A neural network for data association in a multiple-target tracking system

Abstract

A neural network for performing data association in a multitarget tracking system is described. Computer simulations have been conducted, and the results are presented. The solution to the data association problem, and therefore the design of the neural network is based on the minimization of a properly defined energy function. The derivation of the energy function is presented. The scoring function to be optimized is the sum of the probabilities of measurement-to-track file associations. The latter are derivable from a Kalman filter, which maintains the track files. The simulations indicate the ability of the neural network to converge quickly to the optimal hypothesis, which has the maximum score, given a reasonable difference in score between the optimal and nearest suboptimal hypothesis.<>