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Low Rank Optimization for Spectral Sensing by Hongqing Yu is a document available to read on EtoBox.
What is Low Rank Optimization for Spectral Sensing about?
1) This paper proposes a new low rank optimization model called the low rank double Hankel model for robust spectral compressed sensing. 2) The low rank double Hankel model is partially inspired by forward-backward processing and aims to restrict spectral poles on the unit circle. 3) The paper presents convex relaxation approaches based on the low rank double Hankel model and shows their provable accuracy and robustness to noise.
- Author
- Hongqing Yu
- Language
- EN