Abstract
There is always a compromise between unambiguous wide-swath imaging and high cross-range resolution owing to the constraint of minimum antenna area for conventional single-channel spaceborne synthetic aperture radar (SAR) imaging. To overcome the inherent systemic limitation, multi-channel SAR imaging has been developed. Nevertheless, this still suffers from various problems such as high system complexity. To simplify the system structure, a novel algorithm for high resolution multi-ship ScanSAR imaging based on sparse representation is proposed in this paper, where the SAR imaging model is established via maximum a posterior estimation by utilizing the sparsity prior of multi-ship targets. In the scheme, a wide swath is generated in the ScanSAR mode by continuously switching the radar footprint between subswaths. Meanwhile, high cross-range resolution is realized from sparse subapertures by exploiting the sparsity feature of multi-ship imaging. In particular, the SAR observation operator is constructed approximately as the inverse of conventional SAR imaging and then high resolution SAR imaging including range cell migration compensation is achieved by solving the optimization. Compared with multi-channel SAR imaging, the system complexity is effectively reduced in the ScanSAR mode. In addition, enhancement of the cross-range resolution is realized by incorporating the sparsity prior with sparse subapertures. As a result, the amount of data is effectively reduced. Experiments based on measured data have been carried out to confirm the effectiveness and validity of the proposed algorithm.
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Freeman A, Johnson W T K, Huneycutt B, et a1. The myth of the minimum SAR antenna area constraint. IEEE Trans Geosci Rem Sens, 2000, 38: 320–324
Li Z F, Wang H X, Su T, et a1. Generation of Wide-Swath and High-Resolution SAR Images From Multichannel Small Spaceborne SAR Systems. IEEE Geosci Rem Sens Lett, 2005, 2: 82–86
Gerhard K, Nicolas G, Alberto M. Multidimensional waveform encoding: A new digital beamforming technique for synthetic aperture radar remote sensing. IEEE Trans Geosci Rem Sens, 2008, 46: 31–46
Nicolas G, Gerhard K, Alberto M. Multichannel azimuth processing in ScanSAR and TOPS mode operation. IEEE Trans Geosci Rem Sens, 2010, 48: 2994–3008
Candès E, Romberg J, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory, 2006, 52: 489–509
Kim Y Y, Nadar M, Bilgin A. Compressed sensing using a Gaussian scale mixtures model in wavelet domain. In: 17th IEEE International Conference on Image Processing (ICIP), 2010, 26. 3365–3368
Cetin M, Kard W C. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Trans Image Process, 2001, 10: 623–631
Zhang L, Xing M D, Qiu C W, et al. Achieving higher resolution ISAR imaging with limited pluses via compressed sampling. IEEE Trans Geosci Rem Sens Lett, 2009, 6: 567–571
Zhang L, Qiao Z J, Xing M D, et al. High Resolution ISAR imaging with sparse stepped-frequency waveforms. IEEE Trans Geosci Remote Sens, in press
Liao M S, Wang C C, Wang Y, et al. Using SAR images to detect ships from sea clutter. IEEE Geosci Rem Sens Lett, 2008, 5: 194–198
Montgomery D R, Pichel W, Calon C P. The use of satellite-based SAR in support of fisheries enforcement applications. In: IEEE International Geoscience and Remote Sensing Symposium Proceedings (IGARSS), 1998, vol. 3. 1388–1390
Conte E, De Maio A. Exploiting persymmetry for CFAR detection in compound-Gaussian clutter. IEEE Trans on Aerosp Electron Syst, 2003, 39: 719–724
Gini F, Greco M V, Verrazzani L. Detection problem in mixed clutter environment as a Gaussian problem by adaptive pre-processing. Electron Lett, 1995, 31: 1189–1190
Samadi S, Cetin M, Masnadi-Shirazi M A. Sparse representation-based synthetic aperture radar imaging. IET Radar Sonar Navig, 2011, 5: 182–193
Deledalle C A, Denis L, Tupin F. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans Image Process, 2009, 18: 2661–2672
Wang H X, Liang Y, Xing M D, et a1. ISAR imaging via sparse frequency-stepped chirp signal. Sci China Inf Sci, 2012, 55: 877–888
Ji S H, Xue Y, Carin L. Bayesian compressive sensing. IEEE Trans Signal Process, 2008, 56: 2346–2356
Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Rev, 2001, 43: 129–159
Zhu C W. Stable recovery of sparse signals via regularized minimization. IEEE Trans Inform Theory, 2008, 54: 3364–3367
Liao M S, Wang C C, Wang Y, et al. Mitigation techniques for non-Gaussian sea clutter. IEEE Trans Geosci Rem Sens, 2008, 5: 194–198
Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J Sel Top Signal Process, 2007, 1: 586–597
Deng B, Li X, Wang H Q, et al. Fast raw-signal simulation of extended scenes for missile-borne SAR with constant acceleration. IEEE Geosci Rem Sens Lett, 2011, 8: 44–48
Franceschetti G, Guida R, Iodice A, et al. Efficient simulation of hybrid stripmap/spotlight SAR raw signals from extended scenes. IEEE Trans Geosci Rem Sens, 2004, 42: 2385–2396
Prentiss N R. Depth of Field for SAR with Aircraft Acceleration. IEEE Trans Aerosp Electron Syst, 1984, 20: 603–616; 2008, 5: 194–198
Alberto M, Josef M, Rolf S. Extended chirp scaling algorithm for air- and spaceborne SAR data processing in stripmap and ScanSAR imaging modes. IEEE Trans Geosci Rem Sens, 1996, 34: 1123–1136
Zhang L, Xing M D, Qiu C W, et al. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing. IEEE Trans Geosci Rem Sens, 2010, 48: 3824–3838
Moler C, Little J, Bangert S, et al. Pro-Matlab User’s Guide. Sherborn: The Math Works Inc., 1987
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Xu, G., Sheng, J., Zhang, L. et al. Performance improvement in multi-ship imaging for ScanSAR based on sparse representation. Sci. China Inf. Sci. 55, 1860–1875 (2012). https://doi.org/10.1007/s11432-012-4626-3
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DOI: https://doi.org/10.1007/s11432-012-4626-3