## 3 Simulation of Feature Enhancement Extraction Based on Real Ship Data
### Real-Ship LOFAR Spectrum Experiment
Fine-tune according to the discriminative criteria of underwater targets and observe the size of the error, if the error is within the acceptable range, then output the results; if the error is too large, then enter the process of back propagation of the error, the error signal through the output layer, the implied layer to the input layer-by-layer to the weights and bias values to correct, so that the error is gradually reduced within the acceptable range. Finally, the processed matrix is re-decoded and transformed into the image language. Figure 8: LOFAR spectrum of sonar detection. Figure 9: Fine-tuning flow chart. Figure 10 shows the result of the DAE's reconstruction:  
The reconstructed LOFAR graph based on the DAE method is compared with the original graph, and the statistics such as the number of line spectrum, density of line spectral distribution, and the minimum frequency interval in the frequency band of 10-300 Hz are further calculated. For easy observation, the LOFAR comparison plots after and before the reconstruction of the DAE method are shown in Figures 10 and 11, with the former after reconstruction and the latter before reconstruction, respectively. In the comparison with the traditional image processing based methods, it can be seen that reconstructed image obtained by our proposed DAE model is more advantageous. Figure 12 shows the image obtained by the traditional LOFAR line spectrum enhancement method based on the time accumulation algorithm, which employs the traditional histogram equalization algorithm to accumulate and sum the corresponding pixel values of the points in the image over the entire time course, finally achieving the enhancement processing of the line spectrum features in the LOFAR spectrum. In the comparison with the traditional image processing based methods, it can be seen that reconstructed images obtained by our proposed DAE model are more advantageous. As shown in Table 2, within the frequency range of 10-300 Hz, the LOFAR spectrum processed by the DAE method has an 18 line spectrum, with a line spectral density of 0.064 and a minimum frequency interval of 2.5 Hz between adjacent lines. In contrast, the  
Figure 11: LOFAR spectrum before processing. Figure 12: Traditional method after processing. Figure 10: DAE method reconstruction results. result of traditional method is an 11 line spectrum, with a line spectral density of 0.038 and a minimum frequency interval of 7.0 Hz. Consequently, the DAE method significantly enhances the number of line spectrum and increases the discernable line spectral density, demonstrating its superiority over traditional line spectral enhancement techniques. In the before-and-after comparison of extraction results and comparison with traditional time-accumulation algorithms, our proposed DAE-based line spectrum enhancement extraction method has following advantages:  
1. Traditional algorithms have limited enhancement effect on broken line spectrum, whereas our proposed DAE can adaptively drive repair line spectrum with hydroacoustic data to remove image noise and, thus, enhance line spectral features while maximizing the retention of original feature information;
2. The traditional method can enhance the contrast between the line features and background, but the enhancement effect is poor for the weak line spectrum, while our proposed DAE can also increase the contrast between the weak line spectrum and background, which is more intuitive visually, with a higher degree of separation, which is conducive to the identification of weak/very weak line spectrum, Compared to traditional methods, the DAE method can achieve more than double the extractable line spectral density within the 10-300 Hz frequency band;
3. The features after DAE reconstruction are more regular, and the phenomenon of line spectrum crossing and overlapping is reduced, which is conducive to feature extraction at a later stage;
4. The DAE model enhances the line spectrum features of LOFAR spectrum with a faster response time, and the average processing time per sample is 26 ms.