Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

Abstract

Under the background of Gaussian colored environmental magnetic noise, for the problem that a supervised learning algorithm has high requirements on samples and poor practicability, a magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm is proposed. The method uses different feature algorithms to extract the statistical features, time-frequency features and fractal features of the signal, reduces the dimensionality of the features by principal component analysis (PCA) and generates feature fusion tensors. Finally the IForest algorithm is used to achieve target detection. The simulation and experimental results show that the method has a higher detection rate under different SNR of Gaussian color noise, which is approximately 5%-18% higher than that of the traditional feature detection algorithm. This method can train an effective detection model with only a small number of negative samples. Compared with the fully connected neural network (FCN) model trained with unbalanced samples, the detection rate increases by approximately 5%-12%, and it takes less time.