A novel reduced-dimension clutter suppression method based on tensor decomposition

Zhiwei Yang; Yufeng Xu; Jingya Li; Yongfei Mao; Hui Kuang

doi:10.1117/12.3012095

21 December 2023 A novel reduced-dimension clutter suppression method based on tensor decomposition

Zhiwei Yang, Yufeng Xu, Jingya Li, Yongfei Mao, Hui Kuang

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297008 (2023) https://doi.org/10.1117/12.3012095
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Reduced-dimension (RD) space-time adaptive processing (STAP) technique has achieved good clutter suppression performance in real data processing. However, the traditional F$A method suffers from performance degradation in presence of clutter fluctuation. Extended F$A method (E-F$A) is an effective approach to improve the performance in such a clutter environment, but it requires high computational complexity and sample support. In this paper, a novel reduced-dimension method for E-F$A clutter suppression is proposed. Firstly, we extract characteristics of clutter by tensor Tucker decomposition, which can preserve the structural characteristics of the clutter in the spatial and Doppler domains. Then, we select eigenvectors to construct the RD matrix based on principle components (PC) analysis. Finally, RD data is obtained by multiplying the RD matrix with the original data, and the weight vector for clutter suppression can be calculated. The experimental results based on real measured data validate the effectiveness of the proposed method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiwei Yang, Yufeng Xu, Jingya Li, Yongfei Mao, and Hui Kuang "A novel reduced-dimension clutter suppression method based on tensor decomposition", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297008 (21 December 2023); https://doi.org/10.1117/12.3012095

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