Luo Wang, Ge Jian, Liu Huan, Wu Shuang, Wang Hongpeng, Yuan Zhiwen, Luan Xinqun, Dong Haobin, Fukushima Edwardo F
School of Automation, China University of Geosciences, Wuhan 430074, China.
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System, Wuhan 430074, China.
Rev Sci Instrum. 2023 Jun 1;94(6). doi: 10.1063/5.0124517.
Magnetic anomaly detection technologies have been widely used for tracking moving targets. In this paper, we present a fast-tracking method for magnetic abnormalities using a distributed Overhauser magnetometer system based on the genetic algorithm. Our proposed framework of the Overhauser magnetometer system employs multiple sensors to eliminate background interference, and the genetic algorithm efficiently solves magnetic anomaly data without requiring the derivation of the objective function. Test platforms were built to evaluate the distributed Overhauser magnetometer system and the genetic algorithm. Results from the natural outdoor magnetism laboratories showed that the noise of our presented magnetometers was below 0.134 nT. The optimal factors for solution precision and effectiveness in the genetic algorithm were obtained from the simulation. Moreover, the outdoor tracking experiments indicated that the proposed method could accurately and quickly detect the moving ferromagnetic object within 6.9% maximum positioning error in 0.55 m, and the tracking precision of the object velocity can get 5.88% maximum error in 4.33 km/h.
磁异常检测技术已被广泛用于跟踪移动目标。在本文中，我们提出了一种基于遗传算法的分布式奥弗豪泽磁力仪系统快速跟踪磁异常的方法。我们提出的奥弗豪泽磁力仪系统框架采用多个传感器来消除背景干扰，并且遗传算法无需推导目标函数就能有效地解决磁异常数据。搭建了测试平台来评估分布式奥弗豪泽磁力仪系统和遗传算法。自然户外磁学实验室的结果表明，我们所展示的磁力仪的噪声低于0.134纳特斯拉。通过模拟获得了遗传算法中求解精度和有效性的最优因素。此外，户外跟踪实验表明，所提出的方法能够在0.55米内以最大6.9%的定位误差准确快速地检测到移动的铁磁物体，并且物体速度的跟踪精度在4.33千米/小时内最大误差可达5.88%。
