Matching pursuit based on nonparametric waveform estimation

Abstract

Matching pursuit (MP) extracts signal feature components by decomposing the observed signal into a linear expansion of waveforms that are selected from a redundant dictionary of basis functions. It is difficult to predefine a parametric basis function that contains all prior information about the observed signal in practice, which restricted the application of MP. Focusing on this defect of MP, we present a modified algorithm of MP which decomposes the observed signal into a series combinations of waveforms, these waveforms are calculated by the nonparametric waveform estimation (NWE) method and used to best match the signal local structures. As a result, it is not needed to predefine the parametric basis function. With the NWE method, the adaptive adjustment of template signals makes it unnecessary for the method to require priori information, so that in practical applications it has better flexibility and adaptability. The extraction results of simulation signals are compared with the traditional MP decomposition, thus verifying the function of the proposed method. The extraction of testing signals on the rotor rig has again verified the feasibility and effectiveness of the method.