Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation With Missing Observations - 2018


During this Project, we tend to address the matter of spectrum estimation of multiple frequency-hopping (FH) signals within the presence of random missing observations. The signals are analyzed among the bilinear time-frequency (TF) illustration framework, where a TF kernel is intended by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts because of missing observations whereas preserving the FH signal autoterms. The kerneled results are represented in the instantaneous autocorrelation operate domain, that are then processed using a redesigned structure-aware Bayesian compressive sensing algorithm to accurately estimate the FH signal TF spectrum. The proposed methodology achieves high-resolution FH signal spectrum estimation even when a massive portion of data observations is missing. Simulation results verify the effectiveness of the proposed technique and its superiority over existing techniques.

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