Ideal array responses are often desirable to a multiple-input multiple-output (MIMO) system. Unfortunately, it may not be guaranteed in practice as the mutual coupling (MC) effects always exist. Current works concerning MC in the MIMO system only account for the uniform array geometry scenario. In this paper, we generalize the issue of angle estimation and MC self-calibration in a bistatic MIMO system in the case of arbitrary sensor geometry. The MC effects corresponding to the transmit array and the receive array are modeled by two MC matrices with several distinct entities. Angle estimation is then recast to a linear constrained quadratic problem. Inspired by the MC transformation property, a multiple signal classification- (MUSIC-) like strategy is proposed, which can estimate the direction-of-departure (DOD) and direction-of-arrival (DOA) via two individual spectrum searches. Thereafter, the MC coefficients are obtained by exploiting the orthogonality between the signal subspace and the noise subspace. The proposed method is suitable for arbitrary sensor geometry. Detailed analyses with respect to computational complexity, identifiability, and Cramer-Rao bounds (CRBs) are provided. Simulation results validate the effectiveness of the proposed method.

中文翻译：

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任意几何双基地MIMO系统的角度估计与互耦自校准

多输入多输出 (MIMO) 系统通常需要理想的阵列响应。不幸的是，在实践中可能无法保证，因为互耦 (MC) 效应始终存在。目前关于 MIMO 系统中 MC 的工作只考虑了均匀阵列几何场景。在本文中，我们概括了在任意传感器几何形状的情况下双基地 MIMO 系统中的角度估计和 MC 自校准问题。对应于发射阵列和接收阵列的 MC 效应由两个具有多个不同实体的 MC 矩阵建模。然后将角度估计重新转换为线性约束二次问题。受 MC 变换特性的启发，提出了一种多信号分类（MUSIC-）策略，它可以通过两个单独的频谱搜索来估计出发方向（DOD）和到达方向（DOA）。此后，通过利用信号子空间和噪声子空间之间的正交性来获得MC系数。所提出的方法适用于任意传感器几何形状。提供了关于计算复杂性、可识别性和 Cramer-Rao 边界 (CRB) 的详细分析。仿真结果验证了所提出方法的有效性。