A novel temperature drift compensation method based on LSTM for NMR sensor

• Published in: Measurement : journal of the International Measurement Confederation Vol. 240; p. 115573
• Main Authors: Wang, Qipeng, Li, Jianli, Zhang, Shuai, Tian, Hao, Jie, Shaofeng, Qu, Chunyu, Liu, Zhanchao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.01.2025
• Subjects: Long short-term memory (LSTM) network; Nuclear magnetic resonance (NMR) sensors; Temperature compensation; Temperature compensation; Nuclear magnetic resonance (NMR) sensors; Long short-term memory (LSTM) network
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.115573

Nuclear Magnetic Resonance (NMR) sensors have become one of the best choices for angular velocity sensors in future Inertial Navigation Systems (INS) due to their small size and high precision. The performance of NMR sensors can be affected by temperature changes, resulting in temperature drift. Therefore, temperature compensation is essential. Accurate temperature compensation is challenging due to the lack of direct temperature observations in the core sensitive area (cell) and the complex temperature drift model. Considering the time correlation of temperatures inside and outside the cell, a new temperature drift compensation method based on the Long-Short Term Memory (LSTM) network is proposed. The memory characteristics of the LSTM enable it to fully utilize current and previous data to learn complex models. The main contributions of this study are as follows: (1) the mechanism of temperature drift in NMR sensors was analyzed; (2) a multi-time scale input–output model for temperature changes, temperature change rates, and ambient temperature versus sensor output bias was established; (3) a temperature drift model identification method based on LSTM was designed. Experimental results show that, compared to traditional polynomial fitting and memory-free methods, the proposed method improves temperature compensation accuracy by 26.0% to 47.0%. This method effectively reduces the adverse impact of temperature changes on the NMR sensor. •The mechanism of temperature drift in NMR sensors was analyzed.•A input–output model for temperature versus sensor drift was established.•A temperature drift model identification method based on LSTM was designed.