未知杂波环境的GM-PHD平滑滤波器

doi:10.3969/j.issn.1001-2400.2015.05.017

Abstract

Abstract:

Aiming at the Multi-target tracking in the unknown clutter environment, this paper proposes a Gaussian Mixture Probability Hypothesis Density (GM-PHD) forward-backward smoothing algorithm, which improves the poor performance of the PHD filter when the clutter model and the prior knowledge are mismatching by estimating the clutter intensity with the finite mixture model. The forward-backward smoothing recursions are applied to improve the state estimation accuracy. Simulation results show that the proposed algorithm performs well in the unknown clutter environment and better than the conventional Gaussian Mixture PHD Filter without smoothing processing in the unknown clutter environment.

Key words: unknown clutter, Gaussian mixture probability hypothesis density, smoothing, multitarget tracking

LI Cuiyun;JIANG Zhou;LI Bin;ZHOU Xuan. Gaussian mixture PHD smoothing filter in unknown clutter[J].J4, 2015, 42(5): 98-104.

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Gaussian mixture PHD smoothing filter in unknown clutter

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