SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
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DOI：
10.1109/tsp.2010.2090525
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发表时间：
2011-02-01
影响因子：
5.4
通讯作者：
Li, Jian
中科院分区：
文献类型：
--
作者：
Stoica, Petre;Babu, Prabhu;Li, Jian
本文提出了一种新的SParse迭代协方差估计方法，简称SPICE，用于阵列处理。所提出的方法是通过最小化协方差矩阵拟合标准，是特别有用的，在许多快照的情况下，但可以使用，即使在单快照的情况下。SPICE具有其他稀疏估计方法所不具有的几个独特功能：它具有简单而可靠的统计基础，它以自然的方式考虑了数据中的噪声，它不需要用户进行任何困难的超参数选择，而且它具有全局收敛特性。
This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing. The proposed approach is obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many-snapshot cases but can be used even in single-snapshot situations. SPICE has several unique features not shared by other sparse estimation methods: it has a simple and sound statistical foundation, it takes account of the noise in the data in a natural manner, it does not require the user to make any difficult selection of hyperparameters, and yet it has global convergence properties.