Over the years, more than 100 million mines have been set around the world, and many lay forgotten and dormant. Currently, the lifesaving work of finding and disarming unexploded ordnances is a time consuming and costly job, relying on geophysical exploration technology, such as ground-penetrating radar.

Liu et al. propose a multimagneto-inductive sensor array that could help find unexploded ordnances with real-time magnetic field imaging. The imaging system uses a 3-by-3 array of magneto-inductive sensors that can image magnetic fields emitted by permanent magnets – including those of forgotten ferromagnetic bombs. Data from the sensors are sent to a computer that processes and displays the magnetic fields at a rate of several frames per second.

“This system does not need moving modules and is convenient and safe to operate,” said author Huan Liu. “Furthermore, with an enlarged sensor array, it has the potential to be adopted in the field for use on real structures, not just in the laboratory.”

The system is sensitive enough to pick up tiny landmines and can distinguish between different objects that have unique magnetic fingerprints caused by their magnetic anomalies due to shape, size and composition. This allows multiple objects to be identified simultaneously.

In addition to its uses finding unexploded ordnances, the sensor could also be employed in other applications such as medicine, security screening, quality assurance, and other areas of nondestructive evaluation involving magnetic fields.

Currently, the system is limited by low detection distance, for instance, not more than 12 centimeters for some landmines. The authors are already working on improving the detector with an improved magnetic sensor that would allow detections of small magnetic objects at greater distances.

Source: “A multi-magneto-inductive sensor array system for real-time magnetic field imaging of ferromagnetic targets,” by Huan Liu, Changfeng Zhao, Xiaobin Wang, Zehua Wang, Jian Ge, and Haobin Dong, Review of Scientific Instruments (2021). The article can be accessed at https://doi.org/10.1063/5.0039894.