Solving the association problem for a multistatic range-only radar target tracker

Petsios, MN; Alivizatos, EG; Uzunoglu, NK

dc.contributor.author	Petsios, MN	en
dc.contributor.author	Alivizatos, EG	en
dc.contributor.author	Uzunoglu, NK	en
dc.date.accessioned	2014-03-01T01:29:12Z
dc.date.available	2014-03-01T01:29:12Z
dc.date.issued	2008	en
dc.identifier.issn	0165-1684	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19158
dc.subject	Clutter	en
dc.subject	Data association	en
dc.subject	Global nearest neighbor	en
dc.subject	Interacting multiple models	en
dc.subject	Multiple target tracking	en
dc.subject	Multisensor tracking	en
dc.subject	Multistatic radar	en
dc.subject	Range-only tracking	en
dc.subject	Track management	en
dc.subject	Unscented Kalman filter	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Computational complexity	en
dc.subject.other	Kalman filters	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Probability	en
dc.subject.other	Problem solving	en
dc.subject.other	Robustness (control systems)	en
dc.subject.other	Tracking radar	en
dc.subject.other	Global nearest neighbor	en
dc.subject.other	Interacting multiple models	en
dc.subject.other	Multiple target tracking	en
dc.subject.other	Multisensor tracking	en
dc.subject.other	Unscented Kalman filters	en
dc.subject.other	Target tracking	en
dc.title	Solving the association problem for a multistatic range-only radar target tracker	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.sigpro.2008.03.011	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.sigpro.2008.03.011	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	This paper investigates the data association problem for a multiple target tracker, in dense clutter environment, using multiple bistatic range and range-rate measurements from a number of bistatic radars, composing a multistatic radar. Special emphasis is given to the initialization procedures, where a multiple hypothesis logic is adopted to resolve 'ghost'-targets, frequently generated by range-only multisensor systems. The contribution of the targets, paper is twofold. First, the development and implementation of the multiple target tracker algorithm is presented, including filtering, association and track management. The proposed algorithm includes: (i) global nearest neighbor (GNN) based on auction algorithm combined with a heuristic multiple hypothesis logic for new track initialization; (ii) interacting multiple model algorithm combined with iterated unscented Kalman filter (IMM-I-UKF); (iii) and a rule-based track management. Second, a set of performance metrics is chosen to evaluate the performance of the algorithm presented. Monte Carlo simulation demonstrates the track accuracy, probability of correct association, robustness and computational complexity for numerous scenarios of multiple targets (crossing track patterns, closely spaced or manoeuvring targets), for different values of clutter density and probability of detection. Finally, comparisons with other widely used methods prove the superiority of the proposed algorithm. (c) 2008 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Signal Processing	en
dc.identifier.doi	10.1016/j.sigpro.2008.03.011	en
dc.identifier.isi	ISI:000257615100010	en
dc.identifier.volume	88	en
dc.identifier.issue	9	en
dc.identifier.spage	2254	en
dc.identifier.epage	2277	en