空时自适应处理张量波束成形器的外积合成法

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

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。

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.