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 |
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页(从-至) | 2153-2166 |
页数 | 14 |
期刊 | IEEE Transactions on Signal Processing |
卷 | 66 |
期 | 8 |
DOI | |
出版状态 | 已出版 - 15 4月 2018 |