Local-diffusion-based distributed SMC-PHD filtering using sensors with limited sensing range

Tiancheng Li, Victor Elvira, Hongqi Fan, Juan M. Corchado

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

We investigate the problem of distributed multitarget tracking by using a set of netted, 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 approach, 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.

Original languageEnglish
Article number8539980
Pages (from-to)1580-1589
Number of pages10
JournalIEEE Sensors Journal
Volume19
Issue number4
DOIs
StatePublished - 15 Feb 2019

Keywords

  • Average consensus
  • Diffusion
  • Distributed tracking
  • Probability hypothesis density filter
  • Sequential Monte Carlo

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