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Off-Grid Parameter Estimation Based on Joint Sparse Regularization

机译：基于联合稀疏正则化的离网参数估计

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We present a new off-grid parameter estimation method based on joint sparse regularization. More specifically, we consider the application of Direction-Of-Arrival (DOA) estimation from Multiple Measurement Vectors (MMVs). The MMV based DOA estimation problem exhibits joint sparsity which is commonly exploited by mixed-norm regularization, e.g., the l_(2,1) mixed-norm, which is known to suffer from high computational complexity in case of many MMVs or large dictionary matrices. To overcome the computational complexity associated with mixed-norm minimization the SPARse ROW-norm reconstruction (SPARROW) approach has recently been introduced as an equivalent, but less complex problem formulation. For the special case of Uniform Linear Arrays (ULAs) or ULAs with missing sensors, the GridLess-SPARROW (GL-SPARROW) has been presented. In this paper, we extend the SPARROW formulation by an off-grid estimation method for arbitrary array topologies, based on linear interpolation in form of first order Taylor expansion.
机译：我们提出了一种基于联合稀疏正则化的新型离网参数估计方法。更具体地，我们考虑从多个测量向量（MMV）的到达方向（DOA）估计的应用。基于MMV的DOA估计问题表现出共同稀疏性，其通常由混合规范化，例如，L_（2,1）混合规范，其已知在许多MMV或大词典矩阵的情况下遭受高计算复杂度。为了克服与混合规范最小化相关的计算复杂性，最近被引入了稀疏的行 - 范数重建（Sparrow）方法作为等效但不太复杂的问题制定。对于具有缺失传感器的均匀线性阵列（ULAS）或ULAS的特殊情况，已经介绍了无格子 - 麻雀（GL-Sparrow）。在本文中，基于一阶泰勒膨胀形式的线性插值，通过对任意阵列拓扑的离网估计方法扩展麻雀制剂。

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