Data association for multiple target tracking using Hopfield neural network

Data association consists of assigning measurements to the predicted track positions in the multiple target tracking application. The data association problem can be structured in a basic framework very similar to that of the classic travelling salesman problem (TSP). The derivation of the energy function is presented, and the solution is based on a modified Hopfield network which uses the Runge-Kutta method and Aiyer (1990) network´s structure. We demonstrate the feasibility of applying neural network technology to multitarget tracking using simulated and real-life radar data. The modified Hopfield tracker is also observed to have better performance than the original Hopfield network