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

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.