A Hybrid EFIR/DFT Algorithm on Trajectory Prediction of Space Spinning Target

Based on the visual camera, a robust and efficient method for tracking and predicting the trajectory of a space spinning target is proposed in this paper. The initial discrete point set of a motion trajectory is firstly accumulated in real-time, and then the motion is decomposed into translation and rotation. A hybrid extended finite impulse response (EFIR)/discrete Fourier transform (DFT) is used to estimate the states and dynamics parameters in time domain and frequency domain simultaneously. According to the dynamic equations of a free-floating object, we achieve the long-term and precise prediction while the measurement noise and process noise are unknown. The experiment with ground robot is presented to verify the correctness and effectiveness of the proposed method. The results show that the trajectory of a space spinning target can be predicted accurately using our proposed method. Comparing with the traditional extended Kalman filtering algorithm, the presented control method can improve the the speed of the parameter estimation and the accuracy of the trajectory prediction despite the noise covariance and the initial conditions are not exactly known in advance.