Bearings Fusion using the Cross Entropy Method for Underwater Multitarget Tracking

In scenarios with an unknown and time-varying number of targets, this paper proposes an underwater multi-target tracking method based on cross-entropy method data association for multi-array bearing fusion. The proposed method constructs a probability distribution over the association space under conditions of uncertain target numbers. Potential solutions for target birth and death, as well as the association between surviving targets and bearing measurements, are generated by random sampling. This approach enhances the tracking capability under conditions of a large and unknown number of targets. Simulation experiments validate the effectiveness of the proposed algorithm and improved performance compared to traditional methods.