Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation

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.