A novel SMC-PHD filter based on particle compensation

作者：Xu Cong-an; He You^*; Yang Fu-cheng; Jian Tao; Wang Hai-peng; Li Tian-mei

来源：Journal of Central South University, 2017, 24(8): 1826-1836.

DOI：10.1007/s11771-017-3591-9

• random finite set (RFS)
• probability hypothesis density (PHD)
• particle filter (PF)
• particle impoverishment
• particle compensation
• multi-target tracking (MTT)

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.

• 出版日期2017-8
• 单位中国人民解放军火箭军工程大学; 中国人民解放军海军航空工程学院