Cramér-Rao Lower Bound for State-Constrained Nonlinear Filtering
Cramér-Rao Lower Bound for State-Constrained Nonlinear Filtering
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DOI：
10.1109/lsp.2017.2764540
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发表时间：
2017-12
影响因子：
3.9
通讯作者：
Lorenz A. Schmitt;W. Fichter
中科院分区：
文献类型：
--
作者：
Lorenz A. Schmitt;W. Fichter
本文给出了给定可微状态约束下非线性随机系统状态估计的均方误差下界。它的递归公式允许随机过程和测量误差的结合，并被证明是已知的无约束问题的下界的推广。以结合路线图的地面车辆水平位置和速度的噪声测量为例，对边界进行了评估。
This letter presents a mean-square error lower bound for state estimation of nonlinear stochastic systems under given differentiable state constraints. Its recursive formulation permits incorporation of random process and measurement errors and is shown to be a generalization of the known lower bound for unconstrained problems. The bound is evaluated for the example of locating a ground vehicle from noisy measurements of its horizontal position and velocity incorporating a roadmap.