Multisensor adaptive bayesian tracking under time-varying target detection probability

In practical tracking applications, the target detection performance may be unknown and also change rapidly in time. This work considers a network of sensors and develops a target-tracking procedure able to adapt and react to the time-varying changes of the network detection probability. The proposed adaptive tracker is validated using extensive computer simulations and real-world experiments, testing a network of high-frequency radars for maritime surveillance and an underwater network of autonomous underwater vehicles for antisubmarine warfare.