Open AccessBernoulli track‐before‐detect filter for maritime radar
Author(s) -
Ristic Branko,
Rosenberg Luke,
Kim Du Yong,
Wang Xuezhi,
Williams Jason
Publication year - 2020
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2019.0480
Subject(s) - clutter , track before detect , computer science , radar , tracking (education) , radar tracker , stationary target indication , algorithm , filter (signal processing) , detector , noise (video) , estimator , amplitude , bayesian probability , artificial intelligence , point target , continuous wave radar , computer vision , radar imaging , mathematics , telecommunications , physics , synthetic aperture radar , statistics , psychology , pedagogy , quantum mechanics , image (mathematics)
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.