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Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm
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DOI：10.1109/ACCESS.2022.3197630.png)
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Abstract
En 中文
In order to improve the weak magnetic detection ability under the background of Gaussian colored magnetic environment noise, a magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm is proposed in this paper. 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.
Keywords:
Feature extraction
Magnetometers
Time-frequency analysis
Magnetic anomaly detection
Detectors
Fractals
Wavelet packets
Anomaly detection
Principal component analysis
Gaussian processes
Principal Component Analysis
Magnetic noise
Magnetic anomaly detection
feature fusion
unsupervised learning
isolation forest
principal component analysis
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W