基于深度学习的捷变相参雷达1-Bit块稀疏重构

doi:10.12305/j.issn.1001-506X.2022.01.10

Abstract

Abstract:

In recent years, the theory of quantized compressed sensing has received extensive attention and development in the problem of radar target parameter estimation. Its main idea is to quantify the sampled radar echo and model it as an underdetermined equation, then target signal recovery can be solved via quantized compressed sensing algorithms. As sampling data is quantized, the bit width is greatly reduced thus simplifying the system and improving efficiency. This paper formulates the parameter estimation problem for frequency agile coherent radars as an underestimated problem, and proposes a 1-Bit block-sparse reconstruction network based on deep learning, namely B-BAdaLISTA. Compared with the traditional binary iterative hard thresholding algorithm, this reconstruction network has similar reconstruction performance and faster recovery speed. At the same time, the block-sparse structure is integrated into the network structure, which greatly improves the quality of the recovery of target parameters. The simulation experiments verify the recovery performance of the proposed B-BAdaLISTA network under both noiseless and noisy cases.

Key words: frequency agile coherent radar, block sparse, 1-Bit quantization, deep learning

CLC Number:

TP957.51

Rong FU, Tianyao HUANG, Yimin LIU. DNN based 1-bit block sparse recovery in frequency agile coherent radar[J]. Systems Engineering and Electronics, 2022, 44(1): 70-75.

Figures/Tables 3

References 35

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DNN based 1-bit block sparse recovery in frequency agile coherent radar

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