Target trajectory prediction based on neural network and Kalman filtering

Ling-xiao Li; Guang-li Sun; Jiang-peng Song

doi:10.1117/12.2547799

18 December 2019 Target trajectory prediction based on neural network and Kalman filtering

Ling-xiao Li, Guang-li Sun, Jiang-peng Song

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 113420J (2019) https://doi.org/10.1117/12.2547799
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

Kalman filtering is a filtering method based on minimum mean square error. It is a filtering algorithm formed by the state equation of the system, the observation equation and the statistical characteristics of the process noise of the system. It is widely used in the field of target tracking navigation guidance, etc. The Kalman filter requires an accurate state model of the known system, so it has great limitations in practical applications. Because Neural Networks have strong nonlinear mapping capabilities. In this paper, a variety of motion models are selected for reference and simulated by Matlab. The simulation results show that the prediction effect of the filter optimized by neural network is better than that of ordinary Kalman filter.

Citation Download Citation

Ling-xiao Li, Guang-li Sun, and Jiang-peng Song "Target trajectory prediction based on neural network and Kalman filtering", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420J (18 December 2019); https://doi.org/10.1117/12.2547799

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