Abstract
Scattering dependency often exists in both the spatial location and the viewing angle. Based on the assumption of isotropic point scattering model, however, conventional narrow-angle synthetic aperture radar (SAR) imaging algorithms have been no longer suitable to the scattering dependency model. To improve azimuth resolution and capture richer observation information, sparsity-driven (SD) wide-angle SAR (WSAR) imaging algorithms have been developed. Actually, existing SD-based WSAR imaging algorithms are sensitive to the regularization parameters which are required to adjust manually. These methods indeed limit their practical applications. To solve this problem, in this paper, we propose an adaptive WSAR imaging algorithm based on the Boltzmann machine (BM) model. In particular, we model the spatial sparsity and high azimuth correlation of scattering energy by virtual of a special BM prior. Then, the support of sparse representation and imaging parameters including BM parameters, noise variance and the variance of each sparse representation element are jointly estimated by a block-coordinate descent process. Finally, the proposed WSAR imaging algorithm is performed adaptively via sparse representation. Experiments are conducted by synthetic scene and simple tank dataset of high-frequency electromagnetic scattering calculation software. Extensive empirical results demonstrate that the proposed algorithm can achieve better imaging performance than the conventional algorithms in terms of relative mean squared error and support identification error.
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Ash, J. N., Potter, L. C., et al. (2014). Wide-angle synthetic aperture radar imaging: Models and algorithms for anisotropic scattering. IEEE Signal Processing Magazine, 31(4), 16–26.
Carrara, W. G., Goodman, R. S., Majewski, R. M., et al. (1995). Spotlight synthetic aperture radar: Signal processing algorithms. Boston: Artech House.
Çetin, M., Stojanovic, I., Onhon, N. O., Varshney, K. R., Samadi, S., Karl, W. C., et al. (2014). Sparsity-driven synthetic aperture radar imaging: Reconstruction, autofocusing, moving targets, and compressed sensing. IEEE Signal Processing Magazine, 31(4), 27–40.
Cevher, V., Duarte, M. F., Hedge, C., & Baraniuk, R. G. (2009). Sparse signal recovery using Markov random fields. In Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems (pp. 257–264). Vancouver, British Columbia, Canada.
Chartrand, R., & Staneva, V. (2008). Restricted isometry properties and nonconvex compressive sensing. Inverse Problems, 24(3), 1–14.
Cheng, S., Wang, W., & Shao, H. (2016). Large time-bandwidth product OFDM chirp waveform diversity using for MIMO radar. Multidimensional Systems and Signal Processing, 27(1), 145–158.
Cong, X. C., & Liu, J. B. (2014). Millimeter-wave spotlight circular synthetic aperture radar (SCSAR) imaging for Foreign Object Debris on airport runway. In IEEE international conference on signal processing (ICSP) (pp. 1968–1972).
Donoho, D., Maleki, A., & Montanari, A. (2009). Message passing algorithms for compressed sensing. Proceedings of the National Academy of Sciences, 106(45), 18914–18919.
Ertin, E., Austin, C. D., Sharma, S., Moses, R. L., & Potter, L. C. (2007). GOTCHA experience report: Three-dimensional SAR imaging with complete circular apertures. Defense and Security Symposium, International Society for Optics and Photonics, 6568, 656802-1–656802-12.
Fasoula, A., Driessen, H., & van Genderen, P. (2009). De-ghosting of tomographic images in a radar network with sparse angular sampling. In IEEE radar conference (RADAR) (pp. 286–289).
Gerry, M. J., Potter, L. C., Gupta, I. J., & Van Der Merwe, A. (1999). A parametric model for synthetic aperture radar measurements. IEEE Transactions on Antennas and Propagation, 47(7), 1179–1188.
Ge, D. B., & Wei, B. (2002). Electromagnetic wave theory. Beijing: Science Press.
Hammond, G. B., & Jackson, J. A. (2013). SAR canonical feature extraction using molecule dictionaries. In IEEE radar conference (RADAR) (pp. 1–6).
Jackson, J. A., & Moses, R. L. (2012). Synthetic aperture radar 3D feature extraction for arbitrary flight paths. IEEE Transactions on Aerospace and Electronic Systems, 48(3), 2065–2084.
Lin, Z., Zeng, Y., Bi, G., & Yeo, J. (2003). Signal processing for large bandwidth and long duration waveform SAR. Multidimensional Systems and Signal Processing, 14(1), 119–137.
Liu, H. C., Jiu, B., et al. (2014). Super-resolution ISAR imaging based on sparse Bayesian learning. IEEE Transactions on Geoscience and Remote Sensing, 52(8), 5005–5013.
Moses, R., & Ash, J. (2011). An autoregressive formulation for SAR back projection imaging. IEEE Transactions on Aerospace and Electronic Systems, 47(4), 2860–2873.
Moses, R. L., Potter, L. C., & Cetin, M. (2004). Wide-angle SAR imaging. SPIE Defense and Security Symposium, Algorithms for Synthetic Aperture Radar Imagery XI, 5427, 164–175.
Peleg, T., Eldar, Y. C., & Elad, M. (2012). Exploiting statistical dependencies in sparse representations for signal recovery. IEEE Transactions on Signal Processing, 60(5), 2286–2303.
Ponce, O., Prats-Iraola, P., Pinheiro, M., Rodriguez-Cassola, M., Scheiber, R., Reigber, A., et al. (2014). Fully polarimetric high-resolution 3-D imaging withcircular SAR at L-band. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3074–3090.
Potter, L. C., & Moses, R. L. (1997). Attributed scattering centers for SAR ATR. IEEE Transactions on Image processing, 6(2), 79–91.
Saville, M. A., Jackson, J. A., & Fuller, D. F. (2014). Rethinking vehicle classification with wide-angle polarimetric SAR. IEEE Aerospace and Electronic Systems Magazine, 29(1), 41–49.
Stojanovic, I., Cetin, M., & Karl, W. C. (2008). Joint space aspect reconstruction of wide-angle SAR exploiting sparsity. SPIE Defense and Security Symposium, International, Society for Optics and Photonics, 6970, 697005–697005.
Varshney, K. R., Cetin, M., Fisher, J. W., & Willsky, A. S. (2008). Sparse representation in structured dictionaries with application to synthetic aperture radar. IEEE Transactions on Signal Processing, 56(8), 3548–3561.
Xu, D., et al. (2015). Compressive sensing of stepped-frequency radar based on transfer learning. IEEE Transactions on Signal Processing, 63(12), 3076–3087.
Xu, G., Zhang, L., et al. (2011). Bayesian inverse synthetic aperture radar imaging. IEEE Geoscience and Remote Sensing Letters, 8(6), 1150–1154.
Zeng, Y., Lin, Z., Bi, G., Yeo, J., & Lu, S. (2005). Dilation dependent matched filtering for SAR signal processing. IEEE Transactions on Aerospace and Electronic Systems, 41(2), 729–736.
Ziniel, J., & Schniter, P. (2013). Dynamic compressive sensing of time-varying signals via approximate message passing. IEEE Transactions on Signal Processing, 61(21), 5270–5284.
Acknowledgments
The authors would like to thank the anonymous reviewers for their constructive suggestions and insightful comments. The authors would also like to thank Dr. Y. K. Wang, Dr. X. Li and Mr. D. P. Feng for their helpful discussions. This work was supported in part by the Japan Society for the Promotion of Science (JSPS) research Grant (No. 15K06072), and the National Natural Science Foundation of China Grants (61401069, U1533125).
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Cong, X., Gui, G., Luo, Y.J. et al. A novel adaptive wide-angle SAR imaging algorithm based on Boltzmann machine model. Multidim Syst Sign Process 29, 119–135 (2018). https://doi.org/10.1007/s11045-016-0459-3
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DOI: https://doi.org/10.1007/s11045-016-0459-3