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Detection of Jointly Sparse Signals via Locally Most Powerful Tests with Gaussian Noise

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Study on Signal Detection and Recovery Methods with Joint Sparsity

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

In recent years, compressive sensing (CS) has emerged as a new paradigm for sparse signal processing, which aims at obtaining valuable information of sparse signals from a small number of measurements.

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Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Xueqian Wang

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  1. Xueqian Wang
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Wang, X. (2024). Detection of Jointly Sparse Signals via Locally Most Powerful Tests with Gaussian Noise. In: Study on Signal Detection and Recovery Methods with Joint Sparsity. Springer Theses. Springer, Singapore. https://doi.org/10.1007/978-981-99-4117-9_2

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  • DOI: https://doi.org/10.1007/978-981-99-4117-9_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4116-2

  • Online ISBN: 978-981-99-4117-9

  • eBook Packages: EngineeringEngineering (R0)

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