Abstract: 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.

基于块稀疏的超宽带FMCW雷达ISAR大转角成像

[1]Anghel, A., Vasile, G., Cacoveanu, R., et al., 2014. Short-range wideband FMCW radar for millimetric displacement measurements. IEEE Trans. Geosci. Remote Sens., 52(9):5633-5642.

[2]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.

[3]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.

[4]Eldar, Y.C., Kuppinger, P., Bolcskei, H., 2010. Block-sparse signals: uncertainty relations and efficient recovery. IEEE Trans. Signal Process., 58(6):3042-3054.

[5]Elhamifar, E., Vidal, R., 2012. Block-sparse recovery via convex optimization. IEEE Trans. Signal Process., 60(8): 4094-4107.

[6]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).

[7]Herman, M.A., Strohmer, T., 2009. High-resolution radar via compressed sensing. IEEE Trans. Signal Process., 57(6): 2275-2284.

[8]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.

[9]Kim, K.T., Seo, D.K., Kim, H.T., 2005. Efficient classification of ISAR images. IEEE Trans. Antennas Propag., 53(5): 1611-1621.

[10]Li, G., Zhang, H., Wang, X.Q., et al., 2012. ISAR 2D imaging of uniformly rotating targets via matching pursuit. IEEE Trans. Aerosp. Electron. Syst., 48(2):1838-1846.

[11]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.

[12]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.

[13]Li, S.J., Qi, H.R., 2015. A Douglas-Rachford splitting approach to compressed sensing image recovery using low-rank regularization. IEEE Trans. Image Process., 24(11): 4240-4249.

[14]Lu, G.Y., Bao, Z., 1999. Analysis of MTRC compensation algorithm in ISAR. IEEE Radar Conf., p.242-245.

[15]Martorella, M., Berizzi, F., Haywood, B., 2005. Contrast maximisation based technique for 2D ISAR autofocusing. IEE Proc. Radar Son. Navig., 152(4):253-262.

[16]Meta, A., Hoogeboom, P., Ligthart, L.P., 2007. Signal processing for FMCW SAR. IEEE Trans. Geosci. Remote Sens., 45(11):3519-3532.

[17]Middleton, R.J.C., Macfarlane, D.G., Robertson, D.A., 2011. Range autofocus for linearly frequency-modulated continuous wave radar. IET Radar Sonar Navig., 5(3):288-295.

[18]Özdemir, C., 2012. Inverse synthetic aperture radar imaging with MATLAB algorithms. John Wiley & Sons, Hoboken, New Jersey, America.

[19]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.

[20]Ribalta, A., 2011. Time-domain reconstruction algorithms for FMCW-SAR. IEEE Geosci. Remote Sens. Lett., 8(3):396-400.

[21]Wang, H.X., Liang, Y., Xing, M.D., et al., 2011. ISAR imaging via sparse frequency-stepped chirp signal. Sci. China Inform., 55(4):877-888.

[22]Wang, R., Loffeld, O., Nies, H., et al., 2010. Focus FMCW SAR data using the wavenumber domain algorithm. IEEE Trans. Geosci. Remote Sens., 48(4):2109-2118.

[23]Wang, S.L., Li, S.G., Ni, J.L., et al., 2001. A new transform-match Fourier transform. Acta Electron. Sin., 29(3):403-405 (in Chinese).

[24]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).

[25]Xing, M.D., Wu, R.B., Lan, J.Q., et al., 2004. Migration through resolution cell compensation in ISAR imaging. IEEE Geosci. Remote Sens. Lett., 1(2):141-144.

[26]Zhang, S.S., Xiao, B., Zong, Z.L., 2014. Improved compressed sensing for high-resolution ISAR image reconstruction. Chin. Sci. Bull., 59(23):2918-2926.