阅读说明：本技术 一种基于权重融合选择的粒子滤波检测前跟踪方法 (Particle filter tracking-before-detection method based on weight fusion selection ) 是由 石义芳 潘凯 陈霄 于 2020-06-01 设计创作，主要内容包括：本发明公开了一种基于权重融合选择的粒子滤波检测前跟踪方法,在跟踪环节中的粒子权重融合部分对粒子权重加上幂值,计算每个雷达位置与跟踪粒子群位置均值的距离,对其距离按从小到大进行排序,依据此排序对雷达粒子权重加上不同的幂值。在雷达粒子权重融合后,对跟踪粒子群进行筛选,按粒子权重值大小对跟踪粒子群从大到小进行排序,选取排序后的前m个粒子并计算其平均状态。分别计算得到这m个粒子与其粒子平均位置的距离并与一给定阈值Dis进行比较,若其值大于Dis,则令对应的粒子状态等于排序后的前m个粒子平均状态且令粒子权重等于前m个粒子权重均值,群高了跟踪粒子群的粒子权重准确度,提高了跟踪精度。(The invention discloses a particle filter track-before-detect method based on weight fusion selection, which is characterized in that a particle weight fusion part in a tracking link adds power values to particle weights, calculates the distance between each radar position and the mean value of tracking particle swarm positions, orders the distances from small to large, and adds different power values to the radar particle weights according to the order. And after the radar particle weights are fused, screening the tracking particle swarm, sorting the tracking particle swarm from large to small according to the weight values of the particles, selecting the first m particles after sorting and calculating the average state of the particles. And respectively calculating the distances between the m particles and the average positions of the particles, comparing the distances with a given threshold value Dis, and if the distances are greater than Dis, enabling the corresponding particle states to be equal to the average states of the first m sequenced particles and enabling the particle weights to be equal to the average value of the weights of the first m particles, so that the particle weight accuracy of the tracking particle swarm is improved, and the tracking precision is improved.)

1. A particle filter track-before-detect method based on weight fusion selection is characterized by comprising the following steps:

step 1, initializing parameters: a radar scanning period T, an observation total frame number K, a particle number N in a particle swarm, a radar number R, and a radar particle weight fusion power value array L ═ L [ L ]₁,L₂…L_R]Distance, Doppler and azimuth space cell distance are D_r,D_d,D_b；

Step 2, reading the k frame measurement of multiple radars

step 3, setting the tracking target set Taxe at the moment k-1 as f_1,k-1，,f_2,k-1…f_m,k-1M targets are tracked, Tm is m, each target f_i,k-1All have a tracking particle swarm P_i,k-1＝{p_i,1,k-1,p_i,2,k-1…p_i,N,k-1In which p is_i,j,k-1A jth particle representing an ith target;

step 3.1, making i equal to 1, r equal to 1 and j equal to 1;

step 3.2, tracking particle swarm P of ith target_i,k-1＝{p_i,1,k-1,p_i,2,k-1…p_i,N,k-1Performing state transition on each particle in the target particle swarm P to obtain a target tracking particle swarm P_i,k＝{p_i,1,k,p_i,2,k…p_i,N,kEach particle has a state variable of

Step 3.3, calculating and tracking the multi-radar weight of each particle in the particle swarm, and specifically comprising the following steps:

step 3.3.1, compare E of the target jth particle_i,jIf so, entering step 3.3.2, otherwise, calculating the weight of the particle based on the r radar1 and go to step 3.3.4;

step 3.3.2, calculating the distance of the jth particle under the corresponding r radarDoppler deviceAnd orientation value

x_r,y_rRepresents the r-th radar position;

step 3.3.3, calculating particle weight under single radar

Wherein

step 3.3.4, if j < N, j ═ j +1 and go to step 3.3.1, otherwise go to step 3.3.5;

step 3.3.5, if R < R, j is 1, R +1 and go to step 3.3.1, otherwise go to step 3.4;

step 3.4, calculating the weight of the multi-radar fusion particlej＝1,…,N；

Step 3.4.1, calculating the distances between the target at the moment k-1 and R radars respectively:

wherein d is_r,i,kIndicating the distance between the ith target predicted value and the r radar at the k moment,represents the x, y direction position of the r radar,representing the predicted position of the target k moment in the x and y directions obtained by transferring the state information of the ith target k-1 moment;

step 3.4.2, obtaining a set of distances d_i,k＝{d_i,1,k,d_i,2,k…d_i,R,kH, will d_i,kSorting from small to large to obtain a sorting label I ═ I₁,…I_r…,I_R}＝{1,2…R}，I_rIndicating that the r-th radar distance is ranked I in the set_rA bit;

and 3.4.3, sequentially distributing a radar particle weight fusion power value array L-L to each radar according to the radar sequencing index value₁,L₂…L_R]To the power of the corresponding position in, L₁＝L₂＝…＝L_R/2＝2，L_R/2+1＝…＝L_R＝1；

Step 3.4.4, making r equal to 1;

step 3.4.5, normalizing the weight value of the particles corresponding to the r radar:

step 3.4.6, if R < R, then R ═ R +1 and go to step 3.4.5, otherwise go to step 3.4.7;

step 3.4.7, calculating the weight of the fused radar particles:

step 3.4.8, normalizing the particle swarm weight:

step 3.5, managing the tracking particle swarm;

step 3.5.1, according to the weight value of the particlesSequencing the tracking particle swarm of the ith target from big to small to obtain the first H particles

Step 3.5.2, calculating the H particles and the average value of the particles in sequenceThe distance between:

whereinIs a particle groupThe mean value of the positions of (a),respectively represent a group of particlesThe position of the jth particle in (a);

step 3.5.3, set a threshold constant Dis, ifIf the particle size is larger than Dis, the particle size is tracked_i,j,kTo the mean value of the particle states

Step 3.6, adopting system resampling to track the particle swarm P_i,k＝{p_i,1,k,p_i,2,k…p_i,N,kUpdating is carried out;

step 3.7, calculate target f_i,kJudging whether pb is less than a found target threshold Myu, if yes, considering the target as a false target, deleting the target from a target tracking set Taxe, and deleting a tracking particle swarm P_i,kOtherwise, the target is considered to exist, and the target state estimation is obtained

pb M/N type (10)

Wherein M is the presence variable E_i,jA particle number of 1;

step 3.8, if i < Tm, i ═ i +1 and go to step 3.2, otherwise go to step 3.9;

step 3.9, tracking the target set to be each target f in the Taxe_i,k-1Is updated to f_i,kFinally obtaining a tracking target set Taxe at the moment k, wherein the number of tracking targets is Tm;

step 4, detecting a new target at the moment k to generate a detection particle swarm

step 4.1, making Dm equal to 0; dm is the number of targets in the detection target set;

step 4.2, detecting particle swarmIn which each particle is subjected to a state transition to obtain a state variable of each particle asAnd the existence of variable E_h,jWherein x is_h,j,y_h,jThe position of the particles in the x, y direction,

step 4.3, calculating the weight of each particle under each radar in the detection particle swarm

Step 4.3.1, let j equal to 1, r equal to 1, i equal to 1;

4.3.2, calculating the distance between the jth particle and a target i in the combined set of the detection target set Daxe and the tracking target set Taxe and see a formula (11); if it is

x_h,j,y_h,jfor detecting the position of the particles in the x, y directions, x_i,k,y_i,kThe position of a target i in the x and y directions is collected for the detection target set and the tracking target set;

step 4.3.3, if i < Tm + Dm, i ═ i +1 and go to step 4.3.2, otherwise go to step 4.3.4;

step 4.3.4, calculating the distance, Doppler and bearing value of the jth particle based on the r radar:

x_r,y_rrespectively representing the position of the r-th radar in x, y,

step 4.3.5, calculating the weight of the jth particle based on the r radar measurement

WhereinRepresenting the weight of the jth particle for the r-th radar, m, n, p, k being particles

step 4.3.6, if j < N, j ═ j +1 and go to step 4.3.2, otherwise go to step 4.3.7;

step 4.3.7, if R < R, let j equal to 1, R equal to R +1 and go to step 4.3.2;

step 4.3.8, normalizing the particle swarm weight under the r radar, which is shown in formula (7):

step 4.3.9, calculating the weight of the fused jth particle at the moment k:

step 4.4, resampling the particle swarm by adopting a system resampling method;

step 4.5, calculating the detection probability pb of the detected particles according to the formula (10), judging whether pb is smaller than the found target threshold Myu, if yes, going to step 5, otherwise, considering that a new target is detected, and calculating the state estimation of the target

step 4.6, judging whether the detected target set Daxe and the tracked target set Taxe at the moment k are empty, if so, going to step 5, otherwise, continuously judging whether the new target is a target found in the tracked target set Taxe at the moment k or a target detected in the detected target set Daxe, specifically:

step 4.6.1, changing i to 1;

step 4.6.2, calculating the distance equation (12) of the new target and the target i in the union set of the detection target set and the tracking target set, and judging dis_i,kIf the value is less than the verification target threshold value Mk, if so, the target is not a new target, the step 4.2 is skipped, the detection particle group is regenerated to detect the new target, otherwise, the step 4.6.3 is entered;

where { x_i,k，y_i,kX is the position of the target i in the x and y directions in the union set of the detection target set and the tracking target set, x^D，y^DRepresents the x, y direction positions of the new target, respectively;

step 4.6.3, if i < Tm + Dm, i is equal to i +1 and step 4.6.2 is performed, otherwise, step 4.6.4 is performed;

step 4.6.4 New target acquisition detection particle population

and 5, adding the new target into the k-time tracking target set Taxe to obtain an updated k-time tracking target set Taxe ═ f_1,k,f_2,k,...f_mk+nkDetecting particle swarmUpdated to track the particle swarm P_i,k。

Technical Field

The invention belongs to the technical field of tracking before radar detection, relates to the technical field of tracking before multi-radar multi-target particle filter detection, and particularly relates to a particle filter tracking method before detection based on weight fusion selection.

Background

The multi-radar multi-target particle filter pre-detection tracking algorithm is a method for detecting and tracking a plurality of weak targets by using a plurality of radars, and a double-layer particle filter structure, namely a target tracking layer and a target detection layer, is usually adopted. When the target tracking layer performs tracking filtering on the found target, if the target is close in distance, when the target tracking particle group is thrown, the target tracking particle group may be thrown in the adjacent target area. In addition, if the radar is far from these targets, the particle weights calculated from the radar measurements may not accurately reflect the different target locations. When multiple radar particle weight fusion is performed, simple particle weight multiplication may cause a phenomenon that in a particle group tracked by a certain target, the weight of a particle positioned at the edge of the group and close to an adjacent target is larger relative to a particle at the center of the group, and the particle group shifts during resampling, so that a target track gradually shifts to an adjacent target track.

Disclosure of Invention

The invention provides a particle filter track-before-detect method based on weight fusion selection, which considers the problem of target track deviation in the problem of tracking a plurality of targets with close distances.

The method comprises the following specific steps:

step 1, initializing parameters: a radar scanning period T, an observation total frame number K, a particle number N in a particle swarm, a radar number R, and a radar particle weight fusion power value array L ═ L [ L ]₁,L₂…L_R]Distance, Doppler and azimuth space cell distance are D_r,D_d,D_b；

Step 2, reading the k frame measurement of multiple radarsWherein the content of the first and second substances,

the measurement in a measurement unit (m, n, p) of the echo data of the kth frame of the r-th radar is shown, wherein m, n and p respectively show a distance unit, a Doppler unit and a direction unit;

step 3, setting the tracking target set Taxe at the moment k-1 as f_1,k-1，,f_2,k-1…f_m,k-1Tracking m targets in the data, wherein Tm is the number of the targets in the tracking set, and each target f_i,k-1All have a tracking particle swarm P_i,k-1＝{p_i,1,k-1,p_i,2,k-1…p_i,N,k-1In which p is_i,j,k-1The jth particle representing the ith target.

Step 3.1, making i equal to 1, r equal to 1 and j equal to 1;

step 3.2, tracking particle swarm P of ith target_i,k-1＝{p_i,1,k-1,p_i,2,k-1…p_i,N,k-1Performing state transition on each particle in the target particle swarm P to obtain a target tracking particle swarm P_i,k＝{p_i,1,k,p_i,2,k…p_i,N,kEach particle has a state variable ofAnd the existence of variable E_i,j；

Step 3.3, calculating and tracking the multi-radar weight of each particle in the particle swarm, and specifically comprising the following steps:

step 3.3.1, compare E of the target jth particle_i,jIf so, entering step 3.3.2, otherwise, calculating the weight of the particle based on the r radar1 and go to step 3.3.4.

Step 3.3.2, calculating the distance of the jth particle under the corresponding r radar

Doppler deviceAnd orientation value

x_r,y_rRepresenting the r-th radar position.

Step 3.3.3, calculating particle weight under single radar

Wherein

Represents the weight of the jth particle based on the r radar measurement, with m, n, p, k being the time k

The unit position in the radar measurement space; sigma_nRepresents the standard deviation, L_rDenotes the distance-dependent attenuation constant, L_dRepresents a Doppler-dependent attenuation constant, L_bShowing attenuation constant related to azimuth, R (m) showing distance of the target corresponding to the r radar measurement unit, D (n) showing Doppler of the target corresponding to the r radar measurement unit, B (p) showing azimuth value of the target corresponding to the r radar measurement unit, A_r,kThe complex amplitude of the unit is corresponding to the r-th sensor.

Step 3.3.4, if j < N, j equals j +1 and step 3.3.1 is entered, otherwise step 3.3.5 is entered.

Step 3.3.5, if R < R, j is 1, R +1 and step 3.3.1 is entered, otherwise step 3.4 is entered.

Step 3.4, calculating the thunderUp to the fusion particle weight

Step 3.4.1, calculating the distances between the target at the moment k-1 and R radars respectively:

wherein d is_r,i,kIndicating the distance between the ith target predicted value and the r radar at the k moment,

represents the x, y direction position of the r radar,representing the predicted position of the target k in the x and y directions obtained by transferring the state information of the ith target k-1.

Step 3.4.2, obtaining a set of distances d_i,k＝{d_i,1,k,d_i,2,k…d_i,R,kH, will d_i,kSorting from small to large to obtain a sorting label I ═ I₁,…I_r…,I_R}＝{1,2…R}，I_rIndicating that the r-th radar distance is ranked I in the set_rA bit.

And 3.4.3, sequentially distributing a radar particle weight fusion power value array L-L to each radar according to the radar sequencing index value₁,L₂…L_R]To the power of the corresponding position in, L₁＝L₂＝…＝L_R/2＝2， L_R/2+1＝…＝L_R＝1。

And 3.4.4, making r equal to 1.

Step 3.4.5, normalizing the weight value of the particles corresponding to the r radar:

step 3.4.6, if R < R, then R ═ R +1 and go to step 3.4.5, otherwise go to step 3.4.7.

Step 3.4.7, calculating the weight of the fused radar particles:

the fusion power value of the particle weight of the r radar,

indicating the weight of the jth particle based on the r radar measurements.

Step 3.4.8, normalizing the particle swarm weight after fusion:

step 3.5, managing the tracking particle swarm;

step 3.5.1, according to the weight value of the particlesSequencing the tracking particle swarm of the ith target from big to small to obtain the first H particles

Calculating to obtain the state mean value of the H particles

Step 3.5.2, calculating the H particles and the average value of the particles in sequence

The distance between:

whereinIs a particle group

The mean value of the positions of (a),respectively represent a group of particles

The position of the jth particle in (a);

step 3.5.3, set a threshold constant Dis, ifIf the particle size is larger than Dis, the particle size is tracked_i,j,kTo the mean value of the particle states

Step 3.6, adopting system resampling to track the particle swarm P_i,k＝{p_i,1,k,p_i,2,k…p_i,N,kUpdating is carried out;

step 3.7, calculate target f_i,kJudging whether pb is less than a found target threshold Myu, if yes, considering the target as a false target, deleting the target from a target tracking set Taxe, and deleting a tracking particle swarm P_i,kOtherwise, the target is considered to exist, and the target state estimation is obtainedThat is, the state of the target at the next moment, the target existence probability is:

pb M/N type (10)

Wherein M is the presence variable E_i,jA particle number of 1;

step 3.8, if i < Tm, i ═ i +1 and go to step 3.2, otherwise go to step 3.9;

step 3.9, tracking the target set to be each target f in the Taxe_i,k-1Is updated to f_i,kFinally obtaining a tracking target set Taxe at the moment k, wherein the number of tracking targets is Tm;

step 4, detecting a new target at the moment k to generate a detection particle swarm

For detecting new objects, new objects detectedInputting the detection target set Daxe to obtain a detection target setAnd detecting a target particle groupWherein h is the h-th target in the detection target set, and specifically is:

step 4.1, making Dm equal to 0; dm is the number of targets in the detection target set;

step 4.2, detecting particle swarmIn which each particle is subjected to a state transition to obtain a state variable of each particle as

And the existence of variable E_h,jWherein x is_h,j,y_h,jThe position of the particles in the x, y direction,

the speed of the particles in the x and y directions;

step 4.3, calculating the weight of each radar particle in the detection particle swarm

Step 4.3.1, let j equal to 1, r equal to 1, i equal to 1;

4.3.2, calculating the distance between the jth particle and a target i in the combined set of the detection target set Daxe and the tracking target set Taxe and see a formula (11); if it isThen the jth particle weightSetting to 1, and entering a step 4.3.6, otherwise entering a step 4.3.3.

x_h,j,y_h,jFor detecting the position of the particles in the x, y directions, x_i,k,y_i,kThe position of a target i in the x and y directions is collected for the detection target set and the tracking target set;

step 4.3.3, if i < Tm + Dm, i ═ i +1 and go to step 4.3.2, otherwise go to step 4.3.4.

Step 4.3.4, calculating the distance, Doppler and bearing value of the jth particle based on the r radar:

x_r,y_rrespectively representing the position of the r-th radar in x, y,respectively representing the distance, Doppler and azimuth values of the jth particle based on the r radar measurement.

Step 4.3.5, calculate jth particle based on the rWeight of radar measurements

Wherein

Representing the weight of the jth particle for the r-th radar, m, n, p, k being particles

The position of the cell, σ, in the radar measurement space_nRepresenting standard deviation, R (m) represents the distance of the target corresponding to the r-th radar measurement unit, D (n) represents the Doppler of the target corresponding to the r-th radar measurement unit, B (p) represents the azimuth of the target corresponding to the r-th radar measurement unit, A_r,kRepresenting the complex amplitude of the corresponding cell of the r-th sensor.

Step 4.3.6, if j < N, j equals j +1 and step 4.3.2 is entered, otherwise step 4.3.7 is entered.

In step 4.3.7, if R < R, the process proceeds to step 4.3.2, where j is 1 and R is R + 1.

Step 4.3.8, normalizing the particle swarm weight under the r radar, which is shown in formula (7):

step 4.3.9, calculating the weight of the fused jth particle at the moment k:

step 4.4, resampling the particle swarm by adopting a system resampling method;

step 4.5, calculating the detection probability pb of the detection particles according to the formula (10), and judging whether pb isIf not, the target is less than the found target threshold Myu, if yes, go to step 5, otherwise, a new target is deemed to be detected and a state estimate for the target is calculatedGo to step 4.6.

Step 4.6, judging whether the detected target set Daxe and the tracked target set Taxe at the moment k are empty, if so, going to step 5, otherwise, continuously judging whether the new target is a target found in the tracked target set Taxe at the moment k or a target detected in the detected target set Daxe, specifically:

step 4.6.1, changing i to 1;

step 4.6.2, calculating the distance equation (12) of the new target and the target i in the union set of the detection target set and the tracking target set, and judging dis_i,kIf the value is less than the verification target threshold value Mk, if so, the target is not a new target, the step 4.2 is skipped, the detection particle group is regenerated to detect the new target, otherwise, the step 4.6.3 is entered;

where { x_i,k，y_i,kX is the position of the target i in the x and y directions in the union set of the detection target set and the tracking target set, x^D，y^DRepresents the x, y direction positions of the new target, respectively;

step 4.6.3, if i < Tm + Dm, i is equal to i +1 and step 4.6.2 is performed, otherwise, step 4.6.4 is performed;

step 4.6.4 New target acquisition detection particle populationNew objectInput to the detected target setIn the step, Dm is equal to Dm +1, and the step is rotated4.2, circulating until no new target is detected, and outputting a detection target set;

and 5, adding the new target into the k-time tracking target set Taxe to obtain an updated k-time tracking target set Taxe ═ f_1,k,f_2,k,…f_mk+nkDetecting particle swarmUpdated to track the particle swarm P_i,k。

The invention provides a particle filter pre-detection tracking method based on weight fusion selection. And after the radar particle weights are fused, screening the tracking particle swarm, sorting the tracking particle swarm from large to small according to the weight values of the particles, selecting the first m particles after sorting and calculating the average state of the particles. Respectively calculating the distance between the m particles and the average position of the particles, comparing the distance with a given threshold value Dis, and if the value is greater than Dis, enabling the corresponding particle state to be equal to the average state of the first m particles after sorting and enabling the particle weight to be equal to the average value of the weight of the first m particles. Compared with the traditional multi-radar multi-target particle filter pre-detection tracking algorithm, the algorithm can improve the particle weight accuracy of the tracking particle swarm, delete harmful particles, supplement beneficial particles and avoid center deviation of the re-sampled tracking particle swarm, so that the target point trace is ensured not to deviate, the target point trace is accurately associated, and the target tracking precision is improved.

Detailed Description

The invention mainly adopts a computer simulation method for verification, and all the steps are verified correctly on matlab-2016 a. The specific implementation steps are as follows:

(1) initializing system parameters: the radar scanning period T is 2, the number N of the initialization particles is 3000, the target threshold Myu is 0.7, the particle distance target threshold Jyu is 35, the number Xz of the selected particles is 2, the verification target Mk is 50, and Syu is 0.01.

(2) Obtaining kth time measurements for multiple radarsWherein R is the total number of sensors, m, n, p respectively represent a distance unit, a Doppler unit and an orientation unit, D_r,D_d,D_bRespectively range, doppler, and azimuth space cell range.

(3) Taxe ═ f in the tracking target set at the time of k-1_1,k-1，,f_2,k-1…f_Tm,k-1Target f in (1) }_i,k-1Tracking, wherein each target has a tracking particle swarm P_i,k-1＝{p_1,i,k-1,p_2,i,k-1…p_N,i,k-1To the target f_i,k-1The tracking process of (1) is as follows:

(a) calculating the number of columns of a tracking target set Taxe, assigning the number of columns to a variable Tm, wherein Tm represents the number of tracking targets;

(b)i＝1，j＝1，r＝1；

(c) the particle group is subjected to state transition to obtain the state of each particleAnd presence of variable quantity E_i,jWherein x is_i,j,y_i,jRepresenting the position of the particles in the x, y direction,representing the velocity of the particles in the x, y directions;

(d) if E_i,j<0, thenIs 1 and goes to (f);

(e) calculating a weight of a jth particle based on an r-th sensor

Wherein x_r,y_rRespectively representing the position of the r-th radar in x, y,respectively representing the distance, Doppler and orientation values of the jth particle under the ith particle, R (m) representing the distance of the target corresponding to the ith radar measuring unit, D (n) representing the Doppler of the target corresponding to the ith radar measuring unit, B (p) representing the orientation value of the target corresponding to the ith radar measuring unit, A_r,kRepresenting the complex amplitude of the corresponding cell of the r-th sensor. .

(f) If j < N, j equals j +1 and goes to (d), otherwise goes to (g);

(g) if R < R, let j equal 1, R equal R +1 and go to (d), otherwise go to (h);

(h) let r be 1;

(i) normalizing the particle swarm weight under the r radar:

(j) if R < R, then R ═ R +1 and enter (i), otherwise enter (k);

(k) and (3) calculating the distance between the target prediction state at the moment k and each radar:

(l) To d_r,i,kSorting from small to large to obtain a sorting label I_r＝{I₁,I₂…I_R}；

(m) assigning to each radar its number of sets L in turn according to the radar ranking index value_r＝[L₁,L₂…L_R]The power value of the corresponding position in (1);

(n) calculating the weight after the fusion of the particles, whereinRepresents the weight power value of the particles under the r radar:

(o) sorting the particle groups according to the weight values from large to small to obtain the composition of the first m particles

Calculating the state mean value of the m particles

(p) comparing the m particles with a given threshold Dis, and if the m particles are greater than Dis, making the corresponding particle state equal to the average value of the particle states;

(q) obtaining a tracking particle swarm P at the time k by adopting a system resampling method_i,k＝{p_1,i,k,p_2,i,k…p_N,i,k}；

(r) calculating the target f_i,k-1M/N, M being the presence variable E_i,jIf pb is less than the found target threshold Myu, the target is considered as a false target, the target is deleted from the target tracking set Taxe, and the tracking particle group P is deleted_i,kIf the target is greater than the threshold Myu, a target state estimate is obtained

(s) tracking the set of targets as each target f in the Taxe_i,k-1Is updated to f_i,kFinally, a tracking target set Taxe at the moment k is obtained;

(t) updating the number of columns in the target tracking set Taxe and updating the number of targets Tm in the target tracking set;

(4) detecting a new target by using the detection particle group, and starting the new target number Dm to be 0, wherein the process is as follows:

(a) carrying out state transition on the detection particle swarm to obtain the state of each particle asAnd the existence of variable E_h,jWherein x is_h,j,y_h,jThe position of the particles in the x, y direction,is the x, y direction velocity of the particle;

(b) let r be 1, j be 1, i be 1, and target flag alloxit be 0;

(c) calculating the distance between the jth particle and a target i in a union set of a detection target set Daxe and a tracking target set Taxe:

(d) if it is

Then alloxit is 0 and the weight of the jth particle under the r-th radarPutting 1 and switching in (g), otherwise, switching in (e) when alloxit is 1;

(e) if i < Tm + Dm, i ═ i +1 and enter (c), otherwise enter (f);

(f) calculating the weight of the jth particle corresponding to the r radarHeavy load

(g) If j < N, j equals j +1, i equals 1, and go to (c), otherwise go to (h);

(h) if R < R, let j equal 1, i equal 1, R equal R +1 and go to (c), otherwise go to (i);

(i)r＝1；

(j) normalizing the particle swarm weight under the r radar:

(k) if R < R, then R +1 and go to (j), otherwise go to (l);

(l) Fusing the particle swarm weights:

(M) calculating the probability pb of detection of the detected particles, pb being M/N, M being the presence variable E_h,jA particle number of 1;

(n) if pb is less than the thresholdValue go to (5), otherwise a new target is detected, and a state estimate of the target is calculated

Go to (o).

(o) if Tm + Td is equal to 0, go to (5), otherwise go to (p) to determine whether the newly detected target is the found target;

(p) let i be 1 and new target flag be 0;

(q) calculating the distance between the newly detected target and the target i in the detection target set and the tracking target set as follows:

where { x_q，y_qTarget f_i,kX, y direction of (c) { x^D，y^DThe position of the newly detected target in the x and y directions is used as the position of the target;

(r) if dis_i,k<If the Mk is not the new target, if the flag is 1, jumping to the step (a), regenerating a detection particle group to detect the new target, and otherwise, entering the step(s);

(s) if i < Tm + Dm, i ═ i +1, proceed to step (q), otherwise proceed to (t);

(t) if flag is 0, obtaining a detection particle group as a new target

New object

Input to the detected target setWhen the Dm is equal to Dm +1, turning to the step (a), regenerating a detection particle group to detect a new target, and circulating until the new target cannot be detected, and outputting a detection target set;

(5) adding the detection target set Daxe in the period into the tracking target set Taxe to obtain an updated tracking target set Taxe { f }_1,k,f_2,k,…f_mk+nkDetecting particle swarm

Updated to track the particle swarm P_i,k；

Simulation scene:

the total number of 5 radars is located at the origin, under the condition that the number of the targets is 5, the signal-to-noise ratios of the targets are respectively 6dB, 9dB and 12dB, the target distance is short, and under the condition that the difference of the signal-to-noise ratios of the targets is large, the particle weight fusion power value selection and particle swarm management method improves the target tracking precision and reduces the target track offset probability.