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针对自旋目标运动轨迹的跟踪与预测中鲁棒性与时效性问题,本文提出一种在视觉测量目标位姿的基础上,通过混合扩展有限冲击响应(EFIR)/离散傅立叶变换(DFT)估计目标状态与特征参数,进而预测目标轨迹的方法。在视觉相机对目标特征点位姿测量的基础上,将运动过程分解为平动与转动,时域与频域同步估计目标的状态与动力学参数,采用DFT估计与平动相关参量,采用EFIR估计与转动相关参量,根据空间漂浮目标动力学方程,实现在过程噪声与量测噪声未知的复杂条件下对目标轨迹的长期准确预测,并通过地面机器人模拟试验对预测方法的正确性和有效性开展验证。结果表明:利用本文提出的方法实现了对空间自旋目标运动轨迹的准确预测;与传统基于扩展卡尔曼滤波的预测方法相比,在过程噪声、量测噪声未知的条件下,文中提出的方法有效缩短了参数收敛时间,提高了参数估计与轨迹预测精度。
In order to solve the problem of robustness and timeliness in tracking and predicting motion trajectories of spin targets, this paper presents a new method to estimate the target trajectory of spin targets by the hybrid extended finite impulse response (EFIR) / Discrete Fourier Transform (DFT) Target state and characteristic parameters, and then predict the target trajectory. Based on the measurement of the position and orientation of the target feature points by the visual camera, the motion process is decomposed into translation and rotation, the state and dynamics parameters of the target are estimated synchronously in the time domain and the frequency domain, DFT is used to estimate the translational parameters and EFIR According to the floating target kinetic equation, the long-term and accurate prediction of the target trajectory can be achieved under the complicated conditions where the process noise and the measurement noise are unknown. The accuracy and validity of the prediction method are verified by ground robot simulation Carry out verification. The results show that the method proposed in this paper can accurately predict the motion trajectory of space spin target. Compared with the traditional prediction method based on extended Kalman filter, under the condition of process noise and unknown measurement noise, the proposed method Effectively shorten the parameter convergence time and improve the accuracy of parameter estimation and trajectory prediction.