Robust Static Attitude Determination via Robust Optimization
Robust Static Attitude Determination via Robust Optimization
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DOI：
10.3182/20110828-6-it-1002.02001
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发表时间：
2011
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影响因子：
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通讯作者：
Shakil Ahmed;E. Kerrigan
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文献类型：
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作者：
Shakil Ahmed;E. Kerrigan
摘要本文提出了一种基于静态方法的鲁棒姿态确定方法。与动态方法相反，静态方法不依赖于系统动态。这种方法只需要在两个不同的坐标系中测量一些矢量，如地磁场、太阳矢量等。从某些传感器或数学模型获得的这些向量可能由于传感器误差和建模不准确而不准确。我们认为所有这样的错误作为无穷范数有界的不确定性，我们的主要重点是获得一个姿态估计，这是最不敏感的不确定性。我们制定了一个鲁棒优化（RO）问题的二次成本和非线性约束，并提出了一个解决方案，使用四元数方法与仿射不确定性参数化。我们将RO问题转化为一个次优的最小化问题，这是非凸的，但可以解决一个很好的初始猜测使用非线性优化求解器。结果表明，所提出的方法的最坏情况下的不确定性的显着优势。
Abstract We address the problem of robust attitude determination using a static approach. In contrast to the dynamic approach, a static approach does not depend on the system dynamics. This approach only requires measurements of some vectors, such as the earth magnetic field, sun vector, etc in two different coordinate frames. These vectors, obtained from some sensor or a mathematical model, may not be accurate due to sensor errors and modeling inaccuracies. We consider all such errors as infinity-norm bounded uncertainties and our main focus is to obtain an attitude estimate, which is least sensitive to such uncertainties. We formulate a robust optimization (RO) problem with a quadratic cost and nonlinear constraints and propose a solution using a quaternion approach with an affine uncertainty parameterization. We transform the RO problem into a suboptimal minimization problem, which is non-convex but can be solved with a good initial guess using a nonlinear optimization solver. The results show a significant advantage of the proposed approach for the worst case uncertainties.