@inproceedings{d753509eea5a4605bb68b5c1b11a1e94,
title = "Sparse Decomposition Algorithm Based on Joint Sparse Model",
abstract = "Orthogonal Matching Pursuit (OMP) algorithm is the most classical signal recovery algorithm in compressed sensing. It is also applicable to the Joint Sparse Model (JSM) of distributed compressive sensing. However, OMP algorithm suffers from high computational complexity and poor anti-noise ability without considering the correlation between signals. Therefore, by combining the characteristics of the JSM-1 and JSM-2 models, we propose the corresponding joint sparse decomposition algorithms, named JSM1-OMP and JSM2-OMP. The JSM2-OMP algorithm can be viewed as improvement of the JSM1-OMP algorithm. Furthermore, a better JSM-OMP algorithm is proposed by modifying the JSM2-OMP algorithm. The simulation experiments demonstrate the effectiveness of the proposed algorithms.",
keywords = "JSM, JSM-OMP algorithm, JSM1-OMP algorithm, JSM2-OMP algorithm, OMP",
author = "Qiyun Xuan and Si Wang and Yulong Gao and Junhui Cheng",
note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.; 1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019 ; Conference date: 25-05-2019 Through 26-05-2019",
year = "2019",
doi = "10.1007/978-3-030-22968-9\_5",
language = "英语",
isbn = "9783030229672",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "47--56",
editor = "Shuai Han and Liang Ye and Weixiao Meng",
booktitle = "Artificial Intelligence for Communications and Networks - 1st EAI International Conference, AICON 2019, Proceedings",
address = "德国",
}