Comparative Study on Sparse and Recovery Algorithms for Antenna Measurement by Compressed Sensing

被引：0
|
作者：
Zhang, Liang [1 ,2 ]
Wang, Tianting [1 ]
Liu, Yang [1 ]
Kong, Meng [2 ]
Wu, Xianliang [1 ]
机构：
[1] Anhui Univ, Minist Educ Intelligent Comp & Signal Proc, Key Lab, Hefei 230601, Anhui, Peoples R China
[2] Hefei Normal Univ, Anhui Prov Key Lab Simulat & Design Elect Informa, Hefei 230601, Anhui, Peoples R China
来源：
PROGRESS IN ELECTROMAGNETICS RESEARCH M | 2019年 / 81卷
基金：
中国国家自然科学基金;
关键词：
SIGNAL RECOVERY;
D O I：
10.2528/PIERM19041803
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Compressed sensing (CS) is utilized in antenna measurements. The antenna data are compressed using the CS method, and the performances of different sparse and recovery algorithms of CS are used to solve antenna measurements. Experiments are conducted on various types of antennas. The results show that efficiency can be greatly improved by reducing the number of measurement points. The best reconstruction performance is exhibited by the Discrete Wavelet Transform (DWT) algorithm combined with the Compressive Sampling Matching Pursuit (COSAMP) algorithm.
引用
收藏
页码：149 / 158
页数：10
相关论文
共 50 条
  • [41] An Efficient Recovery Algorithms Using Complex to Real Transformation of Compressed Sensing
    Shalaby, Wafaa A.
    Saad, Waleed
    Shokair, Mona
    Dessouky, Moawad I.
    2016 33RD NATIONAL RADIO SCIENCE CONFERENCE (NRSC), 2016, : 122 - 131
  • [42] Joint recovery algorithms using difference of innovations for distributed compressed sensing
    Valsesia, Diego
    Coluccia, Giulio
    Magli, Enrico
    2013 ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS, 2013, : 414 - 417
  • [43] Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions
    Gosse L.
    ANNALI DELL'UNIVERSITA' DI FERRARA, 2013, 59 (1) : 81 - 116
  • [44] Compressed Sensing of Different Size Block-Sparse Signals: Efficient Recovery
    Ziaei, Ali
    Pezeshki, Ali
    Bahmanpour, Saeid
    Azimi-Sadjadi, Mahmood R.
    2010 CONFERENCE RECORD OF THE FORTY FOURTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS (ASILOMAR), 2010, : 818 - 821
  • [45] One-bit Matrix Compressed Sensing Algorithm for Sparse Matrix Recovery
    Wang, Hui c
    Van Huffel, Sabine
    Gui, Guan
    Wan, Qun
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2016, E99A (02) : 647 - 650
  • [46] A MIXED INTEGER LINEAR PROGRAMMING FORMULATION FOR THE SPARSE RECOVERY PROBLEM IN COMPRESSED SENSING
    Karahanoglu, N. Burak
    Erdogan, Hakan
    Birbil, S. Ilker
    2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 5870 - 5874
  • [47] Colour compressed sensing imaging via sparse difference and fractal minimisation recovery
    Liu, Ji-xin
    Li, Xiao-fei
    Han, Guang
    Sun, Ning
    Du, Kun
    Sun, Quan-sen
    IET Image Processing, 2015, 9 (05) : 369 - 380
  • [48] Fast compressed sensing recovery using generative models and sparse deviations modeling
    Cai, Lei
    Fu, Yuli
    Xiang, Youjun
    Zhu, Tao
    Li, Xianfeng
    Zeng, Huanqiang
    2020 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2020, : 447 - 450
  • [49] Comparative Study on Recovery Algorithms for Solving Electromagnetic Scattering Problems by Compressive Sensing
    Hou, Xinli
    Chen, Mingsheng
    Zhu, Yanyan
    Cao, Xinyuan
    Zhao, Hui
    Wu, Xianliang
    PROCEEDINGS OF THE 2016 11TH INTERNATIONAL SYMPOSIUM ON ANTENNAS, PROPAGATION AND EM THEORY (ISAPE), 2016, : 530 - 533
  • [50] Sensing-Assisted Sparse Channel Recovery for Massive Antenna Systems
    Ren, Zixiang
    Qiu, Ling
    Xu, Jie
    Ng, Derrick Wing Kwan
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (11) : 17824 - 17829
12345