Abstract
In this paper, the Subspace Pursuit (SP) recovery of signals with sensing matrix perturbations is analyzed. Previous studies have only considered the robustness of Basis pursuit and greedy algorithms to recover the signal in the presence of additive noise with measurement and/or signal. Since it is impractical to exactly implement the sampling matrix A in a physical sensor, precision errors must be considered. Recently, work has been done to analyze the methods with noise in the sampling matrix, which generates a multiplicative noise term. This new perturbed framework (both additive and multiplicative noise) extends the prior work of Basis pursuit and greedy algorithms on stable signal recovery from incomplete and inaccurate measurements. Our works show that, under reasonable conditions, the stability of the SP solution of the completely perturbed scenario was limited by the total noise in the observation.
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References
Candès, E., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005)
Candès, E.: The restricted isometry property and its implications for compressed sensing. C. R. Math. Acad. Sci. Paris, Serie I 346, 589–592 (2008)
Tropp, J., Gilbert, A.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)
Dai, W., Milenkovic, O.: Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. Info. Theory 55(5), 2230–2249 (2009)
Herman, M., Strohmer, T.: General Deviants: An analysis of perturbations in compressed sensing. IEEE Journal of Selected Topics in Sig. Proc.: Special Issue on Compressive Sensing 4(2) (April 2010)
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© 2011 Springer-Verlag Berlin Heidelberg
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Wang, Q., Liu, Z. (2011). Sampling Matrix Perturbation Analysis of Subspace Pursuit for Compressive Sensing. In: Qi, L. (eds) Information and Automation. ISIA 2010. Communications in Computer and Information Science, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19853-3_86
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DOI: https://doi.org/10.1007/978-3-642-19853-3_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19852-6
Online ISBN: 978-3-642-19853-3
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