Nonorthogonal problem in iterated unscented Kalman filter for passive tracking

Abstract

Abstract The iterated unscented Kalman filter (IUKF) is a promising nonlinear tracking algorithm. However, we find that the IUKF has poor performance in tracking accuracy and will diverge easily when the variance of observation noise is large, because the iterated state prediction is nonorthogonal to the current observation after the first iteration. This will increase the proportion of current observation in state estimate and lead to the tendency for the final iteration result to be closer to the observation compared with the optimal solution, which is a phenomenon termed the nonorthogonal problem here. We solve this problem by augmenting the state vector with the process and observation noise vectors and slightly reconstructing the IUKF formula. Simulation results show that the proposed algorithm has better tracking performance than IUKF. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.