Extended Object Tracking Method Based on Variational Bayesian Expectation Maximization

A variational Bayesian and expectation maximization technology, applied in the field of target tracking, can solve the problem of extended target tracking performance degradation and achieve the effect of improving tracking accuracy

Inactive Publication Date: 2017-08-25
XIDIAN UNIV
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Problems solved by technology

However, the traditional extended target tracking algorithms deal with the situation where the measurement noise covariance is known. In practice, when the measurement noise covariance is unknown, the tracking performance of the extended target will drop sharply

Method used

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  • Extended Object Tracking Method Based on Variational Bayesian Expectation Maximization
  • Extended Object Tracking Method Based on Variational Bayesian Expectation Maximization
  • Extended Object Tracking Method Based on Variational Bayesian Expectation Maximization

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] refer to figure 1 , the specific implementation steps of the present invention include as follows:

[0026] Step 1, at time k=0, initialize the joint probability hypothesis density of target state and measurement noise covariance:

[0027]

[0028] Among them, J 0 Indicates the number of Gaussian components, Represents the weight of the i-th Gaussian component, N( ) represents the Gaussian distribution, Indicates the mean value of the i-th Gaussian component, Represents the covariance of the i-th Gaussian component; IG( ) represents the inverse gamma distribution, Indicates the covariance of the i-th inverse gamma component, is a constant factor of the ith inverse gamma component, is the iteration factor of the i-th inverse gamma component, l represents the l-th dimension of the measurement noise covariance, and d represents the dimension of the ...

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Abstract

The invention discloses an extended target tracking method based on Variational Bayesian Expectation Maximization (VBEM), which mainly solves the problem that in the traditional extended target tracking field, when the covariance of measurement noise is unknown, the tracking performance of the target will decrease. problem of a sharp decline. This method first predicts the relevant parameters of the Gaussian inverse gamma component in the joint probability hypothesis density of the target state and measurement noise covariance; then updates the parameters of the Gaussian inverse gamma component; finally obtains the extended target state and number by pruning and merging . Simulation experiments show that the present invention can well track multi-spread targets under unknown number and unknown measurement noise covariance, and has high tracking precision, and can be used for tracking aircraft and submarine targets.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to a target tracking method, which can be used to track multiple extended targets. Background technique [0002] In the traditional target tracking field, due to the limited resolution of the radar, the target is usually regarded as a point target, that is, each target can only produce one measurement at a time. In recent years, with the development of radar detection technology and the needs of practical applications, more targets are regarded as extended targets, that is, each target can generate multiple measurements at each moment. [0003] In the actual target tracking scene, the number of targets cannot be predicted in advance, so the proposal of random set theory greatly meets the needs of target tracking theory. Among many model assumptions on the target, especially the proposed extended target theory is closer to the needs of the current tracking...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/277
Inventor 李翠芸王晋斌姬红兵王荣
Owner XIDIAN UNIV
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