Particle flow for sequential Monte Carlo implementation of probability hypothesis density
Yang Liu, Wenwu Wang, Yuxin Zhao
Abstract
Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the full multi-target posterior. Recently, the particle flow filter based on the log homotopy provides a new way for state estimation. In this paper, we propose a novel sequential Monte Carlo (SMC) implementation for the PHD filter assisted by the particle flow (PF), which is called PF-SMC-PHD filter. Experimental results show that our proposed filter has higher accuracy than the SMC-PHD filter and is computationally cheaper than the Gaussian mixture PHD (GM-PHD) filter.
BibTeX
@inproceedings{icassp2017_particleflowfors,
title = {Particle flow for sequential Monte Carlo implementation of probability hypothesis density},
author = {Yang Liu and Wenwu Wang and Yuxin Zhao},
booktitle = {ICASSP 2017},
year = {2017}
}