CN103679753A - Track identifying method of probability hypothesis density filter and track identifying system - Google Patents

Abstract

The invention provides a track identifying method of a probability hypothesis density filter suitable for the technical field of multi-sensors. The track identifying method includes the first step of determining a predicted gauss item and an identity identification of the gauss item according to a gauss item and an identity identification of the gauss item at the previous moment and adding a non-exclusive identity identification to each newly produced gauss item, the second step of determining updated gauss items and identity identifications of the updated gauss items according to the predicted gauss item, the newly produced gauss items and the corresponding identity identifications, the third step of cutting and combining the updated gauss items and the identity identifications, the fourth step of regulating the identity identifications whose weight is larger than a preset weight threshold value according to the cut and combined gauss items and the corresponding identity identifications, and the fifth step of extracting the gauss items whose weight is larger than the preset weight threshold value to serve as output of the filter and outputting the corresponding identity identifications. According to the method, the identity identifications are added to the gauss items, target states of different moments are correlated, and accordingly motion trails of targets are obtained.

Description

A kind of track identification method of probability hypothesis density wave filter and track tag system

Technical field

The present invention relates to multiple sensor information amalgamation method, relate in particular to a kind of track identification method and track tag system of probability hypothesis density wave filter.

Background technology

In the situation that existing false-alarm, undetected and number of targets unknown, the probability hypothesis density wave filter that Mahler proposes is the new method that solves target detection and tracking.Probability hypothesis density wave filter has been avoided the direct correlation between observed reading and state value, and its great advantage is from posteriority square, to estimate number of targets.For solving the reluctant problem of integral operation in the prediction of probability hypothesis density wave filter and renewal equation, Vo etc. have proposed particle probability hypothesis density wave filter and Gaussian Mixture probability hypothesis density wave filter.At present, probability hypothesis density wave filter has been obtained application more widely in the field such as the target following in passive positioning, passive radar target following, video tracking, sonar image and group target following.

Yet, although existing probability hypothesis density filtering algorithm can effectively estimate state and the target number of target, but owing to having avoided data correlation, so cannot determine not the corresponding relation of dbjective state in the same time, can not obtain the movement locus of each target.The movement locus problem of establishing target is a key technical problem that needs exploration in probability hypothesis density wave filter and solve.

Summary of the invention

Technical matters to be solved by this invention is to provide a kind of track identification method and track tag system of probability hypothesis density wave filter, is intended to solve while using probability hypothesis density wave filter to multiple target tracking, and target trajectory is difficult to definite problem.

The present invention is achieved in that a kind of track identification method of probability hypothesis density wave filter, comprises the following steps:

Step 1: determine Gauss's item of prediction and the identify label of prediction term according to the identify label of Gauss's item of previous moment and previous moment, and add non-proprietary identify label to Gauss's item of each current time new life;

Step 2: determine the identify label of Gauss's item and the renewal item of renewal according to the identify label of Gauss's item of the identify label of Gauss's item of described prediction, prediction term, new life and new life's item;

Step 3, the identify label of Gauss's item of described renewal and described renewal item is carried out to cutting and merging;

Step 4, Gauss's item and identify label thereof according to cutting and after merging, the identify label that weight is greater than to Gauss's item of default weight threshold is adjusted, and makes identify label have uniqueness;

Step 5, extract weight and be greater than Gauss's item of default weight threshold as the output of wave filter, and identify label after adjusting, that weight is greater than Gauss's item of default weight threshold is exported together.

Further, in described step 1, with k-1, represent previous moment, with k, represent current time, described non-proprietary identify label 0 mark, k-1 Gauss's item set expression is constantly

i=1,2 ..., n _k-1, k-1 identify label set expression is constantly

i=1,2 ..., n _k-1,

not being to show that Gauss's item identity specifies at 0 o'clock, is that 0 o'clock indicate identification is not yet specified, and wherein, i is call number, and value is from 1 to n _k-1, n _k-1represent the k-1 number of Gauss's item constantly,

represent the k-1 weight of Gauss's item constantly,

represent the k-1 average of Gauss's item constantly,

represent the k-1 variance of Gauss's item constantly;

By k-1 Gauss's item constantly, gathered

i=1,2 ..., n _k-1with k-1 identify label set constantly i=1,2 ..., n _k-1obtain Gauss's item set that k predicts constantly i=1,2 ..., n _k-1, and k predicts the identify label set of Gauss's item constantly

i=1,2 ..., n _k-1, wherein

represent that k predicts the weight of Gauss's item constantly, represent that k predicts the average of Gauss's item constantly,

represent that k predicts the variance of Gauss's item constantly, $w_{k|k-1}^i=p_s\·w_{k-1}^i,$ $x_{k|k-1}^i=F_{k-1}{x}_{k-1}^i,$ $P_{k|k-1}^i=Q_{k-1}+F_{k-1}{P}_{k-1}^i{F}_{k-1}^T,$ $l_{k|k-1}^i=l_{k-1}^i;$ Here, p _sfor the probability that target is survived, F _k-1for state-transition matrix, Q _k-1for process noise covariance matrix; K constantly Gauss's item set of new life is

i=1,2 ..., n _{γ, k}, wherein

be given parameter, represent respectively weight, average and the variance of newborn Gauss's item, n _γ, _knumber for newborn Gauss's item; The k constantly identify label set of newborn Gauss's item is

i=1,2 ..., n _{γ, k}, the described k identify label unification of newborn Gauss's item is constantly set to non-proprietary sign 0, i=1,2 ..., n _{γ, k}.

Further, in described step 2, Gauss's item set of constantly predicting according to described k

i=1,2 ..., n _k-1with k Gauss's item set of new life constantly

i=1,2 ..., n _{γ, k}and the identify label set of prediction Gauss item

i=1,2 ..., n _k-1identify label set with newborn Gauss's item i=1,2 ..., n _{γ, k}, obtain Gauss's item set that k upgrades constantly

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}) and upgrade the identify label set of Gauss's item i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}), wherein, m _kfor k moment measuring set Z _kthe number of middle measurement,

represent that k upgrades the weight of Gauss's item constantly,

represent that k upgrades the average of Gauss's item constantly, represent that k upgrades the variance of Gauss's item constantly;

As i≤n _k-1+ n _{γ, k}time, $w_k^i=(1-p_D)\·w_{k|k-1}^i,$ $x_k^i=x_{k|k-1}^i,$ $P_k^i=P_{k|k-1}^i,$ $l_k^i=l_{k|k-1}^i,$ Work as i>n _k-1+ n _{γ, k}time, make q=int[(i-1)/(n _k+ n _{γ, k})], p=i-q * (n _k+ n _{γ, k}), ? $x_k^i=x_{k|k-1}^p+K_p[z_q\left(k\right)-H_k{x}_{k|k-1}^p],$ $P_k^i=(I-K_p{H}_k)P_{k|k-1}^p,$ $w_k^i=\frac{p_D{w}_{k|k-1}^{p}N(\mathrm{\ξ};z_q-H_k{x}_{k|k-1}^p,H_k{P}_{k|k-1}^p{H}_k^T+R_k)}{{\mathrm{\λ}}_c\left(z_q\right)+p_D\underset{e=1}{\overset{n_{k-1}+n_{\mathrm{\γ},k}}{\mathrm{\Σ}}}{w}_{k|k-1}^{e}N(\mathrm{\ξ};z_q-H_k{x}_{k|k-1}^e,H_k{P}_{k|k-1}^e{H}_k^T+R_k)},$ $l_k^i=l_{k|k-1}^p,$ Wherein, int represents to round, p _dfor acquisition probability, λ _cfor clutter density, H _kfor observing matrix, R _kfor the covariance of observation noise, K _pfor filter gain,

be the average of p prediction or newborn Gauss's item, be the variance of p prediction or newborn Gauss's item, I is unit matrix,

be the weight of p prediction or newborn Gauss's item, N represents Gaussian distribution, the state that ξ is target,

be the average of e prediction or newborn Gauss's item,

be the variance of e prediction or newborn Gauss's item, z _qfor k moment measuring set Z _kin q measurement, the number of the Gauss's item after renewal is n _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1).

Further, in described step 3, Gauss's item that k upgrades is constantly expressed as

i=1,2 ..., n _k|k, the identify label set that k upgrades Gauss's item is constantly

i=1,2 ..., n _k|k, n wherein _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1); Delete weight fully little,

gauss's item, wherein, τ is cutting thresholding;

Will be apart from d in Gauss's item of remainder from cutting _ijgauss's item of <U is merged into one, and wherein U is for merging thresholding, and combined distance is defined as

after merging, the identify label of Gauss's item determines that principle is: if the identify label of Gauss's item is 0 entirely before merging, after merging, the identify label of Gauss's item is also 0; Otherwise identify label is not in Gauss's item of 0 before merging, the heavy maximum Gauss's item identify label of weighting is as the identify label of Gauss's item after merging; When Gauss's item is merged, weight is greater than other weight of Gauss Xiang Buyu of 0.5 and is greater than Gauss's item of 0.5 and merges.

Further, in described step 4, according to cutting with merge after Gauss's item, the identify label that weight is greater than to Gauss's item of 0.5 is judged: if the identify label of Gauss's item is greater than 0, show that its identity is definite; If 0, show that its identity not yet specifies, now need to specify a unique non-zero sign as its proprietary identify label to it.

Further, for making proprietary identify label there is uniqueness, after a proprietary identify label is used, proprietary identify label adds 1 automatically, if a plurality of Gauss's items have identical proprietary identify label, the identify label of Gauss's item that weight coefficient is the highest remains unchanged, and the identify label of other Gauss's item is reassigned.

The present invention also provides a kind of track tag system of probability hypothesis density wave filter, comprising:

Prediction module, determines Gauss's item of prediction and the identify label of prediction according to the identify label of Gauss's item of previous moment and previous moment, and adds non-proprietary identify label to each newborn Gauss's item;

Update module, determines the identify label of Gauss's item and the renewal item of renewal according to the identify label of Gauss's item of the identify label of Gauss's item of described prediction, prediction term, new life and new life's item;

Cutting and merging module, carry out cutting and merging to the identify label of Gauss's item of described renewal and described renewal item;

Adjusting module, Gauss's item and identify label thereof according to cutting and after merging, the identify label that weight is greater than to Gauss's item of default weight threshold is adjusted, and makes identify label have uniqueness;

Dbjective state extraction module, extracts weight and is greater than Gauss's item of default weight threshold as the output of wave filter, and identify label after adjusting, that weight is greater than Gauss's item of default weight threshold is exported together.

Further, in described prediction module, with k-1, represent previous moment, with k, represent current time, described non-proprietary identify label 0 mark, k-1 Gauss's item set expression is constantly

i=1,2 ..., n _k-1, k-1 identify label set expression is constantly

i=1,2 ..., n _k-1,

not being to show that Gauss's item identity specifies at 0 o'clock, is that 0 o'clock indicate identification is not yet specified, and wherein, i is call number, and value is from 1 to n _k-1, n _k-1represent the k-1 number of Gauss's item constantly, represent the k-1 weight of Gauss's item constantly,

represent the k-1 average of Gauss's item constantly, represent the k-1 variance of Gauss's item constantly;

By k-1 Gauss's item constantly, gathered i=1,2 ..., n _k-1with k-1 identify label set constantly

i=1,2 ..., n _k-1obtain Gauss's item set that k predicts constantly

i=1,2 ..., n _k-1, and the identify label set of k prediction Gauss item constantly i=1,2 ..., n _k-1, wherein

represent that k predicts the weight of Gauss's item constantly,

represent that k predicts the average of Gauss's item constantly,

represent that k predicts the variance of Gauss's item constantly, $w_{k|k-1}^i=p_s\&CenterDot;w_{k-1}^i,$ $x_{k|k-1}^i=F_{k-1}{x}_{k-1}^i,$ $P_{k|k-1}^i=Q_{k-1}+F_{k-1}{P}_{k-1}^i{F}_{k-1}^T,$ $l_{k|k-1}^i=l_{k-1}^i;$ Here, p _sfor the probability that target is survived, F _k-1for state-transition matrix, Q _k-1for process noise covariance matrix; K constantly Gauss's item set of new life is

i=1,2 ..., n _{γ, k}, wherein

be given parameter, represent respectively weight, average and the variance of newborn Gauss's item, n _{γ, k}number for newborn Gauss's item; The k constantly identify label set of newborn Gauss's item is

i=1,2 ..., n _{γ, k}, and the described k identify label unification of newborn Gauss's item is constantly set to non-proprietary sign 0,

i=1,2 ..., n _{γ, k}.

Further, in described update module, Gauss's item set of constantly predicting according to described k

i=1,2 ..., n _k-1with k Gauss's item set of new life constantly

i=1,2 ..., n _{γ, k}and the identify label set of prediction Gauss item

i=1,2 ..., n _k-1identify label set with newborn Gauss's item

i=1,2 ..., n _{γ, k}, obtain Gauss's item set that k upgrades constantly

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}) and upgrade the identify label set of Gauss's item

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}), wherein, m _kfor k moment measuring set Z _kthe number of middle measurement,

represent that k upgrades the weight of Gauss's item constantly,

represent that k upgrades the average of Gauss's item constantly, represent that k upgrades the variance of Gauss's item constantly;

As i≤n _k-1+ n _{γ, k}time, $w_k^i=(1-p_D)\&CenterDot;w_{k|k-1}^i,$ $x_k^i=x_{k|k-1}^i,$ $P_k^i=P_{k|k-1}^i,$ $l_k^i=l_{k|k-1}^i,$ Work as i>n _k-1+ n _{γ, k}time, make q=int[(i-1)/(n _k+ n _{γ, k})], p=i-q * (n _k+ n _{γ, k}),

? $x_k^i=x_{k|k-1}^p+K_p[z_q\left(k\right)-H_k{x}_{k|k-1}^p],$ $P_k^i=(I-K_p{H}_k)P_{k|k-1}^p,$ $w_k^i=\frac{p_D{w}_{k|k-1}^{p}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^p,H_k{P}_{k|k-1}^p{H}_k^T+R_k)}{{\mathrm{\&lambda;}}_c\left(z_q\right)+p_D\underset{e=1}{\overset{n_{k-1}+n_{\mathrm{\&gamma;},k}}{\mathrm{\&Sigma;}}}{w}_{k|k-1}^{e}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^e,H_k{P}_{k|k-1}^e{H}_k^T+R_k)},$ $l_k^i=l_{k|k-1}^p,$ Wherein, int represents to round, p _dfor acquisition probability, λ _cfor clutter density, H _kfor observing matrix, R _kfor the covariance of observation noise, K _pfor filter gain,

be the average of p prediction or newborn Gauss's item,

be the variance of p prediction or newborn Gauss's item, I is unit matrix,

be the weight of p prediction or newborn Gauss's item, N represents Gaussian distribution, the state that ξ is target,

be the average of e prediction or newborn Gauss's item, be the variance of e prediction or newborn Gauss's item, z _qfor k moment measuring set Z _kin q measurement, the number of the Gauss's item after renewal is n _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1).

Further, in cutting and merging module, Gauss's item that k upgrades is constantly expressed as

i=1,2 ..., n _k|k, the identify label set that k upgrades Gauss's item is constantly i=1,2 ..., n _k|k, n wherein _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1); Delete weight fully little,

gauss's item, wherein, τ is cutting thresholding;

Will be apart from d in Gauss's item of remainder from cutting _ijgauss's item of <U is merged into one, and wherein U is for merging thresholding, and combined distance is defined as

after merging, the identify label of Gauss's item determines that principle is: if the identify label of Gauss's item is 0 entirely before merging, after merging, the identify label of Gauss's item is also 0; Otherwise identify label is not in Gauss's item of 0 before merging, the heavy maximum Gauss's item identify label of weighting is as the identify label of Gauss's item after merging; When Gauss's item is merged, weight is greater than other weight of Gauss Xiang Buyu of 0.5 and is greater than Gauss's item of 0.5 and merges;

In described adjusting module, according to cutting with merge after Gauss's item, the identify label that weight is greater than to Gauss's item of 0.5 is judged: if the identify label of Gauss's item is greater than 0, show that its identity is definite; If 0, show that its identity not yet specifies, now need to specify a unique non-zero sign as its proprietary identify label to it.

Compare with existing probability hypothesis density wave filter, the track identification method of probability hypothesis density wave filter of the present invention utilizes to Gauss's item and adds identify label, will be not in the same time dbjective state associate, thereby obtain the movement locus of each target, and each different target can be made a distinction.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of track identification method in the probability hypothesis density wave filter that provides of the embodiment of the present invention;

Fig. 2 is the block diagram of the track tag system of the probability hypothesis density wave filter that provides of the embodiment of the present invention;

Fig. 3 is the emulated data that the track identification method of probability hypothesis density wave filter of the present invention adopts;

Fig. 4 is that verification and measurement ratio is 0.99 o'clock definite target trajectory of the inventive method.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.

The track identification method of probability hypothesis density wave filter of the present invention adds proprietary identify label can to the Gauss's item as wave filter output, when wave filter prediction, renewal and cutting merge, utilize inheritance to carry out respective handling to the identify label of Gauss's item, during wave filter output, proprietary identify label is exported together in company with dbjective state, can determine the movement locus of each target.

As shown in Figure 1, be this bright preferred embodiment, a kind of track identification method of probability hypothesis density wave filter, comprises the following steps:

Step 1: determine Gauss's item of prediction and the identify label of prediction term according to the identify label of Gauss's item of previous moment and previous moment, and add non-proprietary identify label to each newborn Gauss's item.

With k-1, represent previous moment, with k, represent current time, non-proprietary identify label 0 mark, k-1 Gauss's item set expression is constantly

i=1,2 ..., n _k-1, k-1 identify label set expression is constantly

i=1,2 ..., n _k-1,

not being to show that Gauss's item identity specifies at 0 o'clock, is that 0 o'clock indicate identification is not yet specified, and wherein, i is call number, and value is from 1 to n _k-1, n _k-1represent the k-1 number of Gauss's item constantly,

represent the k-1 weight of Gauss's item constantly,

represent the k-1 average of Gauss's item constantly,

represent the k-1 variance of Gauss's item constantly.

By k-1 Gauss's item constantly, gathered

i=1,2 ..., n _k-1with k-1 identify label set constantly

i=1,2 ..., n _k-1obtain Gauss's item set that k predicts constantly

i=1,2 ..., n _k-1, and k predicts the identify label set of Gauss's item constantly

i=1,2 ..., n _k-1, wherein

represent that k predicts the weight of Gauss's item constantly,

represent that k predicts the variance of Gauss's item constantly, $w_{k|k-1}^i=p_s\&CenterDot;w_{k-1}^i,$ $x_{k|k-1}^i=F_{k-1}{x}_{k-1}^i,$ $P_{k|k-1}^i=Q_{k-1}+F_{k-1}{P}_{k-1}^i{F}_{k-1}^T,$ $l_{k|k-1}^i=l_{k-1}^i.$ Here, p _sfor the probability that target is survived, F _k-1for state-transition matrix, Q _k-1for process noise covariance matrix; K constantly Gauss's item set of new life is

=1,2 ..., n _{γ, k}, wherein be given parameter, represent respectively weight, average and the variance of newborn Gauss's item, n _{γ, k}number for newborn Gauss's item.The k constantly identify label set of newborn Gauss's item is i=1,2 ..., n _{γ, k}, the described k identify label unification of newborn Gauss's item is constantly set to non-proprietary sign 0,

i=1,2 ..., n _{γ, k}.

Step 2: determine the identify label of Gauss's item and the renewal item of renewal according to the identify label of Gauss's item of the identify label of Gauss's item of described prediction, prediction term, new life and new life's item.

Gauss's item set of constantly predicting according to described k

i=1,2 ..., n _k-1with k Gauss's item set of new life constantly

i=1,2 ..., n _{γ, k}and the identify label set of prediction Gauss item

i=1,2 ..., n _k-1identify label set with newborn Gauss's item

i=1,2 ..., n _{γ, k}, obtain Gauss's item set that k upgrades constantly

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}) and upgrade the identify label set of Gauss's item

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}), wherein, m _kfor k moment measuring set Z _kthe number of middle measurement, represent that k upgrades the weight of Gauss's item constantly, represent that k upgrades the average of Gauss's item constantly,

represent that k upgrades the variance of Gauss's item constantly.

As i≤n _k-1+ n _{γ, k}time, $w_k^i=(1-p_D)\&CenterDot;w_{k|k-1}^i,$ $x_k^i=x_{k|k-1}^i,$ $P_k^i=P_{k|k-1}^i,$ $l_k^i=l_{k|k-1}^i;$ Work as i>n _k-1+ n _{γ, k}time, make q=int[(i-1)/(n _k+ n _{γ, k})], p=i-q * (n _k+ n _{γ, k}),

? $x_k^i=x_{k|k-1}^p+K_p[z_q\left(k\right)-H_k{x}_{k|k-1}^p],$ $P_k^i=(I-K_p{H}_k)P_{k|k-1}^p,$ $w_k^i=\frac{p_D{w}_{k|k-1}^{p}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^p,H_k{P}_{k|k-1}^p{H}_k^T+R_k)}{{\mathrm{\&lambda;}}_c\left(z_q\right)+p_D\underset{e=1}{\overset{n_{k-1}+n_{\mathrm{\&gamma;},k}}{\mathrm{\&Sigma;}}}{w}_{k|k-1}^{e}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^e,H_k{P}_{k|k-1}^e{H}_k^T+R_k)},$ $l_k^i=l_{k|k-1}^p,$ Wherein, int represents to round, p _dfor acquisition probability, λ _cfor clutter density (being the clutter number in unit volume), H _kfor observing matrix, R _kfor the covariance of observation noise, K _pfor Kalman filter gain,

be the average of p prediction or newborn Gauss's item, be the variance of p prediction or newborn Gauss's item, I is unit matrix, be the weight of p prediction or newborn Gauss's item, N represents Gaussian distribution, the state that ξ is target,

be the average of e prediction or newborn Gauss's item,

be the variance of e prediction or newborn Gauss's item, z _qfor k moment measuring set Z _kin q measurement, the number of the Gauss's item after renewal is n _k|kindividual, n _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1).

Step 3, the identify label of Gauss's item of described renewal and described renewal is carried out to cutting and merging.

Gauss's item that k upgrades is constantly expressed as

i=1,2 ..., n _k|k, the identify label set that k upgrades Gauss's item is constantly

i=1,2 ..., n _k|k, n wherein _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1); Delete weight fully little,

gauss's item, wherein, τ is cutting thresholding.

Will be apart from d in Gauss's item of remainder from cutting _ijgauss's item of <U is merged into one, and wherein U is for merging thresholding, and combined distance is defined as

after merging, the identify label of Gauss's item determines that principle is: if the identify label of Gauss's item is 0 entirely before merging, after merging, the identify label of Gauss's item is also 0; Otherwise identify label is not in Gauss's item of 0 before merging, the heavy maximum Gauss's item identify label of weighting is as the identify label of Gauss's item after merging.When Gauss's item is merged, weight is greater than other weight of Gauss Xiang Buyu of 0.5 and is greater than Gauss's item of 0.5 and merges.

Step 4, Gauss's item and identify label thereof according to cutting and after merging, the identify label that weight is greater than to Gauss's item of default weight threshold is adjusted, and makes identify label have uniqueness.

Described default weight threshold is 0.5, according to cutting with merge after Gauss's item, the identify label that weight is greater than to Gauss's item of 0.5 is judged: if the identify label of Gauss's item is greater than 0, show that its identity is definite; If 0, show that its identity not yet specifies, now need to specify a unique non-zero sign as its proprietary identification number to it.

In order to guarantee that proprietary mark identity is known, do not repeat, after a proprietary identify label (target lot number) is used, proprietary identify label adds 1 automatically; If a plurality of Gauss's items have identical proprietary identify label (being designated non-zero), for guaranteeing the uniqueness of identify label, the identify label of Gauss's item that weight coefficient is the highest remains unchanged, and the identify label of other Gauss's item is reassigned.

Step 5, extract weight and be greater than Gauss's item of default weight threshold as the output of wave filter, and identify label after adjusting, that weight is greater than Gauss's item of default weight threshold is exported together.The identify label that utilizes each moment wave filter to export, associates each dbjective state that wave filter is exported in the same time, thereby obtains the movement locus of each target.

Track identification method based on above-mentioned probability hypothesis density wave filter, the present invention also provides a kind of track tag system of probability hypothesis density wave filter, as shown in Figure 2, this track tag system comprises: prediction module 201, update module 202, cutting and merging module 203, adjusting module 204, dbjective state extraction module 205.Prediction module 201 is determined Gauss's item of prediction and the identify label of prediction term according to the identify label of Gauss's item of previous moment and previous moment, and adds non-proprietary identify label 0 to each newborn Gauss's item.Update module 202 is determined the identify label of Gauss's item and the renewal item of renewal according to the identify label of Gauss's item of the identify label of Gauss's item of described prediction, prediction term, new life and new life's item.Cutting and merging are carried out in Gauss's item of 203 pairs of described renewals of cutting and merging module and the identify label of described renewal item.Gauss item and the identify label thereof of adjusting module 204 according to cutting and after merging, the identify label that weight is greater than to Gauss's item of default weight threshold is adjusted, and makes identify label have uniqueness.Dbjective state extraction module 205 extracts weights and is greater than Gauss's item of default weight threshold as the output of wave filter, and identify label after adjusting, that weight is greater than Gauss's item of default weight threshold is exported together.

In described prediction module 201, with k, represent current time, k-1 Gauss's item set expression is constantly

i=1,2 ..., n _k-1, k-1 identify label set expression is constantly i=1,2 ..., n _k-1,

not being to show that Gauss's item identity specifies at 0 o'clock, is that 0 o'clock indicate identification is not yet specified, and wherein, i is call number, and value is from 1 to n _k-1, n _k-1represent the k-1 number of Gauss's item constantly,

represent the k-1 weight of Gauss's item constantly,

represent the k-1 average of Gauss's item constantly,

represent the k-1 variance of Gauss's item constantly.

By k-1 Gauss's item constantly, gathered

i=1,2 ..., n _k-1with k-1 identify label set constantly

i=1,2 ..., n _k-1obtain Gauss's item set that k predicts constantly

i=1,2 ..., n _k-1, and the identify label set of k prediction Gauss item constantly

i=1,2 ..., n _k-1, wherein represent that k predicts the weight of Gauss's item constantly,

represent that k predicts the average of Gauss's item constantly,

represent that k predicts the variance of Gauss's item constantly, $w_{k|k-1}^i=p_s\&CenterDot;w_{k-1}^i,$ $x_{k|k-1}^i=F_{k-1}{x}_{k-1}^i,$ $P_{k|k-1}^i=Q_{k-1}+F_{k-1}{P}_{k-1}^i{F}_{k-1}^T,$ $l_{k|k-1}^i=l_{k-1}^i.$ Here, p _sfor the probability that target is survived, F _k-1for state-transition matrix, Q _k-1for process noise covariance matrix.K constantly Gauss's item set of new life is

i=1,2 ..., n _{γ, k}, wherein

be given parameter, represent respectively weight, average and the variance of newborn Gauss's item, n _{γ, k}number for newborn Gauss's item; The k constantly identify label set of newborn Gauss's item is

i=1,2 ..., n _{γ, k}, and the described k identify label unification of newborn Gauss's item is constantly set to non-proprietary sign 0,

i=1,2 ..., n _{γ, k}.

In update module 202, Gauss's item set of constantly predicting according to described k i=1,2 ..., n _k-1with k Gauss's item set of new life constantly

i=1,2 ..., n _{γ, k}and the identify label set of prediction Gauss item

i=1,2 ..., n _k-1identify label set with newborn Gauss's item

i=1,2 ..., n _{γ, k}, obtain Gauss's item set that k upgrades constantly

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}) and upgrade the identify label set of Gauss's item

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}), wherein, m _kfor k moment measuring set Z _kthe number of middle measurement, represent that k upgrades the weight of Gauss's item constantly,

represent that k upgrades the average of Gauss's item constantly,

represent that k upgrades the variance of Gauss's item constantly.

As i≤n _k-1+ n _{γ, k}time, $w_k^i=(1-p_D)\&CenterDot;w_{k|k-1}^i,$ $x_k^i=x_{k|k-1}^i,$ $P_k^i=P_{k|k-1}^i,$ $l_k^i=l_{k|k-1}^i,$ Work as i>n _k-1+ n _{γ, k}time, make q=int[(i-1)/(n _k+ n _{γ, k})], p=i-q * (n _k+ n _{γ, k}), ? $x_k^i=x_{k|k-1}^p+K_p[z_q\left(k\right)-H_k{x}_{k|k-1}^p],$ $P_k^i=(I-K_p{H}_k)P_{k|k-1}^p,$ $w_k^i=\frac{p_D{w}_{k|k-1}^{p}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^p,H_k{P}_{k|k-1}^p{H}_k^T+R_k)}{{\mathrm{\&lambda;}}_c\left(z_q\right)+p_D\underset{e=1}{\overset{n_{k-1}+n_{\mathrm{\&gamma;},k}}{\mathrm{\&Sigma;}}}{w}_{k|k-1}^{e}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^e,H_k{P}_{k|k-1}^e{H}_k^T+R_k)},$ $l_k^i=l_{k|k-1}^p,$ Wherein, int represents to round, p _dfor acquisition probability, λ _cfor clutter density, H _kfor observing matrix, R _kfor the covariance of observation noise, K _pfor Kalman filter gain,

be the average of p prediction or newborn Gauss's item,

be the variance of p prediction or newborn Gauss's item, I is unit matrix,

be the weight of p prediction or newborn Gauss's item, N represents Gaussian distribution, the state that ξ is target,

be the average of e prediction or newborn Gauss's item,

be the variance of e prediction or newborn Gauss's item, z _qfor k moment measuring set Z _kin q measurement, the number of the Gauss's item after renewal is n _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1).

In cutting and merging module 203, Gauss's item that k upgrades is constantly expressed as

i=1,2 ..., n _k|k, the identify label set of Gauss's item that k upgrades is constantly i=1,2 ..., n _k|k, n wherein _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1); Delete weight fully little,

gauss's item, wherein, τ is cutting thresholding.

Will be apart from d in Gauss's item of remainder from cutting _ijgauss's item of <U is merged into one, and wherein U is for merging thresholding, and combined distance is defined as after merging, the identify label of Gauss's item determines that principle is: if the identify label of Gauss's item is 0 entirely before merging, after merging, the identify label of Gauss's item is also 0; Otherwise identify label is not in Gauss's item of 0 before merging, the heavy maximum Gauss's item identify label of weighting is as the identify label of Gauss's item after merging; When Gauss's item is merged, weight is greater than other weight of Gauss Xiang Buyu of 0.5 and is greater than Gauss's item of 0.5 and merges.

In described adjusting module 204, default weight threshold is 0.5, according to cutting with merge after Gauss's item, the identify label that weight is greater than to Gauss's item of 0.5 is judged: if the identify label of Gauss's item is greater than 0, show that its identity is definite; If 0, show that its identity not yet specifies, now need to specify a unique non-zero sign as its proprietary identify label to it.

The present invention adds proprietary identify label can to the Gauss's item as wave filter output, when wave filter prediction, renewal and cutting merge, utilize inheritance to carry out respective handling to the identify label of Gauss's item, during wave filter output, proprietary identify label is exported together in company with dbjective state, by identify label by wave filter not in the same time the dbjective state of output associate, thereby can determine the movement locus of each target.At clutter density λ _c=5 * 10 ^-6, target detection probability is 0.99, in situation that target disappears, the emulated data shown in Fig. 3 is processed appears and have in existing fresh target, the tracking results that the present invention obtains is as shown in Figure 4.As can be seen from Figure 4 the wave filter that, we propose can detect whole 10 batches of targets of observation space and can effectively follow the tracks of.Because wave filter output tape has proprietary identify label, and proprietary identify label provided and do not exported in the same time the relevance between data, thereby can determine the movement locus of each target.Although have decoy in wave filter output data, between decoy and follow-up output data, there is not relevance, there is different identify labels, on Fig. 4, decoy shows as some isolated points, and this isolated point is the point representing with circle.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (10)

1. a track identification method for probability hypothesis density wave filter, is characterized in that, comprises the following steps:

Step 1: determine Gauss's item of prediction and the identify label of prediction term according to the identify label of Gauss's item of previous moment and previous moment, and add non-proprietary identify label to Gauss's item of each current time new life;

Step 2: determine the identify label of Gauss's item and the renewal item of renewal according to the identify label of Gauss's item of the identify label of Gauss's item of described prediction, prediction term, new life and new life's item;

Step 3, the identify label of Gauss's item of described renewal and described renewal item is carried out to cutting and merging;

Step 4, Gauss's item and identify label thereof according to cutting and after merging, the identify label that weight is greater than to Gauss's item of default weight threshold is adjusted, and makes identify label have uniqueness;

Step 5, extract weight and be greater than Gauss's item of default weight threshold as the output of wave filter, and identify label after adjusting, that weight is greater than Gauss's item of default weight threshold is exported together.

2. track identification method according to claim 1, is characterized in that, in described step 1, with k-1, represents previous moment, with k, represents current time, described non-proprietary identify label 0 mark, and k-1 Gauss's item set expression is constantly

i=1,2 ..., n _k-1, k-1 identify label set expression is constantly

i=1,2 ..., n _k-1,

not being to show that Gauss's item identity specifies at 0 o'clock, is that 0 o'clock indicate identification is not yet specified, and wherein, i is call number, and value is from 1 to n _k-1, n _k-1represent the k-1 number of Gauss's item constantly,

represent the k-1 weight of Gauss's item constantly,

represent the k-1 average of Gauss's item constantly,

represent the k-1 variance of Gauss's item constantly;

By k-1 Gauss's item constantly, gathered

i=1,2 ..., n _k-1with k-1 identify label set constantly

i=1,2 ..., n _k-1obtain Gauss's item set that k predicts constantly

i=1,2 ..., n _k-1, and k predicts the identify label set of Gauss's item constantly

i=1,2 ..., n _k-1, wherein

represent that k predicts the weight of Gauss's item constantly,

represent that k predicts the average of Gauss's item constantly,

represent that k predicts the variance of Gauss's item constantly, $w_{k|k-1}^i=p_s\&CenterDot;w_{k-1}^i,$ $x_{k|k-1}^i=F_{k-1}{x}_{k-1}^i,$ $P_{k|k-1}^i=Q_{k-1}+F_{k-1}{P}_{k-1}^i{F}_{k-1}^T,$ $l_{k|k-1}^i=l_{k-1}^i;$ Here, p _sfor the probability that target is survived, F _k-1for state-transition matrix, Q _k-1for process noise covariance matrix; K constantly Gauss's item set of new life is

i=1,2 ..., n _{γ, k}, wherein

be given parameter, represent respectively weight, average and the variance of newborn Gauss's item, n _γ, _knumber for newborn Gauss's item; The k constantly identify label set of newborn Gauss's item is i=1,2 ..., n _{γ, k}, the described k identify label unification of newborn Gauss's item is constantly set to non-proprietary sign 0,

i=1,2 ..., n _{γ, k}.

3. track identification method according to claim 2, is characterized in that, in described step 2, and Gauss's item set of constantly predicting according to described k

i=1,2 ..., n _k-1with k Gauss's item set of new life constantly

i=1,2 ..., n _{γ, k}and the identify label set of prediction Gauss item

i=1,2 ..., n _k-1identify label set with newborn Gauss's item i=1,2 ..., n _{γ, k}, obtain Gauss's item set that k upgrades constantly

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}) and upgrade the identify label set of Gauss's item i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}), wherein, m _kfor k moment measuring set Z _kthe number of middle measurement,

represent that k upgrades the weight of Gauss's item constantly,

represent that k upgrades the average of Gauss's item constantly,

represent that k upgrades the variance of Gauss's item constantly;

As i≤n _k-1+ n _{γ, k}time, $w_k^i=(1-p_D)\&CenterDot;w_{k|k-1}^i,$ $x_k^i=x_{k|k-1}^i,$ $P_k^i=P_{k|k-1}^i,$ $l_k^i=l_{k|k-1}^i,$ Work as i>n _k-1+ n _{γ, k}time, make q=int[(i-1)/(n _k+ n _{γ, k})], p=i-q * (n _k+ n _{γ, k}), ? $x_k^i=x_{k|k-1}^p+K_p[z_q\left(k\right)-H_k{x}_{k|k-1}^p],$ $P_k^i=(I-K_p{H}_k)P_{k|k-1}^p,$ $w_k^i=\frac{p_D{w}_{k|k-1}^{p}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^p,H_k{P}_{k|k-1}^p{H}_k^T+R_k)}{{\mathrm{\&lambda;}}_c\left(z_q\right)+p_D\underset{e=1}{\overset{n_{k-1}+n_{\mathrm{\&gamma;},k}}{\mathrm{\&Sigma;}}}{w}_{k|k-1}^{e}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^e,H_k{P}_{k|k-1}^e{H}_k^T+R_k)},$ $l_k^i=l_{k|k-1}^p,$ Wherein, int represents to round, p _dfor acquisition probability, λ _cfor clutter density, H _kfor observing matrix, R _kfor the covariance of observation noise, K _pfor filter gain, be the average of p prediction or newborn Gauss's item,

be the variance of p prediction or newborn Gauss's item, I is unit matrix,

be the weight of p prediction or newborn Gauss's item, N represents Gaussian distribution, the state that ξ is target,

be the average of e prediction or newborn Gauss's item,

be the variance of e prediction or newborn Gauss's item, z _qfor k moment measuring set Z _kin q measurement, the number of the Gauss's item after renewal is n _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1).

4. track identification method according to claim 3, is characterized in that, in described step 3, Gauss's item that k upgrades is constantly expressed as

i=1,2 ..., n _k|k, the identify label set that k upgrades Gauss's item is constantly

i=1,2 ..., n _k|k, n wherein _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1); Delete weight fully little,

gauss's item, wherein, τ is cutting thresholding;

Will be apart from d in Gauss's item of remainder from cutting _ijgauss's item of <U is merged into one, and wherein U is for merging thresholding, and combined distance is defined as

after merging, the identify label of Gauss's item determines that principle is: if the identify label of Gauss's item is 0 entirely before merging, after merging, the identify label of Gauss's item is also 0; Otherwise identify label is not in Gauss's item of 0 before merging, the heavy maximum Gauss's item identify label of weighting is as the identify label of Gauss's item after merging; When Gauss's item is merged, weight is greater than other weight of Gauss Xiang Buyu of 0.5 and is greater than Gauss's item of 0.5 and merges.

5. track identification method according to claim 1, it is characterized in that, in described step 4, default weight threshold is 0.5, Gauss's item according to cutting and after merging, the identify label that weight is greater than to Gauss's item of 0.5 is judged: if the identify label of Gauss's item is greater than 0, show that its identity is definite; If 0, show that its identity not yet specifies, now need to specify a unique non-zero sign as its proprietary identify label to it.

6. track identification method according to claim 5, it is characterized in that, for making proprietary identify label there is uniqueness, after a proprietary identify label is used, proprietary identify label adds 1 automatically, if a plurality of Gauss's items have identical proprietary identify label, the identify label of Gauss's item that weight coefficient is the highest remains unchanged, and the identify label of other Gauss's item is reassigned.

7. a track tag system for probability hypothesis density wave filter, is characterized in that, comprising:

Prediction module, determines Gauss's item of prediction and the identify label of prediction according to the identify label of Gauss's item of previous moment and previous moment, and adds non-proprietary identify label to each newborn Gauss's item;

Update module, determines the identify label of Gauss's item and the renewal item of renewal according to the identify label of Gauss's item of the identify label of Gauss's item of described prediction, prediction term, new life and new life's item;

Cutting and merging module, carry out cutting and merging to the identify label of Gauss's item of described renewal and described renewal item;

Adjusting module, Gauss's item and identify label thereof according to cutting and after merging, the identify label that weight is greater than to Gauss's item of default weight threshold is adjusted, and makes identify label have uniqueness;

Dbjective state extraction module, extracts weight and is greater than Gauss's item of default weight threshold as the output of wave filter, and identify label after adjusting, that weight is greater than Gauss's item of default weight threshold is exported together.

8. track tag system according to claim 7, is characterized in that, in described prediction module, with k-1, represents previous moment, with k, represents current time, described non-proprietary identify label 0 mark, and k-1 Gauss's item set expression is constantly

i=1,2 ..., n _k-1, k-1 identify label set expression is constantly

i=1,2 ..., n _k-1,

not being to show that Gauss's item identity specifies at 0 o'clock, is that 0 o'clock indicate identification is not yet specified, and wherein, i is call number, and value is from 1 to n _k-1, n _k-1represent the k-1 number of Gauss's item constantly,

represent the k-1 weight of Gauss's item constantly,

represent the k-1 average of Gauss's item constantly,

represent the k-1 variance of Gauss's item constantly;

By k-1 Gauss's item constantly, gathered

i=1,2 ..., n _k-1with k-1 identify label set constantly

i=1,2 ..., n _k-1obtain Gauss's item set that k predicts constantly

i=1,2 ..., n _k-1, and k predicts the identify label set of Gauss's item constantly

i=1,2 ..., n _k-1, wherein

represent that k predicts the weight of Gauss's item constantly,

represent that k predicts the average of Gauss's item constantly,

represent that k predicts the variance of Gauss's item constantly, $w_{k|k-1}^i=p_s\&CenterDot;w_{k-1}^i,$ $x_{k|k-1}^i=F_{k-1}{x}_{k-1}^i,$ $P_{k|k-1}^i=Q_{k-1}+F_{k-1}{P}_{k-1}^i{F}_{k-1}^T,$ $l_{k|k-1}^i=l_{k-1}^i;$ Here, p _sfor the probability that target is survived, F _k-1for state-transition matrix, Q _k-1for process noise covariance matrix; K constantly Gauss's item set of new life is

i=1,2 ..., n _{γ, k}, wherein be given parameter, represent respectively weight, average and the variance of newborn Gauss's item, n _{γ, k}number for newborn Gauss's item; The k constantly identify label set of newborn Gauss's item is

i=1,2 ..., n _{γ, k}, the described k identify label unification of newborn Gauss's item is constantly set to non-proprietary sign 0,

i=1,2 ..., n _{γ, k}.

9. track tag system according to claim 8, is characterized in that, in described update module, and Gauss's item set of constantly predicting according to described k

i=1,2 ..., n _k-1with k Gauss's item set of new life constantly

i=1,2 ..., n _{γ, k}and the identify label set of prediction Gauss item

i=1,2 ..., n _k-1identify label set with newborn Gauss's item i=1,2 ..., n _{γ, k}, obtain Gauss's item set that k upgrades constantly

i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}) and the identify label set of Gauss's item of upgrading i=1 ..., (m _k+ 1) (n _k-1+ n _{γ, k}), wherein, m _kfor k moment measuring set Z _kthe number of middle measurement,

represent that k upgrades the weight of Gauss's item constantly,

represent that k upgrades the average of Gauss's item constantly,

represent that k upgrades the variance of Gauss's item constantly;

As i≤n _k-1+ n _{γ, k}time, $w_k^i=(1-p_D)\&CenterDot;w_{k|k-1}^i,$ $x_k^i=x_{k|k-1}^i,$ $P_k^i=P_{k|k-1}^i,$ $l_k^i=l_{k|k-1}^i,$ Work as i>n _k-1+ n _{γ, k}time, make q=int[(i-1)/(n _k+ n _{γ, k})], p=i-q * (n _k+ n _{γ, k}),

? $x_k^i=x_{k|k-1}^p+K_p[z_q\left(k\right)-H_k{x}_{k|k-1}^p],$ $P_k^i=(I-K_p{H}_k)P_{k|k-1}^p,$ $w_k^i=\frac{p_D{w}_{k|k-1}^{p}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^p,H_k{P}_{k|k-1}^p{H}_k^T+R_k)}{{\mathrm{\&lambda;}}_c\left(z_q\right)+p_D\underset{e=1}{\overset{n_{k-1}+n_{\mathrm{\&gamma;},k}}{\mathrm{\&Sigma;}}}{w}_{k|k-1}^{e}N(\mathrm{\&xi;};z_q-H_k{x}_{k|k-1}^e,H_k{P}_{k|k-1}^e{H}_k^T+R_k)},$ $l_k^i=l_{k|k-1}^p,$ Wherein, int represents to round, p _dfor acquisition probability, λ _cfor clutter density, H _kfor observing matrix, R _kfor the covariance of observation noise, K _pfor filter gain,

be the average of p prediction or newborn Gauss's item,

be the variance of p prediction or newborn Gauss's item, I is unit matrix,

be the weight of p prediction or newborn Gauss's item, N represents Gaussian distribution, the state that ξ is target,

be the average of e prediction or newborn Gauss's item,

be the variance of e prediction or newborn Gauss's item, z _qfor k moment measuring set Z _kin q measurement, the number of the Gauss's item after renewal is n _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1).

10. track tag system according to claim 9, is characterized in that, in cutting and merging module, Gauss's item that k upgrades is constantly expressed as

i=1,2 ..., n _k|k, the identify label set that k upgrades Gauss's item is constantly

i=1,2 ..., n _k|k, n wherein _k|k=(n _k-1+ n _{γ, k}) (m _k+ 1); Delete weight fully little,

gauss's item, wherein, τ is cutting thresholding;

Will be apart from d in Gauss's item of remainder from cutting _ijgauss's item of <U is merged into one, and wherein U is for merging thresholding, and combined distance is defined as

after merging, the identify label of Gauss's item determines that principle is: if the identify label of Gauss's item is 0 entirely before merging, after merging, the identify label of Gauss's item is also 0; Otherwise identify label is not in Gauss's item of 0 before merging, the heavy maximum Gauss's item identify label of weighting is as the identify label of Gauss's item after merging; When Gauss's item is merged, weight is greater than other weight of Gauss Xiang Buyu of 0.5 and is greater than Gauss's item of 0.5 and merges;

In described adjusting module, according to cutting with merge after Gauss's item, the identify label that weight is greater than to Gauss's item of 0.5 is judged: if the identify label of Gauss's item is greater than 0, show that its identity is definite; If 0, show that its identity not yet specifies, now need to specify a unique non-zero sign as its proprietary identify label to it.

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