A multi-Bernoulli Gaussian filter for track-before-detect with superpositional sensors

This paper presents a Gaussian implementation of a multi-Bernoulli track-before-detect filter for multi-target tracking with superpositional sensors. The proposed filter runs independent Bernoulli filters for each potential target. At each update step, each Bernoulli filter shares its predicted measurement information with the rest of the Bernoulli filters so that they can account for the influence of this target in the likelihood. The Bernoulli filters are implemented using unscented Kalman filters. Simulation results show the benefits of the proposed algorithm.