Coherent radar imaging based on compressed sensing

Qian Zhu, Ryan Volz, John David Mathews

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    High-resolution radar images in the horizontal spatial domain generally require a large number of different baselines that usually come with considerable cost. In this paper, aspects of compressed sensing (CS) are introduced to coherent radar imaging. We propose a single CS-based formalism that enables the full three-dimensional (3-D) - range, Doppler frequency, and horizontal spatial (represented by the direction cosines) domain - imaging. This new method can not only reduce the system costs and decrease the needed number of baselines by enabling spatial sparse sampling but also achieve high resolution in the range, Doppler frequency, and horizontal space dimensions. Using an assumption of point targets, a 3-D radar signal model for imaging has been derived. By comparing numerical simulations with the fast Fourier transform and maximum entropy methods at different signal-to-noise ratios, we demonstrate that the CS method can provide better performance in resolution and detectability given comparatively few available measurements relative to the number required by Nyquist-Shannon sampling criterion. These techniques are being applied to radar meteor observations. Key Points Discrete linear radar signal model for holography is proposed Aspects of compressed sensing are introduced to holography Outstanding performance of compressed sensing is proved.

    Original languageEnglish (US)
    Pages (from-to)1271-1285
    Number of pages15
    JournalRadio Science
    Volume50
    Issue number12
    DOIs
    StatePublished - Dec 1 2015

    Fingerprint

    coherent radar
    Compressed sensing
    Radar imaging
    radar
    Radar
    holography
    Holography
    sampling
    Sampling
    Maximum entropy methods
    costs
    Imaging techniques
    maximum entropy method
    high resolution
    meteoroids
    meteor
    cost
    Fast Fourier transforms
    signal-to-noise ratio
    Fourier transform

    All Science Journal Classification (ASJC) codes

    • Condensed Matter Physics
    • Earth and Planetary Sciences(all)
    • Electrical and Electronic Engineering

    Cite this

    Zhu, Q., Volz, R., & Mathews, J. D. (2015). Coherent radar imaging based on compressed sensing. Radio Science, 50(12), 1271-1285. https://doi.org/10.1002/2015RS005688
    Zhu, Qian ; Volz, Ryan ; Mathews, John David. / Coherent radar imaging based on compressed sensing. In: Radio Science. 2015 ; Vol. 50, No. 12. pp. 1271-1285.
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    Zhu, Q, Volz, R & Mathews, JD 2015, 'Coherent radar imaging based on compressed sensing', Radio Science, vol. 50, no. 12, pp. 1271-1285. https://doi.org/10.1002/2015RS005688

    Coherent radar imaging based on compressed sensing. / Zhu, Qian; Volz, Ryan; Mathews, John David.

    In: Radio Science, Vol. 50, No. 12, 01.12.2015, p. 1271-1285.

    Research output: Contribution to journalArticle

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