Bernoulli track‐before‐detect filter for maritime radar. Issue 3 (7th February 2020)

• Record Type: Journal Article
• Authors: Ristic, Branko; Rosenberg, Luke; Kim, Du Yong; Wang, Xuezhi; Williams, Jason
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 3(2020)
• Page Start: 356
• Page End: 363
• Publication Date: 2020-02-07
• Subjects: radar clutter -- target tracking -- Bayes methods -- radar tracking -- marine radar -- object detection; Bernoulli track‐before‐detect filter -- high‐resolution maritime radar -- sea clutter -- target amplitude fluctuations -- target tracking -- recursive Bayesian detector‐estimator -- K‐distribution -- unknown distribution parameters; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2019.0480
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In this work, the authors study the problem of detecting and tracking small targets using high‐resolution maritime radar, where sea clutter is correlated in range, and its amplitude fluctuations are characterised by occasional spikes. To tackle this problem, they develop a Bernoulli track‐before‐detect filter, as the optimal recursive Bayesian detector/estimator of target state and its presence in noise. A realistic clutter model, in the form of the K ‐distribution with unknown distribution parameters, is adopted. Target amplitude fluctuations are also included in the model. The detection and tracking improvement are demonstrated by simulations and compared against a conventional point target tracking algorithm.