volume | PIER Journals

Abstract

In high dynamic environment, due to the rapid relative movement between receiver and transmitter, the DOA (Direction of Arrival) of signals will change even between two consecutive snapshots. Thus, covariance-based DOA estimation algorithms are ineffective. Compressive sensing algorithms, as a kind of novel DOA estimation algorithms, are still effective with only one snapshot. At the same time, it is noted that the DOA changing is limited by relative moving speed and distance between receiver and transmitter. In this paper, a DOA tracking algorithm based on weighted L₁ minimization is proposed which utilizing the DOA changing scope between two consecutive snapshots as a prior to improve the tracking performance. Different from other multiple snapshots compressive sensing algorithms which assumed fixed DOA among multiple consecutive snapshots, the proposed algorithm takes into account the DOA changing among different snapshots. The simulation results demonstrate the advantages of the proposed algorithm.

1. Stoica, P. and and R. L. Moses, Introduction to Spectral Analysis, Prentice-Hall, Englewood Cliffs, NJ, 1997.

2. Schmidt, R., "Multiple emitter location and signal parameter estimation," IEEE Transactions on Antennas and Propagation, Vol. 34, No. 3, 276-280, 1986.
doi:10.1109/TAP.1986.1143830 Google Scholar

3. Roy, R. and T. Kailath, "Esprit-estimation of signal parameters via rotational invariance techniques," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 37, No. 7, 984-995, 1989.
doi:10.1109/29.32276 Google Scholar

4. Stoica, P. and A. Nehorai, "Music, maximum likelihood, and Cramer-Rao bound: Further results and comparisons," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 38, No. 12, 2140-2150, 1990.
doi:10.1109/29.61541 Google Scholar

5. Malioutov, D., M. Cetin, and A. S. Willsky, "A sparse signal reconstruction perspective for source localization with sensor arrays," IEEE Transactions on Signal Processing, Vol. 53, No. 8, 3010-3022, 2005.
doi:10.1109/TSP.2005.850882 Google Scholar

6. Candes, E. J., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, Vol. 52, No. 2, 489-509, 2006.
doi:10.1109/TIT.2005.862083 Google Scholar

7. Chen, S., D. L. Donoho, and A. S. Michael, "Atomic decomposition by basis pursuit," SIAM Journal on Scientific Computing, Vol. 20, No. 1, 33-61, 1998.
doi:10.1137/S1064827596304010 Google Scholar

8. 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.
doi:10.1109/TIT.2007.909108 Google Scholar

9. Needell, D. and J. A. Tropp, "Cosamp: Iterative signal recovery from incomplete and inaccurate samples," Applied and Computational Harmonic Analysis, Vol. 26, No. 3, 301-321, 2008.
doi:10.1016/j.acha.2008.07.002 Google Scholar

10. Vaswani, N., "Kalman filtered compressed sensing," Proceedings of the International Conference on Image Processing, ICIP 2008, 893-896, 2008.
doi:10.1109/ICIP.2008.4711899 Google Scholar

11. Vaswani, N. and W. Lu, "Modified-CS: Modifying compressive sensing for problems with partially known support," IEEE Transactions on Signal Processing, Vol. 58, No. 9, 4595-4607, 2010.
doi:10.1109/TSP.2010.2051150 Google Scholar

12. Candes, E. J., M. B. Wakin, and S. P. Boyd, "Enhancing sparsity by reweighted L₁ minimization," Journal of Fourier Analysis and Applications, Vol. 14, 877-905, 2008.
doi:10.1007/s00041-008-9045-x Google Scholar

13. Candes, E. and J. Romberg, "L₁-magic: Recovery of sparse signals via convex programming,", http://www.acm.caltech.edu/l1magic/, 2005. Google Scholar

14. Asif, M. S. and J. Romberg, "Fast and accurate algorithms for reweighted L₁-norm minimization,", Available: http://arxiv.org/abs/1208.0651, 2012. Google Scholar

15. Asif, M. S. and J. Romberg, "Sparse recovery of streaming signals using L₁-homotopy,", Available: http://arxiv.org/abs/1306.3331. Google Scholar

Dr. Wenyi Wang

Professor Renbiao Wu