Adaptive Magnetic Anomaly Detection Method Using Support Vector Machine
Adaptive Magnetic Anomaly Detection Method Using Support Vector Machine
复制标题
使用支持向量机的自适应磁异常检测方法
DOI：
10.1109/lgrs.2020.3025572
复制
发表时间：
2022
影响因子：
4.8
通讯作者：
Xing Liu
中科院分区：
文献类型：
--
作者：
Liming Fan;Chong Kang;Huigang Wang;Hao Hu;Xiaojun Zhang;Xing Liu
磁异常检测(MAD)已被广泛应用于许多领域的磁目标检测。在低信噪比的情况下，磁异常往往被隐藏在磁噪声中，导致传统MAD方法的检测性能下降。为了提高低信噪比条件下MAD的检测概率，提出了一种基于支持向量机的自适应MAD检测方法。首先，设计了一种基于径向基核函数的支持向量机检测模型。同时，对磁异常信号进行分析，确定以正交基函数能量和磁熵作为磁异常的特征，作为模型的输入。然后，基于带有磁信号的数据集对模型进行训练。最后，将训练好的模型应用到原始磁信号中进行异常检测。与传统方法相比，该方法具有更好的性能和自适应能力。通过实际的磁噪声实验验证了该方法的有效性。结果表明，该方法可以减少虚警情况，提高低信噪比下的检测性能。
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.