Low-Frequency Environmental Magnetic Noise Elimination Based on a Neural Network Algorithm for TMR Sensor Arrays

Tunneling magnetoresistance (TMR) sensors have shown the capability of operating in weak magnetic fields. However, the environmental magnetic noise limits their applications in open field detection. This article proposes a novel background noise cancellation method based on a backpropagation (BP) ne...

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Published inIEEE sensors journal Vol. 24; no. 10; pp. 15994 - 16001
Main Authors Gao, Junqi, Jiang, Zekun, Li, Baoju, Shen, Ying, Wang, Wenxu, Dong, Hao, Wang, Jiazeng, Pan, Lindong, Chen, Jiamin
Format Journal Article
LanguageEnglish
Published New York IEEE 15.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Back propagation networks
Background noise
Backpropagation (BP) neural network
Coherence
Coherence coefficient
environmental magnetic noise elimination
magnetic sensor array
Magnetic sensors
Magnetoresistance
Magnetoresistivity
Noise
Noise reduction
Sensor arrays
Sensors
Signal to noise ratio
Transfer functions
Online AccessGet full text
ISSN1530-437X
1558-1748
DOI10.1109/JSEN.2024.3384749

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Abstract Tunneling magnetoresistance (TMR) sensors have shown the capability of operating in weak magnetic fields. However, the environmental magnetic noise limits their applications in open field detection. This article proposes a novel background noise cancellation method based on a backpropagation (BP) neural network for TMR sensor arrays. According to simulation results, the BP-based noise reduction method can eliminate background noise more effectively than the traditional coherence coefficient method. The signal-to-noise ratio (SNR) of the sensor can, thus, be improved by over 20 dB, especially when detecting extremely low SNR signals. This algorithm is demonstrated using a TMR sensor array, which shows a capability of greatly enhancing the sensor array's limit of detection in open field testing.
AbstractList Tunneling magnetoresistance (TMR) sensors have shown the capability of operating in weak magnetic fields. However, the environmental magnetic noise limits their applications in open field detection. This article proposes a novel background noise cancellation method based on a backpropagation (BP) neural network for TMR sensor arrays. According to simulation results, the BP-based noise reduction method can eliminate background noise more effectively than the traditional coherence coefficient method. The signal-to-noise ratio (SNR) of the sensor can, thus, be improved by over 20 dB, especially when detecting extremely low SNR signals. This algorithm is demonstrated using a TMR sensor array, which shows a capability of greatly enhancing the sensor array's limit of detection in open field testing.
Author Wang, Wenxu
Dong, Hao
Pan, Lindong
Li, Baoju
Wang, Jiazeng
Jiang, Zekun
Gao, Junqi
Shen, Ying
Chen, Jiamin
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Snippet Tunneling magnetoresistance (TMR) sensors have shown the capability of operating in weak magnetic fields. However, the environmental magnetic noise limits...
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SubjectTerms Algorithms
Back propagation networks
Background noise
Backpropagation (BP) neural network
Coherence
Coherence coefficient
environmental magnetic noise elimination
magnetic sensor array
Magnetic sensors
Magnetoresistance
Magnetoresistivity
Noise
Noise reduction
Sensor arrays
Sensors
Signal to noise ratio
Transfer functions
Title Low-Frequency Environmental Magnetic Noise Elimination Based on a Neural Network Algorithm for TMR Sensor Arrays
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https://www.proquest.com/docview/3055169049
Volume 24
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