采用拟蒙特卡罗法的被动多传感器目标跟踪

doi:10.3969/j.issn.1001-2400.2010.06.011

Abstract

Abstract:

This paper employs Quasi-Monte-Carlo (QMC) sampling to replace conventional Monte-Carlo (MC) sampling, thus improving the performance of the Gaussian Particle Filter (GPF). A multi-passive-sensor target tracking algorithm based on the Quasi-Monte-Carlo Gaussian Particle Filter (QMC-GPF) is proposed in connection with the multi-sensor centralized fusion strategy, which resolves the strong nonlinearity and weak observability problem in a multi-passive-sensor tracking system more efficiently. The algorithm not only reduces the computational complexity, but also improves the accuracy and stability of the tracking algorithm, thus getting fast convergence. Moreover, because of the parallel structure, which makes it easier to realize with large-scale integrated circuits.

Key words: passive sensors, Quasi-Monte-Carlo Gaussian particle filter, target tracking

GUO Hui;JI Hong-bing;WU Bin. Quasi-Monte-Carlo Gaussian particle filter based target tracking for the multiple passive sensor[J].J4, 2010, 37(6): 1042-1047.

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Quasi-Monte-Carlo Gaussian particle filter based target tracking for the multiple passive sensor

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