A Source Localization Method Using Complex Variational Mode Decomposition

被引:2
|
作者
Miao, Qiuyan [1 ]
Sun, Xinglin [1 ]
Wu, Bin [1 ]
Ye, Lingyun [1 ]
Song, Kaichen [1 ]
机构
[1] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Hangzhou 310027, Peoples R China
关键词
source localization; complex variational mode decomposition; near-field source; far-field source; compressive sensing; FIELD; ESPRIT;
D O I
10.3390/s22114029
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources' direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Passive method for islanding detection using variational mode decomposition
    Thakur, Amit Kumar
    Singh, Shiv P.
    Shukla, Devesh
    Singh, Sunil Kumar
    [J]. IET RENEWABLE POWER GENERATION, 2020, 14 (18) : 3782 - 3791
  • [2] A queued Variational Mode Decomposition method
    Chen, Wei
    Zhang, Yong
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2024, 361 (12):
  • [3] SEISMIC NOISE ATTENUATION USING AN IMPROVED VARIATIONAL MODE DECOMPOSITION METHOD
    Zhou, Yatong
    Chi, Yue
    [J]. JOURNAL OF SEISMIC EXPLORATION, 2020, 29 (01): : 29 - 47
  • [4] Mode Identification of Denoised SH Guided Waves Using Variational Mode Decomposition Method
    Sun, Hongyu
    Peng, Lisha
    Huang, Songling
    Wang, Shen
    Wang, Qing
    Zhao, Wei
    [J]. 2020 IEEE SENSORS, 2020,
  • [5] Space Decomposition Method by Using Complex Source Expansion
    Carli, Giacomo
    Martini, Enrica
    Maci, Stefano
    [J]. 2008 IEEE ANTENNAS AND PROPAGATION SOCIETY INTERNATIONAL SYMPOSIUM, VOLS 1-9, 2008, : 4439 - 4442
  • [6] Nonlinear Chirp Mode Decomposition: A Variational Method
    Chen, Shiqian
    Dong, Xingjian
    Peng, Zhike
    Zhang, Wenming
    Meng, Guang
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (22) : 6024 - 6037
  • [7] Complex variational mode decomposition for signal processing applications
    Wang, Yanxue
    Liu, Fuyun
    Jiang, Zhansi
    He, Shuilong
    Mo, Qiuyun
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 86 : 75 - 85
  • [8] Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography - Variational Mode Decomposition Technique
    Kaur, Chamandeep
    Singh, Preeti
    Sahni, Sukhtej
    [J]. EUROPEAN NEUROLOGY, 2019, 81 (1-2) : 63 - 75
  • [9] Image Dehazing Using Variational Mode Decomposition
    Suseelan, Hima T.
    Sowmya, V.
    Soman, K. P.
    [J]. 2017 2ND IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), 2017, : 200 - 205
  • [10] Knock Detection Using Variational Mode Decomposition
    Bi F.
    Li X.
    Ma T.
    [J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2018, 38 (05): : 903 - 907