The stochastic CRB for array processing: A textbook derivation
The stochastic CRB for array processing: A textbook derivation
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DOI：
10.1109/97.917699
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发表时间：
2001-05-01
影响因子：
3.9
通讯作者：
Gershman, AB
中科院分区：
文献类型：
--
作者：
Stoica, P;Larsson, EG;Gershman, AB
在阵列处理应用中，方向估计的随机Cramer-Rao界（CRB）是在大约十年前作为最大似然（ML）估计器的（渐近）协方差矩阵间接导出的。试图通过CRB理论直接获得随机CRB未能提供一个简单的推导，因此，在公开文献中没有直接推导出这个有用的性能界限。在这封信中，我们纠正这种情况，提供一个教科书般的直接推导的随机CRB。
The stochastic Cramer-Rao bound (CRB) for direction estimation in array processing applications was indirectly derived some ten years ago as the (asymptotic) covariance matrix of the maximum likelihood (ML) estimator. Attempts to obtain the stochastic CRB directly via the CRB theory fell short of providing a simple derivation and consequently, no direct derivation of this useful performance bound was available in the open literature. In the present letter, we correct this situation by providing a textbook-like direct derivation of the stochastic CRB.