# Underwater Target Recognition Based on Multi-Decision
## 5 Numerical Results
### Multi-Step Decision LOFAR Line Spectrum Enhancement Algorithm Validity Test  
In this section, the audio data of ShipsEar (a database of measured ship radiated noise) is used to verify the effectiveness of the algorithm. In the process of testing the effectiveness of the multi-step decision LOFAR line spectrum enhancement algorithm, morphological component analysis is required. In Section 2.1.1, we detailed the use of the RSSD algorithm to construct the optimal sparse representation of the high and low-resonance components in the ship radiated noise. In the RSSD algorithm, it is necessary to select an appropriate filter scaling factor according to \begin{table}
\begin{tabular}{c c} \hline \hline
**Lab Environment** & **Configuration** \\ \hline Operating system & Ubuntu 16.04 \\ \hline Graphics card & GTX 1080 ti \\ \hline Programming language and & Pycharm 2019.1 + Python 3.6 \\ software & Matlab R2016b \\ \hline \multirow{4}{*}{Deep-learning library and software toolbox} & Keras 2.3 \\  & (tensorflow backed) \\ \cline{1-1} \cline{2-3}  & Librosa Audio processing \\ \cline{1-1} \cline{2-3}  & library (python) \\ \cline{1-1} \cline{2-3}  & TQWT Toolbox \\ \cline{1-1} \cline{2-3}  & (Matlab language) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental hardware platform and software support. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline W & Fishing boat, trawler, mussel harvester, tugboat, dredge \\ X & Motorboat, pilot boat, sailboat \\ Y & Passenger ferry \\ Z & Ocean liner, ro-ro ship \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Four types of ship targets. the waveform characteristics of the signal, to calculate the parameters corresponding to the two types of resonance components, and construct the corresponding wavelet basis functions. In this regard, we have completed the calculation of the parameters with the help of MATLAB. For the signal decomposition algorithm based on resonance, the parameters setting for extracting high-resonance components are \(Q_{h}=4\), \(r_{h}=3\), \(J_{h}=32\), and the parameters setting for extracting low-resonance components are \(Q_{l}=1\), \(r_{l}=3\), \(J_{l}=3\). The energy distribution of the low-resonance component signal and the high-resonance component signal is finally calculated as follows:  
From the energy percentage of each frequency band in Figure 6, the energy distribution of the low-resonance component is mostly concentrated in the higher frequency band (greater than 1000 Hz), while the energy distribution in the low-frequency band is very small. Compared to Figure 7, we find that higher energy distribution of the original signal comes from the low-resonance component. In Figure 8, most of the energy of the high-resonance component is concentrated in the low-frequency narrow band, and the narrow band energy distribution characteristic is usually regarded as a line spectrum. In previous studies, the low-frequency line spectrum is the main manifestation of mechanical noise and propeller cavitation noise in the LOFAR spectrum. It is also an important basis for the identification of ship radiated noise. Therefore, the separated high-resonance component retains the main features of underwater target recognition well. In addition, Spectral Correlation Coefficient (SCC) [40] can also be used to measure the effectiveness of the RSSD algorithm. The physical significance of the spectral correlation coefficient is measuring the similarity of the power spectrum of the two signals, which is defined as follows:  
\[C_{A,B}=\frac{\int_{f_{1}}^{f_{2}}N_{A}(f)*N_{B}(f)df}{\sqrt{\int_{J_{1}}^{f_{ 2}}N_{A}^{2}(f)*\int_{f_{1}}^{f_{2}}N_{B}^{2}(f)df}}, \tag{30}\]  
where \(N_{A}(f)\) and \(N_{B}(f)\) represent the power spectrum of the two types of signals A and B, respectively. \(f_{1}\) and \(f_{2}\) represent the range of the power spectrum. This means that the radiated noise of the two types of ships with a higher degree of difference has a smaller spectral correlation coefficient.