Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation with Missing Observations
Bayesian compressive sensing
Permanent link to this recordhttp://hdl.handle.net/20.500.12613/4682
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Abstract© 1991-2012 IEEE. In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time-frequency (TF) representation framework, where a TF kernel is designed by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts due to missing observations while preserving the FH signal autoterms. The kerneled results are represented in the instantaneous autocorrelation function domain, which are then processed using a redesigned structure-aware Bayesian compressive sensing algorithm to accurately estimate the FH signal TF spectrum. The proposed method achieves high-resolution FH signal spectrum estimation even when a large portion of data observations is missing. Simulation results verify the effectiveness of the proposed method and its superiority over existing techniques.
Citation to related workInstitute of Electrical and Electronics Engineers (IEEE)
Has partIEEE Transactions on Signal Processing
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