Real-time motion trajectory training and prediction using reservoir computing for intelligent sensing equipment

Yuru Mao; Ning Jing; Yongjie Guo

doi:10.1063/5.0233064

Real-time motion trajectory training and prediction using reservoir computing for intelligent sensing equipment

Rev Sci Instrum. 2025 Jan 1;96(1):015113. doi: 10.1063/5.0233064.

Authors

Yuru Mao¹, Ning Jing¹, Yongjie Guo¹

Affiliation

¹ Shanxi Key Laboratory of Intelligent Detection Technology and Equipment, School of Information and Communication Engineering, North University of China, Taiyuan 030051, Shanxi, China.

PMID: 39835946
DOI: 10.1063/5.0233064

Abstract

Real-time moving target trajectory prediction is highly valuable in applications such as automatic driving, target tracking, and motion prediction. This paper examines the projection of three-dimensional random motion of an object in space onto a sensing plane as an illustrative example. Historical running trajectory data are used to train a reserve network. The trained network model is subsequently used to predict future trajectories. In the experiment, a network model trained on 20 000 frames of random running trajectory data was used to predict trajectories for 1-20 future frames, and 5000 frames were used for testing. The results showed prediction errors for 80% of the predictions of less than 0.01%, 0.8%, and 4% for 1, 10, and 20 future frames, respectively.