Ambiguity-Free and Efficient Sparse Phase Retrieval from Affine Measurements under Outlier Corruption

Ming Hsun Yang, Y. W.Peter Hong, Jwo Yuh Wu

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

Conventional sparse phase retrieval schemes can recover sparse signals from the magnitude of linear measurements but only up to a global phase ambiguity. This work proposes a novel approach to achieve ambiguity-free signal reconstruction using the magnitude of affine measurements, where an additional bias term is used as reference for phase recovery. The proposed scheme consists of two stages, i.e., a support identification stage followed by a signal recovery stage in which the nonzero signal entries are resolved. In the noise-free case, perfect support identification is guaranteed using a simple counting rule subject to a mild condition on the signal sparsity, and the exact recovery of the nonzero signal entries can be obtained in closed-form. The proposed scheme is then extended to the sparse noise (or outliers) scenario. Perfect support identification is still ensured in this case under mild conditions on the support size of the sparse outliers. With perfect support estimation, exact signal recovery from noisy measurements can be achieved using a simple majority rule. Computer simulations using both synthetic and real-world data sets are provided to demonstrate the effectiveness of the proposed scheme.

原文???core.languages.en_GB???
主出版物標題2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021
發行者IEEE Computer Society
ISBN(電子)9781728163383
DOIs
出版狀態已出版 - 2021
事件31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021 - Gold Coast, Australia
持續時間: 25 10月 202128 10月 2021

出版系列

名字IEEE International Workshop on Machine Learning for Signal Processing, MLSP
2021-October
ISSN(列印)2161-0363
ISSN(電子)2161-0371

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???event.eventtypes.event.conference???31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021
國家/地區Australia
城市Gold Coast
期間25/10/2128/10/21

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