A NEURAL APPROACH TO THE ASSIGNMENT ALGORITHM FOR MULTIPLE-TARGET TRACKING

A neural network has been designed for performing data association for multiple target tracking on an optimal assignment basis. The optimal assignment hypothesis is that which maximizes the sum of likelihood functions of measurement-to-track file associations. The likelihoods are shown to be derivable from a Kalman filter, which updates and maintains the track files from the measurements assigned by the neural network. Not only are measurements assigned to track files on an optimal basis, but undetected targets and unassigned measurements are identified also. A multiple-target tracking system utilizing the neural network, in conjunction with Kalman filtering, can also automatically delete and initiate track files. 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, along with comparisons to similar neural networks designed by previous investigators. Computer simulations indicate the ability of the neural network to converge quickly to the optimal hypothesis under various conditions, provided that the ambiguity in the scenario is not extreme. The computational complexity involved is moderate.