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Target trajectory prediction based on neural network and Kalman filtering
Author(s): Ling-xiao Li; Guang-li Sun; Jiang-peng Song
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Paper 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.

Paper Details

Date Published: 18 December 2019
PDF: 9 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420J (18 December 2019); doi: 10.1117/12.2547799
Show Author Affiliations
Ling-xiao Li, Tianjin Jinhang Institute of Technical Physics (China)
Guang-li Sun, Tianjin Jinhang Institute of Technical Physics (China)
Jiang-peng Song, Tianjin Jinhang Institute of Technical Physics (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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