Compressed sensing (CS) has attracted significant attention in the radar community. The better understanding of CS theory has led to substantial improvements over existing methods in CS radar. But there are also some challenges that should be resolved in order to benefit the most from CS radar, such as radar signal with low signal to noise ratio (Low-SNR). In this paper, we will focuses on monostatic chaotic multiple-input-multiple-output (MIMO) radar systems and analyze theoretically and numerically the performance of sparsity-exploiting algorithms for the parameter estimation of targets at Low-SNR. The novelty of this paper is that it capitalizes on chaotic coded waveform to construct measurement operator incoherent with noise and singular value decomposition (SVD) to suppress noise. In order to improve the robustness of azimuth estimation, interpolation method is applied to construction of sparse bases. The gradient pursuit (GP) algorithm for reconstruction is implemented at Low-SNR. Finally, the conclusions are all demonstrated by simulation experiments.
2. Jiang, T., S. Qiao, Z.-G. Shi, L. Peng, J. Huangfu, W.-Z. Cui, W. Ma, and L. X. Ran, "Simulation and experimental evaluation of the radar signal performance of chaotic signals generated from a microwave Colpitts oscillator," Progress In Electromagnetics Research, Vol. 90, 15-30, 2009.
3. Yang, , J., , Z. K. Qiu, X. Li, and Z. W. Zhuang, "Uncertain chaotic behaviours of chaotic-based frequency- and phase-modulated signals," IET Signal Processing, Vol. 5, No. 8, 748-756, 2011.
4. Qiao, S., Z. G. Shi, T. Jiang, and L. X. Ran, "A new architecture of UWB radar utilizing microwave chaotic signals and chaos synchronization," Progress In Electromagnetics Research, Vol. 75, 225-237, 2007.
5. Hatam, M., A. Sheikhi, and M. A. Masnadi-Shirazi, "Target detection in pulse-train MIMO radars applying Ica algorithms," Progress In Electromagnetics Research, Vol. 122, 413-435, 2012.
6. Xu, L., J. Li, and P. Stoica, "Target detection and parameter estimation for MIMO radar systems," IEEE Transactions on Aerospace and Electronic Systems, Vol. 44, No. 3, 927-939, Jul. 2008.
7. Yu, Y., A. P. Petropulu, and H. V. Poor, "Measurement matrix design for compressive sensing-based MIMO radar," IEEE Transactions on Signal Processing, Vol. 59, No. 11, 5338-5352, 2011.
8. Strohmer, T., "Radar and compressive sensing --- A perfect couple?," The First International Workshop on Compressed Sensing Applied to Radar (CoSeRa2012), Bonn, Germany, May 2012, available at: http://workshops.fhr.fraunhofer.de/cosera/programme.html.
9. Herman, M. and T. Strohmer, "Compressed sensing radar," Proc. IEEE Int'l Conf. Acoust. Speech Signal Process, 2617-2620, Las Vegas, NV, Mar.-Apr. 2008.
10. Francesco, B., A. Laura, V. R. Wim, O. Matern, and H. Peter, "Digital beam forming and compressive sensing based DOA estimation in MIMO arrays," Radar Conference (EuRAD), 285-288, European, 2011.
11. Yu, Y., A. P. Petropulu, and H. V. Poor, "Compressive sensing for MIMO radar," IEEE International Conference on Acoustics Speech and Signal Processing, 3017-3020, Apr. 2009.
12. Gogineni, S. and A. Nehorai, "Target estimation using sparse modeling for distributed MIMO radar," IEEE Transactions on Signal Processing, Vol. 59, No. 11, 5315-5325, 2011.
13. Yang, M. and G. Zhang, "Compressive sensing based parameter estimation for monostatic MIMO noise radar," Progress In Electromagnetics Research Letters, Vol. 30, 133-143, 2012.
14. Pribic, R., H. Flisijn, and , "Back to Bayes-ics in radar: Advantages for sparse-signal recovery," The First International Workshop on Compressed Sensing Applied to Radar (CoSeRa2012 ), Bonn, Germany, May 2012, available at: http://workshops.fhr.fraunhofer.de/cosera/programme.html.
15. Blumensath, T. and M. E. Davies, "Gradient pursuits," IEEE Transactions on Signal Processing, Vol. 56, No. 6, 2370-2382, 2008.