Xiaodong Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiaodong Liu has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Wanhua Zhu
Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
3 shared publications - Jiarui Liu
Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
2 shared publications - Xiuzhi You
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2 shared publications - Bin Yan
Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China
1 shared publication - Bingyang Chen
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
1 shared publication - Junqian Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
1 shared publication
Latest Publications
- Open Access
Free Induction Decay Optically Pumped Magnetometers: A Review
Free induction decay (FID) optically pumped magnetometers are among the most sensitive quantum sensors available for high-precision magnetic field measurements. This review summarizes recent advances in FID magnetometers, which represent a critical technological route that suppresses systematic errors by temporally separating atomic polarization from magnetic field detection. This review covers light-modulated optical pumping schemes (such as the Bell–Bloom mode), radio-frequency excitation schemes, and magnetic-field-enhanced pumping schemes. Key technological developments, including chip-scale MEMS fabrication, optical multipass architectures, advanced frequency estimation algorithms, and error suppression methods, are also discussed. Finally, emerging trends toward higher integration, environmental robustness, and intelligent signal demodulation are highlighted. These trends provide insights into the development of next-generation high-performance quantum sensors and their growing role in biomedical and geophysical applications, as well as in fundamental scientific research.
Open AccessFree Induction Decay Optically Pumped Magnetometers: A Review
Free induction decay (FID) optically pumped magnetometers are among the most sensitive quantum sensors available for high-precision magnetic field measurements. This review summarizes recent advances in FID magnetometers, which represent a critical technological route that suppresses systematic errors by temporally separating atomic polarization from magnetic field detection. This review covers light-modulated optical pumping schemes (such as the Bell–Bloom mode), radio-frequency excitation schemes, and magnetic-field-enhanced pumping schemes. Key technological developments, including chip-scale MEMS fabrication, optical multipass architectures, advanced frequency estimation algorithms, and error suppression methods, are also discussed. Finally, emerging trends toward higher integration, environmental robustness, and intelligent signal demodulation are highlighted. These trends provide insights into the development of next-generation high-performance quantum sensors and their growing role in biomedical and geophysical applications, as well as in fundamental scientific research.
- Open Access
- 1 March 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (5) , 2653
Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network
Estimating the magnetic moment of a magnetic target is a key aspect of magnetic anomaly detection when accurately locating the target. Current methods primarily rely on vector magnetic field signals or gradient signals, which face challenges such as steering errors and the high costs associated with sensor arrays. This paper proposes a magnetic moment estimation algorithm that combines a scalar magnetic field sensor and the three components of the local geomagnetic field with a convolutional neural network (CNN). The simulation results demonstrate that the proposed algorithm performs well in noise environments with a signal-to-noise ratio (SNR) greater than −5 dB. At an SNR of −5 dB, the relative error in the magnetic moment magnitude is 4.92%, while the absolute errors in the magnetic moment declination and inclination are and , respectively. The vector angle difference is . The algorithm achieves high estimation accuracy even with a scalar sensor and under poor noise conditions, offering a novel and effective approach for future magnetic moment estimation research.- 1 March 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (5) , 2653
Open AccessMagnetic Moment Estimation Algorithm Based on Convolutional Neural Network
Estimating the magnetic moment of a magnetic target is a key aspect of magnetic anomaly detection when accurately locating the target. Current methods primarily rely on vector magnetic field signals or gradient signals, which face challenges such as steering errors and the high costs associated with sensor arrays. This paper proposes a magnetic moment estimation algorithm that combines a scalar magnetic field sensor and the three components of the local geomagnetic field with a convolutional neural network (CNN). The simulation results demonstrate that the proposed algorithm performs well in noise environments with a signal-to-noise ratio (SNR) greater than −5 dB. At an SNR of −5 dB, the relative error in the magnetic moment magnitude is 4.92%, while the absolute errors in the magnetic moment declination and inclination are and , respectively. The vector angle difference is . The algorithm achieves high estimation accuracy even with a scalar sensor and under poor noise conditions, offering a novel and effective approach for future magnetic moment estimation research. - Open Access
- 5 December 2023
- Published by MDPI AG in Electronics
- Vol. 12 (24) , 4900
Inversion of Target Magnetic Moments Based on Scalar Magnetic Anomaly Signals
As a key physical property of underwater ferromagnetic targets, magnetic moment can reflect important information such as the mass and heading of the target. However, most of the current magnetic moment estimation methods rely on vector magnetic field sensors or sensor arrays to measure the magnetic field, which limits its application in remote target magnetic moment calculation on mobile platforms to some extent. To solve this problem, a real-time magnetic moment inversion method based on the high-precision scalar magnetic measurement data of a high-speed moving platform is proposed in this paper. The method allows the estimation of the magnetic moment of underwater ferromagnetic targets by the scalar magnetic measurement data of an optical pump magnetic field sensor mounted on a high-speed moving platform. The experimental results show that this method has high precision in estimating magnetic moment; the average error of the magnetic moment amplitude was only 5.85%, while the average errors of the magnetic moment inclination and magnetic moment deflection were 1.58° and 2.79°, respectively. These results provide a new and effective way to estimate the magnetic moment of underwater ferromagnetic targets and are expected to have important practical applications.
- 5 December 2023
- Published by MDPI AG in Electronics
- Vol. 12 (24) , 4900
Open AccessInversion of Target Magnetic Moments Based on Scalar Magnetic Anomaly Signals
As a key physical property of underwater ferromagnetic targets, magnetic moment can reflect important information such as the mass and heading of the target. However, most of the current magnetic moment estimation methods rely on vector magnetic field sensors or sensor arrays to measure the magnetic field, which limits its application in remote target magnetic moment calculation on mobile platforms to some extent. To solve this problem, a real-time magnetic moment inversion method based on the high-precision scalar magnetic measurement data of a high-speed moving platform is proposed in this paper. The method allows the estimation of the magnetic moment of underwater ferromagnetic targets by the scalar magnetic measurement data of an optical pump magnetic field sensor mounted on a high-speed moving platform. The experimental results show that this method has high precision in estimating magnetic moment; the average error of the magnetic moment amplitude was only 5.85%, while the average errors of the magnetic moment inclination and magnetic moment deflection were 1.58° and 2.79°, respectively. These results provide a new and effective way to estimate the magnetic moment of underwater ferromagnetic targets and are expected to have important practical applications.