4.7 Article

Adaptive Magnetic Anomaly Detection Method Using Support Vector Machine

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3025572

关键词

Magnetometers; Entropy; Magnetic resonance; Support vector machines; Signal to noise ratio; Adaptation models; Magnetic noise; Features selection; magnetic anomaly detection (MAD); magnetic entropy; orthonormal basis function (OBF) energy; support vector machine (SVM)

资金

  1. National Natural Science Foundation of China [51909216]
  2. Natural Science Basis Research Plan in Shaanxi Province of China [2020JQ-151]
  3. Fundamental Research Funds for the Central Universities [31020190QD033]

向作者/读者索取更多资源

Magnetic anomaly detection (MAD) is widely used for detecting magnetic targets, but its performance decreases in low signal-to-noise ratio (SNR) situations. To address this issue, we propose an adaptive MAD method using support vector machine (SVM) and validate its performance with real magnetic noise, showing improved detection performance compared to traditional methods.
Magnetic anomaly detection (MAD) has been widely used for detecting the magnetic targets in many areas. In low signal-to-noise ratio (SNR) situations, the magnetic anomaly is usually buried in the magnetic noise, which leads to a decrease in the detection performance of traditional MAD methods. In order to improve the detection probability of MAD in low SNR, we propose an adaptive method of MAD using support vector machine (SVM). First, we design a detection model using SVM with radial basis function (RBF) kernel function. Meanwhile, we analyze the magnetic anomaly signal and determined the orthonormal basis function (OBF) energy and magnetic entropy as the features of the magnetic anomaly, which are as the input of the model. Then, the model is trained based on the data set with the magnetic signals. Finally, the trained model is applied to the raw magnetic signal to detect the anomaly. Compared to the traditional methods, the proposed method has a better performance and adaptive ability. The proposed method is validated by the real magnetic noise. The results show that the proposed method can reduce the case of false alarm and improve the detection performance in low SNR.

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