Direction of arrival estimation using robust complex Lasso
Direction of arrival estimation using robust complex Lasso
复制标题
使用鲁棒复杂套索估计到达方向
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
E. Ollila
中科院分区：
文献类型：
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作者：
E. Ollila
Lasso（最小绝对收缩和选择算子）一直是同步线性回归估计和变量选择的流行技术。在本文中，我们提出了一种遵循 M 估计精神的鲁棒套索新方法。我们将回归和尺度的 M-Lasso 估计定义为广义零次梯度方程的解。本文的另一个独特之处是我们考虑了复值测量和回归参数，这需要对问题进行仔细的数学表征。提出了一种用于计算 M-Lasso 解的显式且高效的算法，其计算复杂度与用于计算 Lasso 解的最先进算法相当。说明了 M-Lasso 方法对于在单个快照情况下使用传感器阵列进行到达方向 (DoA) 估计的有用性。
The Lasso (Least Absolute Shrinkage and Selection Operator) has been a popular technique for simultaneous linear regression estimation and variable selection. In this paper, we propose a new novel approach for robust Lasso that follows the spirit of M-estimation. We define M-Lasso estimates of regression and scale as solutions to generalized zero sub-gradient equations. Another unique feature of this paper is that we consider complex-valued measurements and regression parameters, which requires careful mathematical characterization of the problem. An explicit and efficient algorithm for computing the M-Lasso solution is proposed that has comparable computational complexity as state-of-the-art algorithm for computing the Lasso solution. Usefulness of the M-Lasso method is illustrated for direction-of-arrival (DoA) estimation with sensor arrays in a single snapshot case.