Parallel computing particle probability hypothesis density filtering multi-target tracking method

Abstract

The invention discloses a parallel computing particle probability hypothesis density filtering multi-target tracking method. In view of the problems that the traditional particle probability hypothesis density filtering can not achieve complete parallel computing and the real-time performance is poor, a multi-core processor is used to realize parallel computing of all processes from importance sampling to a state estimation stage, so as to improve the real-time performance of multi-target tracking. Each parallel processing unit obtains initialized particles and then independently performs importance sampling, weight calculation normalization, resampling and local estimation, the local weight of the resampled particle before resampling is kept and recorded, a central unit calculates the particle weight ratio of each parallel unit participating in the state estimation according to the kept local weight, and then, the local estimation value is weighted and fused according to the weight ratio to obtain final multi-target state global estimation. The real-time performance of multi-target tracking is improved through parallel computing, and the application prospect is good.

Description

technical field [0001] The invention relates to the field of image, radar and sonar signal processing. Background technique [0002] Multi-target tracking is the use of signal processing methods to estimate the state information of multiple targets in image, radar, sonar and other applications. It has been widely used in military and civilian fields, and is a very popular research field in the world. one. [0003] The state estimation method based on the probability hypothesis density (Probability Hypothesis Density, PHD) filter and various improved methods has been most widely used in multi-target tracking. The sequential Monte Carlo implementation method of the PHD filter, that is, the particle Particle-Probability Hypothesis Density (P-PHD) filtering, realizes the recursion of PHD in the form of particles, and uses a set of weighted random samples to approximate the probability hypothesis density distribution, so as to solve the calculation problem of multiple integrals ...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a multi-target tracking method for parallel computing particle probability hypothesis density filtering, which solves the problem that the above particle probability hypothesis density filtering cannot realize complete parallel computing and poor real-time performance, thereby greatly improving the computing speed of the processor and reducing time consumption, which is different from Existing methods, the proposed method can realize the parallel computing of all processes from the importance sampling to the state estimation stage

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