Dynamic waveform selection for manoeuvering target tracking in clutter. Issue 7 (1st August 2013)

• Record Type: Journal Article
• Authors: Wang, Jiantao; Qin, Yuliang; Wang, Hongqiang; Li, Xiang
• Is Part Of: IET radar, sonar & navigation. Volume 7:Issue 7(2013)
• Page Start: 815
• Page End: 825
• Publication Date: 2013-08-01
• Subjects: approximation theory -- cognitive radio -- Gaussian processes -- mean square error methods -- measurement errors -- Monte Carlo methods -- particle filtering (numerical methods) -- probability -- radar clutter -- radar tracking -- statistical analysis; cognitive radar -- waveform diversity -- fixed waveform radar -- dynamic waveform selection algorithm -- tracking error minimisation -- CR manoeuvering target tracking -- resolution cell concepts -- measurement extraction cell -- statistical characteristics -- radar measurements -- CrameÌ?r‐Rao lower bound -- measurement errors -- signal‐to‐noise ratio assumption -- particle filter -- probabilistic data association -- predicted tracking mean‐square error -- Gaussian fitting -- target state -- statistical linearisation -- Monte Carlo simulation -- track loss probability; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2012.0310
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In recent years, cognitive radar (CR) with waveform diversity has exhibited significant performance improvements over the traditional fixed waveform radar and become an area of vigorous research and development. This study presents a dynamic waveform selection algorithm to strive for tracking error minimisation for CR manoeuvering target tracking in clutter. Based on the concepts of resolution cell and measurement extraction cell, the statistical characteristics of radar measurements are discussed without dependence upon the Cramér‐Rao lower bound of the measurement errors and the high signal‐to‐noise ratio assumption. A particle filter combined with probabilistic data association is used as a tracker. To quantify the utility of available waveforms, the predicted tracking mean‐square error, because of its dependence on actual future measurements, is approximated efficiently via Gaussian fitting of the prior density of the target state and statistical linearisation of the measurement equation. Monte Carlo simulation results show that the proposed dynamic waveform selection algorithm can improve tracking performance considerably, especially in terms of track loss probability.