TY - GEN
T1 - Demixing sparse signals from nonlinear observations
AU - Soltani, Mohammadreza
AU - Hegde, Chinmay
N1 - Funding Information:
This work was supported in part by the National Science Foundation under the grants CCF-1566281 and IIP-1632116.
Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Signal demixing is of special importance in several applications ranging from astronomy to computer vision. The goal in demixing is to recover a set of signals from their linear superposition. In this paper, we study the more challenging scenario where only a limited number of nonlinear measurements of the signal superposition are available. Our contribution is a simple, fast algorithm that recovers the component signals from the nonlinear measurements. We support our algorithm with a rigorous theoretical analysis, and provide upper bounds on the estimation error as well as the sample complexity of demixing the components (up to a scalar ambiguity). We also provide a range of simulation results, and observe that the method outperforms a previous algorithm based on convex relaxation.
AB - Signal demixing is of special importance in several applications ranging from astronomy to computer vision. The goal in demixing is to recover a set of signals from their linear superposition. In this paper, we study the more challenging scenario where only a limited number of nonlinear measurements of the signal superposition are available. Our contribution is a simple, fast algorithm that recovers the component signals from the nonlinear measurements. We support our algorithm with a rigorous theoretical analysis, and provide upper bounds on the estimation error as well as the sample complexity of demixing the components (up to a scalar ambiguity). We also provide a range of simulation results, and observe that the method outperforms a previous algorithm based on convex relaxation.
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U2 - 10.1109/ACSSC.2016.7869116
DO - 10.1109/ACSSC.2016.7869116
M3 - Conference contribution
AN - SCOPUS:85016282477
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 615
EP - 619
BT - Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Y2 - 6 November 2016 through 9 November 2016
ER -