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

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., ...

AI Technical Summary

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 targets, and the computational complexity will increase with the increase of targets and the number of targets. The number of clusters or the number of false alarms increases exponentially, which is difficult to apply in engineering

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