2020Electronics LettersOpen access

ℓ 1–2 minimisation for compressed sensing with partially known signal support

Jing Zhang, Shuguang Zhang, Xuran Meng

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Abstract

In this study, the authors mainly discuss the robust signal recovery by minimisation with incorporating prior support information, which is not considered in previous works. A robust recovery condition is established and an recovery error estimation is obtained, in particular, the obtained results generalise the state‐of‐the‐art ones. In addition, by proposing a modified algorithm, the numerical experiments show that incorporating prior support information for minimisation exhibits better recovery performance than standard minimisation.

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What this paper is about

In this study, the authors mainly discuss the robust signal recovery by minimisation with incorporating prior support information, which is not considered in previous works. A robust recovery condition is established and an recovery error estimation is obtained, in particular, the obtained results generalise the state‐of‐the‐art ones. In addition, by proposing a modified algorithm, the numerical experiments show that incorporating prior support information for minimisation exhibits better recovery performance than standard minimisation.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this study, the authors mainly discuss the robust signal recovery by minimisation with incorporating prior support information, which is not considered in previous works. A robust recovery condition is established and an recovery error estimation is obtained, in particular, the obtained results generalise the state‐of‐the‐art ones. In addition, by proposing a modified algorithm, the numerical experiments show that incorporating prior support information for minimisation exhibits better recovery performance than standard minimisation.

Key concepts: Minimisation (clinical trials), Compressed sensing, Signal recovery, Computer science, Electronic engineering, Materials science, Mathematics, Algorithm

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