In this paper, we investigate the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input multiple-output radar with unknown spatially colored noise. By exploiting the sparse structure of the noise covariance matrix, a new de-noising scheme is designed. The signal covariance matrix is recast as a low-rank matrix with missing entries, which can be approximately tackled via solving an optimization problem. Thereafter, DODs and DOAs are obtained with the traditional subspace techniques. The proposed method does not bring any virtual aperture loss; thus, it achieves more accurate estimation performance than several state-of-the-art de-noising methods. Finally, the stochastic Cramer–Rao bound for joint direction finding is derived. Numerical computer simulations verify the effectiveness of the proposed algorithm.

中文翻译：

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具有未知空间有色噪声的双基地 MIMO 雷达测向

在本文中，我们研究了空间彩色噪声未知的双基地多输入多输出雷达的联合出发方向（DOD）和到达方向（DOA）估计问题。利用噪声协方差矩阵的稀疏结构，设计了一种新的去噪方案。信号协方差矩阵被改写为具有缺失项的低秩矩阵，可以通过求解优化问题来近似解决。此后，使用传统的子空间技术获得 DOD 和 DOA。所提出的方法不会带来任何虚拟孔径损失；因此，它比几种最先进的去噪方法实现了更准确的估计性能。最后，推导出联合测向的随机 Cramer-Rao 界。