Bearing-Doppler Target Tracking by ANFIS Based Deep Fuzzy System1

Bearing Doppler passive target tracking offers significant research value due to its concealment and anti-interference ability. However, the nonlinear measurements of such system pose challenges to conventional filtering methods especially when target maneuvers. To address this, the Wang-Mendel fuzzy system-based unscented Kalman filter (WMFS-UKF) utilizes deep convolutional fuzzy systems (DCFS) to address motion model uncertainty and demonstrates good tracking performance for bearing Doppler measurements, but there is still room for improvement. Therefore, this article proposes a deep adaptive neuro fuzzy inference system-based unscented Kalman filter (DANFIS-UKF). The method utilizes ANFIS to solve the problem of rule explosion, and combines with Unscented Transform (UT) to process nonlinear observations. The experimental results show that the proposed algorithm has lower errors and higher association accuracy in complex multi-target tracking scenarios.