Probability hypothesis density multi-target tracking method based on variational Bayesian approximation technology

A technology of probability assumption density and variational Bayesian technology, applied in the field of variable number target tracking with unknown measurement noise, to reduce computational complexity and improve operating efficiency

Active Publication Date: 2013-10-09
江苏华文医疗器械有限公司
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Problems solved by technology

[0004] In view of the above problems, the present invention proposes a probability hypothesis density multi-target tracking method based on variational Bayesian approximation to solve the problem of multi-target tracking with number changes in an unknown measurement noise environment in a real tracking scene, and has a good tracking effect and robustness, which can meet the design requirements of practical engineering systems

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  • Probability hypothesis density multi-target tracking method based on variational Bayesian approximation technology
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  • Probability hypothesis density multi-target tracking method based on variational Bayesian approximation technology

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[0029] 1. Introduction to basic theory

[0030] 1. Variational Bayesian approximation technique

[0031] Suppose the state equation and measurement equation of a single target are expressed as:

[0032] x k+1 Fx k +Gw k 1)

[0033] the y k =h(x k )+v k 2) Among them, represents the state vector of the target at time k, F is a one-step transition matrix, the function h( ) represents the observation model, w k and v k Denote the state noise and measurement noise, respectively, and the corresponding covariances are denoted as Q k and R k . In real tracking scenarios, R k Usually unknown and changing, need to be estimated in due course.

[0034] Assuming that the target dynamic model is independent of the measurement noise covariance, the target state x k and the measurement noise covariance R k The joint posterior probability distribution of can be expressed as:

[0035] p(x k , R k |y 1:k-1 )=∫p(x k |x k-1 )p(R k |R k-1 )p(x k-1 , R k-1 |y 1:k-1 ...

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Abstract

The invention discloses a probability hypothesis density multi-target tracking method based on a variational Bayesian approximation technology, and belongs to the technical field of guidance and intelligent information processing. The probability hypothesis density multi-target tracking method based on the variational Bayesian approximation technology mainly solves the problem that an existing random set filtering method can not achieved varied number multi-target tracking under an unknown quantity measurement noise environment. According to the method, the variational Bayesian approximation technology is introduced, posterior probability hypothesis density of target states and measurement noise covariance is estimated in a combination mode, a Gaussian mixture inverse gamma distribution recurrence closed solution is adopted, and thus the varied number multi-target tracking under the unknown quantity measurement noise environment is achieved. The probability hypothesis density multi-target tracking method based on the variational Bayesian has a good tracking effect and robustness, is capable of meeting the design demands on practical engineering systems and has good engineering application value.

Description

technical field [0001] The invention belongs to the technical field of intelligent information processing, and relates to a variable-number multi-target tracking method with unknown measurement noise. Specifically, it is a multi-target tracking method based on variational Bayesian approximation and probability hypothesis density filtering, which can be used for target detection and tracking in various traffic control, robot navigation and precision guidance systems. Background technique [0002] Early multi-target tracking algorithms mainly realized tracking through the data association technology between targets and measurements, such as nearest neighbor algorithm, joint probability data association algorithm, multi-hypothesis tracking algorithm, etc. However, most of these algorithms are aimed at the multi-target tracking problem with a known number, and the computational complexity is relatively high, so it is difficult to effectively realize the multi-target tracking wit...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 杨金龙葛洪伟李志伟刘风梅
Owner 江苏华文医疗器械有限公司
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