# Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold
## 1 Introduction  
Due to the severe attenuation of radio frequency and optical signals under the sea, the use of audio signals is often a great way to detect under water targets. Sonar (Sound Navigation and Ranging) is a technique that uses sound propagation to navigate, communicate with or detect objects on or under the surface of the water, such as other vessels. These systems record the sound waves using hydrophone and by processing these signals, we can detect, locate, and classify different targets. Sonar is divided into two families of active and passive that in the active type, by sending sound pulses (pings) and analyzing the echoes of them, we can identify the type, distance, and direction of the target. In the passive sonar, which is the topic of the project, underwater acoustic signals received by the hydrophone and after pre-processing, signal can be detected by analyzing the content of the target. The passive sonars use waves and their unwanted vibrations to identify vessels. Sounds do not generate only from the vessels so the factors such as waves, vibrations from the bottom of sea, fishes, and so on cause confusion target detection. Therefore, for target detecting, we need an intelligent adaptive threshold level that at the different situations and environmental parameters to minimize error detection. Typically, the detection of sonar targets is performed using sonar equations. These equations have many variables, such as the source level, the transmission path loss, reflection lose, sound absorption, and so on that some of them are the functions of other variables. In this method, the source level (SL) is a measure of the acoustic intensity of the signal measured one meter away from the source. This parameter assumes that the acoustic energy spreads omnidirectionally outwards away from the source. However, most acoustic sources are designed to focus the acoustic energy into a narrower beam in order to improve efficiency. This effect is accounted for in the sonar equations by the Directivity Index (DI) as a measure of focusing. The DetectionThreshold (DT) is a parameter defined by the system. If the observed signal to noise ratio exceeds the detection threshold, then a target is assumed to be present. The intensity of an acoustic signal is reduced by range. This reduction in the acoustic signal with distance from the source is due to the combined effects of spreading and attenuation and is accounted by the transmission Loss Term (TL). The Target Strength (TS) is a measure of how good an acoustic reflector the target. The echo level will increase with target strength. Figure 1 shows the relationship between the various components of the hydrophone sonar arrays and decisions about the presence/absence of target. Target detection with passive sonar has found to be so interesting for many researchers during last ten years. Generally, target detection algorithms are applied in three domains: in time domain, in frequency domain and fusion time-frequency domain. Later, in time domain, [PERSON] performed a combined experimental and numerical study to investigate Sonar Equation for calculate signal to noise ratio in [1]. [PERSON] in [2] reported that DEMON (Detection Envelope Modulation on Noise) narrowband analysis that furnishes the propeller characteristic: number of shafts, shaft rotation frequency and blade rate of the target. [PERSON] reported in [3] that using Receiving Operating Characteristics (ROC) curve improves the target detection performance in Sonar Equation. [PERSON] et al. performed Two-Pass Split-Windows (TPSW) algorithm and Neural Network to classify under water signal [4]. [PERSON] performed non-Gaussian function for determines Detection Threshold (DT) in [5]. [PERSON] et al. describes the forecasting of probability of target presence in a search area [6] (also referred to as the PT map) when considering both detection and non-detection conditions. [PERSON] et al. describes DEMON algorithm for detecting target in passive sonar [7].