Ultra-wideband FMCW ISAR imaging with a large rotation angle based on block-sparse recovery
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Ultra-wideband frequency modulated continuous wave (FMCW) radar has the ability to achieve high-range resolution. Combined with the inverse synthetic aperture technique, high azimuth resolution can be realized under a large rotation angle. However, the range-azimuth coupling problem seriously restricts the inverse synthetic aperture radar (ISAR) imaging performance. Based on the turntable model, traditional match-filter-based, range Doppler algorithms (RDAs) and the back projection algorithm (BPA) are investigated. To eliminate the sidelobe effects of traditional algorithms, compressed sensing (CS) is preferred. Considering the block structure of a signal at high resolution, a block-sparsity adaptive matching pursuit algorithm (BSAMP) is proposed. By matching pursuit and backtracking, a signal with unknown sparsity can be recovered accurately by updating the support set iteratively. Finally, several experiments are conducted. In comparison with other algorithms, the results from processing the simulation data, some simple targets, and a complex target indicate the effectiveness and superiority of the proposed algorithm.
Key wordsFrequency modulated continuous wave (FMCW) Inverse synthetic aperture radar (ISAR) Match-filter-based algorithm Compressed sensing Block sparsity
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- Anghel, A., Vasile, G., Cacoveanu, R., et al., 2015. Range autofocusing for FMCW radars using time warping and a spectral concentration measure. IEEE Radar Conf., p.581–586. https://doi.org/10.1109/RADAR.2015.7131065Google Scholar
- Do, T.T., Gan, L., Nguyen, N., et al., 2008. Sparsity adaptive matching pursuit algorithm for practical compressed sensing. 42nd Asilomar Conf. on Signals, p.581–587. https://doi.org/10.1109/ACSSC.2008.5074472Google Scholar
- Fu, Y.W., Li, Y.N., Li, X., 2012. A 3D InISAR imaging method for non-uniformly rotating target based on match. Four. Transf. J. Astron., 33(6):769–775 (in Chinese). https://doi.org/10.3873/j.issn.1000-1328.2012.06.012Google Scholar
- Hu, J.M., Jiang, W.D., Fu, Y.W., et al., 2010. A novel range alignment algorithm for ISAR. IEEE 2nd Int. Conf. on Computer Engineering and Technology, p.358–362. https://doi.org/10.1109/ICCET.2010.5486064Google Scholar
- Li, S.J., Qi, H.R., 2014a. Compressed dictionary learning for detecting activations in fMRI using double sparsity. IEEE Global Conf. on Signal and Information Processing, p.434–437. https://doi.org/10.1109/GlobalSIP.2014.7032154Google Scholar
- Li, S.J., Qi, H.R., 2014b. Recursive low-rank and sparse recovery of surveillance video using compressed sensing. Proc Int. Conf. on Distributed Smart Cameras, Article 1. https://doi.org/10.1145/2659021.2659029Google Scholar
- Lu, G.Y., Bao, Z., 1999. Analysis of MTRC compensation algorithm in ISAR. IEEE Radar Conf., p.242–245. https://doi.org/10.1109/NRC.1999.767330Google Scholar
- Qiu, X.H., Zhao, Y., 2006. A non-parametric rotating angle acquisition method for optimal ISAR imaging. IEEE Antennas and Propagation Society Int. Symp., p.2697–2700. https://doi.org/10.1109/APS.2006.1711159Google Scholar
- Wang, S.L., Li, S.G., Ni, J.L., et al., 2001. A new transformmatch Fourier transform. Acta Electron. Sin., 29(3):403–405 (in Chinese). https://doi.org/10.3321/j.issn:0372-2112.2001.03.030Google Scholar
- Wang, W.W., Liao, G.S., Zhang, L., et al., 2012. An imaging method based on compressive sensing for sparse aperture of SAR. Acta Electron. Sin., 40(12):2487–2494 (in Chinese). https://doi.org/10.3969/j.issn.0372-2112.2012.12.021Google Scholar