CN113382354B - Wireless positioning non-line-of-sight signal discrimination method based on factor graph - Google Patents

Wireless positioning non-line-of-sight signal discrimination method based on factor graph Download PDF

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CN113382354B
CN113382354B CN202110639431.2A CN202110639431A CN113382354B CN 113382354 B CN113382354 B CN 113382354B CN 202110639431 A CN202110639431 A CN 202110639431A CN 113382354 B CN113382354 B CN 113382354B
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CN113382354A (en
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李旭
胡悦
胡玮明
胡锦超
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Southeast University
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • 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
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Abstract

The invention discloses a wireless positioning non-line-of-sight signal discrimination method based on a factor graph. The method comprises the steps of firstly constructing a wireless signal/inertial navigation fusion positioning frame based on a factor graph, then fully utilizing the static characteristic of a base station, and carrying out non-line-of-sight judgment on a signal according to the confidence coefficient of a factor graph output result and the prior position information of the base station. The method for judging the wireless positioning non-line-of-sight signal overcomes the problems of weak self-adaption capability and poor real-time performance of the existing method, and further ensures the accuracy and reliability of wireless positioning.

Description

Wireless positioning non-line-of-sight signal discrimination method based on factor graph
Technical Field
The invention relates to the field of navigation positioning, in particular to a method for judging non-line-of-sight signals in wireless positioning.
Background
In recent years, Wireless positioning technologies represented by Wireless Sensor Networks (WSNs) such as cellular, bluetooth, and ultra-wideband have been rapidly developed and widely applied. On one hand, wireless positioning is used as a redundant supplementary means, so that the problem of inaccurate positioning caused by satellite signal quality deterioration in urban environment can be solved; on the other hand, the positioning requirement in a complex large-scale indoor environment can be solved. The wireless positioning system mainly comprises a base station and a receiving station, wherein the base station is fixed at a place with known position information, the receiving station is fixed on an entity with positioning requirements, and the base station and the receiving station perform signal transmission in a wireless mode. The wireless positioning firstly measures the distance between the base station and the receiving station through a wireless sensing network, and then calculates the position of the receiving station according to the distance and the known base station position information, thereby realizing the positioning.
However, the accuracy of wireless positioning is affected by non-line of Sight (NLOS) problems in both outdoor and indoor scenarios. The non-line-of-sight refers to the fact that a wireless signal propagation path between a base station and a receiving station is shielded by a dynamic obstacle and a static obstacle, so that the ranging error is increased, and errors occur in final positioning calculation. Therefore, it is the key to ensure the accuracy and reliability of wireless positioning to effectively distinguish the non-line-of-sight signal. The existing non-line-of-sight signal discrimination methods include the following methods: (1) a statistical-based approach. In view of the difference between the variance and the mean of the line-of-sight signal and the non-line-of-sight signal, the method considers that the non-line-of-sight signal is received when the variation of the statistical information of the signal exceeds a certain threshold. However, the method depends on prior knowledge such as statistical information, the threshold value is difficult to determine, the method cannot be used in a pure non-line-of-sight environment, and the method has certain hysteresis; (2) context-based sensing methods. The method is represented by a 3D map and ray tracing, but needs accurate environment prior information, occupies a large amount of computing resources and is poor in instantaneity; (3) methods based on signal propagation path loss models or on Channel Impulse Response (CIR). Such methods consider path energy at line-of-sight conditions to be significantly greater than energy at non-line-of-sight and decide accordingly. However, the method model is difficult to establish and has no universality; (4) a learning based approach. Machine learning represented by a Support Vector Machine (SVM), deep learning represented by a Long Short-Term Memory network (LSTM), and the like have achieved certain effects on non-line-of-sight discrimination of wireless positioning. However, this type of method requires a large amount of data pre-training, is not interpretable, is difficult to land, and is not very versatile. In summary, the existing methods mainly have two problems: on one hand, due to high requirements on prior information such as statistics/environment and the like, when the environment or working condition changes, the self-adaptive capacity of the method is poor; on the other hand, due to the complex mathematical model and the occupation of a large amount of computing resources, the real-time performance of the method is poor.
Disclosure of Invention
The invention provides a wireless signal non-line-of-sight discrimination method based on a factor graph, which is specifically characterized by comprising the following steps: (1) the self-adaptive capacity is strong. The method fully utilizes the static characteristic of the base station based on the confidence interval of the factor graph output result, only needs to know the prior position information of the base station necessary in wireless positioning, and does not need to know any other prior information such as environment, statistic and the like, so the method has stronger self-adaptive capacity in different environments; (2) the real-time performance is good. The increment smoothing method based on the factor graph integrates the wireless sensing network information and the inertial navigation information, completes non-line-of-sight detection while positioning, has simple model, uncomplicated sensor data composition, does not occupy a large amount of computing resources, and can ensure the real-time performance of judgment.
The wireless positioning non-line-of-sight signal discrimination method provided by the invention solves the problem of poor adaptability of the current non-line-of-sight discrimination, improves the real-time property of discrimination, ensures the accuracy and reliability of wireless positioning, and has a positive promotion effect on the development of a wireless positioning technology.
The idea of the invention is further explained below:
the method comprises the following steps: obtaining prior location information of a base station
The location of the base station is a priori information that is necessary in wireless positioning. In the invention, no matter outdoor application or indoor application, the coordinate of the outdoor application or the indoor application is transferred to a navigation coordinate system, and a specific coordinate system conversion method is visible in a reference document (Huxiaoping navigation technology foundation [ M)]National defense industrial publishing, 2015.). After unifying the coordinate system, note the location of the base station as (x)i，yi，zi) Wherein x, y, z are coordinates of the base station, and the superscript i represents the serial number of the base station.
Step two: construction of a factor graph-based localization model
The factor graph is one of probability graphs, and comprises nodes of two types, namely variable nodes and factor nodes. The detailed principles of the factor graph can be found in the literature references (M.Kaess, H.Johannsson, R.Roberts, V.Ila, J.Leonard, and F.Dellaert, iSAM2: Incremental smoothening and mapping using the Bayes tree [ J ] Intl.J.of Robotics research.31: 217-. The invention constructs a wireless signal/inertial navigation positioning frame based on a factor graph, which comprises the following specific steps:
the first substep: constructing variable nodes
Firstly, constructing variable node X representing positioning target statekMainly comprising coordinate values of the positioning target at the moment k; secondly, constructing a variable node C representing the inertial navigation error parameter statekConstant drift mainly including inertial navigation at time kMigration and random walk.
And a second substep: construction factor node
Firstly, constructing an inertial navigation observation factor node for connecting a positioning target state variable node and an inertial navigation error parameter state variable node, wherein a cost function is as follows:
Figure BDA0003106600200000031
the subscripts in the above formula all indicate the time of day,
Figure BDA0003106600200000032
data representing inertial navigation measurements at time k, i.e. gyroscopes and accelerometers,
Figure BDA0003106600200000033
h () is the prior state at the moment k +1, and h () is the system state transfer function; secondly, constructing INS bias factor nodes for connecting inertial navigation error parameter state variable nodes, wherein a cost function is as follows:
fbias(Ck+1,Ck)＝d(Ck+1-g(Ck))#(8)
g () is an error state update function; and finally, constructing a wireless sensor observation factor node, wherein a cost function is as follows:
Figure BDA0003106600200000034
wherein
Figure BDA0003106600200000035
Represents the observed quantity of the wireless sensor at the time k, hWSN() Expressing the observation equation;
step three: non-line-of-sight signal discrimination
The first substep: calculating the maximum theoretical distance between the base station and the receiving station
Recording the position of the positioning target calculated according to the inertial navigation observation factor node at the moment k in the step as
Figure BDA0003106600200000036
Figure BDA0003106600200000037
The standard deviation of the covariance matrix output by the factor graph is respectively calculated as
Figure BDA0003106600200000038
The position coordinates obey multidimensional Gaussian distribution, the projection on the coordinate axis is an ellipsoid, the sphere center of the ellipsoid is determined by the mean vector of the multidimensional Gaussian distribution, and the length and the direction of each axis are determined by the eigenvalue and the eigenvector of the covariance matrix of the multidimensional Gaussian distribution. Because the coordinate values of the positions are not related to each other, the confidence interval of each value
Figure BDA0003106600200000039
Figure BDA00031066002000000310
Can be calculated as:
Figure BDA00031066002000000311
s is used to define the size of the confidence ellipsoid, which can be obtained by looking up the chi-square distribution table. The maximum theoretical distance from the receiving station to the ith base station at time k is:
Figure BDA0003106600200000041
Figure BDA0003106600200000042
representing the maximum theoretical distance of the receiving station to the ith base station at time k,
Figure BDA0003106600200000043
a possible value for locating the target position in the confidence interval at the moment;
and a second substep: judging non-line-of-sight distance according to theoretical distance and actual distance
Meanwhile, the distance between the receiving station and the base station measured by the ith wireless sensor at the moment k is recorded as rhok iThe measurement error is obtained through the specification of the wireless sensor
Figure BDA0003106600200000044
If so:
Figure BDA0003106600200000045
the data is considered to be acquired under a non-line-of-sight condition and is determined to be a non-line-of-sight signal.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method has strong self-adaptive capacity, the static characteristic of the base station is fully utilized based on the confidence interval of the factor graph output result, only the prior position information of the base station necessary in wireless positioning needs to be known, and in addition, any prior information such as environment, statistic and the like does not need to be known, so the method has strong self-adaptive capacity when facing different environments;
(2) the increment smoothing method based on the factor graph has good real-time performance, integrates wireless sensing network information and inertial navigation information, completes non-line-of-sight detection while positioning, has simple model and uncomplicated sensor data composition, does not occupy a large amount of computing resources, and can ensure the real-time performance of judgment.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In recent years, Wireless positioning technologies represented by Wireless Sensor Networks (WSNs) such as cellular, bluetooth, and ultra-wideband have been rapidly developed and widely applied. On one hand, wireless positioning is used as a redundant supplementary means, so that the problem of inaccurate positioning caused by satellite signal quality deterioration in urban environment can be solved; on the other hand, the positioning requirement in a complex large-scale indoor environment can be solved. The wireless positioning system mainly comprises a base station and a receiving station, wherein the base station is fixed at a place with known position information, the receiving station is fixed on an entity with positioning requirements, and the base station and the receiving station perform signal transmission in a wireless mode. The wireless positioning firstly measures the distance between the base station and the receiving station through a wireless sensing network, and then calculates the position of the receiving station according to the distance and the known base station position information, thereby realizing the positioning.
However, the accuracy of wireless positioning is affected by non-line of Sight (NLOS) problems in both outdoor and indoor scenarios. The non-line-of-sight refers to the fact that a wireless signal propagation path between a base station and a receiving station is shielded by a dynamic obstacle and a static obstacle, so that the ranging error is increased, and errors occur in final positioning calculation. Therefore, it is the key to ensure the accuracy and reliability of wireless positioning to effectively distinguish the non-line-of-sight signal. The existing non-line-of-sight signal discrimination methods include the following methods: (1) a statistical-based approach. In view of the difference between the variance and the mean of the line-of-sight signal and the non-line-of-sight signal, the method considers that the non-line-of-sight signal is received when the variation of the statistical information of the signal exceeds a certain threshold. However, the method depends on prior knowledge such as statistical information, the threshold value is difficult to determine, the method cannot be used in a pure non-line-of-sight environment, and the method has certain hysteresis; (2) context-based sensing methods. The method is represented by a 3D map and ray tracing, but needs accurate environment prior information, occupies a large amount of computing resources and is poor in instantaneity; (3) methods based on signal propagation path loss models or on Channel Impulse Response (CIR). Such methods consider path energy at line-of-sight conditions to be significantly greater than energy at non-line-of-sight and decide accordingly. However, the method model is difficult to establish and has no universality; (4) a learning based approach. Machine learning represented by a Support Vector Machine (SVM), deep learning represented by a Long Short-Term Memory network (LSTM), and the like have achieved certain effects on non-line-of-sight discrimination of wireless positioning. However, this type of method requires a large amount of data pre-training, is not interpretable, is difficult to land, and is not very versatile. In summary, the existing methods mainly have two problems: on one hand, due to high requirements on prior information such as statistics/environment and the like, when the environment or working condition changes, the self-adaptive capacity of the method is poor; on the other hand, due to the complex mathematical model and the occupation of a large amount of computing resources, the real-time performance of the method is poor.
Aiming at the problems, the invention provides a wireless signal non-line-of-sight discrimination method based on a factor graph, which is specifically characterized in that: (1) the self-adaptive capacity is strong. The method fully utilizes the static characteristic of the base station based on the confidence interval of the factor graph output result, only needs to know the prior position information of the base station necessary in wireless positioning, and does not need to know any other prior information such as environment, statistic and the like, so the method has stronger self-adaptive capacity in different environments; (2) the real-time performance is good. The increment smoothing method based on the factor graph integrates the wireless sensing network information and the inertial navigation information, completes non-line-of-sight detection while positioning, has simple model, uncomplicated sensor data composition, does not occupy a large amount of computing resources, and can ensure the real-time performance of judgment.
The wireless positioning non-line-of-sight signal discrimination method provided by the invention solves the problem of poor adaptability of the current non-line-of-sight discrimination, improves the real-time property of discrimination, ensures the accuracy and reliability of wireless positioning, and has a positive promotion effect on the development of a wireless positioning technology.
The idea of the invention is further explained below:
the method comprises the following steps: obtaining prior location information of a base station
The location of the base station is a priori information that is necessary in wireless positioning. In the invention, no matter outdoor application or indoor application, the coordinate of the outdoor application or the indoor application is transferred to a navigation coordinate system, and a specific coordinate system conversion method is visible in a reference document (Huxiaoping navigation technology foundation [ M)]National defense industrial publishing, 2015.). After unifying the coordinate system, note the location of the base station as (x)i，yi，zi) Wherein x, y, z are coordinates of the base station, and the superscript i represents the serial number of the base station.
Step two: construction of a factor graph-based localization model
The factor graph is one of probability graphs, and comprises nodes of two types, namely variable nodes and factor nodes. The detailed principles of the factor graph can be found in the literature references (M.Kaess, H.Johannsson, R.Roberts, V.Ila, J.Leonard, and F.Dellaert, iSAM2: Incremental smoothening and mapping using the Bayes tree [ J ] Intl.J.of Robotics research.31: 217-. The invention constructs a wireless signal/inertial navigation positioning frame based on a factor graph, which comprises the following specific steps:
the first substep: constructing variable nodes
Firstly, constructing variable node X representing positioning target statekMainly comprising coordinate values of the positioning target at the moment k; secondly, constructing a variable node C representing the inertial navigation error parameter statekMainly includes constant drift and random walk of the inertial navigation at the time k.
And a second substep: construction factor node
Firstly, constructing an inertial navigation observation factor node for connecting a positioning target state variable node and an inertial navigation error parameter state variable node, wherein a cost function is as follows:
Figure BDA0003106600200000061
the subscripts in the above formula all indicate the time of day,
Figure BDA0003106600200000062
data representing inertial navigation measurements at time k, i.e. gyroscopes and accelerometers,
Figure BDA0003106600200000063
h () is the prior state at the moment k +1, and h () is the system state transfer function; secondly, constructing INS bias factor nodes for connecting inertial navigation error parameter state variable nodes, wherein a cost function is as follows:
fbias(Ck+1,Ck)＝d(Ck+1-g(Ck))# (14)
g () is an error state update function; and finally, constructing a wireless sensor observation factor node, wherein a cost function is as follows:
Figure BDA0003106600200000064
wherein
Figure BDA0003106600200000071
Represents the observed quantity of the wireless sensor at the time k, hWSN() Expressing the observation equation;
step three: non-line-of-sight signal discrimination
The first substep: calculating the maximum theoretical distance between the base station and the receiving station
Recording the position of the positioning target calculated according to the inertial navigation observation factor node at the moment k in the step as
Figure BDA0003106600200000072
Figure BDA0003106600200000073
The standard deviation of the covariance matrix output by the factor graph is respectively calculated as
Figure BDA0003106600200000074
The position coordinates obey multidimensional Gaussian distribution, the projection on the coordinate axis is an ellipsoid, the sphere center of the ellipsoid is determined by the mean vector of the multidimensional Gaussian distribution, and the length and the direction of each axis are determined by the eigenvalue and the eigenvector of the covariance matrix of the multidimensional Gaussian distribution. Because the coordinate values of the positions are not related to each other, the confidence interval of each value
Figure BDA0003106600200000075
Figure BDA0003106600200000076
Can be calculated as:
Figure BDA0003106600200000077
s is used to define the size of the confidence ellipsoid, which can be obtained by looking up the chi-square distribution table. The maximum theoretical distance from the receiving station to the ith base station at time k is:
Figure BDA0003106600200000078
Figure BDA0003106600200000079
representing the maximum theoretical distance of the receiving station to the ith base station at time k,
Figure BDA00031066002000000710
a possible value for locating the target position in the confidence interval at the moment;
and a second substep: judging non-line-of-sight distance according to theoretical distance and actual distance
Meanwhile, the distance between the receiving station and the base station measured by the ith wireless sensor at the moment k is recorded as
Figure BDA00031066002000000711
The measurement error is obtained through the specification of the wireless sensor
Figure BDA00031066002000000712
If so:
Figure BDA00031066002000000713
the data is considered to be acquired under a non-line-of-sight condition and is determined to be a non-line-of-sight signal.

Claims (1)

1. A wireless positioning non-line-of-sight signal discrimination method based on a factor graph is characterized in that when a non-line-of-sight signal is discriminated, except for necessary base station position information of wireless positioning, the method does not depend on any other prior information, does not occupy a large number of computing resources, and completes discrimination of the non-line-of-sight signal while positioning, and the method comprises the following steps:
the method comprises the following steps: obtaining prior location information of a base station
The position of the base station is the necessary prior information in wireless positioning, whether the application is outdoor application or indoor application, the coordinate of the base station is transferred to a navigation coordinate system, and after the coordinate system is unified, the position of the base station is recorded as (x)i，yi，zi) Wherein x, y and z are coordinates of the base station, and the superscript i represents the serial number of the base station;
step two: construction of a factor graph-based localization model
The factor graph is one of probability graphs and comprises two types of nodes, namely variable nodes and factor nodes, and a wireless signal/inertial navigation positioning frame based on the factor graph is constructed, specifically as follows:
the first substep: constructing variable nodes
Firstly, constructing variable node X representing positioning target statekMainly comprising coordinate values of the positioning target at the moment k; secondly, constructing a variable node C representing the inertial navigation error parameter statekThe method mainly comprises constant drift and random walk of inertial navigation at the moment k;
and a second substep: construction factor node
Firstly, constructing an inertial navigation observation factor node for connecting a positioning target state variable node and an inertial navigation error parameter state variable node, wherein a cost function is as follows:
Figure FDA0003106600190000011
in the above formula, the subscripts all indicate the time,
Figure FDA0003106600190000012
data representing inertial navigation measurements at time k, i.e. gyroscopes and accelerometers,
Figure FDA0003106600190000013
h () is the prior state at the moment k +1, and h () is the system state transfer function; secondly, constructing INS bias factor nodes for connecting inertial navigation error parameter state variable nodes, wherein a cost function is as follows:
fbias(Ck+1,Ck)＝d(Ck+1-g(Ck))#(2)
g () is an error state update function; and finally, constructing a wireless sensor observation factor node, wherein a cost function is as follows:
Figure FDA0003106600190000014
wherein
Figure FDA0003106600190000015
Represents the observed quantity of the wireless sensor at the time k, hWSN() Expressing the observation equation;
step three: non-line-of-sight signal discrimination
The first substep: calculating the maximum theoretical distance between the base station and the receiving station
Recording the position of the positioning target calculated according to the inertial navigation observation factor node at the moment k in the step as
Figure FDA0003106600190000016
Figure FDA0003106600190000021
The standard deviation of the covariance matrix output by the factor graph is respectively calculated as
Figure FDA0003106600190000022
The position coordinates obey multidimensional Gaussian distribution, the projection on the coordinate axis is an ellipsoid, the sphere center of the ellipsoid is determined by the mean vector of the multidimensional Gaussian distribution, and the length and the direction of each axis are determined by the eigenvalue and the eigenvector of the covariance matrix of the multidimensional Gaussian distribution; because the coordinate values of the positions are not related to each other, the confidence interval of each value
Figure FDA0003106600190000023
Figure FDA0003106600190000024
Can be calculated as:
Figure FDA0003106600190000025
s is used for defining the scale of the confidence ellipsoid and can be obtained by inquiring a chi-square distribution table; the maximum theoretical distance from the receiving station to the ith base station at time k is:
Figure FDA0003106600190000026
Figure FDA0003106600190000027
representing the maximum theoretical distance of the receiving station to the ith base station at time k,
Figure FDA0003106600190000028
a possible value for locating the target position in the confidence interval at the moment;
and a second substep: judging non-line-of-sight distance according to theoretical distance and actual distance
Meanwhile, the distance between the receiving station and the base station measured by the ith wireless sensor at the moment k is recorded as
Figure FDA0003106600190000029
The measurement error is obtained through the specification of the wireless sensor
Figure FDA00031066001900000210
If so:
Figure FDA00031066001900000211
the data is considered to be acquired under a non-line-of-sight condition and is determined to be a non-line-of-sight signal.
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