期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 66, 期 8, 页码 2153-2166出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2018.2806351
关键词
Frequency hopping; spectrum estimation; missing observations; Bayesian compressive sensing; time-frequency distribution; kernel design
资金
- National Natural Science Foundation of China [61671060, 61421001, 61331021]
- Natural Science Foundation of Beijing Municipality [4172052]
- National Science Foundation [AST-1547420]
- China Scholarship Council
- Direct For Mathematical & Physical Scien
- Division Of Astronomical Sciences [1547420] Funding Source: National Science Foundation
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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