Authors: Đogatović, Marko
Stanojević, Milorad
Mladenović, Nenad 
Title: A variable neighborhood search particle filter for bearings-only target tracking
Journal: Computers and Operations Research
Volume: 52
First page: 192
Last page: 202
Issue Date: 1-Dec-2014
Rank: M21
ISSN: 0305-0548
DOI: 10.1016/j.cor.2013.11.013
In this paper a novel filtering procedure that uses a variant of the variable neighborhood search (VNS) algorithm for solving nonlinear global optimization problems is presented. The base of the new estimator is a particle filter enhanced by the VNS algorithm in resampling step. The VNS is used to mitigate degeneracy by iteratively moving weighted samples from starting positions into the parts of the state space where peaks and ridges of a posterior distribution are situated. For testing purposes, bearings-only tracking problem is used, with two static observers and two types of targets: non-maneuvering and maneuvering. Through numerous Monte Carlo simulations, we compared performance of the proposed filtering procedure with the performance of several standard estimation algorithms. The simulation results show that the algorithm mostly performed better than the other estimators used for comparison; it is robust and has fast initial convergence rate. Robustness to modeling errors of this filtering procedure is demonstrated through tracking of the maneuvering target. Moreover, in the paper it is shown that it is possible to combine the proposed algorithm with an interacted multiple model framework.
Keywords: Maneuvering target tracking | Nonlinear filtering | Particle filter | Variable neighborhood search
Publisher: Elsevier
Project: Intelligent HUman-Machine Mechatronic System for Medical Applications (HUMANISM) 

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