Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering

Abstract:

As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm in performance, and the simulation results show that, the improved filtering algorithm has good tracking performance and tracking accuracy.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2212-2215
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2212
• Online since: July 2014
• Authors: Song Gao*, Chao Bo Chen, Qian Gong
• Keywords: Interactive Multiple Model, Probability Density Hypothesis, Random Sets, Sequential Monte Carlo, Target Tracking

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