A novel robust adaptive filter in interference environments

The target motion uncertainty is a crucial factor affecting the accuracy of target tracking, and the interacting multiple-model (IMM) algorithm is an effective means to address this issue. However, the radar is often subject to various electromagnetic interference in practical applications, leading to a significant degradation in quality of partial dimensions in measurements. This degradation results in distortion in the model probability computation in the IMM framework, leading to a decrease in tracking accuracy. This paper integrates the robust filtering and adaptive filtering by using reliable dimensions of measurement information, and adaptively adjusts the measurement noise covariance matrix and the state prediction covariance matrix. Simulation results demonstrate that compared to traditional IMM algorithms, the proposed algorithm can effectively handle potential outliers in radar measurements and mitigate the adverse effects of target maneuvers, achieving a higher tracking accuracy in the interference environment.