Distributed Box Particle Filtering for Target Tracking in Sensor Networks | Zendy

Ying Liu | Zendy; Hao Liu | Zendy

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Distributed Box Particle Filtering for Target Tracking in Sensor Networks

Author(s) -

Ying Liu,

Hao Liu

Publication year - 2015

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2015/829013

Subject(s) - computer science , tracking (education) , particle filter , wireless sensor network , interval (graph theory) , bayesian probability , real time computing , distributed computing , kalman filter , artificial intelligence , computer network , mathematics , psychology , pedagogy , combinatorics

Distributed target tracking is a significant technique and is widely used in many applications. Combined with the interval analysis, box particle filtering (BPF) has been proposed to solve the problem of Bayesian filtering when the uncertainties in the measurements are intervals; that is, the measurements are interval-based vectors. This paper is targeted for extending the existing BPF based on a single sensor to a distributed sensor network. We propose a distributed BPF (d-BPF) that each sensor communicates with its direct neighbors to collaboratively estimate the states of the target. The feasibility of the proposed distributed BPF is justified, and some numerical simulations are presented to show its effectiveness in target tracking.

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