Abstract
The study investigates the trajectory estimation problem of a noncooperative gliding flight vehicle with complex and atypical maneuvers. An active switching multiple model (ASMM) method is proposed. This method employs a motion behavior model set (MBMS), a motion behavior recognition algorithm, and an active switching estimation and fusion algorithm. First, a recognizable MBMS, which can capture all the motion behaviors of a gliding flight vehicle, is established. Then, a motion behavior recognition algorithm based on recurrent neural networks (RNNs) is developed to obtain the current probability of each motion behavior. Then, an active switching estimation and fusion algorithm is proposed, in which the adopted models are actively chosen at each time instant according to a model selection strategy. Last, the proposed ASMM method is applied to a noncooperative gliding flight vehicle. The simulation results show that the proposed method has higher estimation precision and better dynamic performance.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61473099, 61333001). The Titan Xp used for the RNNs training is donated by the NVIDIA Corporation.
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Zheng, T., Yao, Y., He, F. et al. Active switching multiple model method for tracking a noncooperative gliding flight vehicle. Sci. China Inf. Sci. 63, 192202 (2020). https://doi.org/10.1007/s11432-019-1515-2
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DOI: https://doi.org/10.1007/s11432-019-1515-2