Adaptive Markov IMM Based Multiple Fading Factors Strong Tracking CKF for Maneuvering Hypersonic-Target Tracking

Hypersonic targets have complex motion states and high maneuverability. The traditional interactive multi-model (IMM) has low tracking accuracy a slow convergence speed. Therefore, this paper proposes strong cubature Kalman filter (CKF) adaptive (AIMM) based on multiple fading factors. Firstly, analyzes the structure of CKF algorithm, introduces factor algorithm into covariance matrix time update measurement update, adjusts gain online in real time, which can reduce decline infilter caused by model mismatch. Secondly, Singer model, “current” statistical (CS) Jerk are selected set IMM introduced singular value decomposition (SVD) to solve problem that Cholesky cannot be performed due dimension expansion. Last, an for Markov is proposed. transition probability was adaptively modified likelihood function enhance proportion matching models. simulation results show proposed enhanced models improved 16.51% speed 37.5%.