## 1 Introduction
However, using multiple ping data causes a reduction in the classification rate for a single ping. Moreover, using multiple pings causes the unfiltered signal in a single ping to accumulate across multiple pings. There are many approaches to detecting mines using a side-scan sonar with high-resolution data [10; 11; 12]. However, these approaches are mainly employed for short-range detection purposesusing high-resolution image data. This makes it difficult to apply to low-frequency active sonar for long-range target detection using low-resolution data. A variety of studies have been conducted to assess different properties of the sonar feature of the mine depending on its location on the seabed and angle of incidence of the ping. [PERSON] et al. [13] used the spectral feature information on the bottom of the sea to separate the target from the clutter. In [13], the target signals were generated using the mathematical model of a cylindrical object proposed by [PERSON] [14], and the clutter signals were generated based on the \(K\)-distribution reverberation model introduced by [PERSON] and [PERSON] [15; 16]. To evaluate the classification performance, a logistic regression model trained with the simulated data was applied to the experimental data. It is very difficult to obtain experimental data for training in an underwater environment, so this approach may be an alternative method in situations where experimental data are scarce. In addition, a method based on the time-reversal technique has been proposed to improve the detection of cylindrical objects on the seafloor [17]. Although many studies have been conducted on the short-range detection of fixed objects on the sea floor such as mines, studies reported on long-range detection of moving targets in an underwater environment are insufficient. The level of underwater noise is dependent on environmental factors such as sea state and surrounding vessels. These factors influence the intensity and pattern of the underwater target's echo. Additionally, the strength of signals reflected from the target also depends on the type of target and the angle of incidence of the transmitted signal. These factors affect the echo strength and echo pattern of the target. This leads to a variable signal-to-noise ratio (SNR) in the signal processing of the sonar and make it more difficult to continuously detect and classify the target. The target signal with low SNR does not provide enough features to distinguish it from clutter. For this reason, many researchers have tried to solve classification problems for echoes above a preset threshold by the sonar console operator. These matched filter output data above a preset threshold are selected by the sonar console operator for detection, tracking, classification, and console display. The process of selection of these signals is not accurate, as the threshold is adjusted according to the sonar console operator's experience. If the SNR of the target's echo is below the threshold set by the sonar console operator, the sonar console operator cannot detect and classify the target. Therefore, the matched filter output data is very important as it affects the performance of the sonar console operator's manual detection, tracking and classification. It is very important to continuously detect, track and classify targets, especially in sonars used for military purposes. To detect long-range targets using active sonar, we use long PRI, meaning that fewer data can be obtained over time, and it is difficult to quickly notice changes in environmental noise and target strength. To overcome this problem, we propose a target classification algorithm that can be applied to all signals above the noise level regardless of the preset threshold by the sonar console operator and SNR of the signal in one ping. In this paper, we propose a method of obtaining feature information from human auditory characteristics.