## 1 Introduction  
In active sonar systems, clutter degrades the performance of target detection and overwhelms sonar operators conducting antisubmarine warfare (ASW). Clutter consists of reflected signals from underwater surfaces, the bottom, rocks, fish, and other ships not of interest to sonar operators. Traditionally, underwater target detection depends on the decisions of well-trained sonar operators. This method can be highly inaccurate due to the need for continuous monitoring of the operating sonar console. Moreover, varying underwater environments and target moving patterns make continuous detection and classification difficult, highlighting the need for effective detection and classification algorithms in such an environment. Underwater moving target detection is challenging for the following reasons:  
1. Complex underwater environment: Underwater target detection is a complex pattern classification problem involving time-varying and mixed environments. Environmental noise causes acoustic signal waveform distortion, loss of signal information, and incomplete received acoustic signals due to the complexity of the sound propagation medium. 2. Unknown target orientation: Normally the target's orientation is unknown. Aspect-dependent target strength results in a variable signal-to-noise ratio (SNR) and causes the loss of feature information. 3. Continuous target tracking: Continuous classification and tracking of weak target echoes should be desirable because a target can be a threat and can take action upon recognizing it has been detected. 4. Less feature information: Active sonar for long-range target detection has low-resolution data, so a feature vector extraction algorithm is necessary. 5. Long interval ping-to-ping: In order to detect long-range targets, a long pulse repetition interval (PRI) is used; this means that a relatively small amount of data is acquired over time. 6. Lack of data in many cases: It is difficult to acquire sufficient and representative sea experiment data. Many algorithms have been applied to active sonar detection and classification. Detection using a Markov random field [1], a contrast box detector based on statistical features of reverberation, and morphology detection of an object [2; 3] were studied for the separation of targets from reverberation. The morphology detector distinguishes the characteristics of the target and reverberation signal and processes them under the condition that the target signal is in an isolated area and the reverberation signal has a distribution of several clutters. These techniques are effective for reverberation removal but have the disadvantage of lowering the target detection rate in a single ping. Multiple ping imaging techniques using temporal and spatial features of clutter and the target have been proposed [4; 5]. The cumulative processing of multiple ping data reduces the classification rate in a single ping. It also has the disadvantage of using multiple ping signals because signals that are not filtered out of a single ping are used in multiple pings. There are also many approaches to mine detection using side-scan sonar [6; 7; 8] with high-resolution data. Mine detection approaches are for a near field detection environment that processes high-resolution image data. Therefore, it is difficult to apply in active sonar for long-range target detection with low-resolution data. Much research has been carried out since a mine's sonar signature exhibits variable characteristics according to its position and the ping's angle of incidence at the seafloor. [PERSON] et al. [9] studied target separation from clutter using spectral feature information on the seabed floor. In [9], the mathematical model of a cylindrical object proposed by [PERSON] [10] was used to generate target signals, and clutter signals based on the K-distribution model introduced by [PERSON] and [PERSON] [11; 12] were generated.