Tensor structure-based ground clutter suppression approaches for pulse doppler radar

ABSTRACT

For pulse doppler (PD) radar, two tensor structure-based methods are proposed to improve the clutter suppression performance. One is tensor singular value decomposition (TSVD) and the other is tensor robust principal component analysis (TRPCA). Two algorithms both stack the range-Doppler matrices of multiple coherent processing intervals (CPI) into a tensor structure, which helps make the most of the temporal information across CPIs. TSVD utilizes the strong temporal correlation of ground clutter data to estimate the clutter subspace. Meanwhile, TRPCA exactly separates both low-rank clutter component and sparse target components from the time series range-Doppler tensor structure. Experimental results indicate that compared with conventional matrix-based schemes such as singular value decomposition (SVD) or singular value decomposition (RPCA), the proposed methods have significant advantages in keeping the target echo while suppressing the clutter. In addition, the computational complexity of each algorithm is analysed in detail.

KEYWORDS:

• Pulse doppler radar
• ground clutter suppression
• tensor singular value decomposition
• tensor robust principal component analysis
• target detection

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China (62001229), Natural Science Foundation of Jiangsu Province (BK20230915), and China Postdoctoral Science Foundation (2020M681604).