Maneuvering Target Tracking in the Presence of Glint

Abstract

The problem of maneuvering target tracking in the presence of glint noise is addressed in this paper. The main challenge in this problem stems from its nonlinearity and non-Gaussianity. In this work, the nonlinear Gaussian mixture Kalman filter (NL-GMKF) is applied to the problem of maneuvering target tracking in the presence of glint. The algorithm is based on the Gaussian mixture model for the posterior state vector distribution. The tracking performance of the NL-GMKF is evaluated and compared to the interacting multiple modeling (IMM) with extended Kalman filter (EKF), particle filter (PF) and the EKF. It is shown that the NL-GMKF outperforms other tested methods.