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

• Published in: IEEE 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
• Language: English
• 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
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3384749

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.