Earticle

초록

영어

In order to expand the dynamic range of the GMI sensor in longitudinally excitated amorphous wire and improve its precision, waveforms of the GMI sensor are analyzed on the background of weak magnetic field measurement. Then three features extraction methods are studied in detail. According to the advantages and disadvantages of different methods, an improved method which combines the energy features of the wavelet decomposition and the amplitude features is proposed. First, fit the amplitude change ratio curve respectively with Gaussian function and polynomial function, which not only solves the problem of nonlinearity, but also improves the measurement accuracy. Considering the difference of signals’ in-pulse features at different positions, the ‘db5’ wavelet is introduced to decompose the signals. Then the BP neural network trained by the energy features of the wavelet is used to locate the target’s approximate position, as a result, the problem of multi-value is solved. At last, experiments of target detection in weak magnetic field prove that the method proposed is effective.

목차

Abstract
 1. Introduction
 2. Features Detecting
  2.1. Signal Processing based on Amplitude Features
  2.2. Signal Processing based on Power Features
  2.3. Signal Processing based on the Coefficients of Wavelet Decomposition
 3. Signal Processing Method based on the Amplitude and the Wavelet Features
  3.1. Fit the First Interval and the Second Interval Respectively
  3.2. Target Detection based on the Final Fitting Model
 4. Conclusions
 References

키워드

저자

• Xiusheng Duan [ Ordnance Engineering College, Hebei, Shijiazhuang, 050003, China ]
• Jing Xiao [ Ordnance Engineering College, Hebei, Shijiazhuang, 050003, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605