Abstract Achieving a fair balance between accuracy and computational complexity of limited measurement signals by algorithms for the bistatic multiple-input multiple-output (MIMO) radar under unknown noise effect has been a seemingly difficult task for most covariance methods. In this paper, the aim is to present an efficient method to achieve an improved estimation of the joint direction of departure (DOD) and direction of arrival (DOA) for the bistatic MIMO radar with an unknown ‘Toeplitz’ colored noise effect. First, by taking advantage of the static property of the noise effect, a tensor reconstruction-based imaginary Hermitian matrix is developed to eliminate the unknown noise effect. Then the 2D angle estimation problem is then reduced to a 1D sparse recovery problem where the target sparsity is exploited. Further, we formulate a reverse 1D pairwise Simultaneous Orthogonal Matching Pursuit and sparse Bayesian learning algorithms to reconstruct the sparse signal and estimate the joint DOD-DOA of the target. In contrast with the existing tensor-based methods, the proposed approach not only is robust to the influence of Toeplitz colored noise, resolves targets with limited measurement data but also ensures superior performance with lower computational complexity. A numerical simulation conducted under varying conditions verifies the effectiveness of the proposed approach.

中文翻译：

---

基于张量重建的稀疏表示方法联合 DOD 和 DOA 估计双基地 MIMO 雷达的未知噪声影响

摘要 在未知噪声影响下，通过双基地多输入多输出 (MIMO) 雷达的算法在有限测量信号的精度和计算复杂度之间实现公平的平衡，对于大多数协方差方法来说似乎是一项艰巨的任务。在本文中，目的是提出一种有效的方法来实现对具有未知“托普利茨”色噪声效应的双基地 MIMO 雷达的联合出发方向 (DOD) 和到达方向 (DOA) 的改进估计。首先，利用噪声效应的静态特性，开发了基于张量重建的虚厄米矩阵来消除未知噪声效应。然后将二维角度估计问题简化为利用目标稀疏性的一维稀疏恢复问题。更多，我们制定了反向一维成对同时正交匹配追踪和稀疏贝叶斯学习算法来重建稀疏信号并估计目标的联合 DOD-DOA。与现有的基于张量的方法相比，所提出的方法不仅对 Toeplitz 有色噪声的影响具有鲁棒性，解决了测量数据有限的目标，而且以较低的计算复杂度确保了卓越的性能。在不同条件下进行的数值模拟验证了所提出方法的有效性。所提出的方法不仅对 Toeplitz 有色噪声的影响具有鲁棒性，解决了测量数据有限的目标，而且还以较低的计算复杂度确保了卓越的性能。在不同条件下进行的数值模拟验证了所提出方法的有效性。所提出的方法不仅对 Toeplitz 有色噪声的影响具有鲁棒性，解决了测量数据有限的目标，而且还以较低的计算复杂度确保了卓越的性能。在不同条件下进行的数值模拟验证了所提出方法的有效性。