Table of Contents Author Guidelines Submit a Manuscript
International Journal of Antennas and Propagation
Volume 2015 (2015), Article ID 421740, 10 pages
http://dx.doi.org/10.1155/2015/421740
Research Article

Guaranteed Stability of Sparse Recovery in Distributed Compressive Sensing MIMO Radar

Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received 27 May 2014; Revised 11 October 2014; Accepted 29 October 2014

Academic Editor: Matteo Pastorino

Copyright © 2015 Yu Tao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Fishler, A. Haimovich, R. Blum, D. Chizhik, L. Cimini, and R. Valenzuela, “MIMO radar: an idea whose time has come,” in Proceedings of the IEEE Radar Conference, pp. 71–78, April 2004. View at Scopus
  2. J. Li and P. Stoica, “MIMO radar—diversity means superiority,” in Proceedings of the 14th Adaptive Sensor Array Processing Workshop (ASAP '06), pp. 1–6, Lincoln, Mass, USA, 2006.
  3. X.-R. Li, Z. Zhang, W.-X. Mao, X.-M. Wang, J. Lu, and W.-S. Wang, “A study of frequency diversity MIMO radar beamforming,” in Proceedings of the IEEE 10th International Conference on Signal Processing (ICSP '10), pp. 352–356, Beijing, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Sharma, “Analysis of MIMO radar ambiguity functions and implications on clear region,” in Proceedings of the IEEE International Radar Conference (RADAR '10), pp. 544–548, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Li, G. Liao, and H. Griffiths, “Bistatic MIMO radar spacetime adaptive processing,” in Proceedings of the IEEE Radar Conference (RadarCon '11), pp. 498–501, Westin Crown Center in Kansas City, 2011.
  6. Y. Yu, A. P. Petropulu, and H. V. Poor, “Compressive sensing for MIMO radar,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '09), pp. 3017–3020, Taipei, Taiwan, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. C.-Y. Chen and P. P. Vaidyanathan, “Compressed sensing in MIMO radar,” in Proceedings of the 42nd Asilomar Conference on Signals, Systems and Computers (ASILOMAR '08), pp. 41–44, Pacific Grove, Calif, USA, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Gogineni and A. Nehorai, “Target estimation using sparse modeling for distributed {MIMO} radar,” IEEE Transactions on Signal Processing, vol. 59, no. 11, pp. 5315–5325, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. D. Baron, M. F. Duarte, M. B. Wakin, S. Sarvotham, and R. G. Baraniuk, “Distributed compressive sensing,” Tech. Rep. TREE-0612, Rice University, Houston, Tex, USA, 2006. View at Google Scholar
  10. J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655–4666, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, “Fast reconstruction of piecewise smooth signals from random projections,” in Proceedings of the Signal Processing with Adaptative Sparse Structured Representations (SPARS '05), Rennes, France, November 2005.
  12. D. L. Donoho, Y. Tsaig, I. Drori, and J. L. Starck, “Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 1094–1121, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. D. Needell and J. A. Tropp, “CoSa{MP}: iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis. Time-Frequency and Time-Scale Analysis, Wavelets, Numerical Algorithms, and Applications, vol. 26, no. 3, pp. 301–321, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  14. T. Blumensath, “Accelerated iterative hard thresholding,” Signal Processing, vol. 92, no. 3, pp. 752–756, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Jung, K. Sung, K. S. Nayak, E. Y. Kim, and J. C. Ye, “K-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI,” Magnetic Resonance in Medicine, vol. 61, no. 1, pp. 103–116, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  16. Z. Zhang and B. D. Rao, “Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning,” IEEE Journal on Selected Topics in Signal Processing, vol. 5, no. 5, pp. 912–926, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. F. Duarte, S. Sarvotham, D. Baron, M. B. Wakin, and R. G. Baraniuk, “Distributed compressed sensing of jointly sparse signals,” in Proceedings of the 39th Asilomar Conference on Signals, Systems and Computers, pp. 1537–1541, November 2005. View at Scopus
  18. D. L. Donoho, M. Elad, and V. N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 6–18, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus