Linear-correction Extended Kalman Filter for Target Tracking Using TDOA and FDOA Measurements

This paper considers the target tracking based on TDOA (Time Difference of Arrival) and FDOA (Frequency Difference of Arrival) measurements for a single target in a distributed sensor network. A linear-correction EKF algorithm that involves closed-form weighted least squares (WLS) optimization only is developed to solve the divergence problem of the extended kalman filter (EKF) in performing estimation of the state of a nonlinear system. The proposed method firstly obtains initial state estimation result by EKF using the WLS approach and subtracting its state error, then redoes the filtering process again to overcome the error causing by the part linearization operation for measurement matrix in EKF. The specific expressions TDOA-FDOA-based is provided at last. Simulation results demonstrate the good performance of the proposed method.