Near Space Hypersonic Vehicle Target Tracking Adaptive Non-Zero Mean Model

As a typical maneuver model, the second-order Markov model, which is based on acceleration periodic autocorrelation, is an effective way used for tracking near space hypersonic vehicle(NSHV) target tracking. It is found that, however, the model responds slowly to the target maneuvering and has weak maneuverability. To solve this problem, the adaptive non-zero mean damped oscillation model (ANM-DO), which based on the idea of mean compensation, is proposed. Then the difference of the mean compensation method between the first order Markov model and the two order Markov model is analyzed, and the physical essence of adaptive nonzero mean is discussed from time domain and frequency domain. Furthermore, to further investigate the performance of the ANM-DO model, we deduced the systematic dynamic errors of ANM-DO taking Kalman Filter (KF) as filtering algorithm. On this basis, the superior performance of ANM-DO model is verified in terms of maneuverability. Finally, simulation experiments in different scenarios show that the ANM-DO model shows lower filtering errors tracking near space hypersonic jump gliding targets, and verified the adaptability of the model proposed in this paper.