Instantaneous frequency and time-frequency signature estimation using compressive sensing

This paper considers compressive sensing for time-frequency signal representation (TFSR) of nonstationary radar signals which can be considered as instantaneously narrowband. Under-sampling and random sampling of the signal stem from avoiding aliasing and relaxing Nyquist sampling constraints. Unlik...

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Authors: Moeness Amin, Srdjan Stankovic, Branka Jokanovic
Conference Name: Radar Sensor Technology XVII
Conference Location: Baltimore, MD
Conference Dates: April 29, 2013 - May 1, 2013
Proceedings Title: Proceedings of the SPIE - The International Society for Optical Engineering
Format: Conference Proceeding
Language: English
Published: 2013
Subjects:
Summary: This paper considers compressive sensing for time-frequency signal representation (TFSR) of nonstationary radar signals which can be considered as instantaneously narrowband. Under-sampling and random sampling of the signal stem from avoiding aliasing and relaxing Nyquist sampling constraints. Unlike previous work on compressive sensing (CS) and TFSR based on the ambiguity function, reduced observations in the underlying problem are time-domain data. In the reconstruction process, Orthogonal Matching Pursuit (OMP) is used. Since the frequency index in the first iteration of OMP is the same as the one obtained by finding the frequency position of the highest Spectrogram peak, it becomes necessary to consider several OMP iterations to improve over Spectrograms performance. We examine various methods for estimating IF from higher number of OMP iterations, including the S-method. The paper also applies CS for signal time-frequency signature estimations corresponding to human gait radar returns.
ISBN: 9780819495051
DOI: 10.1117/12.2016636