Adapting the multi-Bernoulli filter to phased array observations using MUSIC as pseudo-likelihood

In this paper, we consider Bayesian multi-target tracking using phased array of sensors. Although joint Bayesian filtering is theoretically the optimal approach to multi-target tracking, the method suffers from high computational complexity for large numbers of targets. The PHD and multi-Bernoulli filters avoid this complexity by operating in the dimensionality of a single target space. However, these filters do not possess a mathematical framework to operate directly on signals from the phased sensor array. Therefore, it is necessary for the sensor signals to be first converted to a beamformer image (in the multi-Bernoulli filter) which is then thresholded (in the PHD filter). Moreover, resolving close targets in a beamformer image is difficult. This paper proposes the use of MUSIC as a pseudo-likelihood in the multi-Bernoulli filter. The merits of this proposal are that the multi-Bernoulli filter is able to operate more directly on sensor array signals, and that close targets are effectively resolved. We show the efficacy of our approach in reverberant and noisy environments.