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Proceedings Paper

Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation
Author(s): Shengheng Liu; Yimin D. Zhang; Tao Shan; Si Qin; Moeness G. Amin
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Paper Abstract

Frequency-hopping (FH) is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems due to its capability of low probability of intercept, reduced interference, and desirable ambiguity property. In this paper, we consider the blind estimation of the instantaneous FH spectrum without the knowledge of hopping patterns. The FH signals are analyzed in the joint time-frequency domain, where FH signals manifest themselves as sparse entries, thus inviting compressive sensing and sparse reconstruction techniques for FH spectrum estimation. In particular, the signals' piecewise-constant frequency characteristics are exploited in the reconstruction of sparse quadratic time-frequency representations. The Bayesian compressive sensing methods are applied to provide high-resolution frequency estimation. The FH spectrum characteristics are used in the design of signal-dependent kernel within the framework of structure-aware sparse reconstruction.

Paper Details

Date Published: 4 May 2016
PDF: 9 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570N (4 May 2016); doi: 10.1117/12.2228339
Show Author Affiliations
Shengheng Liu, Beijing Institute of Technology (China)
Temple Univ. (United States)
Yimin D. Zhang, Temple Univ. (United States)
Tao Shan, Beijing Institute of Technology (China)
Si Qin, Villanova Univ. (United States)
Moeness G. Amin, Villanova Univ. (United States)


Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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