A novel SMC-PHD filter based on particle compensation

As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, the particle impoverishment problem introduced by the resampling step, together with the high computational burden problem, ma...

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Bibliographic Details
Published in:Journal of Central South University Vol. 24; no. 8; pp. 1826 - 1836
Main Authors: Xu, Cong-an, He, You, Yang, Fu-cheng, Jian, Tao, Wang, Hai-peng, Li, Tian-mei
Format: Journal Article
Language:English
Published: Changsha Central South University 01-08-2017
Springer Nature B.V
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Summary:As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, the particle impoverishment problem introduced by the resampling step, together with the high computational burden problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this work, a novel SMC-PHD filter based on particle compensation is proposed to solve above problems. Firstly, according to a comprehensive analysis on the particle impoverishment problem, a new particle generating mechanism is developed to compensate the particles. Then, all the particles are integrated into the SMC-PHD filter framework. Simulation results demonstrate that, in comparison with the SMC-PHD filter, proposed PC-SMC-PHD filter is capable of overcoming the particle impoverishment problem, as well as improving the processing rate for a certain tracking accuracy in different scenarios.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-017-3591-9