A Noise Estimation Method Based on Envelope Pseudo-measurement System in Adaptive Kalman Filter

For adaptive Kalman filter algorithm, the accurate estimation of the measurement noise covariance matrix (R) is particularly important. Recently, researchers have developed an enhanced algorithm that can adaptively estimate R using second-order mutual-difference (SOMD) sequences generated by redundant measurements. However, the applicability of this algorithm to most real-world systems is limited because it relies on redundant measurements. In order to introduce SOMD algorithm into non-redundant measurement system, the envelope pseudo-measurement noise covariance estimation (EPMNCE) is constructed in this paper. By extracting the upper and lower envelope of the signal, the median of each sampling point is calculated to form a pseudo-measurement system. On this basis, the feedback detection mechanism is introduced to judge the validity of the envelope, and the virtual detection and missing detection are corrected to ensure that the observed values are within the upper and lower envelope range. By comparing the simulation results of Extended Kalman filtering (EKF), Innovative-based adaptive estimation (IAE) and EPMNCE, it is found that the results of EPMNCE are smoother and closer to the actual value. The application of EPMNCE can improve the accuracy and stability of noise covariance estimation.