## 1 Introduction  
Detection of objects in the unfavorable environment is an important issue that receives great interest in various fields, including the military and civilian areas. Detecting an object on a seafloorcomposed of sand and mud in the shallow water is a significant technology in the military detection field [1; 2; 3]. In this problem, the reflections coming from the clutter surrounding a target and the ambient noise are the main hurdles. Furthermore, when the target is lying on the seabed in the shallow water, the detection is more difficult because the noise radiated by the target is maintained to be minimal and its target strength is relatively reduced by a narrow incident angle of the signal from the active sonar source. Thus, effective detection methods under such an environment are required to be developed. The actual underwater environment considered in this study is the Yellow Sea in Korea. The depth of the Yellow Sea is less than 100 m, so it may be anticipated that small submarines can find places on the sea floor to exploit. Thus, the target considered here is like a cylinder with a length of 20 m and a radius of 2 m. For detecting this type of target at a distance from 500 to 800 m, an active sonar is designed with the linearly frequency modulated (LFM) signal of a band from 6 kHz to 20 kHz and a planar array of 9 m by 11 m [3; 4; 5], which has a bearing-range resolution of 0.67 m\({}^{2}\). Figure 1 schematically illustrates the underwater environment considered in this study. Since the detection area is from 500 m to 800 m, the grazing angle decreases from 10.2\({}^{\circ}\) to 6. 4\({}^{\circ}\), which can hardly be distinguished by the sonar system due to a relatively wide vertical beam width. Since this sonar system uses a mid-frequency LFM signal, imaging is impossible and the detection method is based on the approach proposed in [3; 6]. There are several approaches to the similar problem. One of them is the application of a side scan sonar to acquire images of the detection fields [7; 8; 9]. Unfortunately, obtaining clean images by a side scan sonar requires slow scan speed and additional efforts are required to recognize a target submerged into the sediments. Various studies have been carried out from the viewpoint of classification. In [10], [PERSON] et al. investigated four information fusion methods for classification of underwater objects through two sidescan sonar images. [PERSON] et al. used various machine learning models to create prediction maps and prediction uncertainty maps [11]. The study presented in [12] proposed an automated target detection approach that combines image based feature extraction, independent component analysis, and unsupervised classification. It is demonstrated its suitability for detecting small targets in side scan sonar imagery. In [13], [PERSON] and [PERSON] introduced a new context-dependent classification algorithm and demonstrated its performance using measured data. In this situation, the approach employed by the Applied Physics Lab. (APL) at the University of Washington, Seattle, WA, USA draws attention for detecting sea mines at the bottom of the seabed in the shallow sea [1; 2]. This approach is based on the mathematical target and seabed models to obtain the features of the bottoming target, which are simultaneously verified with the measurements. Through this study, the target signature database is constructed to enable the detection probability of the active sonar system to increase. However, this approach has a disadvantage of investigating whether each received signal matches any component in the database. This is very time-consuming. Thus, in order to save this time and effort, the signal energy-based threshold scheme can be applied. Figure 1: Schematic for the underwater situation considered in the study. That is, for a specific received signal that has greater energy than the prespecified threshold only, comparison with the database is carried out, but this still needs comparison time.