Title :
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
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;
Conference_Titel :
TENCON 2013 - 2013 IEEE Region 10 Conference (31194)
Conference_Location :
Xi´an
Print_ISBN :
978-1-4799-2825-5
DOI :
10.1109/TENCON.2013.6718885
