## 1 Introduction
A logistic regression model with discrete Fourier transform (DFT) features trained with the simulated data was applied to experimental data to evaluate classification performance. It is difficult to acquire experiment data for training in underwater environments. Therefore, the results show that this approach could be an alternative method due to a lack of experiment data. In other target echo analyses, many statistical models representing target backscattering have been studied, such as Weibull; log-normal (LN) models with varying levels of physical basis, such as the Rician distribution; and Swerling models and their extensions [13; 14; 15]. A scheme based on a time-reversal technique was proposed to improve the detection of a cylindrical object placed proud on the seafloor [16]. However, there is a lack of research on long-range target detection that considers the aspect angle of a moving target and the variety of underwater environments. The underwater noise level is a function of the environment's elements, such as the sea state and surrounding sea traffic; this ultimately affects the intensity and patterns of echoes. The target's echo strength mainly depends on the type of target and the aspect angle of the incident-transmitted signal. These factors affect the SNR of the target echo and echo patterns. They eventually lead to variable SNR in a sonar's signal processing and make it more difficult to continuously detect and classify targets. In particular, low SNR signals do not yield enough feature information to distinguish them from clutter. For this reason, much research has studied returns above a preset threshold to solve the classification problem. These signals in the matched filter output data are selected for tracking, classification, and console display by sonar operators. However, the extraction process of these signals is inaccurate since the operators empirically adjust the threshold. If target echo SNR is under the threshold by the operators, the target cannot be detected and classified. Thus, extracted data from the matched filter output are very important because the data affect the operator's manual tracking and classification performance. In most military applications, it is important to continuously track and classify the target. The long PRI required to detect long-range targets means that less data can be obtained over time, so it is hard to know the changes in environmental noise and target strength soon enough to take action. For this reason, we propose a multilayer classification algorithm, which is applicable to all signals without presetting the threshold in one ping's data regardless of the signal SNR. In this paper, the approach to obtaining feature information incorporates two factors. One is that since a target submarine has a typical type, size, material and volume, the distributed energy pattern of its echo can be different than that from clutter. Second, the sources affecting the SNR of echoes are the ambient noise and target strength according to the ping's incident angle. These sources are reflected in the signal processing output and can be extracted as feature vectors from different echo areas. In general, the target return strength for a ping abean (long side) of a submarine target is 15 dB while a head-on ping off the front is known to be 5 dB [17]. This means that the SNR of the target signal varies over at least a 10-dB difference according to the aspect angle of a submarine, and thus may lead to classification performance degradation. Even if the energy in the edge region of the target echo is reduced by noise or decreased by the target aspect, the feature information of the remaining echo energy region near the center point can be retained. We focus on the surviving classifiable feature components from variations in target echo strength by extracting the spatial feature information in the sonar's matched filter output. Much research has concentrated on the separation of feature vectors by labeling target signals and clutter signals of sufficient intensity.