<title>Neural network approach to multiple-target-tracking problems</title>

Abstract

Multiple target tracking (MTT) has received much attention recently for various applications in the military as well as the Strategic Defense Initiative areas. Data association is one of the critical computation in MTT problems, because erroneous data associations often result in lost tracks. The joint probabilistic data association (JPDA) algorithm is a good approach to solve the data association problem. However, the computation complexity of this algorithm increases rapidly with the number of targets and radar returns. Neural networks have been considered to approximate the JPDA and ease the computation burden through the parallel processing. In this paper, we propose a neural network data association (NNDA) algorithm for the solution of the data association problems. Simulation results show the following three controversial issues: first, NNDA can track multiple targets with performance compatible with JPDA. Second, when the prediction filter can not provide an enough accurate prediction data ( This sometimes occurs when the prediction model can not match the tracking environment precisely enough), the neural computation provides better performace than JPDA. Third, the performance of NNDA is not affected by the number while that of JPDA degrades with the increase of target number. As a whole, this paper presents a neural network which not only possesses the intrinsic ability of parallel computation but also provides conditionally better tracking performance than JPDA.