The novelty of this letter is that it capitalizes on noise waveform to construct measurement operator at the transmitter and presents a new method of how the analogue to digital converter (ADC) sampling rate in the monostatic multiple-input multiple-output (MIMO) noise radar can be reduced --- without reduction in waveform bandwidth --- through the use of compressive sensing (CS). The proposed method equivalently converts the measurement operator problems into radar waveform design problems. The architecture is particularly apropos for signals that are sparse in the target scene. In this letter, Estimates of both target directions and target amplitudes using CS for monostatic MIMO noise radar are presented. Sparse bases are constructed using array steering vectors. Orthogonal least squares (OLS) algorithm for reconstruction of both target directions and target amplitudes is implemented. Finally, the conclusions are all demonstrated by simulation experiments.
2. Kulpa, K., Z. Gajo, and M. Malanowski, "Robustification of noise radar detection," IET Radar, Sonar & Navigation, Vol. 2, No. 4, 284-293, 2008.
doi:10.1049/iet-rsn:20070135
3. Chen, W. J. and R. M. Narayanan, "Antenna placement for minimizing target localization error in UWB MIMO noise radar," IEEE Antennas and Wireless Propagation Letters, Vol. 10, 135-138, 2011.
doi:10.1109/LAWP.2011.2119390
4. Surender, S. C. and R. M. Narayanan, "UWB noise-OFDM netted radar: Physical layer design and analysis," IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 2, 1380-1400, 2011.
doi:10.1109/TAES.2011.5751265
5. Chen, C. Y. and P. P. Vaidyanathan, "Compressed sensing in MIMO radar," Proc. 42nd IEEE Asilomar Conf. on Signals, Systems, and Computers, 41-44, 2008.
doi:10.1109/ACSSC.2008.5074356
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, 2008.
doi:10.1109/TAES.2008.4655353
7. Li, B., Y. X. Zou, and Y. S. Zhu, "Direction estimation under compressive sensing framework: A review and experimental results," Proceeding of the IEEE International Conference on Information and Automation, ICIA, 63-68, 2011.
doi:10.1109/ICINFA.2011.5948964
8. Yu, Y., A. P. Petropulu, and H. V. Poor, "Reduced complexity angle-doppler-range estimation for MIMO radar that employs compressive sensing," Proc. 43nd IEEE Asilomar Conf. on Signals, Systems, and Computers, 1196-1200, 2009.
9. 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.
doi:10.1109/TSP.2011.2162328
10. Francesco, B., et al., "Digital beam forming and compressive sensing based DOA estimation in MIMO arrays," European Radar Conference, EuRAD, 285-288, 2011.
11. Yu, Y., A. P. Petropulu, and H. V. Poor, "Compressive sensing for MIMO radar," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 3017-3020, 2009.
doi:10.1109/ICASSP.2009.4960259
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.
doi:10.1109/TSP.2011.2164070
13. Protter, M., I. Yavneh, and M. Elad, "Closed-form MMSE estimation for signal denoising under sparse representation modeling over a unitary dictionary," IEEE Transactions on Signal Processing, Vol. 58, No. 7, 3471-3484, 2010.
doi:10.1109/TSP.2010.2046596
14. Gowreesunker, B. V. and A. H. Tewfik, "Learning sparse representation using iterative subspace identification," IEEE Transactions on Signal Processing, Vol. 58, No. 6, 3055-3065, 2010.
doi:10.1109/TSP.2010.2044251
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