## 3 Simulation of Feature Enhancement Extraction Based on Real Ship Data
### Real-Ship LOFAR Spectrum Experiment  
The loss curve in Section 3.1 verifies the convergence of the model, and this section verifies the accuracy of the model's G-BRBM representation of underwater targets. Comparative simulation and comparison experiments of sonar detection spectral feature enhancement based on deep autoencoder network with measured sonar detection LOFAR spectrum of large underwater vehicles were used as an example. The signal of large underwater vehicles involved in this study was acquired from a hydrophone array, which was approximately 15 km away from the target. The test was conducted under sea state level 3. By setting the parameters of hydrophone array element number, array sensitivity, detection distance, target sound source strength, and test sea state, this paper constructs a real-ship validation environment for enhanced extraction of weak line spectral features of the large underwater vehicles by the DAE model under low signal-to-noise ratio condition. Figure 3 depicts the schematic diagram of obtaining sonar detection signals of large underwater vehicles in this study. The experimental environment parameters are enumerated in Table 1.  
The traditional LOFAR line spectrum enhancement approach employing a time accumulation algorithm initially transforms the color LOFAR graph into a grayscale representation to facilitate line spectrum enhancement. Subsequently, a histogram equalization algorithm is employed to effectively expand the commonly employed brightness, enhance local contrast without impacting the overall contrast, and render the image visually distinct. Last, image convolution denoising is performed to eliminate Gaussian white noise and \"pepper salt\" additive noise induced by background noise within the image, yielding the \begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Quantity** & **Parameters** \\ \hline Number of hydrophones & 64 \\ Distance & 15 km \\ Sea state level & 3 \\ The target’s source level & 160 dB \\ Test frequency band & 10–300 Hz \\ Frequency resolution & 0.1 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental environment and parameters. Figure 3: Schematic diagram of sonar detection of large underwater vehicles. Figure 2: Loss curve for the DAE model. In Figure 8, it is clear that there are some distinctive characteristic spectrum throughout the time course, but because of the large number of interferences in the marine environment, the line spectrum features show the characteristics of interferences, breaks, deformations, and sporadic features. In order to further achieve the feature enhancement for subsequent extraction, the LOFAR spectrum is reconstructed with the coding and decoding mechanism of the DAE network. After the deep autoencoding network obtained by the layer-by-layer greedy algorithm for the G-BRBM, samples of real ship data for typical operating conditions are separated by certain frequency intervals, which are described in Figure 9. The specific steps are as follows:  
1. The generated LOFAR spectrum is encoded with a DAE, the image language is transformed into a matrix form as an input for subsequent calculations and the LOFAR spectrum is deflated into a 400 \(\times\) 400 \(\times\) 3 RGB image through the resize operation. The matrix size depends on the frequency resolution of LOFAR spectrum, the selected bandwidth on the one hand, and the selected time duration on the other. In order to make the denoising network model process natural images and calculate the nonlinear data model with stronger computational ability, the autoencoder designed in this paper incorporates an activation function f(x) such as sigmoid(x) at the end of each layer;
2. The generated matrix is used as input, and the layer-by-layer greedy algorithm is used to process the G-BRBM to obtain the DAE network, which kernel computes the separated subsets one-by-one and obtains the output, so that the background noise in original data is suppressed while the feature line spectrum is enhanced;
3.