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Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping Spectrum Estimation with Missing Observations
Liu, S Zhang, YD Shan, T Tao, R
Liu, S
Zhang, YD
Shan, T
Tao, R
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Pre-print
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2018-04-15
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DOI
10.1109/TSP.2018.2806351
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.
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Institute of Electrical and Electronics Engineers (IEEE)
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IEEE Transactions on Signal Processing
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