Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling

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Resumé

This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.
OriginalsprogEngelsk
TidsskriftEuropean Signal Processing Conference (EUSIPCO)
Vol/bind21
Antal sider5
ISSN2076-1465
StatusUdgivet - 2013
BegivenhedEuropean Signal Processing Conference EUSIPCO 2013 - Marrakech, Moroco
Varighed: 9 sep. 201313 sep. 2013

Konference

KonferenceEuropean Signal Processing Conference EUSIPCO 2013
ByMarrakech, Moroco
Periode09/09/201313/09/2013

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Compressed sensing
Computational complexity
Sampling
Signal reconstruction
Glossaries
Degradation

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    title = "Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling",
    abstract = "This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.",
    keywords = "compressed sensing, multi-coset, sampling, nonuniform sampling, reconstruction algorithm",
    author = "Ruben Grigoryan and Jensen, {Tobias Lindstr{\o}m} and Thomas Arildsen and Torben Larsen",
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    Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling. / Grigoryan, Ruben; Jensen, Tobias Lindstrøm; Arildsen, Thomas; Larsen, Torben.

    I: European Signal Processing Conference (EUSIPCO), Bind 21, 2013.

    Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

    TY - GEN

    T1 - Reducing the Computational Complexity of Reconstruction in Compressed Sensing Nonuniform Sampling

    AU - Grigoryan, Ruben

    AU - Jensen, Tobias Lindstrøm

    AU - Arildsen, Thomas

    AU - Larsen, Torben

    PY - 2013

    Y1 - 2013

    N2 - This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.

    AB - This paper proposes a method that reduces the computational complexity of signal reconstruction in single-channel nonuniform sampling while acquiring frequency sparse multi-band signals. Generally, this compressed sensing based signal acquisition allows a decrease in the sampling rate of frequency sparse signals, but requires computationally expensive reconstruction algorithms. This can be an obstacle for real-time applications. The reduction of complexity is achieved by applying a multi-coset sampling procedure. This proposed method reduces the size of the dictionary matrix, the size of the measurement matrix and the number of iterations of the reconstruction algorithm in comparison to the direct single-channel approach. We consider an orthogonal matching pursuit reconstruction algorithm for single-channel sampling and its modification for multi-coset sampling. Theoretical as well as numerical analyses demonstrate order of magnitude reduction in execution time for typical problem sizes without degradation of the signal reconstruction quality.

    KW - compressed sensing

    KW - multi-coset

    KW - sampling

    KW - nonuniform sampling

    KW - reconstruction algorithm

    M3 - Conference article in Journal

    VL - 21

    JO - Proceedings of the European Signal Processing Conference

    JF - Proceedings of the European Signal Processing Conference

    SN - 2076-1465

    ER -