Ballistic extrapolation based on extended Kalman smoothing

Yuqiang Wang; Sisheng Song

doi:10.1117/12.2657083

28 November 2022 Ballistic extrapolation based on extended Kalman smoothing

Yuqiang Wang, Sisheng Song

Author Affiliations +

Proceedings Volume 12503, International Conference on Network Communication and Information Security (ICNCIS 2022); 125030H (2022) https://doi.org/10.1117/12.2657083
Event: International Conference on Network Communication and Information Security (ICNCIS 2022), 2022, Qingdao, China

Abstract

To address the problems of estimation errors of the Extended Kalman Filter (EKF) and the estimation errors of the gun position due to the long extrapolation distance, a ballistic extrapolation algorithm based on extended Kalman smoothing is proposed. The algorithm adopts a filtering model of seven-dimensional state vectors including the ballistic coefficients, takes the end point of the forward filtering as the start point of the smoothing, post-processes the filtered data by RTS smoothing algorithm, and then takes the midpoint of the smoothed data as the start point of the ballistic extrapolation, and uses the fourth order Runge-Kutta algorithm for ballistic extrapolation. The simulation results show that the extrapolation accuracy of the algorithm is significantly improved compared with the original algorithm.

Citation Download Citation

Yuqiang Wang and Sisheng Song "Ballistic extrapolation based on extended Kalman smoothing", Proc. SPIE 12503, International Conference on Network Communication and Information Security (ICNCIS 2022), 125030H (28 November 2022); https://doi.org/10.1117/12.2657083

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