The application of data-driven TF analysis methods in LFM signal parameter estimation

Author

Chen Hao ; Guo Jun-hai

Author_Institution

Beijing Inst. of Tracking & Telecommun. Technol., Beijing, China

fYear

2013

fDate

22-25 Oct. 2013

Firstpage

1

Lastpage

4

Abstract

Based on Empirical wavelet transform (EWT) and sparse time-frequency analysis method, two new LFM parameter estimation methods are proposed. EWT method builds adaptive wavelets and decomposes the LFM signal into different modes, which can be processed through energy-oriented principal component extraction (EPCE) method to estimate the parameters of LFM signal. The second method tries to find the sparsest representation of multi-scale data within dictionary consisting of AM-FM intrinsic mode functions (IMF) through solving nonlinear L1 optimization problem. Comparisons are made with EEMD based EPCE method to show the usefulness of these two methods.

Keywords

parameter estimation; principal component analysis; signal processing; wavelet transforms; AM-FM intrinsic mode functions; EPCE method; EWT; IMF; LFM signal parameter estimation; adaptive wavelets; data driven TF analysis method application; empirical wavelet transform; energy oriented principal component extraction; time frequency analysis method; Estimation; Frequency modulation; Noise measurement; Signal to noise ratio; Time-frequency analysis; Wavelet transforms; LFM; empirical mode decomposition; empirical wavelet transform; sparse representation of signal; srincipal component extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2013 - 2013 IEEE Region 10 Conference (31194)

Conference_Location

Xi´an

ISSN

2159-3442

Print_ISBN

978-1-4799-2825-5

Type

conf

DOI

10.1109/TENCON.2013.6718885

Filename

6718885