Bernoulli filter for tracking maritime targets using point measurements with amplitude

Statistical performance of the Bernoulli Track-Before-Detect (TBD) algorithm for maritime radar is superior to that of the Bernoulli tracking filter which processes, in a conventional manner, the point measurements (PM) produced by a detector. However, the Bernoulli TBD is orders of magnitude computationally more intensive. In this paper we develop a hybrid algorithm that takes advantage of the amplitude information (exploited by the TBD algorithm), but operates in the framework of conventional point-measurement Bayesian tracking. The resulting filter is formulated using the Rao-Blackwell decomposition: a particle filter estimates the target amplitude, while the target position and velocity, given the amplitude, are estimated using a Gaussian mixture filter. Numerical results show that the statistical and computational efficiency of the new hybrid filter is between the two extremes, namely, the Bernoulli TBD and the Bernoulli PM tracker.