论文标题
稳定嵌入的无气味卡尔曼过滤
Unscented Kalman filter with stable embedding for simple, accurate and computationally efficient state estimation of systems on manifolds in Euclidean space
论文作者
论文摘要
本文提出了一种简单，准确和计算有效的方法，以将欧几里得空间中的普通无气体滤波器应用于在流形上的动力学发展的系统。我们使用称为稳定嵌入的数学理论，以使无味的卡尔曼滤波器保持良好的估计值，同时表现出良好的估计估计，从而使状态估计值保持近距离估计。我们通过将其应用于卫星系统模型并将其与其他专门针对歧管上系统设计的非意识到的卡尔曼过滤器进行比较，确认了我们设计的过滤器的性能。我们设计的过滤器的估计误差很低，使状态估计值保持与预期的歧视密切关系，并消耗少量的计算时间。同样，我们设计的过滤器非常简单易用，因为我们的过滤器直接采用了在欧几里得空间中设计的现成的标准的无意义的卡尔曼滤波器，而没有任何特定的歧管结构构造的离散方法或坐标转换。
This paper proposes a simple, accurate and computationally efficient method to apply the ordinary unscented Kalman filter developed in Euclidean space to systems whose dynamics evolve on manifolds.We use the mathematical theory called stable embedding to make a variant of unscented Kalman filter that keeps state estimates in closeproximity to the manifold while exhibiting excellent estimation performance. We confirm the performance of our devised filter by applying it to the satellite system model and comparing the performance with other unscented Kalman filters devised specifically for systems on manifolds. Our devised filter has a low estimation error, keeps the state estimates in close proximity to the manifold as expected, and consumes a minor amount of computation time. Also our devised filter is simple and easy to use because our filter directly employs the off-the-shelf standard unscented Kalman filter devised in Euclidean space without any particular manifold-structure-preserving discretization method or coordinate transformation.
