【作者】 李建国；

【导师】 崔祜涛；

【作者基本信息】 哈尔滨工业大学 ， 航空宇航科学与技术， 2014， 博士

【摘要】 探测器自主着陆能力对于星体安全着陆与采样返回至关重要，已经成为深空探测任务重要的设计目标。由于火星距离地球遥远，探测器着陆时间短，火星环境复杂多变，对探测器自主实时着陆导航提出了新的挑战。本文以火星探测器下降着陆段为研究背景，对探测器位姿估计中涉及到的基础理论和关键技术进行了深入的研究，致力于发展新一代精度高、自主性强、环境适应能力强的着陆导航方法，论文的主要内容包括以下几个方面：研究了基于视线矢量观测的探测器姿态确定方法。针对非线性测量模型选取问题，利用微分几何曲率测量理论，定量比较了两种测量模型的非线性强度大小。考虑到利用路标特征点测量信息的姿态确定问题，发展了基于视线矢量观测的姿态四元数估计算法。首先从信息融合的角度出发，将约束扩展卡尔曼滤波算法应用到状态矢量部分受约束的乘性姿态估计中，设计了一种乘性姿态约束滤波算法。然后借鉴QUEST(Quaternion Estimator)批处理算法结构，构建广义二次约束优化目标函数，通过求解特征值/特征矢量问题，提出了一种扩展QUEST最优姿态估计算法。结合火星轨道器获得的高分辨率特征点三维位置信息，研究了基于光学与惯性测量信息融合的绝对导航方法。详细推导了着陆点固连坐标系中的高精度系统动力学模型和测量模型。针对不同测量频率的敏感器信息融合，提出了一种处理测量延迟的状态矢量增广算法。并将6自由度相对位姿参数作为状态进行实时估计，解决了敏感器之间的空间校准。利用李导数可观性矩阵秩条件作为数学工具，从理论上分析了组合导航系统的可观性。最后对着陆导航算法的精度影响因素进行了仿真验证。研究了探测器在陌生环境和有限观测条件下的着陆导航方法。通过将相对运动测量与惯性导航相融合，提出了一种基于帧间单应变换的着陆导航算法。针对单应矩阵分解过程带来的数值病态问题，直接将单应矩阵估计值作为测量值，构建了与状态显性相关的测量模型。运用图像拼接来增加图像重叠区域，提高了可利用的特征点测量信息。针对导航系统的强非线性和乘性噪声问题，通过对采样空间进行解耦，消除了随机变量之间的相关性。研究了基于多帧图像特征点跟踪的着陆导航算法。首先对基于即时定位与地图构建(Simultaneous localization and mapping, SLAM)的导航算法进行了改进，利用固定滞后平滑原理，自适应调节状态矢量维数，提出了一种基于多帧滑动窗口优化的着陆导航算法。为了便于在着陆过程中进行实时反馈控制，通过增加一个特征点位置延迟初始化过程，将导航求解过程和地图构建过程解耦，设计了一种基于多目几何约束的着陆导航滤波算法。构建了描述多帧几何约束关系的测量模型，并借助于零空间投影方法进一步降低了算法计算复杂度。更多还原

【Abstract】 Spacecraft autonomous landing capability, a critical prerequisite for Mars safelanding and sample return, has been considered as the important design goal for theMars exploration missions. Due to the large distance between the Mars and earth,the short landing time, and the complex and changeable Mars environment, newchallenges for spacecraft autonomous real-time landing navigation are proposed.After an in-depth investigation of the Mars probe descent and landing phase, thebasic theory and key technology involved in spacecraft pose estimation are studiedin this dissertation in order to develop the new generation autonomoushigh-precision landing navigation method with strong adaptive capacity to thechange of environment. The major research contents of this dissertation are given asfollows:The attitude determination methods based on line-of-sight vector observationsare studied. According to the choice of nonlinear vector measurement models, thenonlinearity of two measurement models were compared using the differentialgeometry curvature nonlinearity measure theory. Considering the determinationproblem using the landmark features measurement information, the attitudequaternion estimation algorithms are developed based on line-of-sight vectormeasurements. By applying the constrained extended Kalman filter algorithm to thepart constrained multiplicative attitude estimation, a multiplicative quaternionconstrained filter algorithm is first designed from the view of information fusion.Then a generalized optimization object function under quadratic constraints isdeveloped through borrowing the traditional QUEST batch algorithm framework,and an extended QUEST optimal attitude estimation algorithm is presented bysolving the eigenvalue-eigenvector problem.The absolute navigation method based on optical and inertial measuremeninformation fusion is studied by combination of high resolution3D feature pointslocation information from Mars orbiter. For the information fusion of sensors withdifferent measurement frequency, a state vector augmentation algorithm is presentedto deal with the measurement delay and the six degrees-of-freedom sensorscalibration parameters are also estimated in real-time. Then the observability of theintegrated navigation system is investigated theoretically by the observability matrixrand condition based on Lie derivatives, and the landing navigation algorithm’sperformance under various operational conditions are validated by simulation.The landing navigation method in unknown environment and limitedobservation condition is studied. By fusing relative motion measurement and inertial navigation, a landing navigation algorithm based on inter-frame homographytransformation is proposed. The homography matrix estimation is directly fed intothe filter as vision measurement to solve the poorly conditioned problem inhomography decomposition. Then the measurement model explicitly related to thestate of the lander is provided. The image mosaicking processes are used to enlargethe images overlap regions and therefore increase the available feature measurementinformation. In view of the strongly nonlinear and multiplicative noise problem oflanding navigation system, the correlation between random variables is eliminatedby decoupling the sample space.The landing navigation algorithms based on multi-frame feature tracking arestudied from the perspectives of batch processing and filtering. First of all, thenavigation algorithm based on simultaneous localization and mapping (SLAM) isimproved. The dimensions of the state vectors are adaptively regulated by thefixed-lag smoothing principle, and a landing navigation algorithm based onmulti-frame sliding windows optimization is presented. In order to realize thereal-time feedback control during the landing process, the navigation system andmap construction processes are decoupled by adding a delayed initializationestimation of the features position, and a landing navigation filter algorithm basedon multi-view geometry constraint is designed. A measurement model relating themulti-frame geometric constraint is proposed and the computational complexity ofthe navigation algorithm is further reduced with the null space projection method.更多还原