Volume 30 Issue 5
Sep.  2021
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LI Fan, XIONG Jiajun, LAN Xuhui, BI Hongkui, TAN Xiansi. Hypersonic Vehicle Trajectory Prediction Algorithm Based on Hough Transform[J]. Chinese Journal of Electronics, 2021, 30(5): 918-930. doi: 10.1049/cje.2021.07.003
Citation: LI Fan, XIONG Jiajun, LAN Xuhui, BI Hongkui, TAN Xiansi. Hypersonic Vehicle Trajectory Prediction Algorithm Based on Hough Transform[J]. Chinese Journal of Electronics, 2021, 30(5): 918-930. doi: 10.1049/cje.2021.07.003

Hypersonic Vehicle Trajectory Prediction Algorithm Based on Hough Transform

doi: 10.1049/cje.2021.07.003
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This work is supported by the National High-tech R & D Program of China (No.2015AA7056045, No.2015AA8017032P), and Postgraduate Funding Project (No.JW2018A039).

  • Received Date: 2019-07-31
    Available Online: 2021-09-02
  • Trajectory prediction is a prerequisite for missile and high-speed vehicle guidance interception. To address the trajectory problem for the near space hypersonic unpowered gliding vehicle. Firstly, the trajectory prediction mechanism is analyzed. Based on the existing NSHV trajectory prediction method, the key techniques of trajectory prediction are discussed from the perspectives of prediction parameter selection and prediction parameter description. Then, a trajectory prediction method based on Hough transform is proposed. The core of the method is to select the longitude latitude, and high direction positions as prediction parameters, and use the Hough transform to fit the prior basis functions. In the latitude and longitude direction, the rationality of predicting parameters is discussed from two aspects:position extreme point and lateral maneuverability. In the height direction, for the NSHV oscillation drop characteristic, two Hough transforms are used to separate the periodic term from the linear term. In addition, we give the approximate interval of the parameter values of the method, and design a parameter adaptive scheme. Finally, trajectory prediction is performed on two different control patterns. The error of the prediction time of 100s is within 25km, and the results show the effectiveness of the proposed method.
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