We develop a new tensor model for slow-time multiple-input multiple-output (MIMO) radar, and apply it for joint direction-of-departure (DOD), and direction-of-arrival (DOA) estimation. This tensor model aims to exploit the independence of phase modulation matrix, and receive array in the received signal for slow-time MIMO radar. Such tensor can be decomposed into two tensors of different ranks, one of which has identical structure to that of the conventional tensor model for MIMO radar, and the other contains all phase modulation values used in the transmit array. We then develop a modification of the alternating least squares algorithm to enable parallel factor decomposition of tensors with extra constants. The Vandermonde structure of the transmit, and receive steering matrices (if both arrays are uniform, and linear) is then utilized to obtain angle estimates from factor matrices. The multi-linear structure of the received signal is maintained to take advantage of tensor-based angle estimation algorithms, while the shortage of samples in Doppler domain for slow-time MIMO radar is mitigated. As a result, the joint DOD, and DOA estimation performance is improved as compared to existing angle estimation techniques for slow-time MIMO radar. Simulation results verify the effectiveness of the proposed method.

中文翻译：

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通过PARAFAC分解在慢时MIMO雷达中联合DOD和DOA估计

我们为慢时多输入多输出 (MIMO) 雷达开发了一种新的张量模型，并将其应用于联合出发方向 (DOD) 和到达方向 (DOA) 估计。该张量模型旨在利用相位调制矩阵的独立性，并在接收信号中为慢时MIMO雷达接收阵列。这种张量可以分解为两个不同等级的张量，其中一个与传统的MIMO雷达张量模型具有相同的结构，另一个包含发射阵列中使用的所有相位调制值。然后我们开发了交替最小二乘算法的修改，以启用具有额外常数的张量的并行因子分解。发射和接收控制矩阵的 Vandermonde 结构（如果两个阵列是均匀的，和线性）然后用于从因子矩阵中获得角度估计。保持接收信号的多重线性结构，利用基于张量的角度估计算法，同时缓解慢时MIMO雷达多普勒域样本不足的问题。因此，与现有的用于慢时 MIMO 雷达的角度估计技术相比，联合 DOD 和 DOA 估计性能得到了提高。仿真结果验证了所提方法的有效性。与现有的用于慢时 MIMO 雷达的角度估计技术相比，DOA 估计性能得到了提高。仿真结果验证了所提方法的有效性。与现有的用于慢时 MIMO 雷达的角度估计技术相比，DOA 估计性能得到了提高。仿真结果验证了所提方法的有效性。