Online Adaptive Kalman Filter for Target Tracking With Unknown Noise Statistics

Abstract

Considering that the external hostile environment will lead to rapid attenuation of sensor signals, which will make the noise parameters we set different from the actual noise parameters. In this letter, a novel online adaptive Kalman filter (AKF) is investigated with the main focus on inaccurate nonzero mean Gaussian white noise inherent in the filtering model. In the proposed AKF, we employed the expectation maximization algorithm to construct the noise parameter iteration expressions and obtain an approximate solution of the noise parameter. Finally, the derived AKF can effectively estimate the one-step prediction mean vector, the one-step prediction error covariance matrix, and the measurement noise covariance matrix. A classical target tracking simulation results show the effectiveness and stability of the derived AKF.