Abstract:As one of the most critical issues for target track, α-jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α-jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α-jerk model.

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For missile's accuracy assessment, an accurate separation about the guidance of systematic errors is a critical part. Based on the vehicles from a mobile launcher platform, this paper proposes a nonlinear error separation model and a corresponding method in consideration of the ill-conditioning of the environmental function matrix, and the coupling of the guidance instrumental errors and the initial errors. The nonlinear model is built in combination with the tracking data. For the error separation problem with ill-conditioning, the traditional nonlinear methods can only slightly weaken the degree of ill-conditioning rather than solve it. To address this issue, this paper puts forward a novel guidance systematic error separation method based on the artificial fish swarm algorithm (AFSA). We first provide a brief introduction to AFSA and then analyze the convergence and the optimality of parameter estimation. Furthermore, we present the details of our novel algorithm that can address the guidance systematic error separation problem. We conduct a set of simulations to verify our approach. The simulation results confirm that our approach, which is based on AFSA, can improve the error separation accuracy effectively and perform better than the Bayesian estimation based on the traditional linear model and the Bayesian maximum a posteriori estimation based on the nonlinear model.

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