A generalized CHNN method for track-to-track association

A very important aspect of multisensor data fusion is track-to-track association and track fusion in distributed multisensor-multitarget environments. There is a assumption for the proposed approach based on Hopfield neural network that every sensor detect the same targets, but in practice, it is not always realizable. This paper propose a generalized approach based on continuous state Hopfield neural network (CHNN) to solve this problem. Furthermore, the algorithm is generalized to system of three sensors. Also, the Mahalanobis distance is redefined in this paper to accelerate the convergence of the Hopfield networks. Computer simulation results indicate that this approach successfully solves the track-to-track association problem, and it can be generalized in distributed mutisensor-multitarget environment.