基于PARAFAC分解的慢时间MIMO雷达联合DOD与DOA估计
Joint DOD and DOA Estimation in Slow-Time MIMO Radar via PARAFAC Decomposition
AI总结：
针对慢时间MIMO雷达多普勒域样本不足问题，提出基于新张量模型与改进交替最小二乘的PARAFAC分解方法，利用阵列范德蒙德结构实现性能更优的联合DOD与DOA估计，仿真验证了有效性。
摘要：
我们为慢时间多输入多输出（MIMO）雷达构建了一种新的张量模型，并将其应用于联合发射方向（direction-of-departure, DOD）与接收方向（direction-of-arrival, DOA）估计。该张量模型旨在利用慢时间MIMO雷达接收信号中相位调制矩阵与接收阵列的独立性。此类张量可分解为两个不同秩的张量，其中一个的结构与传统MIMO雷达张量模型完全相同，另一个则包含发射阵列使用的所有相位调制值。随后我们改进了交替最小二乘算法，以实现带有额外常数的张量的平行因子（PARAFAC）分解。当发射和接收导向矩阵均具备范德蒙德结构时（即两个阵列均为均匀线阵），可利用该结构从因子矩阵中获取角度估计值。该方法保留了接收信号的多线性结构，可充分发挥基于张量的角度估计算法的优势，同时缓解了慢时间MIMO雷达在多普勒域样本不足的问题。因此，与现有的慢时间MIMO雷达角度估计技术相比，其联合DOD和DOA估计性能得到提升。仿真结果验证了所提方法的有效性。
英文摘要：
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.
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