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2015-06-23
Off -Grid Direction-of-Arrival Estimation Using a Sparse Array Covariance Matrix
By
Progress In Electromagnetics Research Letters, Vol. 54, 15-20, 2015
Abstract
An off-grid direction-of-arrival (DOA) estimation method that utilizes a sparse array covariance matrix is proposed. In this method, the array covariance matrix is sparsely represented in the form of a vector and then modified to become an off-grid DOA estimation model according to the first-order Taylor series. By solving for the two sparse vectors in the resulting array covariance matrix, the off-grid DOA estimation can thus be achieved. We present an alternating iterative algorithm that exploits the alternating update of a convex optimization problem and a least-squares problem to solve for these two sparse vectors. Our method also extends the aperture. The effectiveness and efficiency of the proposed method are demonstrated in the simulation results.
Citation
Xiaoyu Luo, Xiao Chao Fei, Lu Gan, and Ping Wei, "Off -Grid Direction-of-Arrival Estimation Using a Sparse Array Covariance Matrix," Progress In Electromagnetics Research Letters, Vol. 54, 15-20, 2015.
doi:10.2528/PIERL15030306
References

1. Krim, H. and M. Viberg, "Two decades of array signal processing research: The parametric approach," IEEE Trans. Signal Process. Mag., Vol. 13, No. 4, 67-94, 1996.
doi:10.1109/79.526899

2. Schmidt, R., "Multiple emitter location and signal parameter estimation," IEEE Trans. Antennas Propag., Vol. 34, No. 3, 276-280, 1989.
doi:10.1109/TAP.1986.1143830

3. Stoica, P. and A. Nehorai, "MUSIC, maximum likelihood, and Cramer-Rao bound," IEEE Trans. Acoust., Lett., Vol. 34, No. 3, 276-280, 1986.

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

5. Stoica, P., P. Babu, and J. Li, "SPICE: A sparse covariance-based estimation method for array processing," IEEE Trans. Signal Process., Vol. 59, No. 2, 629-638, 2011.
doi:10.1109/TSP.2010.2090525

6. Yin, J.-H. and T.-Q. Chen, "Direction-of-arrival estimation using a sparse representation of array covariance vectors," IEEE Trans. Signal Process., Vol. 59, No. 9, 4489-4493, 2011.
doi:10.1109/TSP.2011.2158425

7. He, Z.-Q., Q.-H. Liu, L.-N. Jin, and S. Ouyang, "Low complexity method for DOA estimation using array covariance matrix sparse representation," Electronics Letters, Vol. 49, No. 3, 228-230, 2013.
doi:10.1049/el.2012.4032

8. Carlin, M., P. Rocca, G. Oliveri, F. Viani, and A. Massa, "Directions-of-arrival estimation through bayesian compressive sensing strategies," IEEE Trans. Antennas Propag., Vol. 61, No. 7, 3828-3838, 2013.
doi:10.1109/TAP.2013.2256093

9. Tang, G., B. N. Bhaskar, P. Shah, and B. Recht, "Compressed sensing off the grid," IEEE Trans. Inf. Theory, Vol. 59, No. 11, 7465-7490, 2013.
doi:10.1109/TIT.2013.2277451

10. Zhu, H., G. Leus, and G. Giannakis, "Sparsity-cognizant total least-squares for perturbed compressive sampling," IEEE Trans. Signal Process., Vol. 59, No. 5, 2002-2016, 2011.
doi:10.1109/TSP.2011.2109956

11. Zheng, J.-M. and M. Kaveh, "Directions-of-arrival estimation using a sparse spatial spectrum model with uncertainty," IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2848-2851, 2011.

12. Tan, Z. and A. Nehorai, "Sparse direction of arrival estimation using co-prime arrays with off-grid targets," IEEE Trans. Signal Process. Lett., Vol. 21, No. 1, 26-29, 2014.
doi:10.1109/LSP.2013.2289740

13. Tan, Z., P. Yang, and A. Nehorai, "Joint sparse recovery method for compressed sensing with structured dictionary mismatches," IEEE Trans. Signal Process., Vol. 62, No. 19, 4997-5008, 2014.
doi:10.1109/TSP.2014.2343940

14. Yang, Z., L.-H. Xie, and C.-S. Zhang, "Off-grid direction of arrival estimation using sparse Bayesian inference," IEEE Trans. Signal Process., Vol. 61, No. 1, 38-43, 2013.
doi:10.1109/TSP.2012.2222378

15. Zhang, Y., Z.-F. Ye, X. Xu, and N. Hu, "Off-grid DOA estimation using array covariance matrix and block-sparse Bayesian learning," Signal Process., Vol. 98, 197-201, 2014.
doi:10.1016/j.sigpro.2013.11.022

16. Jagannath, R. and K. V. S. Hari, "Block sparse estimator for grid matching in single snapshot DoA estimation," IEEE Trans. Signal Process. Lett., Vol. 20, No. 11, 1038-1041, 2013.
doi:10.1109/LSP.2013.2279124

17. Yang, Z., C.-S. Zhang, and L.-H. Xie, "Robustly stable signal recovery in compressed sensing with structured matrix perturbation," IEEE Trans. Signal Process., Vol. 60, No. 9, 4658-4671, 2012.
doi:10.1109/TSP.2012.2201152

18. Donoho, D. L., M. Elad, and V. N. Temlyakov, "Stable recovery of sparse overcomplete representations in the Presence of Noise," IEEE Trans. Inf. Theory, Vol. 52, No. 1, 6-18, 2006.
doi:10.1109/TIT.2005.860430

19. Tropp, J. A., "Just relax: Convex programming methods for identifying sparse signals in noise," IEEE Trans. Inf. Theory, Vol. 52, No. 3, 1030-1048, 2006.
doi:10.1109/TIT.2005.864420