Abstract
In this paper, a novel sparse recovery method is proposed for direction of arrival (DOA) estimation on the assumption that source signals are uncorrelated and the number of snapshots is enough. We demonstrate in this paper how to solve DOA estimation problem with multiple measurement vectors (MMV) from the perspective of the correlation rather than from the view of data which appears in almost all sparse signal recovery methods. By using this method we remove the effect of noise and attain both DOA estimation and signal power. Theoretical analysis and experimental results demonstrated that our approach has a number of advantages compared other source localization schemes, including increased resolution, lower bias and variance, unknowing the number of sources, better performance in low SNR, decreased computational complexity.
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References
Krim, H., Viberg, M.: Two Decades of Array Signal Processing Research: The Parametric Approach. IEEE Signal Process. Mag. 13, 67–94 (1996)
Donoho, D.L., Elad, M., Temlyakvo, V.: Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise. IEEE Trans. Inf. Theory 52, 6–18 (2006)
Candés, E.J., Wakin, M.B.: An Introduction to Compressive Sampling. IEEE Signal Process. Mag. 25, 21–30 (2008)
Gorodnitsky, I.F., Rao, B.D.: Sparse Signal Reconstruction from Limited Data Using FOCUSS: a Re-weighted Minimum Norm Algorithm. IEEE Trans. Signal Process. 45, 600–616 (1997)
Tipping, M.E.: Sparse Bayesian Learning and the Relevance Vector Machine. J. Mach. Learn. Res. 1, 211–244 (2001)
Cotter, S.F., Rao, B.D., Kjersti, E., Kreutz-Delgado, K.: Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors. IEEE Trans. Signal Process. 53, 2477–2488 (2005)
Wipf, D.P., Rao, B.D.: An Empirical Bayesian Strategy for Solving the Simultaeours Sparse Approximation Problems. IEEE Trans. Signal Process. 55, 3704–3716 (2007)
CVX: Matlab Software for Disciplined Convex Programming, http://cvxr.com/cvx
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Yin, M., Xu, D., Ye, Z. (2012). A Sparse Recovery Method of DOA Estimation for Uncorrelated Sources with Multi-snapshot. In: Jin, D., Lin, S. (eds) Advances in Electronic Engineering, Communication and Management Vol.2. Lecture Notes in Electrical Engineering, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27296-7_37
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DOI: https://doi.org/10.1007/978-3-642-27296-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27295-0
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