the random node for a plurality of times at random distances from the random node to obtain a plurality of signal intensity values;
After calculating the average value of the signal intensity values to obtain an intensity average value, constructing an intensity coordinate system by taking the intensity average value as an ordinate and taking the logarithm of the random distance as an abscissa;
and adding the signal intensity values serving as coordinate points to the intensity coordinate system and connecting the intensity coordinate system to form a coordinate curve, and calculating the longitudinal intercept of the coordinate curve based on a linear fitting method of a least square method to obtain an invariant alpha.
Optionally, the calculating the entropy value of each AP node using a nonlinear function includes:
counting the maximum value and the minimum value of the RSSI value of each AP node, and generating an RSSI vector by using the maximum value and the minimum value;
After normalizing the RSSI vector to obtain a normalized vector, mapping the normalized vector to a y-axis of a preset coordinate system by using a nonlinear function, wherein the preset coordinate system is a coordinate system set for a preset compression coefficient u value;
Counting the number of AP nodes in M sections of cells on a y axis, and calculating the ratio of the number of the AP nodes to the sampling times to obtain sampling frequency;
substituting the sampling frequency into a preset information entropy formula to calculate and obtain the entropy value of the AP node.
Optionally, constructing a loss function by using a preset number of round equations of the target nodes, and solving a minimum value of the loss function to obtain positioning coordinates, including:
Constructing a circle corresponding to the target node by using a preset radius value with the target node as a circle center, and acquiring an equation corresponding to the circle to obtain a circle equation, wherein the preset radius value is calculated based on an intensity average value of the target node and a random parameter, and the random parameter is an integer randomly generated according to an indoor environment to be positioned;
subtracting the preset number of the round equations from each other to obtain a plurality of linear equations, and converting the plurality of linear equations into a matrix equation set;
And solving the matrix equation set by using a least square method to obtain a solving coordinate, defining a loss function by using the solving coordinate, and obtaining a positioning coordinate by solving the minimum value of the loss function.
Optionally, the loss function is generated as shown in the following formula:
In the above-mentioned method, the step of, The preset radius value is shown as the following formula:
in the above formula, alpha is an invariant, For intensity average, n i is a random parameter.
It will be clearly understood by those skilled in the art that, for convenience and brevity, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Further, an embodiment of the present application further provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, which processor when executing the program implements a robust indoor positioning method as described in the above embodiments.
Further, the embodiment of the present application also provides a computer-readable storage medium storing a computer-executable program for causing a computer to perform the robust indoor positioning method according to the above embodiment.
It will be appreciated by those skilled in the art that embodiments of the present application may also be provided including a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), devices and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims (10)

1. A robust indoor positioning method, the method comprising:
Acquiring a plurality of AP nodes, wherein each AP node is an AP node with an RSSI value smaller than a preset power threshold value;
after calculating the entropy of each AP node by using a nonlinear function, extracting a preset number of AP nodes as target nodes according to the entropy;
constructing a loss function by using a preset number of round equations of the target nodes, and solving the minimum value of the loss function to obtain positioning coordinates, wherein the round equations are matrix equations of circles constructed by taking the target nodes as circle centers.
2. The robust indoor positioning method according to claim 1, wherein the acquiring a plurality of AP nodes comprises:
Constructing a propagation model of a Gaussian white noise corresponding channel, and acquiring a plurality of node signals on the basis of the propagation model, wherein each node signal is a signal of an AP node;
After determining the RSSI value corresponding to each node signal, screening a plurality of AP nodes with RSSI values smaller than a preset power threshold from the plurality of node signals as target node signals, and determining the AP node of each target node signal to obtain a plurality of AP nodes.
3. The robust indoor positioning method of claim 2, wherein the propagation model is represented by the formula:
rssiAPi＝PT-Pd0-10nilog10di+X＝α-10nilog10di+N(0,δ2);
In the above equation, P T is the transmission power of the signal, P d0 is the loss at the distance APd 0, X is white gaussian noise, and α is an invariant of the channel parameters of the propagation model.
4. A robust indoor positioning method according to claim 3, wherein the operation of determining the invariant α comprises:
After determining any random node in an area to be positioned, acquiring signal intensity values of the random node for a plurality of times at random distances from the random node to obtain a plurality of signal intensity values;
After calculating the average value of the signal intensity values to obtain an intensity average value, constructing an intensity coordinate system by taking the intensity average value as an ordinate and taking the logarithm of the random distance as an abscissa;
and adding the signal intensity values serving as coordinate points to the intensity coordinate system and connecting the intensity coordinate system to form a coordinate curve, and calculating the longitudinal intercept of the coordinate curve based on a linear fitting method of a least square method to obtain an invariant alpha.
5. The robust indoor positioning method according to claim 1, wherein the calculating the entropy value of each AP node using a nonlinear function comprises:
counting the maximum value and the minimum value of the RSSI value of each AP node, and generating an RSSI vector by using the maximum value and the minimum value;
After normalizing the RSSI vector to obtain a normalized vector, mapping the normalized vector to a y-axis of a preset coordinate system by using a nonlinear function, wherein the preset coordinate system is a coordinate system set for a preset compression coefficient u value;
Counting the number of AP nodes in M sections of cells on a y axis, and calculating the ratio of the number of the AP nodes to the sampling times to obtain sampling frequency;
substituting the sampling frequency into a preset information entropy formula to calculate and obtain the entropy value of the AP node.
6. The robust indoor positioning method according to claim 1, wherein constructing a loss function using a round equation of a preset number of the target nodes, and solving a minimum value of the loss function, to obtain positioning coordinates, comprises:
Constructing a circle corresponding to the target node by using a preset radius value with the target node as a circle center, and acquiring an equation corresponding to the circle to obtain a circle equation, wherein the preset radius value is calculated based on an intensity average value of the target node and a random parameter, and the random parameter is an integer randomly generated according to an indoor environment to be positioned;
subtracting the preset number of the round equations from each other to obtain a plurality of linear equations, and converting the plurality of linear equations into a matrix equation set;
And solving the matrix equation set by using a least square method to obtain a solving coordinate, defining a loss function by using the solving coordinate, and obtaining a positioning coordinate by solving the minimum value of the loss function.
7. The robust indoor positioning method of claim 1, wherein the loss function is generated as follows:
In the above-mentioned method, the step of, The preset radius value is shown as the following formula:
in the above formula, alpha is an invariant, For intensity average, n i is a random parameter.
8. A robust indoor positioning apparatus, the apparatus comprising:
The acquisition module is used for acquiring a plurality of AP nodes, wherein each AP node is an AP node with an RSSI value smaller than a preset power threshold value;
the node extraction module is used for extracting a preset number of AP nodes as target nodes according to the entropy value after calculating the entropy value of each AP node by using a nonlinear function;
And the positioning module is used for constructing a loss function by utilizing a preset number of round equations of the target nodes, solving the minimum value of the loss function, and obtaining positioning coordinates, wherein the round equations are matrix equations of circles constructed by taking the target nodes as circle centers.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a robust indoor positioning method according to any of the claims 1-7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer-executable program for causing a computer to perform the robust indoor positioning method according to any one of claims 1-7.
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