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.

In the field of aviation anti-submarine warfare, magnetic detection systems often result in a large number of false alarms due to the inability to effectively distinguish between a local geomagnetic anomaly in the background field and a target magnetic anomaly. To address this problem, this paper proposes a matching detection method for the magnetic anomaly of underwater targets. This method is based on the measured data of the magnetic anomaly in the sea area, and the geomagnetic reference map of the area is generated by interpolation of the Kriging method; then, the background field signal extracted from the geomagnetic reference map and the real-time signal acquired by detection are matched by the similarity metric algorithm; finally, the target detection is realized by using the feature fusion-isolation forest algorithm. Compared with the conventional detection methods, such as time–frequency analysis, the detection rate is improved by nearly 15% and the false alarm rate is reduced by about 40%, indicating that matching detection can effectively filter out the magnetic anomaly in the background field, reduce the false alarm, and improve the detection rate in the real geomagnetic environment, which has certain application value.