# Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold
## 5 Discussion and Conclusions  
In this paper, a novel algorithm for target detection in time and frequency domain is proposed. In order to compare, the results of this method are compared with three general and practical passive sonar detection methods as sonar equation [20], detection based on the DEMON and independent component analyze (ICA) [14], and target detection based on TPSW and neural networks [4] are expressed briefly in the following. Sonar equation is the basic method for calculation detection threshold. The equation for determining the performance of passive sonar is \(DT=SL-TL-(NL-DI)\), where \(SL\) is the source level, \(TL\) is the transmission loss, \(NL\) is the noise level, \(DI\) is the directivity index of the array (an approximation to the array gain), and \(DT\) is the detection threshold. In this paper, optimum \(DT\) is selected by ROC curve. Table 3 shows the values of parameters of sonar equation in the Persian Gulf. In [14], for target detection, DEMON analysis is applied. DEMON algorithm is a narrow-band filter that reduces the ambient noises. Then, output signal are performed over the independent sources (ICA). The ICA provides a linear representation of non-Gaussian data, so that components are statistically independent, or as much independent as possible. The detection method is described in [4]. In the preprocessing and feature extraction stage, TPSW algorithm is used to extract tonal features from the average power spectral density. In the classification stage, neural network classifiers are used to evaluate the classification results, inclusive of the hyper plane based Classifier-Multilayer Perceptron (MLP). To compare proposed method with the three expressed methods same database is used. In this investigation, three categories of test data are considered. The first category includes vessels with  
Figure 19: \(DP\) for 19 test signals (1 to 14 are the target signals and 15 to 17 are only ambient noise). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **DI** & **NL** & **TL** & **SL** \\ \hline Value (dB) & 200 & 19.02 & 90 & 31.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value of parameters of sonar equation in the Persian Gulf. DI: directivity index; NL: noise level; TL: transmission loss; SL: source level. small size and fast (e.g., barges), the second category includes medium vessels (e.g., cart), and third includes large vessels with low speed. Figure 20 show ROC curve compares the result of the proposed algorithm and method expressed in [4; 14; 20] applied on same database. It is seen in Figure 20 that the result of proposed method (adaptive fusion) is obviously better than other methods. As shown in Table 4, the true detection rate is 58.75%, 61.27%, and 35.71% for [4; 14; 20], respectively, whereas for the proposed method is 85.4%. The results show that proposed method has improved true detection rate about 24% compared to the other best detection method. **Acknowledgments:** The authors are grateful to the Department of Electrical and Computer Engineering Birjand University for financial support. The authors thank the Noar Institute Research for experimental support. Finally, the authors would like to express their sincere gratitude to all of those who have offered selfless help during the course of this research. **Author Contributions:** All authors ([PERSON], [PERSON]) contributed to the research work. [PERSON] conceived and designed the prototype observation; [PERSON] performed the prototype tests and analyzed the data; [PERSON] contributed analysis tools; and [PERSON] drafted the paper. **Conflicts of Interest:** The authors declare no conflict of interest.