A Factor Analysis Framework for Power Spectra Separation and Multiple Emitter Localization
Publication in refereed journal

香港中文大學研究人員

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摘要Spectrum sensing for cognitive radio has focused on detection and estimation of aggregate spectra, without regard for latent component identification. Unraveling the constituent power spectra and the locations of ambient transmitters can be viewed as the next step towards situational awareness, which can facilitate efficient opportunistic transmission and interference avoidance. This paper focuses on power spectra separation and multiple emitter localization using a network of multi-antenna receivers. A PARAllel FACtor analysis (PARAFAC)-based framework is proposed, which offers an array of attractive features, including identifiability guarantees, ability to work with asynchronous receivers, and low communication overhead. Dealing with corrupt receiver reports due to shadowing or jamming can be a practically important concern in this context, and addressing it requires new theory and algorithms. A robust PARAFAC formulation and a corresponding factorization algorithm are proposed for this purpose, and identifiability of the latent factors is theoretically established for this more challenging setup. In addition to pertinent simulations, real experiments with a software radio prototype are used to demonstrate the effectiveness of the proposed approach.
著者Fu X, Sidiropoulos ND, Tranter JH, Ma WK
期刊名稱IEEE Transactions on Signal Processing
出版年份2015
月份12
日期15
卷號63
期次24
出版社IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
頁次6581 - 6594
國際標準期刊號1053-587X
電子國際標準期刊號1941-0476
語言英式英語
關鍵詞cognitive radio; emitter localization; nonnegativity; robust estimation; spectra separation; Spectrum estimation; tensor factorization
Web of Science 學科類別Engineering; Engineering, Electrical & Electronic

上次更新時間 2021-20-01 於 01:35