This article addresses the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation with nested bistatic multiple input multiple output (MIMO) radar using tensor decomposition. We first employ the two-level nested transmit and receive arrays to develop the sum-difference coarray for constructing the Toeplitz and spatial smoothing matrices. We then generalize the three-way tensor model from DOD and DOA dimensions, and derive the optimized tensor by maximizing the number of detectable targets, where the existing COMFAC technique is exploited for angle estimation. We show that the proposed method can identify more targets and achieve better performance by enforcing the three-way structure information compared with the subspace-based algorithms. We also show that the conventional tensor model is just a special case. Finally, we derive the coarray Cramér–Rao Bound (CRB) for the nested MIMO radar, and also conduct a study for the conditions under which the CRB exists. Numerical simulations are provided to validate the theoretical analysis and demonstrate the performance improvement.

中文翻译：

---

嵌套MIMO雷达：协同阵列，张量建模和角度估计

本文解决了使用张量分解的嵌套双基地多输入多输出（MIMO）雷达的联合出发方向（DOD）和到达方向（DOA）估计的问题。我们首先采用两级嵌套的发射和接收阵列来开发求和差协阵列，以构造Toeplitz和空间平滑矩阵。然后，我们从DOD和DOA维度推广三向张量模型，并通过最大化可检测目标的数量来导出优化张量，其中现有COMFAC技术可用于角度估计。我们表明，与基于子空间的算法相比，该方法可以通过执行三向结构信息来识别更多目标并实现更好的性能。我们还表明，传统的张量模型只是一个特例。最后，我们推导了用于嵌套MIMO雷达的共数组Cramér–Rao Bound（CRB），并且对存在CRB的条件进行了研究。提供数值模拟以验证理论分析并证明性能改进。