## 1 Introduction  
The attenuation of radio waves is more severe underwater compared to air, so that only very close distance targets can be detected. Therefore, sound waves rather than radio waves are used to detect underwater targets [1]; sound waves can be detected at relatively long distances, although the transmission distance of sound waves is also dependent on the underwater environment. The equipment used to detect underwater targets using sound waves is called sonar, which can be divided into two main categories: active sonar and passive sonar. In the latter case, the sound signal generated by the target is received and detected, while in the former case, the acoustic signal is transmitted and the echo returned from the target is detected. When the acoustic signal from an underwater target is low, it would be difficult to detect with a passive sonar, but active sonar could be used instead. As underwater targets become more and more quiet, they are difficult to detect using passive sonar, and so active sonar must be used. When using active sonar to detect underwater targets, some echoes are not only reflected from underwater targets, but also by the sea surface, sea bottom, sea bed topography, reefs, shoals of fish, and other ships that are not of interest to the sonar console operator. Signals reflected by a cause other than the target are called clutter. Distinguishing between target and clutter signals is very difficult because there are many clutter signals other than target signals while using active sonar to detect underwater targets [2; 3; 4]. Therefore, clutter degrades target detection performance in active sonar systems and makes target detection difficult for sonar operators performing anti-submarine warfare (ASW). In general, the detection of underwater targets is up to the decision of a trained sonar console operator. This underwater target detection method can be inaccurate because it requires the sonar operator to continuously monitor the console screen. In addition, it is difficult to continuously detect and classify the movement of a target in various underwater environments. Therefore, an effective detection and classification algorithm is required in these environments. Detecting a maneuvering target underwater is difficult for the following reasons:  
* Target detection is a complex pattern classification problem due to changes over time and various underwater environments. The complexity of the acoustic transmission environment leads to loss of signal information, distortion of the acoustic signal waveform, and incomplete receipt of acoustic signals. * Once a target is detected it will take evasive action; therefore it is necessary to continuously classify and track weak target echoes. * Since the detection of long-range targets using low-frequency active sonar is low-resolution data, feature extraction algorithms are required. * Long pulse repetition intervals (PRIs) are used to detect long-range targets, which results in relatively small data accumulation over time. * It is very difficult to obtain data from various sea experiments on underwater targets. Various algorithms have been developed and applied in the field of active sonar detection and classification. These include methods that detect targets from reverberation like morphological and statistical approaches to improve detection [5], a contrast box detector based on the statistical features of reverberation [6], and detection using Markov random field [7]. The morphological detector distinguishes between the characteristics of the target signal and the reverberation signal, and processes them under the condition that the target signal is in an isolated area and the reverberation signal has multiple clutter distributions. This method is effective in removing reverberation, but it has the disadvantage of slowing down the target detection rate with a single ping. A classification method using temporal and spatial features of targets and clutters from multiple pings has also been proposed [8; 9].