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获取高精度事后姿态数据是提高遥感平台成像质量的必要条件之一,离线处理可有效降低敏感器测量误差,从而获得更高的姿态确定精度。基于滤波的校正方法中,星敏感器低频误差(LFE)与陀螺漂移将产生耦合影响导致校正精度低,本文针对该问题推导了耦合误差的数学模型,并设计了一种两步双向平滑事后处理算法,将陀螺漂移与低频误差分两步校正,通过反复滤波剥离陀螺漂移与低频误差。同时,针对低频误差参数收敛速度慢、噪声参数调节困难的问题,利用一种基于极大似然估计(MLE)的固定窗口自适应双向滤波算法进行处理以获得更好的噪声估计,提高了收敛速度和收敛精度。文中仿真工况下,离线姿态确定精度可达到0.8″(3σ),低频误差参数完全收敛时间不超过4个轨道周期。
Obtaining high-precision posterior attitude data is one of the necessary conditions to improve the imaging quality of remote sensing platform. Off-line processing can effectively reduce the measurement error of the sensor, so as to obtain higher attitude determination accuracy. In the filter-based correction method, the low-frequency error (LFE) of the star sensor and the gyro drifting will produce the coupling effect, which leads to the low calibration accuracy. In this paper, a mathematical model of the coupling error is deduced and a two-step bidirectional smoothing aftertreatment The algorithm corrects the gyro drift and low frequency error in two steps and repeats the gyro drift and low frequency error by iterative filtering. At the same time, aiming at the problem of slow convergence of low-frequency error parameters and difficulty in adjusting the noise parameters, a fixed-window adaptive bidirectional filtering algorithm based on maximum likelihood estimation (MLE) is adopted to obtain better noise estimation and improved convergence Speed and convergence accuracy. Under simulation conditions, the accuracy of offline attitude determination can reach 0.8 "(3σ), and the complete convergence time of low-frequency error parameters does not exceed 4 track periods.