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Four-dimensional SAR imaging algorithm using Bayesian compressive sensing

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  • X.-Z. Ren
  • L.-N. Chen

Abstract

The compressive sensing (CS) based 4-D synthetic aperture radar (SAR) imaging method performs well in the case of high signal-to-noise ratios (SNR). However, in the presence of strong noises, the performance of CS-based method degrades and the number of false targets increases rapidly. In this paper, a novel 4-D SAR imaging method is proposed based on Bayesian compressive sensing (BCS). Assume that the target scattering field follows the Cauchy distribution, the 4-D SAR imaging is transformed into signal reconstruction via maximum a posteriori estimation. In addition, Poisson disk sampling is utilized to generate the radar positions of 4-D SAR in the baseline-time plane. Experimental results show that the proposed method is capable of effective suppression of the noise by exploiting the sparseness prior distribution of the image scene, and a well-focused image could also be achieved even under the condition of low SNR.

Suggested Citation

  • X.-Z. Ren & L.-N. Chen, 2014. "Four-dimensional SAR imaging algorithm using Bayesian compressive sensing," Journal of Electromagnetic Waves and Applications, Taylor & Francis Journals, vol. 28(13), pages 1661-1676, September.
    • Handle: RePEc:taf:tewaxx:v:28:y:2014:i:13:p:1661-1676
    DOI: 10.1080/09205071.2014.938174
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