Rate-Distortion Analysis of Discrete-HMM Pose Estimation via Multiaspect Scattering Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two dictionaries matching pursuit for sparse decomposition of signals
Signal Processing - Special section: Distributed source coding
Classification of acoustic emissions using modified matching pursuit
EURASIP Journal on Applied Signal Processing
Matched representations and filters for guided waves
IEEE Transactions on Signal Processing
IEEE Transactions on Fuzzy Systems
Underwater broadband source localization based on modal filtering and features extraction
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
Adaptive wavelet transform method to identify cracks in gears
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
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The method of matching pursuits utilizes a nonlinear iterative procedure to project a given waveform onto a particular dictionary. For scattering problems, the most appropriate dictionary is composed of wave objects that are consistent with the underlying wave phenomenology. A signal scattered from most targets of interest can be decomposed in terms of wavefronts, resonances, and chirps-and each of these subclasses can be further subdivided based on characteristic wave physics. Here, we investigate the efficacy of applying the method of matching pursuits with a wave-based dictionary for the processing of scattering data. The performance of this algorithm is examined for scattering data with and without additive noise