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Implementation of sparse recovery method with high‐resolution time‐frequency energy distributions for helicopter
Author(s) -
Wang Yanqing,
Yang Shuhui,
Yin Hongcheng,
Huo Chaoying
Publication year - 2020
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2741
Subject(s) - time–frequency analysis , algorithm , energy (signal processing) , interference (communication) , ambiguity function , computer science , frequency modulation , signal (programming language) , sampling (signal processing) , fast fourier transform , fourier transform , modulation (music) , matrix (chemical analysis) , resolution (logic) , wigner distribution function , mathematics , artificial intelligence , acoustics , telecommunications , computer vision , filter (signal processing) , physics , statistics , radar , bandwidth (computing) , materials science , channel (broadcasting) , mathematical analysis , composite material , quantum , quantum mechanics , programming language , waveform
In this paper, an algorithm named the sparse Wigner‐Ville distribution (WVD) is applied for the time‐frequency analysis of signals. In this algorithm, the sampling for ambiguity function (AF) is regarded as a sparsity measurement of the WVD, and the Fourier transform matrix is treated as a sparsity redundant dictionary. The effectiveness of the algorithm is verified by using the liner frequency modulation and sinusoidal frequency modulation signals. Then the algorithm is employed to deal with the time‐frequency analysis of helicopters. The experiment results show that the time‐frequency images of helicopters obtained by utilizing this algorithm exhibit higher resolution without cross‐interference terms compared with that by WVD.