Detection of magnetic resonance sounding signal by a self-tuning template matching algorithm

作者：Zhang Faquan^*; Zhang Hairu; Wang Guofu; Ye Jincai

来源：Journal of Computational Information Systems, 2015, 11(9): 3113-3120.

DOI：10.12733/jcis14042

• Feature parameters
• Linear regression models
• Linear relationships
• Magnetic resonance sounding (MRS)
• Magnetic resonance soundings
• Powerline interference
• Stochastic noise
• Template-matching algorithms

A self-tuning template matching algorithm for extracting the feature parameters of the Magnetic Resonance Sounding (MRS) signal is investigated, which takes full use of the linear relationship between the logarithmic MRS signal and its sampling time. It makes the unary linear regression model for analyzing original MRS signal and employs the self-tuning template matching algorithm to delete the undependable measurements. Simulation results show the algorithm has a much lower average error rate than the traditional ones. It can extract effective feature parameters from the acquired MSR signal under poor SNR (SNR=1 dB) at the average error rate 30.1% and single period efficient rate 60.5%. In additions, the application results show that the algorithm is suitable for the original MRS signal, which contains the stable stochastic noise, the power line interference signal and the spike noise. It also can greatly improve the effectiveness of the detecting equipment.

• 出版日期2015