COMPRESSED SYNTHETIC APERTURE RADAR IMAGING BASED ON MAXWELL EQUATION
DOI:
https://doi.org/10.11113/jt.v78.8922Keywords:
Born approximation, compressive sensing, fast time and slow time sampling, Maxwell equation, Synthetic aperture radaAbstract
Within few years backward, researches had presented the ability of compressive sensing to handle the large data problem on high resolution synthetic aperture radar (SAR) imaging. The main issue on CS framework that should be dealt with the SAR imaging is on the requirement of linearization on the measurement system. This paper proposes a new approach on formulating the compressed SAR echo imaging system which is derived from the Maxwell’s equations with continuous signal along the SAR antenna movement. Born approximation is applied to approximate the linear form of the SAR echo imaging system. In addition, the compressed sampling is formed by reducing the sampling rate of received radar signals randomly simultaneously on both of low sampling of fast time and slow time signals and by reducing the pulse period interval of transmitted signals. The simulation’s result shows that a better focused reconstructed sparse target can be achieved compared with the conventional match filter based Range Doppler (RD) method.
References
Cumming G. and Wong F. H. 2005. Digital Processing of Synthetic Aperture Radar Data. Norwood, MA: Artech House.
Curlander J. C.and McDonough R. N. 1991. Synthetic Aperture Radar: Systems and Signal Processing. New York, John Wiley & Sons.
Cheney M. and Borden B. 2009. Fundamentals of Radar Imaging. Society for Industrial and Applied Mathematics.
Cheney M. and Borden B. 2009. Problems in synthetic-aperture radar imaging. Inverse Problems. 25(12): 123005.
Candes E. J.and Tao T. 2006. Near-optimal signal recovery from random projections: Universal encoding strategies?. IEEE Transactions on Information Theory. 52(12): 5406–5425.
Donoho D. L. 2006. Compressed Sensing. IEEE Transactions on Information Theory. 52(4): 1289–1306.
Herman M. A.and Strohmer T. 2009. High-Resolution Radar via Compressed Sensing, IEEE Trans. Signal Process. 57(6): 2275–2284.
Wei S.-J., Zhang X.-L., Shi, J. and Xiang G. 2010. Sparse Reconstruction for SAR Imaging Based on Compressed Sensing. Progress In Electromagnetics Research. 109: 63–81.
Sun B., Cao Y., Chen J., Li C., and Qiao Z. 2014. Compressive Sensing Imaging For General Synthetic Aperture Radar Echo Model Based on Maxwell’s equations, EURASIP Journal on Advances in Signal Processing. 2014(1): 1-6.
Victoria Stodden David Donoho Y. T. 1999. SparseLab, http://sparselab.stanford.edu/. Stanford University, 2010.
G. Franceschetti and R. Lanari, Synthetic Aperture Radar Processing. Taylor & Francis.
Friedlander F. G. and Joshi M. S. 1998. Introduction to the Theory of Distributions. Cambridge University Press.
Treves F. 1975. Basic Linear Partial Differntial Equations
Gonzalez R. C. and Woods R. E. 2006. Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc.
Martinez A. and Marchand J. 1993. SAR Image Quality Assessment, Revista de teledeteccion: Revista de la Asociacion Espanola de Teledeteccion. 2(2): 1-5.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.