Comparative Out-of-Sequence Estimation Techniques for Multi-Sensor Target Tracking

In this paper, performance comparisons are carried out between two out-of-sequence estimation filtering techniques based on the principles of the Extended Kalman Filter (EKF) and the Sigma-point Kalman filter (SPKF), in a mobile platform tracking application where distributed radars are used to estimate both linear and highly nonlinear movements of an aircraft. Two scenarios were considered: 1) aircraft movements fit a white noise acceleration model; and 2) aircraft movement follows a coordinated turn model with unknown turn rate. In addition, we evaluate the individual performance of the out-of-order filters against the ideal cases obtained by running the EKF and SPKF with reordered measurements in a chronological sequence. Simulation results show that the algorithms used for dealing with out-of-sequence measurements closely resemble the performance of the non-out-of-order filters. In terms of estimation accuracy, the out-of-order algorithm based on the SPKF outperforms the one based on the EKF when a highly nonlinear aircraft movement is observed. For nearly linear systems, there is not a significant difference between the two approaches.