Fast Sparse Non-Negative Least Squares via ADMM for High Resolution DOA Estimation
Author:
Affiliation:
1. School of Electronic Information Engineering, Foshan University, Foshan, China
2. Leihua Electronic Technology Research Institute, Aviation Industry Corporation of China, Wuxi, China
Funder
National Natural Science Foundation of China
Featured Innovation Project of the Department of Education of Guangdong Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10043099/10017189.pdf?arnumber=10017189
Reference26 articles.
1. Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization
2. A Fast Sparse Covariance-Based Fitting Method for DOA Estimation via Non-Negative Least Squares
3. Sparse Recovery by Means of Nonnegative Least Squares
4. Sparse, collaborative, or nonnegative representation: Which helps pattern classification?
5. Low complexity method for DOA estimation using array covariance matrix sparse representation
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