A high-precision magnetic target classification system based on coarse-fine two stages

By using a coarse-to-fine magnetic target classification system, the system rapidly inverts magnetic moment parameters using the Grey Wolf algorithm and physical information neural network, and combines spatiotemporal convolutional neural network and multi-head self-attention mechanism for dynamic focusing. This solves the problem of traditional magnetic induction devices being susceptible to interference and achieves high-precision and high-reliability target classification.

CN121580177BActive Publication Date: 2026-05-01NANJING UNIV +1
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Patent Information

Application Number
CN202610113825.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-28
Publication Date
2026-05-01
Estimated Expiration
2046-01-28

AI Technical Summary

Technical Problem

Traditional magnetic induction triggering devices are susceptible to environmental interference, leading to false alarms or missed alarms, which reduces the reliability and practicality of the system.

Method used

A high-precision magnetic target classification system based on coarse and fine levels is adopted. The gray wolf algorithm and physical information neural network are used to quickly invert the magnetic moment parameters. The spatiotemporal convolutional neural network and multi-head self-attention mechanism are combined to dynamically focus on key magnetic field mutations for target classification.

Benefits of technology

It improves the anti-interference capability and recognition accuracy in complex environments, reduces the false alarm and false alarm rates, and enhances the reliability and practicality of the system.

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Abstract

The application provides a high-precision magnetic target classification system based on coarse-precision two-stage, relates to the technical field of magnetic target classification, and comprises a magnetic field data acquisition component, a magnetic field data acquisition component configured to acquire magnetic field data of a target to be detected; a magnetic field data processing component, a magnetic field data processing component configured to sequentially perform coarse processing and fine processing according to the magnetic field data to obtain a global context enhanced feature vector corresponding to the target to be detected; the coarse processing comprises using a grey wolf algorithm and a physical information neural network to perform rapid inversion of magnetic distance parameters; the fine processing comprises using a space-time convolution neural network and a multi-head self-attention mechanism to perform dynamic focusing on key magnetic field mutations; and a target classification component, a target classification component configured to determine a target classification probability according to the global context enhanced feature vector to obtain a target classification result of the target to be detected. The application solves the problems of low reliability and practicability of the existing magnetic induction triggering technology through the above system.
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