Open AccessDetection of Ship Shaft-rate Electromagnetic Field Signal Based on NLMS Algorithm
Author(s) -
Kaisong Wang,
Guohua Zhou,
Wenshi Zeng,
Xihao Lu,
Xiaochen Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1846/1/012097
Subject(s) - signal (programming language) , noise (video) , algorithm , computer science , adaptive filter , field (mathematics) , adaptive algorithm , line (geometry) , electromagnetic field , electromagnetic environment , electromagnetic spectrum , underwater , acoustics , electronic engineering , engineering , artificial intelligence , telecommunications , mathematics , physics , geometry , quantum mechanics , pure mathematics , image (mathematics) , programming language , oceanography , geology
Ship shaft-rate electromagnetic field signal contains a lot of valuable target feature information, and effectively extracting its line spectrum components plays an extremely important role in underwater target recognition. In order to effectively separate the characteristic line spectrum of ship shaft-rate electromagnetic field from broadband background noise, this paper studies and analyzes the shaft-rate electric field by using the line spectrum enhancement algorithm based on adaptive filtering. Firstly, the application principle of adaptive line spectrum enhancement and NLMS algorithm in ship shaft-rate signal detection is briefly described. Then, an adaptive line spectrum enhancer is constructed according to the improved NLMS algorithm, and the obtained shaft-rate electric field data are processed and analyzed by this method. Finally, it is verified by simulation data and experimental data. The results show that the algorithm can effectively suppress the background noise, significantly improve the signal-to-noise ratio, and enhance the detection ability of ship electromagnetic field signals in practical applications.