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

Shengheng Liu, Yimin D. Zhang, Tao Shan*, Ran Tao

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

103 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)2153-2166
页数14
期刊IEEE Transactions on Signal Processing
66
8
DOI
出版状态已出版 - 15 4月 2018

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