Sparse signal recovery algorithms can be used to improve radar imaging quality by using the sparse property of strong scatterers. Traditional sparse inverse synthetic aperture radar (ISAR) imaging algorithms mainly consider the recovery of sparse scatterers. However, the scatterers of an ISAR target usually exhibit block or group sparse structure. By utilizing the inherent block sparse structure of ISAR target images, an iterative reweighted lp(0 < p ≤ 1) block sparse signal recovery algorithm is proposed to enhance imaging quality in this paper. Firstly, an ISAR imaging signal model is established with the aid of sparse basis, and the imaging is mathematically converted into block reweighted cost function optimization problem. Then, an iterative algorithm is used to solve the reweighted function minimization problem. In each iteration, the weights are updated based on the closed form solution of the previous iteration. The proposed method is effective to exploit the underlying block sparse structures which does not need the prior knowledge of the number of the blocks. Real data ISAR imaging results are provided to verify that the proposed algorithm in this paper can achieve better images than the images obtained by several popular sparse signal recovery algorithms.
2. Kim, D., D. Seo, and H. Kim, "Efficient classification of ISAR images," IEEE Transaction on Antennas and Propagation, Vol. 53, No. 5, 1611-1621, 2005.
3. Wang, H., Y. Qun, M. Xing, and S. Zhang, "ISAR imaging via sparse probing frequencies," IEEE Geosci. Remote Sens. Lett., Vol. 8, No. 3, 451-455, 2011.
4. Needell, D. and R. Vershynin, "Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit," IEEE Journal of Selected Topics in Signal Processing, Vol. 4, No. 9, 317-334, 2009.
5. Tropp, J. A. and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, Vol. 53, No. 12, 4655-4666, 2007.
6. Demba, B., B. Behtash, and L. Patrick, "Convergence and stability of iteratively re-weighted least squares algorithms," IEEE Trans. Signal Process., Vol. 62, No. 1, 183-159, 2014.
7. Zhang, X. Z., J. H., Qin, and G. J. Li, "SAR Target classification using bayesian compressive sensing with scattering centers features," Progress In Electromagnetics Research, Vol. 136, 385-407, 2013.
8. Cetin, M., I. Stojanovic, O. Onhon, K. Varshney, S. Samadi, W. C. Karl, and A. S. Willsky, "Sparsity-driven synthetic aperture radar imaging," IEEE Signal Processing Magazine, Vol. 31, No. 4, 27-40, 2014.
9. Liu, J., X. Li, S. Xu, and Z. Zhuang, "ISAR imaging of non-uniform rotation targets with limited pulses via compressed sensing," Progress In Electromagnetics Research B, Vol. 41, 285-305, 2012.
10. Zhang, L., et al., "Achieving higher resolution isar imaging with limited pulses via compressed sampling," IEEE Geosci. Remote Sens. Lett., Vol. 6, No. 3, 57-571, 2009.
11. Ma, C. Z., T. S. Yeo, Y. B. Zhao, and J. J. Feng, "MIMO radar 3D imaging based on combined amplitude and total variation cost function with sequential order one negative exponential form," IEEE Trans. Image Process., Vol. 23, No. 5, 2168-2183, 2014.
12. Tan, X., W. Roberts, J. Li, and P. Stoica, "Sparse learning via iterative minimization with application to MIMO radar imaging," IEEE Trans. Signal Process., Vol. 59, No. 3, 1088-1101, 2011.
13. Eldar, Y., P. Kuppinger, and H. Bolcskei, "Block-sparse signals: Uncertainty relations and efficient recovery," IEEE Trans. Signal Process., Vol. 58, No. 6, 3042-3054, 2010.
14. Eldar, Y. and M. Mishali, "Robust recovery of signals from a structured union of subspaces," IEEE Transactions on Information Theory, Vol. 55, No. 1, 5302-5316, 2009.
15. Baraniuk, R. G., V. Cevher, M. F. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Transactions on Information Theory, Vol. 56, No. 4, 1982-2001, 2010.
16. Mohimani, H., M. Babaie-Zadeh, and C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed l0 norm," IEEE Trans. Signal Process., Vol. 57, No. 1, 289-301, 2009.
17. Wipf, D. P. and B. D. Rao, "Sparse Bayesian learning for basis selection," IEEE Trans. Signal Process., Vol. 52, No. 8, 2153-2164, 2004.
18. Stojnic, M., F. Parvaresh, and B. Hassibi, "On the reconstruction of block-sparse signals with an optimal number of measuremets," IEEE Trans. Signal Process., Vol. 57, No. 8, 3075-3085, 2009.
19. Gong, P. C. and Z. H. Shao, "Target estimation by iterative reweighted lq minimization for MIMO radar," Signal Processing, Vol. 101, 35-41, 2014.
20. Chen, S., D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM Review, 129-159, 2001.
21. Stankovic, L., "On the ISAR image analysis and recovery with unavailable or heavily corrupted data," IEEE Transactions on Aerospace and Electronic Systems, Vol. 51, No. 3, 2093-2106, 2015.