空时自适应处理张量波束成形器的外积合成法

doi:10.7527/S1000-6893.2019.22939

Abstract

Abstract: 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.

Key words: STAP, tensor sub-beam synthesis, model-R decomposition, low rank filter, beamformer

CLC Number:

V243.2

BI Quanyang, LI Dan, ZHANG Jianqiu. An outer product synthesis approach to tensor beamformer for space-time adaptive processing[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(10): 322939-322939.

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

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