The problem of direction of arrival (DOA) estimation has been studied for decades as an essential technology in enabling radar, wireless communications, and array signal processing related applications. In this paper, the DOA estimation problem in the scenario with gain-phase errors is considered, and a sparse model is formulated by exploiting the signal sparsity in the spatial domain. By proposing a new atomic norm, named as GP-ANM, an optimization method is formulated via deriving a dual norm of GP-ANM. Then, the corresponding semidefinite program (SDP) is given to estimate the DOA efficiently, where the SDP is obtained based on the Schur complement. Moreover, a regularization parameter is obtained theoretically in the convex optimization problem. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and sparse-based methods in the scenario with gain-phase errors.

中文翻译：

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具有增益相位误差的 DOA 估计的新原子范数

数十年来，作为实现雷达、无线通信和阵列信号处理相关应用的一项基本技术，人们一直在研究到达方向 (DOA) 估计问题。本文考虑了增益相位误差场景下的DOA估计问题，利用空间域的信号稀疏性建立了稀疏模型。通过提出一种新的原子范数，命名为 GP-ANM，通过推导 GP-ANM 的对偶范数来制定优化方法。然后，给出相应的半定规划（SDP）以有效估计 DOA，其中 SDP 是基于 Schur 补码获得的。而且，在凸优化问题中理论上得到了一个正则化参数。仿真结果表明，该方法优于现有方法，