An improved pseudolinear Kalman filter algorithm for 3D Doppler-bearing target tracking

This paper presents an instrumental-variable Kalman filter (IVKF) algorithm for three-dimensional (3D) Doppler-bearing target tracking. Initially, a 3D passive localization and tracking model is established for a nearly constant-velocity moving target using the angle of arrival (AOA) and Doppler frequency measurements obtained from a static observer. The pseudolinear Kalman filter (PLKF) algorithm is employed to achieve target tracking, and based on the bias-compensated PLKF (BC-PLKF), an IVKF algorithm is presented, which constructs IV matrixes to address biases resulting from the correlation between measurement vectors and pseudolinear noises. Simulation results demonstrate that the proposed recursive estimator has higher accuracy and robustness to initialization errors compared to traditional extended Kalman filter (EKF), with root mean squared error fairly close to the posterior Cramér-Rao lower bound (PCRLB).