Multiple target tracking using recurrent neural networks

Multiple target tracking (MTT) encounters the data association problem when the target measurement associations are uncertain because of the measurement noises, the targets' proximity, and the initial condition uncertainty. Standard approaches to MTT rely upon evaluations of association probabilities between targets and measurements whereas the SME filter developed by Kamen relies upon the choice of particular symmetric measurements and the extended Kalman filter (EKF) as a nonlinear filter. This paper centers on improvements of this latter strategy by using recurrent neural networks instead of the EKF. We argue that too much uncertainty in the initial condition prevents even the optimal filter from having an acceptable performance. To overcome this problem, we use the concept of set estimation and present comparative performances of several strategies.