CN109687844A - A kind of intelligent maneuver method for tracking target - Google Patents
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Abstract
The invention discloses a kind of intelligent maneuver method for tracking target, transition probability matrix and model probability are modeled and initialized to the maneuvering target tracked first, input interaction is carried out to system mode and covariance according to the transition probability of system and model probability, obtain the filter input of admixture estimated value and its covariance estimated value as M concurrent working based on model, calculate state filtering value and its covariance based on model, then model probability is updated, computation model probability change rate, and transition probability is modified using model probability change rate, then it is normalized, finally carry out output interaction, obtain final state estimation and its variance, the present invention solves existing in the prior art since the fixed caused interacting multiple model algorithm of transition probability matrix is smooth The weak problem of noise immune, realizes the adaptive of transition probability, effectively improves the ability of Tracking Maneuvering Targets.
Description
Technical Field
The invention belongs to the technical field of maneuvering target tracking, and particularly relates to an intelligent maneuvering target tracking method.
Background
Maneuvering target tracking is always one of the hot points of research, and an interactive multi-model algorithm is the most common maneuvering target tracking algorithm at present, and has the advantage of realizing concurrent filtering of multiple models, and the system state estimation value is the weighting of the state estimation values based on the models. The interactive multi-model algorithm is characterized in that jumping between models is guided by a finite Markov chain. The interactive multi-model algorithm is essentially a filtering algorithm with special parameters, which are the transition probability matrix and have profound influence on the performance of the algorithm. The transition probability matrix of the standard interactive multi-model algorithm is set by prior information and is fixed, which obviously does not accord with the actual situation and influences the improvement of the algorithm performance. The interactive multi-model algorithm can generate a plurality of posterior information after iteration, the adaptation of the algorithm can be realized by correcting the transition probability by adopting the posterior information, and the improvement is realized by amplifying the inertia of the system essentially, so that the noise smoothing capability of the algorithm can be effectively improved, and the accuracy of tracking the maneuvering target is improved.
Disclosure of Invention
The invention aims to provide an intelligent maneuvering target tracking method, which solves the problem of weak smooth noise capability of an interactive multi-model algorithm caused by fixed transition probability matrix in the prior art, realizes the self-adaption of transition probability and effectively improves the capability of tracking maneuvering targets.
The technical scheme adopted by the invention is that the intelligent maneuvering target tracking method is implemented according to the following steps:
step 1, modeling a maneuvering target to be tracked to obtain M models, and initializing a transition probability matrix and a model probability;
step 2, performing input interaction on the system state and the covariance according to the transition probability and the model probability of the system to obtain a model-based mixed state estimation valueAnd its covariance estimate P0j(k-1)；
Step 3, the model-based mixed state estimation value obtained by the calculation in the step 2And its covariance estimate P0j(k-1) works as M pieces in parallelCalculating model-based state filter valuesAnd its covariance Pj(k)；
Step 4, updating the model probability according to the posterior information obtained in the step 3;
step 5, calculating a model probability change rate according to the model probability obtained in the step 4, correcting the transition probability by adopting the model probability change rate, and then carrying out normalization processing on the transition probability;
step 6, performing output interaction on the state estimation value based on the model and the variance thereof obtained in the step 3 and the model probability obtained in the step 4 to obtain a final state estimation value and the variance thereof;
and 7, repeating the steps 2 to 6 to iteratively track the maneuvering target until the iteration is finished.
The present invention is also characterized in that,
the step 1 is implemented according to the following steps:
step 1.1, modeling a system of a target needing maneuvering tracking to obtain M models:
x(k+1)＝F(k,mk)x(k,mk)+G(k,mk)w(k,mk)
z(k)＝H(k,mk)x(mk)+v(k,mk)
wherein,andrespectively representing a state vector and a measurement vector of a system at the moment k, n and m are positive integers, mkRepresents the current model of the system at time k, and the value is {1, 2.., M }, F (k, M) }k)、G(k,mk)、H(k,mk) Are all model mkSystem matrix at time k, w (k, m)k)、v(k,mk) Respectively represent the model mkThe process noise and the measurement noise of (1), which are uncorrelated white Gaussian noise with a mean value of zero and a variance matrix of Q (k, m), respectivelyk),R(k,mk)；
Step 1.2, initializing a transition probability matrix pi:
Π＝[πij]M×M
wherein, piijRepresenting the probability of model i jumping to model j.
Step 1.3, initializing a transition probability matrix mu:
μ＝[μi]1×M
wherein, muiRepresenting the probability of model i.
The step 2 is implemented according to the following steps:
step 2.1, according to the transition probability matrix pi and the model probability muiCalculating the mixing probability mui|j；
Step 2.2, obtaining the mixing probability mu according to the step 2.1i|jComputing model-based hybrid state estimatesAnd its covariance estimate P0j(k-1)。
Step 3 is specifically implemented according to the following steps:
the estimated value of the mixed state based on the model calculated in the step 2And its covariance estimate P0j(k-1) calculating model-based state filter values as input to M parallel-operating Kalman filtersAnd its covariance Pj(k) Error mean vj(k) And error covariance Sj(k).。
Step 4 is specifically implemented according to the following steps:
step 4.1, obtaining the error mean value v based on the model according to the step 3j(k) And error covariance Sj(k) Computing a model-based likelihood function Λj(k)；
Step 4.2, the likelihood function Lambda based on the model obtained by calculation according to the step 4.1j(k) Updating the model probability mui(k)。
Step 5 is specifically implemented according to the following steps:
step 5.1, calculating the probability change rate of the model according to the model probability obtained in the step 4, and defining a transition probability correction function f as follows:
step 5.2, the transfer probability is corrected by adopting the correction function obtained in the step 5.1, and normalization processing is carried out to obtain the transfer probability pi of intelligent processingij(k)：
πij(k)'＝fj(k)*πij(k-1),i＝1,2,…M
Normalization:
step 6 is implemented according to the following steps:
filtering the model-based state obtained in step 3And its covariance Pj(k) And the model probability mu obtained in the step 4i(k) Weighting to obtain the final state estimation value
The invention has the beneficial effects that the transition probability matrix in the standard interactive multi-model algorithm is determined according to the prior information, which does not accord with the actual situation and seriously influences the performance of the algorithm. According to research, the model probability change rate is adopted to generate a correction function, the transition probability is corrected, the transition probability self-adaption can be achieved, noise is effectively smoothed, model misjudgment is reduced, algorithm performance is improved, and therefore the maneuvering target tracking capacity is improved.
Drawings
FIG. 1 is a flow chart of an intelligent maneuvering target tracking method of the invention;
FIG. 2 is a schematic diagram of a standard interactive multi-model algorithm in the intelligent maneuvering target tracking method of the invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses an intelligent maneuvering target tracking method, which is implemented by the following steps of:
step 1, modeling a maneuvering target to be tracked to obtain M models, initializing a transition probability matrix and a model probability, and specifically implementing the following steps:
step 1.1, modeling a system of a target needing maneuvering tracking to obtain M models:
x(k+1)＝F(k,mk)x(k,mk)+G(k,mk)w(k,mk)
z(k)＝H(k,mk)x(mk)+v(k,mk)
wherein,andrespectively representing a state vector and a measurement vector of a system at the moment k, n and m are positive integers, mkRepresents the current model of the system at time k, and the value is {1, 2.., M }, F (k, M) }k)、G(k,mk)、H(k,mk) Are all model mkSystem matrix at time k, w (k, m)k)、v(k,mk) Respectively represent the model mkThe process noise and the measurement noise of (1), which are uncorrelated white Gaussian noise with a mean value of zero and a variance matrix of Q (k, m), respectivelyk),R(k,mk)；
Step 1.2, initializing a transition probability matrix pi:
Π＝[πij]M×M
wherein, piijRepresenting the probability of model i jumping to model j.
Step 1.3, initializing a transition probability matrix mu:
μ＝[μi]1×M
wherein, muiRepresenting the probability of model i;
step 2, performing input interaction on the system state and the covariance according to the transition probability and the model probability of the system to obtain a model-based mixed state estimation valueAnd its covariance estimate P0j(k-1), specifically comprising the following steps:
step 2.1, according to the transition probability matrix pi and the model probability muiCalculating the mixing probability mui|j；
Step 2.2, obtaining the mixing probability mu according to the step 2.1i|jComputing model-based hybrid state estimatesAnd its covariance estimate P0j(k-1)；
Step 3, the model-based mixed state estimation value obtained by the calculation in the step 2And its covariance estimate P0j(k-1) computing model-based state filter values as M filter inputs operating in parallelAnd its covariance Pj(k) The method is implemented according to the following steps:
the estimated value of the mixed state based on the model calculated in the step 2And its covariance estimate P0j(k-1) calculating model-based state filter values as input to M parallel-operating Kalman filtersAnd its covariance Pj(k) Error mean vj(k) And error covariance Sj(k).；
And 4, updating the model probability according to the posterior information obtained in the step 3, and specifically implementing the following steps:
step 4.1, obtaining the error mean value v based on the model according to the step 3j(k) And error covariance Sj(k) Computing a model-based likelihood function Λj(k)；
Step 4.2, the likelihood function Lambda based on the model obtained by calculation according to the step 4.1j(k) Updating the model probability mui(k)；
Step 5, calculating a model probability change rate according to the model probability obtained in the step 4, correcting the transition probability by adopting the model probability change rate, and then carrying out normalization processing on the transition probability, wherein the method is implemented according to the following steps:
step 5.1, calculating the probability change rate of the model according to the model probability obtained in the step 4, and defining a transition probability correction function f as follows:
step 5.2, the transfer probability is corrected by adopting the correction function obtained in the step 5.1, and normalization processing is carried out to obtain the transfer probability pi of intelligent processingij(k)：
πij(k)'＝fj(k)*πij(k-1),i＝1,2,…M
Normalization:
and 6, performing output interaction on the state estimation value and the variance thereof based on the model obtained in the step 3 and the model probability obtained in the step 4 to obtain a final state estimation value and a variance thereof, and specifically performing the following steps:
filtering the model-based state obtained in step 3And its covariance Pj(k) And the model probability mu obtained in the step 4i(k) Weighting to obtain the final state estimation value
And 7, repeating the steps 2 to 6 to iteratively track the maneuvering target until the iteration is finished.
The interactive multi-model algorithm is the most widely applied maneuvering target tracking algorithm, the transition probability matrix is an important parameter of the interactive multi-model algorithm, the parameter has great influence on the performance of the algorithm, but the parameter in the standard interactive multi-model algorithm is set by prior information and is fixed, which is not in accordance with the actual situation, in order to improve the performance of the interactive multi-model algorithm, the scheme provides that the posterior information is utilized to realize transition probability self-adaptation, the inertia of the system is increased, and therefore the capability of the algorithm for smoothing noise is effectively improved.
Claims (7)
1. An intelligent maneuvering target tracking method is characterized by being implemented according to the following steps:
step 1, modeling a maneuvering target to be tracked to obtain M models, and initializing a transition probability matrix and a model probability;
step 2, performing input interaction on the system state and the covariance according to the transition probability and the model probability of the system to obtain a model-based mixed state estimation valueAnd its covariance estimate P0j(k-1)；
Step 3, the model-based mixed state estimation value obtained by the calculation in the step 2And its covariance estimate P0j(k-1) computing model-based state filter values as M filter inputs operating in parallelAnd its covariance Pj(k)；
Step 4, updating the model probability according to the posterior information obtained in the step 3;
step 5, calculating a model probability change rate according to the model probability obtained in the step 4, correcting the transition probability by adopting the model probability change rate, and then carrying out normalization processing on the transition probability;
step 6, performing output interaction on the state estimation value based on the model and the variance thereof obtained in the step 3 and the model probability obtained in the step 4 to obtain a final state estimation value and the variance thereof;
and 7, repeating the steps 2 to 6 to iteratively track the maneuvering target until the iteration is finished.
2. The intelligent maneuvering target tracking method according to claim 1, characterized in that the step 1 is implemented specifically according to the following steps:
step 1.1, modeling a system of a target needing maneuvering tracking to obtain M models:
x(k+1)＝F(k,mk)x(k,mk)+G(k,mk)w(k,mk)
z(k)＝H(k,mk)x(mk)+v(k,mk)
wherein,andrespectively representing a state vector and a measurement vector of a system at the moment k, n and m are positive integers, mkRepresents the current model of the system at time k, and the value is {1, 2.., M }, F (k, M) }k)、G(k,mk)、H(k,mk) Are all model mkSystem matrix at time k, w (k, m)k)、v(k,mk) Respectively represent the model mkThe process noise and the measurement noise of (1), which are uncorrelated white Gaussian noise with a mean value of zero and a variance matrix of Q (k, m), respectivelyk),R(k,mk)；
Step 1.2, initializing a transition probability matrix pi:
Π＝[πij]M×M
wherein, piijRepresenting the probability of model i jumping to model j.
Step 1.3, initializing a transition probability matrix mu:
μ＝[μi]1×M
wherein, muiRepresenting the probability of model i.
3. The intelligent maneuvering target tracking method according to claim 2, characterized in that the step 2 is specifically implemented according to the following steps:
step 2.1, according to the transition probability matrix pi and the model probability muiCalculating the mixing probability mui|j；
Step 2.2, obtaining the mixing probability mu according to the step 2.1i|jComputing model-based hybrid state estimatesAnd its covariance estimate P0j(k-1)。
4. The intelligent maneuvering target tracking method according to claim 3, characterized in that the step 3 is implemented specifically according to the following steps:
calculating the step 2Resulting model-based hybrid state estimatesAnd its covariance estimate P0j(k-1) calculating model-based state filter values as input to M parallel-operating Kalman filtersAnd its covariance Pj(k) Error mean vj(k) And error covariance Sj(k).。
5. The intelligent maneuvering target tracking method according to claim 4, characterized in that the step 4 is specifically implemented according to the following steps:
step 4.1, obtaining the error mean value v based on the model according to the step 3j(k) And error covariance Sj(k) Computing a model-based likelihood function Λj(k)；
Step 4.2, the likelihood function Lambda based on the model obtained by calculation according to the step 4.1j(k) Updating the model probability mui(k)。
6. The intelligent maneuvering target tracking method according to claim 5, characterized in that the step 5 is implemented specifically according to the following steps:
step 5.1, calculating the probability change rate of the model according to the model probability obtained in the step 4, and defining a transition probability correction function f as follows:
step 5.2, the transfer probability is corrected by adopting the correction function obtained in the step 5.1, and normalization processing is carried out to obtain the transfer probability pi of intelligent processingij(k)：
πij(k)'＝fj(k)*πij(k-1),i＝1,2,…M
Normalization:
7. the intelligent maneuvering target tracking method according to claim 6, characterized in that the step 6 is implemented specifically according to the following steps:
filtering the model-based state obtained in step 3And its covariance Pj(k) And the model probability mu obtained in the step 4i(k) Weighting to obtain the final state estimation value
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