## 4 Simulation and Result Analysis
### Contribution Rate Analysis  
Based on the signals of the three magnetic fields obtained from the aforementioned simulations, feature extraction is conducted according to the contribution rate evaluation process. The three components of the submarine's ELF magnetic field--the wake magnetic field, propeller shaft rotation effect magnetic field, and internal equipment radiation magnetic field--typically exhibit a superposition of distinct periodic line spectra and low-frequency band spectra when measured using magnetic sensors. For periodic signals, the line spectrum and the amplitude at the corresponding frequency points serve as a type of frequency-domain feature. Additionally, the centroid frequency and half-energy bandwidth of the low-frequency band spectrum also possess characteristics related to underwater platforms. Due to the complexity of magnetic noise in actual environments and the low signal-to-noise ratio of underwater platform ELF magnetic fields, higher-order statistical features are extracted from the time-domain signals. Therefore, the line spectrum and its amplitude, centroid frequency, half-energy bandwidth, and higher-order statistical features are extracted as the frequency-domain features of the underwater platform's ELF magnetic field. According to the calculation method for the threat matrix described previously, different detection systems and scenarios result in varying signal sensitivities and processing methods, leading to different threat matrices. Therefore, when determining the detection mode and scenario, the threat level of different signal features can be established. The submarine's fixed detection array is taken as an example. In the fixed mode, the magnetic detection system remains stationary, while the submarine's relatively slow speed (3 m/s) allows the system and the submarine to be considered relatively fixed within a certain signal acquisition time. This fixed mode typically operates at a distance from the target, resulting in weaker signals. However, as the magnetic detection system does not move, it can employ various techniques to better eliminate background noise. Research into the generation mechanism and propagation patterns of extremely-low-frequency magnetic fields from underwater platforms reveals that the magnetic field signals consist of a direct current (DC) component superimposed with fluctuating components, with the DC component being predominant. In the absence of relative spatial changes, the measured signal represents a time series with minimal amplitude variations, indicating that the DC component remains constant for the measurement system while only the fluctuating components change [25]. Due to the presence of clear periodic frequency characteristics in the fluctuating terms, it is possible to remove the constant DC component and utilize the frequency-domain characteristics of the signal for detection. This approach is suitable for monitoring critical waterways and defending against intrusions in important straits, such as by deploying surface/underwater magnetic detection arrays. Therefore, for fixed detection, the band spectrum information (centroid frequency and half-energy bandwidth) in the frequency-domain characteristics is considered the primary indicator, followed by the time-frequency characteristics and time-domain characteristics [16]. Hence, the designed threat matrix is denoted as \(T\), and the corresponding feature weight vector is denoted as \(w\). The maximum eigenvalues of the threat matrix and the feature matrix, along with their corresponding characteristic vectors, are calculated, and a consistency test is performed on the maximum eigenvalues. The elements within the Threat Matrix T correspond to the following six characteristics: line spectrum and its amplitude, half-energy bandwidth, centroid frequency, standard deviation, skewness factor, and kurtosis factor. The CR of the threat matrix \(\approx\) 0.0376 \(<\) 0.1, which meets the consistency test. \[T = \left[