Open AccessJoint Fr FT ‐ FFT basis compressed sensing and adaptive iterative optimization for countering suppressive jamming
Author(s) -
Zhao Yang,
Shang Chaoxuan,
Han Zhuangzhi,
Yin Yuanwei,
Han Ning,
Xie Hui
Publication year - 2019
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2018-0357
Subject(s) - jamming , compressed sensing , computer science , joint (building) , algorithm , noise (video) , signal (programming language) , radar , fast fourier transform , basis (linear algebra) , artificial intelligence , engineering , mathematics , telecommunications , architectural engineering , physics , geometry , image (mathematics) , thermodynamics , programming language
Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation ( LFM ) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.