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Tytuł artykułu

Nonuniform Spatial Sampling in a Ground-Based Noise SAR

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Języki publikacji
EN
Abstrakty
EN
The paper presents an idea of nonuniform spatial sampling applied to a noise synthetic aperture radar. In certain cases it is desirable to limit the number of spatial (alongtrack) domain samples acquired in a SAR radar because of external constraints on sampling frequency or on the overall number of samples – e.g. in order to economy on time or power consumed. Lowering number of samples taken may, however, lead to spatial aliasing and incorrect reconstruction of the image. Nonuniform sampling allows to reduce the aliasing effect and reconstruct the image better. This technique can be applied with standard reconstruction methods, but it works best together with Compressive Sensing reconstruction algorithms. The idea will be verified with an experimental noise SAR built at ISE PW.
Twórcy
Bibliografia
  • [1] K. Kulpa, K. Lukin, W. Miceli, and T. Thayaparan, “Editorial: Signal Processing in Noise Radar Technology,” IET Radar, Sonar & Navigation, vol. 2, no. 4, pp. 229–232, 2008.
  • [2] K. Kulpa, K. Lukin, J. Misiurewicz, Z. Gajo, A. Mogila, and P. Vyplavin, “Quality Enhancement of Image Generated with Bistatic Ground Based Noise Waveform SAR,” IET Radar, Sonar & Navigation, vol. 2, no. 4, pp. 263–273, 2008.
  • [3] Ł. Maślikowski, M. Malanowski, K. S. Kulpa, and C. Contartese, “Preliminary Results of Ground-Bsed Noise SAR Experiments,” in 8th European Conference on Synthetic Aperture Radar (EuSAR). Aachen, Germany: VDE Publishing House, 2010, pp. 596–599.
  • [4] Ł. Maślikowski and K. Kulpa, “Bistatic Quasi-Passive Noise SAR Experiment,” in 11th International Radar Symposium (IRS), June 2010, pp. 1–3.
  • [5] Ł. Maślikowski and M. Malanowski, “Sub-Band Phase Calibration in Stepped Frequency GB Noise SAR,” in European Radar Conference (EuRAD), October 2010, pp. 200–203.
  • [6] E. Candes and M. Wakin, “An Introduction to Compressive Sampling,” Signal Processing Magazine, vol. 25, no. 2, pp. 21–30, March 2008.
  • [7] J. Misiurewicz, “Unambiguous Doppler Frequency Estimation in an MTI Radar,” in Proceedings of International RADAR 97 Conference, Edinburgh, Scotland, 1997, pp. 530–534.
  • [8] L. T. Younkins, “Velocity Etimation for Radar Systems with Staggered Pulse Repetition Frequency,” in Proceedings of International RADAR 97 Conference, Edinburgh, Scotland, 1997, pp. 425–428.
  • [9] C. Lemke, T. Mahr, and H.-G. Kölle, “Coherent Prameter Estimation for Radar Signals,” in Proceedings of International Radar Symposium, Cologne, Germany, 2007, pp. 145–150.
  • [10] D. Li, Y. Hou, and W. Hong, “The Sparse Array Aperture Synthesis with Space Constraint,” in 8th European Conference on Synthetic Aperture Radar (EuSAR). Aachen, Germany: VDE Publishing House, 2010, pp. 950–953.
  • [11] B. Zhang, H. Jiang, W. Hong, and Y. Wu, “Synthetic Aperture Radar Iaging of Sarse Cmpressed Sensing,” in 8th European Conference on Synthetic Aperture Radar (EuSAR). Aachen, Germany: VDE Publishing House, 2010, pp. 689–692.
  • [12] L. Prünte, “Application of Compressed Sensing to SAR/GMTI-Data,” in 8th European Conference on Synthetic Aperture Radar (EuSAR). Aachen, Germany: VDE Publishing House, 2010, pp. 465–469.
  • [13] X. Zhu and R. Bamler, “Super-Resolution for 4-D SAR Tomography via Compressive Sensing,” in 8th European Conference on Synthetic Aperture Radar (EuSAR). Aachen, Germany: VDE Publishing House, 2010, pp. 273–276.
  • [14] E. Candes and T. Tao, “Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies?” IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406–5425, December 2006.
  • [15] E. Candes, J. Romberg, and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, February 2006.
  • [16] D. Donoho, “Compressed Sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, April 2006.
  • [17] G. Peyre, “Toolbox Sarsity – Sparsity-Based Signal Pocessing Related Functions,” 12 April 2010.
  • [18] A. Majumdar, “Orthogonal Least Squares Algorithms for Sparse Signal Reconstruction,” 12 April 2010.
  • [19] W. Yin, S. Morgan, J. Yang, and Z. Yin, “Practical Compressive Sensing with Toeplitz and Circulant Matrices,” 12 April 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA0-0049-0016
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