Local-Diffusion-based Distributed SMC-PHD Filtering Using Sensors with Limited Sensing Range
Abstract:
We investigate the problem of distributed multitarget tracking by using a set of spatially dispersed, collaborative sensors with limited sensing range (LSR), where each sensor runs a sequential Monte Carlo-probability hypothesis density filter and exchanges relevant posterior information with its neighbors. The key challenge stems from the LSR of neighbor sensors whose fields of view (FoVs) are partially/non-overlapped and therefore they may observe different targets at the same time. With regard to the local common FoVs among neighbor sensors, the proposed distributed fusion scheme, called local diffusion, performs one iteration of neighbor communication per filtering step in either of two means. One is given by immediate particle exchange, in which a reject-control operation is devised to reduce the number of communicating particles. The other is done by converting the particle distribution to Gaussian functions for parametric information exchange and fusion. The performance of both approaches has been experimentally investigated via simulation for different LSR situations and compared with cutting-edge approaches.
T. Li, V. Elvira, H. Fan and J. M. Corchado, "Local-Diffusion-Based Distributed SMC-PHD Filtering Using Sensors With Limited Sensing Range," in IEEE Sensors Journal, vol. 19, no. 4, pp. 1580-1589, 15 Feb.15, 2019.
doi: 10.1109/JSEN.2018.2882084
@ARTICLE{Li2019local,
author={T. Li and V. Elvira and H. Fan and J. M. Corchado},
journal={IEEE Sensors Journal},
title={Local-Diffusion-Based Distributed \cal{SMC-PHD} Filtering Using Sensors With Limited Sensing Range},
year={2019},
volume={19},
number={4},
pages={1580-1589},
doi={10.1109/JSEN.2018.2882084},
ISSN={1530-437X},
month={Feb.},}
转载本文请联系原作者获取授权,同时请注明本文来自李天成科学网博客。
链接地址:https://wap.sciencenet.cn/blog-388372-1151548.html?mobile=1
收藏