高精度姿态测量设备获取的姿态信息对提升光学遥感卫星图像几何质量和目标定位精度具有重要的意义。在卫星定姿精度需求不断提升的应用背景下，本文以姿态确定理论为研究基础，以多源数据联合处理为核心，重点突破了星敏感器、陀螺以及角位移传感器联合定姿关键技术，实现了提高卫星姿态精度和稳定性的研究目标。论文完成的主要工作和创新点包括：

（1）结合姿态运动学方程和敏感器量测模型，构造线性联合定姿模型；在分析扩展卡尔曼滤波算法原理的基础上，设计实现了基于双向扩展卡尔曼滤波的星敏感器和陀螺联合定姿算法，能够有效提高姿态数据的利用性，与传统的扩展卡尔曼滤波算法相比，提升了联合定姿的精度。

（2）将神经网络理论引入姿态确定领域，提出了基于极限学习机的改进双向自适应扩展卡尔曼滤波的联合定姿算法，有效抑制星敏量测噪声，改善定姿模型存在系统误差的问题。仿真试验表明，该方法具有良好的泛化能力，定姿精度较双向自适应扩展卡尔曼滤波在俯仰和偏航方向提升10%左右，且稳定性更好。

（3）以星敏感器四元数作为量测方程，考虑高阶误差，推导了非线性定姿模型；利用采样点传递状态概率密度来替代线性化处理方式，设计实现了基于无迹卡尔曼滤波的星敏感器和陀螺联合定姿算法。仿真试验表明，算法精度优于传统的扩展卡尔曼滤波算法，适用于强非线性系统，改善了联合定姿精度。

（4）针对强非线性非高斯系统，引入粒子滤波的思想，设计实现了基于粒子滤波的星敏感器和陀螺联合定姿算法，仿真试验验证该算法较通常的非线性滤波处理方法，如无迹卡尔曼滤波算法的精度进一步提升。同时，与扩展卡尔曼滤波、双向扩展卡尔曼滤波、无迹卡尔曼滤波等算法进行对比，分析各自特点及优势，开展了不同采样率、不同精度的陀螺和星敏数据的联合定姿性能仿真分析，总结算法的适应性。

（5）依据精密定姿理论，设计实现了高频角位移数据仿真方法；结合角位移传感器模型特点，通过降采样处理的方式，利用扩展卡尔曼滤波、双向扩展卡尔曼滤波以及无迹卡尔曼滤波算法实现了星敏和角位移的联合定姿，有效地得到了高精度高频率的姿态数据。

 

The attitude information obtained by high precision attitude measurement equipment is of great significance to improve the optical remote sensing satellite’s image geometric quality and the target location accuracy. Due to the background of the increasing demand for satellite attitude accuracy, this thesis takes the attitude determination theory as the research basis, takes the multi-source data integrated processing as the core research thought, this thesis focuses on breaking through the integrated attitude determination algorithms of star sensor, gyro and angular displacement sensor, and realizes the research goal of improving satellite attitude accuracy and stability. The main research contents of this thesis include:

(1) Combined with the attitude kinematics equation and attitude sensor measurement model, this thesis construct a linear system of integrated attitude determination. On the basis of analyzing the principle of extended kalman filter algorithm, an integrated attitude determination algorithm of star sensor and gyro based on forward-backward extended kalman filter is designed and implemented, which can effectively improve the utilization of attitude data. Compared with the traditional extended kalman filter, the accuracy of integrated attitude determination is improved.

(2) The neural network theory is introduced into the field of integrated attitude determination, and an improved forward-backward adaptive extended kalman filter integrated attitude determination based on the limit learning machine is proposed, which can effectively suppress the star sensor measurement noise and improve the problem of systematic error in attitude determination model. The simulation results show that the proposed method has good generalization ability, and the accuracy of attitude determination is about 10% higher than that of forward-backward adaptive extended kalman filter in pitch and yaw direction, and its stability is better.

(3) Taking the quaternion of star sensor as the measurement equation and considering the higher order error, the nonlinear attitude determination model is derived. By using the sampling point transfer state probability density to replace the linearization method, the integrated attitude determination algorithm of star sensor and gyro based on unscented kalman filter is designed and implemented. Simulation results show that the accuracy of the algorithm is better than that of the traditional extended kalman filter algorithm.

(4) For strongly nonlinear non-gaussian systems, the idea of particle filter is introduced to design and implement the integrated attitude determination algorithm of star sensor and gyro based on particle filter. Simulation experiments verify that the algorithm is more accurate than the usual nonlinear filtering methods, such as unscented kalman filter algorithm. At the same time, compared with extended kalman filter, forward-backward extended kalman filter, unscented kalman filter and other algorithms, analyzed their characteristics and advantages, carried out different sampling rates, different precision of gyro and star sensor data integrated attitude determination performance simulation analysis, summed up the adaptability of the algorithm.

(5) The simulation method of high frequency angular displacement data is designed and realized based on the theory of precise attitude determination. Combined with the characteristics of the angular displacement sensor model, the extended kalman filter, forward-backward extended kalman filter and unscented kalman filter algorithm are used to realize the integrated attitude determination of star sensor and angular displacement, which can effectively obtain high precision and high frequency attitude data.

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