# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 1 Introduction  
There has been an increasing interest recently in research on maritime vessels. A submarine is an advanced instrument for maritime deterrence as a specialized platform for the surveillance of sudden attacks. The submarine remains hidden while scanning the environment to effectively perform its tasks. Then, it operates silently using acoustic and electromagnetic waves, avoiding any signal emission while passively listening to the environment. While previous studies have shown promising results in ship classification using synthetic or simplified SoNaR data [1, 2], there is a lack of validation on real-world, multiclass datasets recorded under operational conditions. This gap limits the deployment of these models in practical naval surveillance scenarios. Therefore, this study explored machine learning for ship classification using underwater acoustic signals. The objective was to test a set of algorithms that may help SoNaR operators estimate the classes of ships. Underwater environments present a higher conductivity for acoustic waves than for electromagnetic waves [3]. High-frequency electromagnetic waves are severely attenuated within a few meters under normal underwater conditions [4]. Active SoNaR transmits an acoustic wave and receives its echo to gather information about the transmission medium. Passive SoNaR only listens to acoustic waves from the environment and conceals its operations. Underwater acoustic signal processing is a significant international security issue because it enables the detection, tracking, and classification of underwater threats, thereby safeguarding strategic maritime assets, such as submarine cables and offshore energy facilities [5]. Passive SoNaR aids in protecting assets from sabotage or unauthorized access by monitoring and identifying potential threats. Passive SoNaR provides intelligence-gathering purposes by monitoring the activities of foreign vessels and submarines. A precise classification of these vessels yields valuable insights into the capabilities and intentions of other nations, thereby contributing to an overall national security strategy. Concerns about maritime security have recently escalated owing to the rise in illicit activities, including smuggling, piracy, and trafficking. The detection and identification of ships operating in a given area is a pivotal aspect of maritime security. Traditional vessel identification methods, such as visual inspection and radar systems, face limitations in terms of visibility, particularly under adverse conditions. The acoustic signatures and unique sound patterns emitted by vessels are the most commonly used alternatives for identification purposes because each vessel possesses a distinctive acoustic signature [6]. A plethora of SoNaR technologies are widely used for seafloor mapping. SoNaR Side Scan (SSS) can provide an environmentally friendly alternative for enumerating marine populations [7]. Synthetic Aperture SoNaR (SAS) was used to model underwater acoustic propagation [8]. Additionally, research on pipeline technology and Multiple-Input Multiple-Output (MIMO) SoNaR has proposed an enhancement of the effectiveness of underwater communication systems (UWCs) [9]. The acoustic signal acquired by the SoNaR was processed to detect possible targets, as it is shown in Figure 1. Ship classification indicates potential threats to early warning systems. The accurate classification of encountered vessels enables the planning of appropriate measures to effectively mitigate threats. A human SoNaR operator often faces uncertainty when classifying contacts, which may hinder appropriate actions. A possible solution is to use a support system fed by the signal to extract features for classifying the contacted ship. Trained operators often detect and categorize submarine threats using low-frequency analysis and recording (Lofar) displays of spectral and temporal profiles of passive SoNaR waves [10].