空时自适应处理张量波束成形器的外积合成法

为了解决空时自适应处理（Space-Time Adaptive Processing，STAP）对足量平稳训练快拍的要求，给出了一种设计STAP张量波束成形器的新算法——空时自适应处理张量子波束合成（TSS-STAP）法。分析表明：STAP中所需要的张量波束成形器，可首先在张量的各个子维度上分别进行子波束成形器的设计，然后再由张量的外积运算合成各子波束成形器而得到。进一步分析表明：由于本文算法可在较低自由度（DoF）的子维度上对张量波束成形器进行设计，因此降低了设计所需要的训练快拍数和计算复杂度，同时也实现了有效的去相关处理，使得其在非均匀杂波环境下有更好的目标检测性能。在仿真实验中，所提算法有效提升了目标检测结果，同时降低了目标检测所消耗的时间。

An outer product synthesis approach to tensor beamformer for space-time adaptive processing

In order to solve the requirement of Space-Time Adaptive Processing (STAP) for a sufficient number of stationary training snapshots. In this paper, a new method, named as Tensor Sub-beam Synthesis-STAP (TSS-STAP), for designing STAP tensor beamformer is proposed. Analysis shows that the tensor beamformer required in STAP can be synthesized by the tensor outer product operation of the sub-beamformers, where each of the sub-beamfomer is designed in the each sub-dimension of a tensor, since the proposed beamformer is designed in the sub-dimension of a tensor which has lower Degrees of Freedom (DoF), so that the required training snapshots and computational complexity are reduced. Moreover, it is also shows that the proposed beamformer can effectively do decorrelation processing, so that a better target detection performance in non-uniform clutter environment is obtained. Simulation results show that the proposed method effectively improves the target detection result and reduces the time consumed by the target detection.