Target tracking with glint noise using an RBF neural network
Wei Yan, Zhaoda Zhu · 2002
In this paper, the problem of target tracking with glint noise is considered. We apply a radial basis function (RBF) neural network to evaluate the nonlinear score function, which is used as the correction term in the state estimation of robust Kalman filter. Simulation results are presented to demonstrate the performance of the evaluation of the score function.