VP16-07 Magnetic Anomaly Target Motion State Detection Based on 3D Convolutional Neural Network
Creators: Hexuan Sun
Jing Qiu
Shuanglong Huang
Xinjie Zeng
Beibei Fan and Cong Cao
Publisher: name: IEEE
Year: 2025

Magnetic anomaly detection, as a new detection technology with excellent environmental adaptability and concealment, can be used to detect and track ferromagnetic targets in invisible environments. However, it is extremely challenging to calculate the target's motion state information based on the passively detected magnetic field signals. To address this problem, we propose a magnetic anomaly target motion state detection method based on 3D convolutional neural network(3D CNN). 3D CNN is an advanced technology that can extract and analyze temporal and spatial features in image sequences, to achieve classification and action recognition. A magnetic field sensor array is used to collect a group of magnetic field signals with relative position information, which are converted into time-related images after preprocessing. These images are then input into a 3D CNN, which uses the target mobility and the detection correlation of the sensor array to detect the interested motions from the continuous motion stream, and then classifies the interested motions to complete the target motion state recognition. Simulation experiments show that the proposed method can effectively identify the motion state of ferromagnetic targets, improve the efficiency and accuracy of the identification of the motion state of magnetic anomaly targets, and provide theoretical basis and empirical reference for the further application of magnetic anomaly data such as the identification, tracking and positioning of ferromagnetic targets.References: 1. Y. J. Xu, Z. Wang and S. C. Liu, Ieee Geosci Remote S 19 (2022). 2. Y. LeCun, Y. Bengio and G. Hinton, Nature 521 (7553), 436-444 (2015). 3. Z. Y. Wang, Z. L. Kong and S. Chandra, Proc Int Conf Data, 1418-1429 (2019).
Magnetic anomaly detection, as a new detection technology with excellent environmental adaptability and concealment, can be used to detect and track ferromagnetic targets in invisible environments. However, it is extremely challenging to calculate the target's motion state information based on the passively detected magnetic field signals. To address this problem, we propose a magnetic anomaly target motion state detection method based on 3D convolutional neural network(3D CNN). 3D CNN is an advanced technology that can extract and analyze temporal and spatial features in image sequences, to achieve classification and action recognition. A magnetic field sensor array is used to collect a group of magnetic field signals with relative position information, which are converted into time-related images after preprocessing. These images are then input into a 3D CNN, which uses the target mobility and the detection correlation of the sensor array to detect the interested motions from the continuous motion stream, and then classifies the interested motions to complete the target motion state recognition. Simulation experiments show that the proposed method can effectively identify the motion state of ferromagnetic targets, improve the efficiency and accuracy of the identification of the motion state of magnetic anomaly targets, and provide theoretical basis and empirical reference for the further application of magnetic anomaly data such as the identification, tracking and positioning of ferromagnetic targets.References: 1. Y. J. Xu, Z. Wang and S. C. Liu, Ieee Geosci Remote S 19 (2022). 2. Y. LeCun, Y. Bengio and G. Hinton, Nature 521 (7553), 436-444 (2015). 3. Z. Y. Wang, Z. L. Kong and S. Chandra, Proc Int Conf Data, 1418-1429 (2019).