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Robust Frequency Estimation of Multi-sinusoidal Signals Using Orthogonal Matching Pursuit with Weak Derivatives Criterion

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成果类型:
期刊论文
作者:
Fu, Lihua;Zhang, Meng;Liu, Zhihui;Li, Hongwei*
通讯作者:
Li, Hongwei
作者机构:
[Liu, Zhihui; Fu, Lihua; Li, Hongwei] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
[Zhang, Meng] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
[Li, Hongwei] China Univ Geosci, Hubei Subsurface Multiscale Imaging Key Lab, Wuhan 430074, Peoples R China.
通讯机构:
[Li, Hongwei] C
China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
China Univ Geosci, Hubei Subsurface Multiscale Imaging Key Lab, Wuhan 430074, Peoples R China.
语种:
英文
关键词:
Orthogonal matching pursuit;Multi-sinusoidal signals;Frequency estimation;Multi-grid dictionary learning
期刊:
Circuits, Systems, and Signal Processing
ISSN:
0278-081X
年:
2019
卷:
38
期:
3
页码:
1194-1205
基金类别:
open research project of The Hubei Key Laboratory of Intelligent Geo-Information Processing [KLIGIP2016A01, KLIGIP2016A02]; CCNU from the colleges' basic research and operation of MOE [230-20205160288, CCNU15A05022]
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
In this paper, the weak derivatives (WD) criterion is introduced to solve the frequency estimation problem of multi-sinusoidal signals corrupted by noises. The problem is therefore modeled as a new least squares optimization task combined with WD. To overcome the potential basis mismatch effect caused by discretization of the frequency parameters, a modified orthogonal matching pursuit algorithm is proposed to solve the optimization problem by coupling it with a novel multi-grid dictionary training strategy. The proposed algorithm is validated on a set of simulated datasets with white noise an...

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