Application of neural networks to multitarget tracking
T.K. Robb, Mohamad Farooq · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
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 form clutter or false alarms. In this paper, a neural network based multi target tracking algorithm employing a Hopfield network is presented. The energy function of the Hopfield network is derived form a comparison of the constraints of the data association problem to those of the well-known traveling salesman problem. By minimizing the energy function, through the process of simulate 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.