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Extended target tracking method based on glmb filter and gibbs sampling

A technology of extended target and multi-extended target, which is applied in the field of multi-extended target tracking based on the tag random finite set framework, can solve the problems that the point target model is no longer applicable and no longer satisfies the point target assumption, and achieves the effect of effective tracking

Active Publication Date: 2021-06-29
HANGZHOU DIANZI UNIV
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Problems solved by technology

Because the traditional tracking algorithm no longer satisfies the point target assumption, the traditional point target model is no longer applicable

Method used

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  • Extended target tracking method based on glmb filter and gibbs sampling
  • Extended target tracking method based on glmb filter and gibbs sampling
  • Extended target tracking method based on glmb filter and gibbs sampling

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specific Embodiment approach

[0087] The present invention proposes a finite mixture model multi-extended target tracking method based on GLMB filtering and Gibbs sampling. Estimation of object shape. Its specific implementation is as follows:

[0088] Step 1 System Modeling

[0089] Step 1.1 Target Dynamic Model

[0090] Under the random finite set (RFS) framework, the state of multiple extended targets at time k is represented by the following RFS set:

[0091]

[0092] Over time, the state set X k Contains all the dynamic information of multiple targets at time k. At the next moment, some targets will die or continue to survive and their states will change, and there will also be some regeneration targets and newborn targets. The state model of the target RFS can be written as follows:

[0093]

[0094] Among them, S k|k-1 (x), B k|k-1 (x) and Γ k Indicates the survival, regeneration and newborn of the target, respectively.

[0095] Considering the situation of tracking N(k) extended targe...

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Abstract

The invention discloses an extended target tracking method based on GLMB filtering and Gibbs sampling, target number estimation, and extended target shape estimation problems, and proposes a multi-extended target tracking method based on a label random finite set framework. The method mainly includes Two aspects: dynamic modeling of multiple extended targets and tracking estimation of multiple extended targets. Firstly, combined with the generalized label multi-Bernoulli filter, the measurement finite mixture model of the extended target is established, and the parameters of the finite mixture model are deduced by using Gibbs sampling and Bayesian information criterion to learn and track the state of the multi-extended target, and then adopt The equivalent measurement method is used to replace the measurement produced by the extended target, and the shape of the extended target is modeled by ellipse approximation to realize the estimation of the extended target shape. The simulation experiment shows that the method provided by the present invention can effectively track multiple extended targets, accurately estimate the state and shape of the extended targets, and can obtain the track trajectory of the target.

Description

technical field [0001] The invention belongs to the field of multi-extended target tracking. Aiming at the state estimation of multi-extended targets under clutter conditions, the estimation of the number of targets, and the estimation of the shape of extended targets, a label based random finite set (Labelled random finite sets, L-RFS) is proposed. Multi-extension object tracking method under the framework. Background technique [0002] Traditional target tracking algorithms generally assume that the tracked target is a point target, that is, one target produces at most one measurement, but with the continuous development of modern sensor technology, the increasing resolution of radar enables us to obtain multiple measurements from a single target. A measurement, that is, a target produces more than one measurement point in a sampling period, this type of target is called an extended target. Extended target tracking can provide us with precise movement information and shap...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/02G01S13/70
CPCG01S7/02G01S13/70
Inventor 陈一梅刘伟峰王煦东
Owner HANGZHOU DIANZI UNIV
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