Bernoulli track‐before‐detect filter for maritime radar
Branko Ristić, Luke Rosenberg, Du Yong Kim, Xuezhi Wang, Jason Louis Williams · IET Radar Sonar & Navigation · 2019
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.