Optimization neural net for multiple-target data association: real-time optical lab results

Abstract The Hopfield neural network was first used for optimization in solving the famous Traveling Salesman Problem.We have applied a similar approach to the solution of another problem, namely data association for multiple targets.Simulation data are presented which demonstrate the network's ability to successfully determine the optimum dataassociation solutions, with target noise present. Simulations also indicate the ability to solve the problem on a low accuracy (analog optical) processor. Optical implementation issues are discussed, and an bptical architecture is presented with laboratory results. Introduction \t=n+1 t=n xx x association matrixtarget estimates target measurements Figure 1 : ifiustration of the Data Association Problem.The data association (DA) problem for multitarget tracking (MU) is illustrated in Fig. 1 . Existing targets in agiven time frame must be associated with target measurements obtained in the next time frame. The existing targetdata are target state estimates which have been computed using previous measurement data. The properly associated