Application of neural networks to multi-target tracking

In a typical multi-target tracking problem, the presence of random interference introduces uncertainty into the origin of the measurements. A data association technique is then required to associate each measurement with the appropriate target or to discard it as arising from clutter or false alarms. In this paper, a neural network based multitarget tracking algorithm employing a Hopfield network is presented. The energy function of the Hopfield network is derived from a comparison of the constraints of the data association problem to those of the well-known travelling salesman problem (TSP). By minimising the energy function, through the process of simulated annealing, the data association probabilities are computed and applied to a Kalman filter tracker for each target. The performance of the proposed algorithm is compared to the conventional techniques. Simulation results show that the proposed neural network tracker has satisfactory performance as compared to the Joint Probabilistic Data Association filter.