论文标题
合奏和无知的卡尔曼过滤中的最佳感测精度
Optimal Sensing Precision in Ensemble and Unscented Kalman Filtering
论文作者
论文摘要
我们考虑选择一组最佳传感器精确度的问题，以使用集合Kalman滤波器和一个无用的Kalman滤波器估算非线性动力学系统的状态，该滤波器分别使用随机和确定性的集合。具体而言，目标是在运行时选择，这是一组稀疏的传感器精确度，以满足对估计状态协方差的某些约束。在本文中，我们表明，当我们将L1标准在精确矢量上用作造成稀疏性的替代措施时，这种传感器精度选择问题是一个半决赛编程问题。我们在多个时间步长上制定了传感器选择方案，以对终端估计状态协方差的某些约束。
We consider the problem of selecting an optimal set of sensor precisions to estimate the states of a non-linear dynamical system using an Ensemble Kalman filter and an Unscented Kalman filter, which uses random and deterministic ensembles respectively. Specifically, the goal is to choose at run-time, a sparse set of sensor precisions for active-sensing that satisfies certain constraints on the estimated state covariance. In this paper, we show that this sensor precision selection problem is a semidefinite programming problem when we use l1 norm over precision vector as the surrogate measure to induce sparsity. We formulate a sensor selection scheme over multiple time steps, for certain constraints on the terminal estimated state covariance.
