A track-to-track association algorithm with chaotic neural network

A great deal of attentions is currently focused on multisensor data fusion. A very important aspect of it is track-to-track association and track fusion in distributed multisensor-multitarget environments. The approach based on Hopfield neural network has been developed. But the performance of this approach is limited because Hopfield neural network is often trapped in the local minima. This paper try to solve this problem with an approach based on chaotic neural network (CNN). Furthermore, in order to improve the performance of neural network, the association statistic between tracks from different sensors is modified. Computer simulation results indicate that this approach is more efficient than the algorithm based on continuous Hopfield neural network (CHNN).