A New Adaptive Fading Instrumental Variable Pseudolinear Kalman Filter for 3D AOA Target Tracking

The instrumental variable pseudolinear Kalman filter (IV-PLKF) algorithm, used for 3D angle-of-arrival (AOA) target tracking, has been proven to be more robust to initialization errors, with superior estimation performance and lower computational complexity compared to other state-of-the-art methods. However, the IV-PLKF algorithm requires prior knowledge of the state and angle measurement noise information, which is not available in practice. Improper selection of these values or mismatches due to time-varying changes can significantly impact the stability and estimation performance of the algorithm. To address this issue, this paper proposes a new adaptive fading (AF-) IV-PLKF algorithm that adaptively mitigates the possible scale mismatches in the state and measurement noise covariance matrices and the IV parameters. Simulation results demonstrate that the proposed algorithm outperforms the conventional IV-PLKF under mismatched state and measurement noise covariance scenarios. Moreover, the proposed method can even achieve comparable estimation performance to that of IV-PLKF with perfect knowledge of the noise information.