Abstract:In order to solve the problem of an uncertain initial state and big errors for hypersonic glide vehicle (HGV) tracking, a hybrid model algorithm is proposed by combining a single model algorithm with a multiple model algorithm. To develop the tracking algorithm with the Cubature Kalman filter, in every model filter the process equation is established based on the HGV aerodynamic model and the measurement equation is established based on the radar measurement principle. The proposed hybrid model algorithm is developed by using the multiple model algorithm in the initial tracking stage and using the single model algorithm in the stable tracking stage, and they are divided by a proposed parameter. The former can avoid divergence and reduce the errors caused by the uncertain initial state. The latter can track the HGV at higher accuracy. The simulation indicates that the proposed hybrid model has high speed accuracy in the whole tracking stage and high position accuracy in the stable tracking stage. The average position root mean square error (RMSE) using the hybrid model algorithm is almost the same as that using the single model algorithm but the average speed RMSE using the single model algorithm is about 30% greater than that using the hybrid model algorithm. In a system for defending the HGV, the speed accuracy has more effect on the trajectory prediction as time goes on. Thus, the hybrid model algorithm is an engineering algorithm for HGVs with high accuracy. In future research, the hybrid model algorithm will be studied for general maneuvering target tracking.

Abstract:In order to defend the hypersonic glide vehicle (HGV), a cost-effective single-model tracking algorithm using Cubature Kalman filter (CKF) is proposed in this paper based on modified aerodynamic model (MAM) as process equation and radar measurement model as measurement equation. In the existing aerodynamic model, the two control variables attack angle and bank angle cannot be measured by the existing radar equipment and their control laws cannot be known by defenders. To establish the process equation, the MAM for HGV tracking is proposed by using additive white noise to model the rates of change of the two control variables. For the ease of comparison several multiple model algorithms based on CKF are presented, including interacting multiple model (IMM) algorithm, adaptive grid interacting multiple model (AGIMM) algorithm and hybrid grid multiple model (HGMM) algorithm. The performances of these algorithms are compared and analyzed according to the simulation results. The simulation results indicate that the proposed tracking algorithm based on modified aerodynamic model has the best tracking performance with the best accuracy and least computational cost among all tracking algorithms in this paper. The proposed algorithm is cost-effective for HGV tracking.

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