Particle optimizing probability hypothesis density multi-target tracking method based on variation filtering

A probabilistic hypothesis density, multi-target tracking technology, applied in the field of multi-target tracking, can solve the problems of increased computational complexity and difficulty in engineering applications, and achieve the effect of improving the effect of state estimation

Inactive Publication Date: 2014-07-02
HARBIN ENG UNIV
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Problems solved by technology

At present, the mainstream multi-target tracking processing methods mainly include: domain method, data association method, multi-hypothesis tracking method, etc., and these algorithms involve the correlation of multiple target

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  • Particle optimizing probability hypothesis density multi-target tracking method based on variation filtering
  • Particle optimizing probability hypothesis density multi-target tracking method based on variation filtering
  • Particle optimizing probability hypothesis density multi-target tracking method based on variation filtering

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Embodiment Construction

[0021] The present invention will be further described in detail below in conjunction with accompanying drawings and examples.

[0022] Under the framework of random set theory, the PHD filtering algorithm takes the state value set of all targets at the current moment as a state RFS variable, and the observation value set at the current moment as an observation RFS variable, and puts the multi-target tracking problem in the Bayesian It is solved under the framework of Siss filter. The only difference from the Bayesian optimal filter is that the Bayesian filter transfers the posterior probability density function of a single target state, while the PHD filter transfers the PHD of the multi-target state, that is, a part of the multi-target posterior density. step moment. figure 1 It shows the realization principle under the Monte Carlo method, that is, the particle PHD filter principle, D in the figure k-1 ( ) and D k (·) represent the PHD function of the target at time k-1 a...

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Abstract

The invention provides a particle optimizing probability hypothesis density multi-target tracking method based on variation filtering. According to the multi-target tracking method, distribution parameters of target state variables are taken as random variables, posteriori distribution of the random variables is solved through a variation Bayes method, optimized filtering state distribution is obtained after estimated values of the parameters are determined, and particles are sampled randomly by taking a state distribution function as an importance function of a truth approaching posteriori PHD function, so that most of the sampled particles are distributed at high-likelihood-probability positions, observation information is used reasonably, the phenomenon that according to a traditional particle probability density hypothesis method, the particles are sampled at low-likelihood-probability positions to cause particle degeneracy is avoided, and finally the performance of the particle probability hypothesis density multi-target tracking method is improved.

Description

technical field [0001] The invention relates to a multi-target tracking method using a variational Bayesian method and a particle probability hypothesis density filtering method. Background technique [0002] Target tracking problems are widely used in many production and application fields of human beings. According to target attributes, they can usually be divided into two categories: single target tracking and multi-target tracking. In the actual application environment, for sensors such as radar, sonar, and infrared detection, multi-target tracking is more common, and the processing methods are naturally more complicated than single-target tracking. The so-called multi-target tracking is to estimate the number of targets at the current moment and the state of each target from the observations mixed with clutter. The current mainstream multi-target tracking processing methods mainly include: domain method, data association method, multi-hypothesis tracking method, etc., ...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 周卫东沈忱黄蔚金诗宇刘学敏蔡佳楠
Owner HARBIN ENG UNIV
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