CN114666896A - Target localization method for parameter estimation of wireless signal transmission in non-line-of-sight environment - Google Patents

Abstract

The invention discloses a target positioning method for wireless signal transmission parameter estimation in a non-line-of-sight environment, which mainly solves the problem that signal transmission parameters are unknown in wireless sensor network target positioning in the non-line-of-sight environment. The method comprises the following steps: primarily determining the position of a signal source through an arrival time measurement model in a non-line-of-sight environment, and determining the transmission path loss coefficient of a wireless signal by combining the position and a received signal strength measurement model; inputting the path loss coefficient and the signal source position into a received signal strength measurement model to obtain a wireless signal transmitting power estimated value; and re-establishing a received signal strength measurement model according to the determined wireless signal transmitting power and the path loss coefficient, constructing a target function by combining the arrival time measurement model, and determining the position of a signal source by adopting a dichotomy. The invention can realize time domain combined target positioning by extracting energy domain measurement information under the condition that wireless signal transmission parameters are unknown, and effectively ensures the robustness of target positioning in a non-line-of-sight environment.

Description

Target localization method for parameter estimation of wireless signal transmission in non-line-of-sight environment

technical field

The invention belongs to the field of communication technology, and further relates to a positioning method in wireless communication, in particular to a target positioning method for estimation of wireless signal transmission parameters in a non-line-of-sight environment, which can be used for an unknown wireless signal transmission path loss model in a non-line-of-sight environment Sensor network object localization in the case.

Background technique

In recent years, with the rapid development of sensor networks and Internet of Things technologies, the demand for location services from various smart devices and high-tech applications has grown at an unexpected rate. Due to the increasingly complex electromagnetic environment, the non-line-of-sight propagation of wireless signals is an important factor affecting the positioning accuracy. Usually, multi-domain information fusion methods are used to improve the positioning accuracy. Among them, when the path loss parameter in the wireless signal propagation process is unknown, it is difficult to construct the received signal strength measurement model, and the energy domain measurement information cannot be effectively extracted for target positioning, which leads to a decrease in positioning accuracy. Therefore, it is extremely important to study joint target localization methods for parameter estimation of wireless signal transmission in non-line-of-sight environments.

Lu Zhigang's "Location Algorithm Based on RSS-TOA in Non-Line-of-Sight Environment" establishes the received signal strength and time of arrival measurement results as a non-convex optimization problem about the target position, and then uses the second-order cone relaxation theory to convert the original non-convex optimization problem. After converting to a convex optimization problem, a suboptimal solution to the original problem is obtained. This method focuses on data processing and algorithm verification under the condition of known wireless signal transmission parameters to obtain the sub-optimal solution of the problem, and does not give the closed-form solution of the target final position parameters. "NLOS Error Mitigation in Hybrid RSS-TOA-Based Localization Through Semi-Definite Relaxation" by Mayur Katwe, Pradnya Ghare et al. uses the joint received signal strength and time of arrival measurement information to introduce non-line-of-sight balance parameters to solve the maximum likelihood estimation problem It is transformed into a nonlinear weighted least squares problem, and a semidefinite relaxation method is used to determine the signal source position. However, this method does not study the target location when the wireless signal transmission parameters in the received signal strength measurement model are unknown.

The patent publication number is CN112986906A The Chinese patent titled "An RSS-TOA joint positioning method for semi-positive definite planning", firstly establishes the linear expression of the received signal strength measurement information and the time of arrival measurement information, and constructs the least weighted error term After the quadratic localization problem, auxiliary variables are introduced to transform it into a constrained optimization problem. Finally, the weighted least squares problem is transformed into a positive semi-definite programming problem by using the convex optimization technique. This method has high computational complexity and only considers the positioning performance in an ideal environment. The positioning accuracy decreases or even fails in a non-line-of-sight environment where the wireless signal transmission path loss model is unknown.

SUMMARY OF THE INVENTION

The purpose of the present invention is to aim at the above-mentioned deficiencies of the prior art, and to propose a target positioning method for wireless signal transmit power, transmission path loss coefficient and non-line-of-sight deviation estimation in a non-line-of-sight environment, so as to effectively extract energy domain measurement information Realize joint target positioning in time domain.

The basic idea of realizing the present invention is as follows: the position coordinates of the signal source are preliminarily determined through the time-of-arrival positioning model in the non-line-of-sight environment; And the weighted least squares method is used to determine the transmission path loss coefficient of the wireless signal; the path loss coefficient and the position coordinates of the signal source are substituted into the received signal strength measurement model, and the maximum likelihood estimate of the transmission power of the wireless signal is iteratively solved; The signal transmission power and path loss coefficient are used to re-establish the received signal strength measurement model, and the time-of-arrival measurement model is combined to construct the objective function, and the dichotomy method is used to determine the position of the signal source. The invention solves the target positioning problem of the wireless sensor network in the non-line-of-sight environment, and can determine the average value of the wireless signal transmission power, the transmission path loss coefficient and the non-line-of-sight deviation, and ensures the robustness of the target positioning in the non-line-of-sight environment. sex.

In order to achieve the above object, the technical scheme of the present invention includes the following:

(1) Extract energy time domain measurement information:

According to the wireless sensor network target positioning model in the non-line-of-sight environment, the sensor receiver obtains the time domain measurement information di by recording the reception time of the source signal, and simultaneously obtains the energy domain measurement information _{P i by} recording the energy of the received signal;

(2) Preliminarily determine the position coordinates of the signal source:

(2.1) According to the time domain measurement information d _i , the weighted least squares method is used to construct the minimization function of the signal source location in the time domain:

表示信号源至N个传感器节点的时域非视距偏差平均值，α_i表示时域非视距偏差；

表示时域加权权重；Among them, s represents the position coordinate of the signal source in the three-dimensional coordinate system, denoted as [x, y, z] ^T ; a _i represents the position coordinate of the sensor node in the three-dimensional coordinate system, denoted as [x _i , y _i , z _i ] ^T ; i=1,2,...,N represents the sensor node number; N is the number of sensor nodes, and N is a positive integer greater than or equal to 3; || · || is the Euclidean norm;

represents the average value of the time-domain non-line-of-sight deviation from the signal source to N sensor nodes, and α _i represents the time-domain non-line-of-sight deviation;

represents the time domain weighted weight;

(2.2) Convert the minimization function of signal source location in the time domain into a generalized trust region sub-problem, and initialize the average α′ of the non-line-of-sight deviation in the time domain, that is, let α′=0; times, and let j=1;

根据下式求出当前时域非视距偏差平均值

(2.3) Use the bisection method to solve the generalized trust region subproblem to obtain the estimated value of the jth signal source position

Calculate the average value of the current time-domain non-line-of-sight deviation according to the following formula

若满足，将此时的估计值

确定为信号源位置的初步估计结果

并直接执行步骤(3)；反之，更新时域非视距偏差平均值，即令

并对j加1后返回步骤(2.3)；(2.4) Determine whether the positioning accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as a preliminary estimate of the signal source location

And directly execute step (3); on the contrary, update the average value of time-domain non-line-of-sight deviation, that is, let

Add 1 to j and return to step (2.3);

(3) Multi-point cooperation to obtain received signal strength difference information:

代入能域接收信号强度测量信息P_i，其它传感器节点与参考节点之间的接收信号强度差值信息P_i1表示为：Taking sensor node 1 as the reference node, the initial estimation result of the signal source position

Substituting into the received signal strength measurement information P _i in the energy domain, the received signal strength difference information P _i1 between other sensor nodes and the reference node is expressed as:

Among them, a ₁ represents the position coordinate of the sensor node 1 in the three-dimensional coordinate system; γ represents the transmission path loss coefficient; _{Δl i1} = _{l i} -l _{1 represents} the measurement noise difference; deviation difference;

(4) Determine the transmission path loss coefficient:

(4.1) Use the weighted least squares method to construct the minimization function of the loss coefficient as follows:

表示能域加权权重；P′_i1＝P_i1-Δβ，Δβ表示非视距偏差差值的平均值；in,

represents the energy domain weighting weight; P′ _i1 =P _i1 -Δβ, Δβ represents the average value of non-line-of-sight deviation difference;

(4.2) Convert the minimization function of the loss coefficient into a weighted least squares problem, and initialize the average value Δβ of the non-line-of-sight deviation difference in the energy domain, that is, let Δβ=0; use m to represent the estimated value of the loss coefficient of the wireless signal transmission path The actual number of times, and let m = 1;

再利用损耗系数估计值按下式求出当前能域非视距偏差差值的平均值

(4.3) Use the iterative weighted least squares method to solve the problem in step (4.2), and obtain the estimated value of the mth wireless signal transmission path loss coefficient

Then use the estimated value of the loss coefficient to obtain the average value of the current energy domain non-line-of-sight deviation as follows:

若满足，将此时的估计值

确定为传输路径损耗系数的估计结果

并直接执行步骤(5)；反之，更新能域非视距偏差差值的平均值，即令

并对m加1后返回步骤(4.3)；(4.4) Judging whether the estimation accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as the estimated result of the transmission path loss coefficient

And directly execute step (5); on the contrary, update the average value of the non-horizontal deviation difference in the energy domain, that is, let

And return to step (4.3) after adding 1 to m;

(5) Determine the transmission power of the wireless signal:

和步骤(4)得到的传输路径损耗系数的估计结果

代入能域接收信号强度测量信息P_i中，并将非视距偏差β_i用平均值β'替换，构建无线信号发射功率的最小化函数表达式如下：(5.1) Use the preliminary estimation result of the signal source position obtained in step (2)

and the estimated result of the transmission path loss coefficient obtained in step (4)

Substitute into the received signal strength measurement information P _i in the energy domain, and replace the non-line-of-sight deviation β _i with the average value β' to construct the minimization function expression of the wireless signal transmit power as follows:

P′_i＝P_i+β'，

in

P' _i =P _i +β',

(5.2) Initialize the average value β' of non-line-of-sight deviations in the energy domain, that is, let β'=0; use q to represent the actual number of wireless signal transmit power estimates, and let q=1;

再利用发射功率估计值按下式求出当前能域非视距偏差平均值

(5.3) Obtain P _i ″ according to the defined P _i ', calculate the expression constructed in step (5.1), and obtain the estimated value of the qth wireless signal transmission power

Then use the estimated value of the transmit power to obtain the average value of the current energy domain non-line-of-sight deviation as follows:

若满足，将此时的估计值

确定为信号源的无线信号发射功率估计结果

并直接执行步骤(6)；反之，更新能域非视距偏差平均值，即令

并对q加1后返回步骤(5.3)；(5.4) Judging whether the estimation accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

The estimation result of the transmit power of the wireless signal determined as the signal source

And directly execute step (6); on the contrary, update the average value of the non-line-of-sight deviation in the energy domain, that is, let

And add 1 to q and return to step (5.3);

(6) Modified energy time domain measurement model:

和无线信号发射功率

代入能域接收信号强度测量模型中，时域测量信息中的非视距偏差α_i用其平均值α替换，能域测量信息中的非视距偏差β_i用其平均值β替换，按下式确定修正后的时域到达时间测量信息

能域接收信号强度测量信息

The transmission path loss coefficient determined in step (4) and step (5)

and wireless signal transmit power

Substitute into the received signal strength measurement model in the energy domain, replace the non-line-of-sight deviation α _i in the time domain measurement information with its average value α, and replace the non-line-of-sight deviation β _i in the energy domain measurement information with its average value β, press to determine the corrected time-domain time-of-arrival measurement information

Energy Domain Received Signal Strength Measurement Information

in

(7) Determine the position coordinates of the signal source:

(7.1) Construct the signal source localization minimization function expression capable of time domain information fusion:

(7.2) Convert the signal source localization minimization function capable of time domain information fusion into a generalized trust region sub-problem, and initialize the average α and β of the energy time domain non-line-of-sight deviation, that is, let α=0, β=0; denoted by k Calculate the actual number of times the signal source position estimate is made, and let k = 1;

根据下式求出当前能域非视距偏差平均值

和时域非视距偏差平均值

(7.3) Use the bisection method to solve the generalized trust region sub-problem, and obtain the position estimate of the kth signal source

Calculate the average value of the current energy domain non-line-of-sight deviation according to the following formula

and time-domain non-line-of-sight deviation mean

若满足，将此时的估计值

确定为信号源位置的最终估计结果

反之，更新能时域非视距偏差平均值，即令

并对k加1后返回步骤(7.3)；(7.4) Determine whether the positioning accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as the final estimate of the signal source location

On the contrary, update the average value of the non-line-of-sight deviation in the energy time domain, that is,

And return to step (7.3) after adding 1 to k;

(8) The final estimation result of the output signal source position

Compared with the prior art, the present invention has the following advantages:

First, the target positioning method for transmission parameter estimation in a non-line-of-sight environment proposed by the present invention requires neither the prior information of wireless signal transmission power and path loss coefficient nor the prior information of non-line-of-sight deviation, and can be solved iteratively. To determine the transmission parameters of the wireless signal and the coordinates of the target location;

Second, when the present invention uses the energy-time domain joint measurement information to locate the signal source, it not only considers the problem that the energy-domain measurement model cannot be constructed due to the unknown wireless signal transmission parameters, but also considers the influence of the non-line-of-sight environment, and introduces non-line-of-sight environment. The line-of-sight deviation balance parameter effectively reduces the influence of the inaccurate time-of-arrival measurement parameters in the non-line-of-sight environment, which leads to the decrease or even failure of the positioning accuracy.

Description of drawings

Fig. 1 is the realization flow chart of the method of the present invention;

Fig. 2 is the root mean square error curve of the positioning performance under the change of the standard deviation σ _i of the measurement noise of the present invention;

Fig. 3 is the root mean square error curve of the path loss parameter when the standard deviation σ _i of the measurement noise changes according to the present invention;

Fig. 4 is the root mean square error curve of the positioning performance of the present invention under the change of the non-line-of-sight deviation maximum value β _i ;

FIG. 5 is the root mean square error curve of the path loss parameter under the condition of the variation of the non-line-of-sight deviation maximum value β _i according to the present invention.

Detailed ways

Below with reference to accompanying drawing, the technical scheme of the present invention is described in detail:

Referring to FIG. 1, the present invention proposes a target positioning method for estimation of wireless signal transmission parameters in a non-line-of-sight environment. The implementation steps include:

Step 1: Extract energy time domain measurement information:

According to the wireless sensor network target positioning model in the non-line-of-sight environment, the energy time domain measurement information is extracted from the signal transmitted by the signal source to the sensor node, that is, the sensor receiver obtains the time domain measurement information by recording the reception time of the source signal. d _i , while obtaining energy domain measurement information P _i by recording the energy of the received signal;

Obtain time domain measurement information d _i and energy domain measurement information P _i , as follows:

d _i =||sa _i ||+n _i +α _i ,

的高斯分布；l_i为能域中包含的对数型阴影衰落，服从均值为零、方差为

的高斯分布；α_i表示时域非视距偏差，β_i为能域非视距偏差，非视距偏差存在确定的边界值，即0≤α_i≤α_max、0≤β_i≤β_max，且α_i在0～α_max之间任意取值，β_i在0～β_max之间任意取值，α_max和β_max分别表示时域和能域中非视距偏差的最大值。Among them, s represents the position coordinate of the signal source in the three-dimensional coordinate system, denoted as [x, y, z] ^T ; a _i represents the position coordinate of the sensor node in the three-dimensional coordinate system, denoted as [x _i , y _i , z _i ] ^T ; i=1,2,...,N represents the sensor node number; N is the number of sensor nodes, which is a positive integer ≥ 3; || · || is the Euclidean norm; r ₀ is the unit distance, and r ₀ is 1m; P ₀ is the transmitted signal strength of the signal source at unit distance; γ is the transmission path loss coefficient, in this embodiment, γ is 3; n _i is the time domain The measurement noise of , subject to a mean of zero and a variance of

The _Gaussian distribution of

Gaussian distribution; α _i represents the non-line-of-sight deviation in the time domain, β _i is the non-line-of-sight deviation in the energy domain, and there are certain boundary values for the non-line-of-sight deviation, namely 0≤α _i ≤α _max , 0≤β _i ≤β _max , and α _i can take any value between 0 and α _max , and _{β i} can take any value between ₀ and β _max .

Step 2: Preliminarily determine the position coordinates of the signal source:

(2.1) The non-line-of-sight deviation α _i in the time domain arrival time measurement information d _i is replaced by the average value α', and the weighted least squares method is used to construct the minimization function of the signal source location in the time domain as follows:

表示时域加权权重；

表示信号源至N个传感器节点的时域非视距偏差平均值；in,

represents the time domain weighted weight;

Represents the average time-domain non-line-of-sight deviation from the signal source to N sensor nodes;

(2.2) Transform the minimization function of signal source localization in the time domain into a generalized trust region subproblem:

为约束条件，y_t为含有三维坐标系中信号源位置的辅助变量，(·)^T表示·的转置；加权矩阵W_t表示为：Among them, ||W _t (A _t y _t -h _t )|| ² is the objective function;

is the constraint condition, y _t is an auxiliary variable containing the position of the signal source in the three-dimensional coordinate system, (·) ^T represents the transpose of ·; the weighting matrix W _t is expressed as:

The matrices A _t and h _t in the objective function are:

The matrices D and g in the constraints are:

Wherein, I represents an identity matrix, 0 represents an all-zero matrix, K represents a matrix dimension, and in this embodiment, K is 3;

(2.3) Initialize the time-domain non-line-of-sight deviation average α′, that is, let α′=0; let j represent the actual number of times to calculate the estimated value of the signal source position, and let j=1;

得到

采用二分法对步骤(2.2)中的表达式进行计算，得到第j次信号源位置的估计值

再利用信号源位置估计值

按下式求出当前时域非视距偏差平均值

(2.4) By definition

get

Use the bisection method to calculate the expression in step (2.2) to obtain the estimated value of the jth signal source position

Reuse the source position estimate

Calculate the average value of the current time-domain non-line-of-sight deviation as follows

若满足，将此时的估计值

确定为信号源位置的初步估计结果

并直接执行步骤3；反之，执行步骤(2.6)；(2.5) Determine whether the positioning accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as a preliminary estimate of the signal source location

And directly execute step 3; otherwise, execute step (2.6);

并对j加1后返回步骤(2.4)；(2.6) Update the average value of time-domain non-line-of-sight deviation, that is, let

Add 1 to j and return to step (2.4);

Step 3: Multi-point cooperation to obtain received signal strength difference information:

代入能域接收信号强度测量信息P_i，其它传感器节点与参考节点之间的接收信号强度差值信息P_i1表示为：Taking sensor node 1 as the reference node, the initial estimation result of the signal source position

Substituting into the received signal strength measurement information P _i in the energy domain, the received signal strength difference information P _i1 between other sensor nodes and the reference node is expressed as:

Among them, a ₁ represents the position coordinate of the sensor node 1 in the three-dimensional coordinate system; γ represents the transmission path loss coefficient; _{Δl i1} = _{l i} -l _{1 represents} the measurement noise difference; deviation difference;

Step 4: Determine the transmission path loss factor:

(4.1) The non-line-of-sight deviation difference Δβ _i1 in the received signal strength difference information is replaced by the average value Δβ, and the weighted least squares method is used to construct the minimization function expression of the loss coefficient as follows:

P′_i1＝P_i1-Δβ，

Among them, the energy domain weighted weight

P' _i1 =P _i1 -Δβ,

(4.2) Determine the weighted least squares problem expression:

where the weighting matrix W _r is expressed as:

The matrices A _r and _hr in the objective function are:

in

(4.3) Initialize the average value Δβ of the non-line-of-sight deviation difference in the energy domain, that is, let Δβ=0; use m to represent the actual number of times to calculate the estimated value of the wireless signal transmission path loss coefficient, and let m=1;

再利用损耗系数估计值按下式求出当前能域非视距偏差差值的平均值

(4.4) Obtain P″ _{i1 according to the defined P′ i1 ,} use the iterative weighted least squares method to calculate the expression in step (4.2), and obtain the estimated value of the mth wireless signal transmission path loss coefficient

Then use the estimated value of the loss coefficient to obtain the average value of the current energy domain non-line-of-sight deviation as follows:

若满足，将此时的估计值

确定为传输路径损耗系数的估计结果

并直接执行步骤5；反之，执行步骤(4.6)；(4.5) Judging whether the estimation accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as the estimated result of the transmission path loss coefficient

And directly execute step 5; otherwise, execute step (4.6);

并对m加1后返回步骤(4.4)；(4.6) Update the average value of the non-horizontal deviation difference in the energy domain, that is, let

And return to step (4.4) after adding 1 to m;

Step 5: Determine the wireless signal transmit power:

和步骤4得到的传输路径损耗系数的估计结果

代入能域接收信号强度测量信息P_i中，并将非视距偏差β_i用平均值β'代替，构建无线信号发射功率的最小化函数表达式如下：(5.1) Use the preliminary estimation result of the signal source position obtained in step 2

and the estimated result of the transmission path loss coefficient obtained in step 4

Substitute into the received signal strength measurement information _{Pi in the energy domain, and replace the non-line-of-sight deviation β i with} the average value β' to construct the minimization function expression of the wireless signal transmit power as follows:

P_i'＝P_i+β'，

in

P _i '=P _i +β',

(5.2) Initialize the average value β' of non-line-of-sight deviations in the energy domain, that is, let β'=0; use q to represent the actual number of wireless signal transmit power estimates, and let q=1;

再利用发射功率估计值按下式求出当前能域非视距偏差平均值

(5.3) Obtain P _i ″ according to the defined P _i ', calculate the expression constructed in step (5.1), and obtain the estimated value of the qth wireless signal transmission power

Then use the estimated value of the transmit power to obtain the average value of the current energy domain non-line-of-sight deviation as follows:

若满足，将此时的估计值

确定为信号源的无线信号发射功率估计结果

并直接执行步骤6；反之，执行步骤(5.6)；(5.4) Judging whether the estimation accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

The estimation result of the transmit power of the wireless signal determined as the signal source

And directly execute step 6; otherwise, execute step (5.6);

并对q加1后返回步骤(5.4)；(5.6) Update the average value of the non-line-of-sight deviation in the energy domain, that is,

And add 1 to q and return to step (5.4);

Step 6: Modify the energy time domain measurement model:

和无线信号发射功率

代入能域接收信号强度测量模型中，时域测量信息中的非视距偏差α_i用平均值α代替，能域测量信息中的非视距偏差β_i用平均值β代替，按下式确定修正后的时域到达时间测量信息

能域接收信号强度测量信息

The transmission path loss coefficient determined in steps 4 and 5

and wireless signal transmit power

Substitute into the received signal strength measurement model in the energy domain, the non-line-of-sight deviation α _i in the time domain measurement information is replaced by the average value α, and the non-line-of-sight deviation β _i in the energy domain measurement information is replaced by the average value β, determined as follows Corrected time-domain time-of-arrival measurement information

Energy Domain Received Signal Strength Measurement Information

in

Step 7: Determine the location coordinates of the signal source:

(7.1) The weighted least squares method is used to construct the signal source localization minimization function expression capable of time domain information fusion as follows:

(7.2) The generalized trust region subproblem expression is obtained as follows:

Where ||W(AY-P)|| ² is the objective function; Y ^T DY+2g ^T Y=0 is the constraint condition, Y is the auxiliary variable containing the position of the signal source in the three-dimensional coordinate system, and the weighting matrix W is expressed as:

The matrices A and P in the objective function are:

The matrices D and g in the constraints are:

Wherein, I represents an identity matrix, 0 represents an all-zero matrix, and K represents a matrix dimension. In this embodiment, K takes a value of 3.

(7.3) Initialize the time-domain non-line-of-sight deviation averages α and β, that is, let α=0, β=0; let k represent the actual number of times to calculate the estimated value of the signal source position, and let k=1;

得到d″_i、r″_i和

采用二分法对步骤(7.2)中的表达式进行计算，得到第k次信号源的位置估计值

再利用信号源位置估计值按下式求出当前能域非视距偏差平均值

和时域非视距偏差平均值

(7.4) According to the definition of d′ _i , r′ _i and

get d″ _i , r″ _i and

Use the bisection method to calculate the expression in step (7.2) to obtain the position estimate of the kth signal source

Then use the estimated value of the signal source position to obtain the average value of the current energy domain non-line-of-sight deviation as follows:

and time-domain non-line-of-sight deviation mean

若满足，将此时的估计值

确定为信号源位置的最终估计结果

并直接执行步骤8；反之，执行步骤(7.6)；(7.5) Judging whether the positioning accuracy meets the expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as the final estimate of the signal source location

And directly execute step 8; otherwise, execute step (7.6);

并对k加1后返回步骤(7.4)；(7.6) Update the average value of non-line-of-sight deviations in the energy time domain, that is,

And add 1 to k and return to step (7.4);

Step 8: Output the final estimate of the source location

The effect of the present invention is further described below in conjunction with the simulation experiment:

A. Simulation conditions

Each simulation builds a measurement model according to step 1. All nodes in the wireless sensor network are randomly and uniformly placed in the three-dimensional area of B×B×B, and the number of Monte Carlo simulations is L. The rest of the simulation parameters are set: B=30, L=1000, P ₀ =30dBm, γ=3, r ₀ =1m, N=6. In addition, the energy time-domain non-line-of-sight deviations are randomly and uniformly distributed in [0, Bias _max ] in each Monte Carlo simulation. The performance evaluation index of this method is the root mean square error (RMSE), which is defined as:

表示第l次蒙特卡洛仿真中信号源真实位置s的估计值；in

represents the estimated value of the true position s of the signal source in the lth Monte Carlo simulation;

表示第l次蒙特卡洛仿真中信号发射功率P₀的估计值；in

represents the estimated value of the signal transmission power P ₀ in the l-th Monte Carlo simulation;

表示第l次蒙特卡洛仿真中路径损耗系数γ的估计值。in

represents the estimated value of the path loss coefficient γ in the lth Monte Carlo simulation.

B. Simulation content

(1) Simulation Experiment 1

This experiment adopts the method of Example 1 to carry out the simulation experiment. When the maximum time-domain non-line-of-sight deviation Bias _max = 5 (dB, m), the joint positioning algorithm proposed by the present invention is simulated under the condition that the measurement noise standard deviation σ _i is different, and the simulation results are shown in Figures 2 and 3 Figure 2 is the root mean square error performance curve of signal source location estimation, and Figure 3 is the root mean square error performance curve of wireless signal transmission parameter estimation.

(2) Simulation Experiment 2

时，对本发明所提出的联合定位算法在非视距偏差最大值Bias_max不同的情况下进行仿真，仿真结果如图3、4所示，图3是信号源位置估计的均方根误差性能曲线，图4是无线信号传输参数估计的均方根误差性能曲线。This experiment adopts the method of Example 2 to carry out the simulation experiment. The standard deviation of the noise measured in the time domain is

When the joint positioning algorithm proposed by the present invention is simulated under the condition that the maximum value of the non-line-of-sight deviation Bias _max is different, the simulation results are shown in Figures 3 and 4, and Figure 3 is the root mean square error performance curve of the signal source position estimation , Figure 4 is the root mean square error performance curve of wireless signal transmission parameter estimation.

C. Simulation results

It can be seen from Figure 2 that with the increase of the standard deviation σ _i of the measurement noise, the performance of the time-domain localization algorithm and the time-domain joint localization algorithm both deteriorate. However, it can still be proved that in the non-line-of-sight environment, the performance of the time-domain joint positioning algorithm proposed by the present invention is better. And the proposed algorithm does not need the prior information of the true value of wireless signal transmit power and transmission path loss coefficient in the received signal strength measurement model.

It can be seen from Fig. 3 that with the increase of measurement noise σ _i , the estimation performance of wireless signal transmit power and transmission path loss coefficient gradually deteriorates. Combining with the simulation results in Fig. 2, it can be seen that although the performance of estimating the transmission parameters of the wireless signal is degraded, the positioning performance of the algorithm proposed in the present invention is still better than the time domain positioning performance.

It can be seen from Figure 4 that with the increase of the maximum value of the non-line-of-sight deviation Bias _max , the performance of the time-domain localization algorithm and the time-domain joint localization algorithm both deteriorate. However, it can still be proved that in the non-line-of-sight environment, the performance of the time-domain joint positioning algorithm proposed by the present invention is better. And the proposed algorithm does not need the prior information of the true value of wireless signal transmit power and transmission path loss coefficient in the received signal strength measurement model.

It can be seen from Figure 5 that with the increase of the maximum value of the non-line-of-sight deviation Bias _max , the estimation performance of the wireless signal transmit power and transmission path loss coefficient is relatively stable. Combined with the simulation results in FIG. 3 , it is proved from the side that the estimation performance of the algorithm proposed in the present invention for wireless signal transmission parameters is mainly due to the influence of measurement errors, and is less affected by non-line-of-sight errors.

Based on the above simulation results and analysis, the effectiveness, reliability and robustness of the method of the present invention are verified. And it is proved that in the non-line-of-sight environment, using the energy-time-domain joint positioning method proposed by the present invention can effectively determine the wireless signal transmit power, transmission path loss coefficient and the average value of non-line-of-sight deviation in the received signal strength measurement model, ensuring that The localization accuracy of wireless sensor network targets in non-line-of-sight environments.

The parts of the present invention that are not described in detail belong to the common knowledge of those skilled in the art.

The above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Obviously, for those skilled in the art, after understanding the content and principles of the present invention, they may not deviate from the principles of the present invention, In the case of the structure, various corrections and changes in form and details are made, but these corrections and changes based on the idea of the present invention are still within the scope of protection of the claims of the present invention.

Claims (6)

1. a target location method for wireless signal transmission parameter estimation under non-line-of-sight environment, is characterized in that, comprises the steps: (1) Extract energy time domain measurement information: According to the wireless sensor network target positioning model in the non-line-of-sight environment, the sensor receiver obtains the time domain measurement information di by recording the reception time of the source signal, and simultaneously obtains the energy domain measurement information _{P i by} recording the energy of the received signal; (2) Preliminarily determine the position coordinates of the signal source: (2.1) According to the time domain measurement information d _i , the weighted least squares method is used to construct the minimization function of the signal source location in the time domain:

Among them, s represents the position coordinate of the signal source in the three-dimensional coordinate system, denoted as [x, y, z] ^T ; a _i represents the position coordinate of the sensor node in the three-dimensional coordinate system, denoted as [x _i , y _i , z _i ] ^T ; i=1,2,...,N represents the sensor node number; N is the number of sensor nodes, and N is a positive integer greater than or equal to 3; || · || is the Euclidean norm;

represents the average value of the time-domain non-line-of-sight deviation from the signal source to N sensor nodes, and α _i represents the time-domain non-line-of-sight deviation;

represents the time domain weighted weight; (2.2) Convert the minimization function of signal source location in the time domain into a generalized trust region sub-problem, and initialize the average α′ of the non-line-of-sight deviation in the time domain, that is, let α′=0; times, and let j=1; (2.3) Use the bisection method to solve the generalized trust region subproblem to obtain the estimated value of the jth signal source position

Calculate the average value of the current time-domain non-line-of-sight deviation according to the following formula

(2.4) Determine whether the positioning accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as a preliminary estimate of the signal source location

And directly execute step (3); on the contrary, update the average value of time-domain non-line-of-sight deviation, that is, let

Add 1 to j and return to step (2.3); (3) Multi-point cooperation to obtain received signal strength difference information: Taking sensor node 1 as the reference node, the initial estimation result of the signal source position

Substituting into the received signal strength measurement information P _i in the energy domain, the received signal strength difference information P _i1 between other sensor nodes and the reference node is expressed as:

Among them, a ₁ represents the position coordinate of the sensor node 1 in the three-dimensional coordinate system; γ represents the transmission path loss coefficient; _{Δl i1} = _{l i} -l _{1 represents} the measurement noise difference; deviation difference; (4) Determine the transmission path loss coefficient: (4.1) Use the weighted least squares method to construct the minimization function of the loss coefficient as follows:

in,

represents the energy domain weighting weight; P′ _i1 =P _i1 -Δβ, Δβ represents the average value of non-line-of-sight deviation difference; (4.2) Convert the minimization function of the loss coefficient into a weighted least squares problem, and initialize the average value Δβ of the non-line-of-sight deviation difference in the energy domain, that is, let Δβ=0; use m to represent the estimated value of the loss coefficient of the wireless signal transmission path The actual number of times, and let m = 1; (4.3) Use the iterative weighted least squares method to solve the problem in step (4.2), and obtain the estimated value of the mth wireless signal transmission path loss coefficient

Then use the estimated value of the loss coefficient to obtain the average value of the current energy domain non-line-of-sight deviation as follows:

(4.4) Judging whether the estimation accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as the estimated result of the transmission path loss coefficient

And directly execute step (5); on the contrary, update the average value of the non-horizontal deviation difference in the energy domain, that is, let

And return to step (4.3) after adding 1 to m; (5) Determine the transmission power of the wireless signal: (5.1) Use the preliminary estimation result of the signal source position obtained in step (2)

and the estimated result of the transmission path loss coefficient obtained in step (4)

Substitute into the received signal strength measurement information P _i in the energy domain, and replace the non-line-of-sight deviation β _i with the average value β' to construct the minimization function expression of the wireless signal transmit power as follows:

in

P' _i =P _i +β',

(5.2) Initialize the average value β' of non-line-of-sight deviations in the energy domain, that is, let β'=0; use q to represent the actual number of wireless signal transmit power estimates, and let q=1; (5.3) Obtain P″ _i according to the defined P′ _i , and calculate the expression constructed in step (5.1) to obtain the estimated value of the qth wireless signal transmission power

Then use the estimated value of the transmit power to obtain the average value of the current energy domain non-line-of-sight deviation as follows:

(5.4) Judging whether the estimation accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

The estimation result of the transmit power of the wireless signal determined as the signal source

And directly execute step (6); on the contrary, update the average value of the non-line-of-sight deviation in the energy domain, that is, let

And add 1 to q and return to step (5.3); (6) Modified energy time domain measurement model: The transmission path loss coefficient determined in step (4) and step (5)

and wireless signal transmit power

Substitute into the received signal strength measurement model in the energy domain, replace the non-line-of-sight deviation α _i in the time domain measurement information with its average value α, and replace the non-line-of-sight deviation β _i in the energy domain measurement information with its average value β, press to determine the corrected time-domain time-of-arrival measurement information

Energy Domain Received Signal Strength Measurement Information

in

(7) Determine the position coordinates of the signal source: (7.1) Construct the signal source localization minimization function expression capable of time domain information fusion:

(7.2) Convert the signal source localization minimization function capable of time domain information fusion into a generalized trust region sub-problem, and initialize the average α and β of the energy time domain non-line-of-sight deviation, that is, let α=0, β=0; denoted by k Calculate the actual number of times the signal source position estimate is made, and let k = 1; (7.3) Use the bisection method to solve the generalized trust region sub-problem, and obtain the position estimate of the kth signal source

Calculate the average value of the current energy domain non-line-of-sight deviation according to the following formula

and time-domain non-line-of-sight deviation mean

(7.4) Determine whether the positioning accuracy meets expectations, that is, whether it meets the

If satisfied, use the estimated value at this time

Determined as the final estimate of the signal source location

On the contrary, update the average value of the non-line-of-sight deviation in the energy time domain, that is,

And return to step (7.3) after adding 1 to k; (8) The final estimation result of the output signal source position

2. method according to claim 1 is characterized in that: step (1) obtains time domain measurement information d _i and energy domain measurement information P _i , is specifically as follows: d _i =||sa _i ||+n _i +α _i ,

Among them, r ₀ is the unit distance; P ₀ is the transmitted signal strength of the signal source at the unit distance; γ is the transmission path loss coefficient; n _i is the measurement noise in the time domain; _li is the logarithmic shadow contained in the energy domain. Fading; α _i represents the non-line-of-sight deviation in the time domain; β _i is the non-line-of-sight deviation in the energy domain; and α _i is any value between 0 and α _max , and β _i is any value between 0 and β _max , _max and β _max represent the maximum value of the non-line-of-sight deviation in the time and energy domains, respectively. 3. method according to claim 1 is characterized in that: in step (2.2), the minimization function of signal source localization in time domain is converted into generalized trust region sub-problem, and is realized as follows:

Among them, ||W _t (A _t y _t -h _t )|| ² is the objective function;

is the constraint condition, y _t is an auxiliary variable containing the position of the signal source in the three-dimensional coordinate system, (·) ^T represents the transpose of ·; the weighting matrix W _t is expressed as:

The matrices A _t and h _t in the objective function are:

The matrices D and g in the constraints are:

Among them, I represents the identity matrix, 0 represents the all-zero matrix, and K represents the matrix dimension. 4. The method according to claim 1, characterized in that: the energy domain weighting in step (4.1)

The average value Δβ of the difference from the non-line-of-sight deviation is determined according to the following formulas:

5. The method according to claim 1, characterized in that: the weighted least squares problem described in step (4.2) is specifically as follows:

where W _r represents the weighting matrix:

The matrices A _r and _hr in the objective function are:

in

6. The method according to claim 1, characterized in that: the generalized trust region sub-problem described in the step (7.2), the form is as follows:

Where ||W(AY-P)|| ² is the objective function; Y ^T DY+2g ^T Y=0 is the constraint condition, Y is the auxiliary variable containing the position of the signal source in the three-dimensional coordinate system, and the weighting matrix W is expressed as:

The matrices A and P in the objective function are:

The matrices D and g in the constraints are:

where I represents the identity matrix, 0 represents the all-zero matrix, and K represents the matrix dimension.

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