CN110488222B - UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition - Google Patents

UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition Download PDF

Info

Publication number
CN110488222B
CN110488222B CN201910764643.6A CN201910764643A CN110488222B CN 110488222 B CN110488222 B CN 110488222B CN 201910764643 A CN201910764643 A CN 201910764643A CN 110488222 B CN110488222 B CN 110488222B
Authority
CN
China
Prior art keywords
nlos
svm
uwb
condition
follows
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910764643.6A
Other languages
Chinese (zh)
Other versions
CN110488222A (en
Inventor
魏璇
林志赟
韩志敏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Dianzi University
Original Assignee
Hangzhou Dianzi University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Dianzi University filed Critical Hangzhou Dianzi University
Priority to CN201910764643.6A priority Critical patent/CN110488222B/en
Publication of CN110488222A publication Critical patent/CN110488222A/en
Application granted granted Critical
Publication of CN110488222B publication Critical patent/CN110488222B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Remote Sensing (AREA)
  • Signal Processing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Complex Calculations (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a UWB positioning method combining SVM and barycentric coordinates under NLOS conditions, and belongs to the technical field of wireless sensor networks. The method comprises the following steps: identifying whether the collected measured value is NLOS or LOS propagation condition by using SVM algorithm, and relieving the measurement error caused by NLOS; step two: constructing a distance square matrix by using the measurement value under the LOS condition and the measurement value after the NLOS is relieved, and obtaining the relative position of each UWB node by using an MDS algorithm; step three: calculating a generalized barycentric coordinate about an unknown node according to the relative position obtained by the MDS algorithm; step four: and calculating the actual coordinates of the unknown nodes by combining the generalized barycentric coordinates and the known coordinates of each anchor node. The method provided by the invention can effectively identify NLOS in a complex environment and effectively improve the positioning accuracy of UWB.

Description

UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition
Technical Field
The invention relates to the technical field Of wireless sensor networks, in particular to a UWB (Ultra Wide Band, UWB) positioning method combining SVM (support Vector machine) and barycentric coordinates under the condition Of NLOS (Non-Line-Of-Sight).
Background
With the rapid development of wireless sensor network technology, wireless sensor positioning technology has become a research hotspot at present, and has important significance and very wide application prospect in various fields such as commerce, industry, military and the like. In recent years, UWB has been developed rapidly, and has the advantages of high transmission rate, low power consumption, strong interference immunity, strong multipath resolution, strong penetration capability, etc., which make it have excellent performance in wireless sensor positioning. However, in some complex environments (such as indoor, underground tunnel, etc.), due to the dense obstacles, the wireless sensor can randomly switch between LOS (Line-Of-Sight) propagation and NLOS propagation. In the NLOS environment, due to the absence of a direct signal path, propagation time delay is caused, and a positive deviation is generated, so that a ranging error of UWB increases, which greatly affects positioning accuracy.
It is a current research hotspot to correctly identify and mitigate NLOS in a mixed LOS and NLOS environment. The algorithms researched at present are mostly divided into two types, one is an identification algorithm, the measurement values under LOS and NLOS conditions are distinguished through the identification algorithm, and then the measurement values under LOS conditions are only used for positioning, so that the method can often cause positioning failure when the NLOS conditions are serious, and the positioning performance is greatly influenced; the other is a mitigation algorithm, all data is input into the mitigation algorithm for processing, the algorithm is usually high in computational complexity, and the positioning performance is deteriorated with the increase of NLOS measurement data.
The invention aims to solve the UWB positioning problem under the NLOS propagation condition, and provides a UWB positioning method combining an SVM and barycentric coordinates under the NLOS condition. The method identifies the measured value under the NLOS propagation condition by using the SVM algorithm, can effectively relieve the error caused by the NLOS, and simultaneously carries out positioning by using the measured value under the LOS condition and the relieved NLOS measured value. The method creatively combines the two algorithms of identification and mitigation, and greatly improves the positioning precision.
Disclosure of Invention
The invention aims to provide a UWB positioning method combining an SVM (support vector machine) and a barycentric coordinate under an NLOS (non-line-of-sight) condition aiming at the defects of the prior art, so as to solve the problem of inaccurate positioning caused by the influence of the NLOS on UWB positioning in a complex environment.
The purpose of the invention is realized by the following technical scheme: a UWB positioning method combining SVM and barycentric coordinates under NLOS condition comprises the following steps:
step 1: the method comprises the steps of using UWB radio to carry out multiple measurements in an indoor LOS environment and an NLOS environment respectively, collecting Channel Impulse Response (CPR) in the LOS environment and the NLOS environment respectively, and extracting characteristic samples representing propagation conditions according to the CPR.
Step 2: an SVC (Support Vector Classifier, SVC) is constructed, feature samples extracted under NLOS and LOS conditions and corresponding labels respectively form feature matrixes, and the feature matrixes are input into the SVC for training to obtain the trained SVC.
And step 3: an SVM (Support Vector regression, SVR) is constructed, and the feature samples extracted under the NLOS and LOS conditions and the corresponding output values (distance between UWB nodes) are input into an SVM for training to obtain the trained SVR.
And 4, step 4: collecting new channel impulse response (CPR), extracting a characteristic sample representing a propagation condition, inputting the characteristic sample into a trained SVC, classifying whether a measured value belongs to a LOS propagation condition or an NLOS propagation condition by using the trained SVC, and judging whether the number of the measured values under the LOS propagation condition meets a positioning requirement (for example, if the positioning space is a two-dimensional space, the number of the measured values is not less than 3, if the positioning space is a three-dimensional space, the number of the measured values is not less than 4), if the requirement is met, constructing a distance square matrix by using the measured values under the LOS propagation condition, and inputting the distance square matrix into an MDS (Multi-Dimension Scaling, MDS) algorithm to obtain a relative position; if the number of the measured values under the LOS propagation condition can not meet the positioning requirement, the trained SVR is used for relieving the measured values under the NLOS condition, namely the feature samples under the NLOS condition are input into the trained SVR to obtain new corresponding output values, the relieved measured values and the measured values under the LOS condition are selected to construct a distance square matrix, and the relative position is obtained through an MDS algorithm.
And 5: and calculating barycentric coordinates of the unknown node (the node with unknown coordinates and to be positioned) by using the relative position acquired by the MDS algorithm, and calculating the actual position coordinates of the unknown node by using the barycentric coordinates and the coordinates of the known anchor node (the node with known coordinates).
Further, in step 1, the features extracted according to CPR include:
received signal strength epsilonrThe calculation formula is as follows:
Figure BDA0002171548200000021
where r (t) is the received signal amplitude at time t;
the distance d between nodes is calculated according to the following formula:
d＝c(ti-t0)
where c is the speed of light, tiTo receive time, t0Is a response request time;
maximum amplitude r of received signalmaxThe calculation formula is as follows:
Figure BDA0002171548200000031
the peak value κ is calculated as follows:
Figure BDA0002171548200000032
wherein mu|r|As an average of the amplitudes of the received signals,
Figure BDA0002171548200000033
t is the variance of the amplitude of the received signal, and T is the sampling time;
average excess delay time TMEDThe calculation formula is as follows:
Figure BDA0002171548200000034
wherein ψ (t) ═ r (t) ceiling2/εr
Root mean square delay spread time TRMSThe calculation formula is as follows:
Figure BDA0002171548200000035
further, the step 2 specifically includes the following steps:
constructing a nonlinear classifier using a Radial Basis (RBF) kernel, performing a feature transformation using the Radial Basis (RBF) kernel, the Radial Basis (RBF) kernel K (x ', Y') being as follows:
K(x′，Y′)＝exp(-γ||x′-Y′||2)
wherein, gamma is a nuclear parameter, x 'is an input characteristic sample, and Y' is a corresponding label value.
The objective function of the SVM classifier SVC is as follows:
Figure BDA0002171548200000036
s.t.Yk(wTxk+b)-1≥0，k＝1，2，…，n
wherein w and b are classifier parameters and are obtained through training; x is the number ofkFor the kth feature sample, YkIs the label of the kth characteristic sample, and n is the total number of the characteristic samples.
And optimizing the objective function by adopting a Lagrange dual function and a Radial Basis Function (RBF) kernel function, wherein the optimization result is as follows:
Figure BDA0002171548200000037
wherein alpha iskIs a lagrange multiplier.
Further, the step 3 specifically includes the following steps:
constructing a regressor SVR structure and parameters of a support vector machine using a Radial Basis (RBF) kernel function, K (x ', y'), as follows:
Figure BDA0002171548200000041
where γ is a kernel parameter, σ is a width of a kernel function, x 'is an input feature sample, and y' is a corresponding output value, i.e., a distance between UWB nodes.
The objective function of the SVM regressor SVR is as follows:
Figure BDA0002171548200000042
s.t.|(wTxk+b)-yk|≤ε，k＝1，2，…，n
wherein w and b are regressor parameters and are obtained by training; n is the total number of characteristic samples, xkFor the kth feature sample, ykAnd epsilon is the error range for the kth corresponding output value.
And optimizing the objective function by adopting a Lagrange dual function and a Radial Basis Function (RBF) kernel function, wherein the optimization result is as follows:
Figure BDA0002171548200000043
wherein alpha iskIs a lagrange multiplier.
Further, in step 4, the distance square matrix is input to the MDS algorithm to obtain the relative position, which specifically includes:
obtaining a distance square matrix:
Figure BDA0002171548200000044
wherein d isijThe distance between the ith UWB node and the jth UWB node is calculated;
calculating a central matrix J:
Figure BDA0002171548200000045
n is the total number of UWB nodes, I is the identity matrix, 1NA matrix of nx 1 and a value of 1;
normalized distance squared matrix:
Figure BDA0002171548200000046
carrying out singular value decomposition on the X to obtain a singular value Lambda and a singular value vector V;
X＝VAVT
arranging the singular values Lambda in the order from large to small, and extracting the maximum singular value and the corresponding singular value vector V which have the same number with the dimension of the positioning space (for example, the number is 2 if the positioning space is a two-dimensional space, and the number is 3 if the positioning space is a three-dimensional space)1Constructing the extracted singular value into a diagonal matrix Lambda1Diagonal matrix Λ to be constructed1And singular value vector V1Multiplying to obtain a relative coordinate matrix Q of each UWB node:
Figure BDA0002171548200000047
wherein Q represents a formula of:
Figure BDA0002171548200000051
(x′i，y′i) Is the relative position of the unknown node (x'1，y′1)，(x′2，y′2)，…，(x′N-1，y′N-1) The relative position of the 1 st, 2 nd, … th, N-1 st anchor nodes.
Further, in step 5, the calculating of barycentric coordinates of unknown nodes using the relative positions obtained by the MDS algorithm specifically includes: regarding the relative position of the unknown node as the generalized barycenter of other nodes, and solving the generalized barycenter coordinates by the following formula:
a1*x′1+a2*x′2+…+aN-1*x′N-1＝x′i
a1*y′1+a2*y′2+…+aN-1*y′N-1＝y′i
a1+a2+…+aN-1＝1
wherein (a)1，a2，...，aN-1) Is a generalized barycentric coordinate.
Further, in step 5, the actual position coordinates are calculated by the following formula:
xi＝a1*x1+a2*x2+…+aN-1*xN-1
yi＝a1*y1+a2*y2+…+aN-1*yN-1
wherein (x)i，yi) For the actual position coordinates of the unknown node, (x)1，y1)，(x2，y2)，…，(xN-1，yN-1) Is the real coordinate of the 1 st, 2 nd, … th, N-1 th anchor node.
The invention has the beneficial effects that: the invention combines SVM and gravity center coordinate method, effectively distinguishes the measured value under NLOS propagation condition from the measured value under LOS condition by using SVM classifier, and relieves the measured value under NLOS propagation condition by using SVM regressor, thus greatly reducing the distance measurement error brought by NLOS. Meanwhile, an unknown node is positioned by using a generalized gravity center coordinate method based on MDS, and the problem that the positioning fails due to the fact that no intersection point or a plurality of intersection points exist in the traditional geometric positioning algorithm frequently is solved. The two algorithms are creatively combined, so that the positioning performance is ensured, and the UWB positioning precision under the NLOS propagation condition is greatly improved. Along with the development of portable equipment, UWB's range of application is more and more extensive, like personnel's tracking, unmanned aerial vehicle, unmanned vehicle's location etc.. The invention has wide application range and has great effect on economic development.
Drawings
Fig. 1 is a flowchart of a UWB positioning method combining SVM and barycentric coordinates under NLOS conditions according to an embodiment of the present invention;
FIG. 2 is a diagram of an SVM training process;
FIG. 3 is a signal diagram of a non-direct wave and a direct wave under propagation conditions;
FIG. 4 is a flow chart of an MDS algorithm;
FIG. 5 is a representation of barycentric coordinates of an unknown node with respect to an anchor node.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
As shown in fig. 1, the UWB locating method combining SVM and barycentric coordinates under NLOS condition provided by the present invention includes the following steps:
(1) the method comprises the steps of respectively carrying out a plurality of times of extensive measurement in an indoor LOS environment and an NLOS environment by adopting UWB radio conforming to FCC, respectively collecting CPR (signal diagram shown in figure 3 under a group of non-direct wave and direct wave propagation conditions) under the NLOS condition and the LOS condition, and extracting characteristic samples capable of representing the propagation conditions according to the CPR, wherein the main extracted characteristics comprise: the signal receiving intensity, the maximum signal receiving amplitude, the peak value, the average excess delay time, the root mean square delay spread time and the like are as follows:
received signal strength epsilonrThe calculation formula is as follows:
Figure BDA0002171548200000061
where r (t) is the received signal amplitude at time t;
the distance d between nodes is calculated according to the following formula:
d＝c(ti-t0)
where c is the speed of light, tiTo receive time, t0Is a response request time;
maximum amplitude r of received signalmaxThe calculation formula is as follows:
Figure BDA0002171548200000062
the peak value κ is calculated as follows:
Figure BDA0002171548200000063
wherein mu|r|For receiving messagesThe average of the magnitude of the signal,
Figure BDA0002171548200000064
t is the variance of the amplitude of the received signal, and T is the sampling time;
average excess delay time TMEDThe calculation formula is as follows:
Figure BDA0002171548200000065
wherein ψ (t) ═ r (t) ceiling2/εr
Root mean square delay spread time TRMSThe calculation formula is as follows:
Figure BDA0002171548200000066
(2) constructing an SVM Classifier SVC (Support Vector Classifier, SVC), inputting feature samples extracted under NLOS and LOS conditions and feature matrixes formed by the corresponding labels into an SVM for training, and obtaining the trained SVC, as shown in fig. 2, specifically as follows:
constructing a nonlinear classifier using a Radial Basis (RBF) kernel, performing a feature transformation using the Radial Basis (RBF) kernel, the Radial Basis (RBF) kernel K (x ', Y') being as follows:
K(x′，Y′)＝exp(-γ||x′-Y′||2)
wherein, gamma is a nuclear parameter, x 'is an input characteristic sample, and Y' is a corresponding label value.
The objective function of the SVM classifier SVC is as follows:
Figure BDA0002171548200000071
s.t.Yk(wTxk+b)-1≥0，k＝1，2，…，n
wherein w and b are classifier parameters and are obtained through training; x is the number ofkAs the kth feature sample，YkIs the label of the kth characteristic sample, and n is the total number of the characteristic samples.
And optimizing the objective function by adopting a Lagrange dual function and a Radial Basis Function (RBF) kernel function, wherein the optimization result is as follows:
Figure BDA0002171548200000072
wherein alpha iskIs a lagrange multiplier.
(3) An SVR (Support Vector Regressor, SVR) is constructed, and a feature sample extracted under NLOS and LOS conditions and a corresponding output value (distance between UWB nodes) are input into an SVM for training, so as to obtain a trained SVR, as shown in fig. 2, specifically as follows:
constructing a regressor SVR structure and parameters of a support vector machine using a Radial Basis (RBF) kernel function, K (x ', y'), as follows:
Figure BDA0002171548200000073
where γ is a kernel parameter, σ is a width of a kernel function, x 'is an input feature sample, and y' is a corresponding output value, i.e., a distance between UWB nodes.
The objective function of the SVM regressor SVR is as follows:
Figure BDA0002171548200000074
s.t.|(wTxk+b)-yk|≤ε，k＝1，2，…，n
wherein w and b are regressor parameters and are obtained by training; n is the total number of characteristic samples, xkFor the kth feature sample, ykAnd epsilon is the error range for the kth corresponding output value.
And optimizing the objective function by adopting a Lagrange dual function and a Radial Basis Function (RBF) kernel function, wherein the optimization result is as follows:
Figure BDA0002171548200000075
wherein alpha iskIs a lagrange multiplier.
(4) Collecting a new channel impulse response (CPR), extracting a characteristic sample capable of representing a propagation condition, inputting the characteristic sample into a trained classifier SVC, classifying whether the collected measured value belongs to a LOS propagation condition or an NLOS propagation condition, judging whether the number of the measured values under the LOS propagation condition meets a positioning requirement (for example, if the positioning space is a two-dimensional space, the number of the measured values is not less than 3, and if the positioning space is a three-dimensional space, the number of the measured values is not less than 4), if the requirement is met, constructing a distance square matrix by using the measured values under all the LOS propagation conditions, and inputting the distance square matrix into an MDS (Multi-Dimension Scaling, MDS) algorithm to obtain the relative position of a corresponding node; if the number of the measured values under the LOS propagation condition can not meet the positioning requirement, the trained SVR is used for relieving the measured values under the NLOS condition, namely the feature samples under the NLOS condition are input into the trained SVR to obtain new corresponding output values, then the relieved measured values and the measured values under the LOS condition are selected to construct a distance square matrix, and the relative position of the corresponding node is obtained through an MDS algorithm.
As shown in fig. 4, which is a flow of an MDS algorithm, the present invention uses an MDS-based generalized barycentric coordinate method to locate UWB, wherein the MDS algorithm is specifically implemented as follows:
step 1, squaring the measured distance processed by the SVM to obtain a distance square matrix D.
Figure BDA0002171548200000081
Wherein d isijThe distance between the ith UWB node and the jth UWB node is calculated;
step 2, calculating a central matrix J:
Figure BDA0002171548200000082
where N is the total number of UWB nodes, I is the identity matrix, 1NA matrix of nx 1 and a value of 1;
step 3, normalizing the distance square matrix X:
Figure BDA0002171548200000083
step 4, carrying out singular value decomposition on the X to obtain a singular value Lambda and a singular value vector V:
X＝VAVT
step 5, sorting the singular values Λ in the descending order, and extracting the maximum singular value and the corresponding singular value vector V which have the same number with the dimension of the positioning space (for example, the number is 2 if the positioning space is a two-dimensional space, and the number is 3 if the positioning space is a three-dimensional space)1Constructing the extracted singular value into a diagonal matrix Lambda1Diagonal matrix Λ to be constructed1And singular value vector V1Multiplying to obtain a relative coordinate matrix Q of each UWB node:
Figure BDA0002171548200000084
wherein Q represents a formula of:
Figure BDA0002171548200000085
(x′i，y′i) Is the relative position of the unknown node (x'1，y′1)，(x′2，y′2)，…，(x′N-1，y′N-1) The relative position of the 1 st, 2 nd, … th, N-1 st anchor nodes.
(5) And (3) taking the unknown node as the generalized gravity center of the anchor node (any 3 anchor nodes need to meet the condition of non-collinearity) by using the relative position of each node obtained by the MDS algorithm, and then calculating the generalized gravity center coordinate of the unknown node relative to the anchor node. And calculating the real coordinates of the unknown nodes by using the generalized barycentric coordinates and the real coordinates of the related anchor nodes.
Fig. 5 is a diagram showing barycentric coordinates of unknown nodes with respect to anchor nodes, where each node is a representation of a relative position obtained through MDS in a coordinate system in a specific example, node i is an unknown node, and nodes i, j, and k are anchor nodes. The MDS algorithm does not change the distance relationship between any two points, so that the generalized barycentric coordinate calculated by using the relative coordinates obtained by the MDS algorithm is consistent with the generalized barycentric coordinate between the actual coordinates. Wherein, the calculation of the generalized barycentric coordinates is as follows:
a1*x′1+a2*x′2+…+aN-1*x′N-1＝x′i
a1*y′1+a2*y′2+…+aN-1*y′N-1＝y′i
a1+a2+…+aN-1＝1
wherein (a)1，a2，...，aN-1) Is a generalized barycentric coordinate.
The actual position coordinates are calculated by:
xi＝a1*x1+a2*x2+…+aN-1*xN-1
yi＝a1*y1+a2*y2+…+aN-1*yN-1
wherein (x)i，yi) For the actual position coordinates of the unknown node, (x)1，y1)，(x2，y2)，…，(xN-1，yN-1) Is the real coordinate of the 1 st, 2 nd, … th, N-1 th anchor node.
Finally, the above description is a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily modify the technical solution of the present invention or substitute the same within the technical scope of the present invention, and the technical solution should be covered by the claims of the present invention.

Claims (7)

1. An UWB positioning method combining SVM and barycentric coordinates under NLOS condition is characterized in that the method comprises the following steps:
step 1: using UWB radio to respectively carry out multiple measurements in an indoor LOS environment and an NLOS environment, respectively collecting CPR (channel impulse response) in the LOS environment and the NLOS environment, and extracting characteristic samples representing propagation conditions according to the CPR;
step 2: constructing an SVC classifier SVC, inputting feature matrixes formed by feature samples extracted under NLOS and LOS conditions and corresponding labels into the SVM for training to obtain the trained SVC; the method specifically comprises the following steps:
constructing a nonlinear classifier using a Radial Basis (RBF) kernel, performing a feature transformation using the Radial Basis (RBF) kernel, the Radial Basis (RBF) kernel K (x ', Y') being as follows:
K(x′，Y′)＝exp(-γ||x′-Y′||2)
wherein gamma is a nuclear parameter, x 'is an input characteristic sample, and Y' is a corresponding label value;
the objective function of the SVM classifier SVC is as follows:
Figure FDA0003038202000000011
s.t.Yk(wTxk+b)-1≥0，k＝1，2，...，n
wherein w and b are classifier parameters and are obtained through training; x is the number ofkFor the kth feature sample, YkThe label of the kth characteristic sample is obtained, and n is the total number of the characteristic samples;
and optimizing the objective function by adopting a Lagrange dual function and a Radial Basis Function (RBF) kernel function, wherein the optimization result is as follows:
Figure FDA0003038202000000012
wherein alpha iskIs a lagrange multiplier;
and step 3: constructing an SVM (support vector machine) regressor SVR, inputting feature samples extracted under NLOS (non line of sight) and LOS (line of sight) conditions and corresponding output values into an SVM (support vector machine) for training to obtain a trained SVR, wherein the output values are distances between UWB nodes;
and 4, step 4: collecting new channel impulse response (CPR), extracting a characteristic sample representing a propagation condition, inputting the characteristic sample into a trained SVC, classifying whether a measured value belongs to an LOS (LoS) propagation condition or an NLOS (NLOS) propagation condition by using the trained SVC, judging whether the number of the measured values under the LOS propagation condition meets a positioning requirement, if so, constructing a distance square matrix by using the measured value under the LOS propagation condition, and inputting the distance square matrix into an MDS (minimum signal strength) algorithm to obtain a relative position; if the number of the measured values under the LOS propagation condition can not meet the positioning requirement, the trained SVR is used for relieving the measured values under the NLOS condition, namely the feature samples under the NLOS condition are input into the trained SVR to obtain new corresponding output values, then the relieved measured values and the measured values under the LOS condition are selected to construct a distance square matrix, and the relative position is obtained through an MDS algorithm;
and 5: and calculating barycentric coordinates of the unknown node by using the relative position acquired by the MDS algorithm, and calculating the actual position coordinates of the unknown node by using the barycentric coordinates and the coordinates of the known anchor node.
2. The UWB positioning method of claim 1 wherein the SVM and barycentric coordinates combination under NLOS condition comprises the following steps, wherein the step 1, the feature extracted according to CPR comprises:
received signal strength epsilonrThe calculation formula is as follows:
Figure FDA0003038202000000021
where r (t) is the received signal amplitude at time t;
the distance d between nodes is calculated according to the following formula:
d＝c(ti-t0)
where c is the speed of light, tiTo receive time, t0Is a response request time;
maximum amplitude r of received signalmaxThe calculation formula is as follows:
Figure FDA0003038202000000022
the peak value κ is calculated as follows:
Figure FDA0003038202000000023
wherein mu|r|As an average of the amplitudes of the received signals,
Figure FDA0003038202000000024
t is the variance of the amplitude of the received signal, and T is the sampling time;
average excess delay time TMEDThe calculation formula is as follows:
Figure FDA0003038202000000025
wherein ψ (t) ═ r (t) ceiling2/εr；
Root mean square delay spread time TRMSThe calculation formula is as follows:
Figure FDA0003038202000000026
3. the UWB positioning method combining SVM and barycentric coordinates under NLOS condition according to claim 1, wherein said step 3 specifically comprises the following contents:
constructing a regressor SVR structure and parameters of a support vector machine using a Radial Basis (RBF) kernel function, K (x ', y'), as follows:
Figure FDA0003038202000000031
wherein, gamma is a kernel parameter, sigma is the width of a kernel function, x 'is an input characteristic sample, and y' is a corresponding output value;
the objective function of the SVM regressor SVR is as follows:
Figure FDA0003038202000000032
s.t.|(wTxk+b)-yk|≤ε，k＝1，2，...，n
wherein w and b are regressor parameters and are obtained by training; n is the total number of characteristic samples, xkFor the kth feature sample, ykIs the kth corresponding output value, and epsilon is the error range;
and optimizing the objective function by adopting a Lagrange dual function and a Radial Basis Function (RBF) kernel function, wherein the optimization result is as follows:
Figure FDA0003038202000000033
wherein alpha iskIs a lagrange multiplier.
4. The UWB positioning method combining SVM and barycentric coordinates under NLOS conditions according to claim 1, wherein in said step 4, said positioning requirement is specifically: the positioning space is a two-dimensional space, so that the measured value is required to be not less than 3, and the positioning space is a three-dimensional space, so that the measured value is required to be not less than 4.
5. The UWB positioning method according to claim 1, wherein the SVM and the barycentric coordinate combination under the NLOS condition are used to obtain the UWB positioning method, and the distance square matrix is input into the MDS algorithm to obtain the relative position in the step 4, specifically as follows:
obtaining a distance square matrix:
Figure FDA0003038202000000034
wherein d isijThe distance between the ith UWB node and the jth UWB node is calculated;
calculating a central matrix J:
Figure FDA0003038202000000035
n is the total number of UWB nodes, I is the identity matrix, 1NA matrix of nx 1 and a value of 1;
normalized distance squared matrix:
Figure FDA0003038202000000036
carrying out singular value decomposition on the X to obtain a singular value Lambda and a singular value vector V;
X＝VAVT
arranging the singular values Lambda in the order from large to small, and extracting the maximum singular value with the same number as the positioning space dimensionality and the corresponding singular value vector V1Constructing the extracted singular value into a diagonal matrix Lambda1Diagonal matrix Λ to be constructed1And singular value vector V1Multiplying to obtain a relative coordinate matrix Q of each UWB node
Figure FDA0003038202000000041
Wherein Q represents a formula of:
Figure FDA0003038202000000042
(x′i，y′i) Is the relative position of the unknown node (x'1，y′1)，(x′2，y′2)，…，(x′N-1，y′N-1) The relative position of the 1 st, 2 nd, … th, N-1 st anchor nodes.
6. The UWB positioning method of claim 5 wherein the SVM and the barycentric coordinate combination under the NLOS condition is characterized in that, in the step 5, the calculation of the barycentric coordinate about the unknown node by using the relative position obtained by the MDS algorithm is specifically as follows: regarding the relative position of the unknown node as the generalized barycenter of other nodes, and solving the generalized barycenter coordinates by the following formula:
a1*x′1+a2*x′2+…+aN-1*x′N-1＝x′i
a1*y′1+a2*y′2+…+aN-1*y′N-1＝y′i
a1+a2+…+aN-1＝1
wherein (a)1，a2，...，aN-1) Is a generalized barycentric coordinate.
7. The UWB positioning method of claim 6 wherein the SVM and the barycentric coordinate combination under the NLOS condition is based on the following formula, and the actual position coordinate is calculated in the step 5 by the following formula:
xi＝a1*x1+a2*x2+…+aN-1*xN-1
yi＝a1*y1+a2*y2+…+aN-1*yN-1
wherein (x)i，yi) For the actual position coordinates of the unknown node, (x)1，y1)，(x2，y2)，…，(xN-1，yN-1) Is 1, 2, …, N-1The true coordinates of the anchor node.
CN201910764643.6A 2019-08-19 2019-08-19 UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition Active CN110488222B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910764643.6A CN110488222B (en) 2019-08-19 2019-08-19 UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910764643.6A CN110488222B (en) 2019-08-19 2019-08-19 UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition

Publications (2)

Publication Number Publication Date
CN110488222A CN110488222A (en) 2019-11-22
CN110488222B true CN110488222B (en) 2021-06-22

Family

ID=68551506

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910764643.6A Active CN110488222B (en) 2019-08-19 2019-08-19 UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition

Country Status (1)

Country Link
CN (1) CN110488222B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111294731B (en) * 2020-01-16 2020-12-01 珠海格力电器股份有限公司 UWB indoor positioning method, device, electronic equipment and storage medium
CN111432364B (en) * 2020-03-25 2021-05-11 哈尔滨工程大学 Radial basis function neural network-based non-line-of-sight error suppression method
CN111541988B (en) * 2020-04-17 2021-11-23 北京理工大学重庆创新中心 Three-dimensional indoor positioning method based on barycentric coordinate and Taylor expansion
CN113676832B (en) * 2020-05-15 2023-04-14 大唐移动通信设备有限公司 Measurement reporting method, measurement reporting equipment and positioning server
CN112929826B (en) * 2021-03-23 2022-07-15 浙江工业大学 Indoor hybrid positioning method based on UWB triangulation positioning and fingerprint information
CN114928811B (en) * 2022-05-05 2024-05-07 南京理工大学 Ultra-wideband mine underground personnel accurate positioning method based on neural network

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101561495A (en) * 2009-06-01 2009-10-21 长讯通信服务有限公司 Method for three-dimensionally positioning network node of wireless sensor
CN106772231A (en) * 2016-12-23 2017-05-31 深圳市金溢科技股份有限公司 Electronic tag, base station, alignment system and method based on UWB
CN106950537A (en) * 2017-04-28 2017-07-14 浙江大学 A kind of Distributed localization method based on UWB
CN107563437A (en) * 2017-08-31 2018-01-09 广州中海达定位技术有限公司 Ultra wide band non line of sight discrimination method based on random forest
EP3287736A1 (en) * 2016-08-24 2018-02-28 The Boeing Company Dynamic, persistent tracking of multiple field elements
CN108700652A (en) * 2015-12-09 2018-10-23 欧利景无线有限公司 The methods, devices and systems for detecting and monitoring for wireless event

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101561495A (en) * 2009-06-01 2009-10-21 长讯通信服务有限公司 Method for three-dimensionally positioning network node of wireless sensor
CN108700652A (en) * 2015-12-09 2018-10-23 欧利景无线有限公司 The methods, devices and systems for detecting and monitoring for wireless event
EP3287736A1 (en) * 2016-08-24 2018-02-28 The Boeing Company Dynamic, persistent tracking of multiple field elements
CN106772231A (en) * 2016-12-23 2017-05-31 深圳市金溢科技股份有限公司 Electronic tag, base station, alignment system and method based on UWB
CN106950537A (en) * 2017-04-28 2017-07-14 浙江大学 A kind of Distributed localization method based on UWB
CN107563437A (en) * 2017-08-31 2018-01-09 广州中海达定位技术有限公司 Ultra wide band non line of sight discrimination method based on random forest

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Zhongling Liu等.《A Novel Ultra-Wideband-Based Localization and Tracking Scheme with Channel Classification》.《IEEE》.2017,第1-5页. *

Also Published As

Publication number Publication date
CN110488222A (en) 2019-11-22

Similar Documents

Publication Publication Date Title
CN110488222B (en) UWB positioning method combining SVM (support vector machine) and barycentric coordinate under NLOS (non line of sight) condition
CN106793082B (en) Mobile equipment positioning method in WLAN/Bluetooth heterogeneous network environment
CN107703480B (en) Mixed kernel function indoor positioning method based on machine learning
CN103874118B (en) Radio Map bearing calibrations in WiFi indoor positionings based on Bayesian regression
CN109672973B (en) Indoor positioning fusion method based on strongest AP
Zou et al. A mutual information based online access point selection strategy for WiFi indoor localization
CN104519571B (en) A kind of indoor orientation method based on RSS
CN103220777A (en) Mobile device positioning system
CN103501538A (en) Indoor positioning method based on multipath energy fingerprints
CN106941718A (en) A kind of mixing indoor orientation method based on signal subspace fingerprint base
CN110297213A (en) Radiation source positioning device and method based on the unmanned aerial vehicle platform for loading relatively prime linear array
CN106231549A (en) A kind of 60GHz pulse indoor orientation method based on restructing algorithm
CN107861098B (en) Intelligent court positioning system and method
CN110049549A (en) More fusion indoor orientation methods and its system based on WiFi fingerprint
Han et al. A wireless fingerprint location method based on target tracking
Yang et al. Wireless sensor network localization based on pso algorithm in nlos environment
Zhong et al. WiFi indoor localization based on K-means
CN107121662A (en) Single passive location method based on spatial domain rarefaction representation
CN104793177A (en) Microphone array direction finding method based on least square methods
Wang et al. Study on improvement of fingerprint matching algorithm in wireless LAN based indoor positioning system
CN108632763A (en) A kind of indoor positioning weighting k nearest neighbor method based on WiFi fingerprints
Zeng et al. NLOS identification and mitigation based on CIR with particle filter
CN106019222B (en) A kind of quadratic programming localization method based on location algorithm residual error
CN107682822A (en) A kind of compressed sensing outdoor positioning method based on electromagnetic field intensity
Wu et al. An indoor localization method based on RSSI of adjustable power WiFi router

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant