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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. <\/jats:p>","DOI":"10.1155\/2015\/829013","type":"journal-article","created":{"date-parts":[[2015,7,21]],"date-time":"2015-07-21T21:02:34Z","timestamp":1437512554000},"page":"829013","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Distributed Box Particle Filtering for Target Tracking in Sensor Networks"],"prefix":"10.1177","volume":"11","author":[{"given":"Ying","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}]}],"member":"179","published-online":{"date-parts":[[2015,7,21]]},"reference":[{"key":"B1-2015-829013","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2006.215"},{"key":"B2-2015-829013","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1195991"},{"key":"B3-2015-829013","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3437-9"},{"volume-title":"An Introduction to the Kalman Filter","year":"1995","author":"Welch G.","key":"B4-2015-829013"},{"volume-title":"Kalman Filtering with Real Time Applications","year":"2008","author":"Chui C. 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