# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 1 Introduction
Submarine defense depends greatly on SoNaR-based surveillance, and it may be significantly improved by automatic models that recognize the target from its acoustic signature. A class-modular Multi-Layer Perceptron (MLP) network reported a ship classification accuracy of 93% in [1], whose analysis was based on real signals produced by eight vessel  
Figure 1: Basic block diagram of a SoNaR system. classes. Passive SoNaR target classification using an MLP trained with the salp swarm algorithm achieved an accuracy of 97.12% in [11], where the signals were made in a closed water circuit cavitation tunnel ([[https://www.sciencedirect.com/topics/engineering/cavitation-tunnel](https://www.sciencedirect.com/topics/engineering/cavitation-tunnel)]([https://www.sciencedirect.com/topics/engineering/cavitation-tunnel](https://www.sciencedirect.com/topics/engineering/cavitation-tunnel)) accessed on 10 January 2025) model NA-10 England, when three classes were simulated. An unsupervised generative framework utilizing a variational autoencoder (VAE) was proposed to create more disentangled representations for downstream classification tasks in [2], which obtained F1 scores much greater than 0.9 for most of the ten target classes. Few signals are typically available to train classifiers due to the operational difficulty of recording diverse vessel scenarios and the classified nature of military SoNaR data. Since these recordings often contain sensitive acoustic signatures, they are restricted as part of broader national security protocols. When the amount of data is limited, generative adversarial networks (GANs) can be used [12] to synthesize numerous passive SoNaR records and train Convolutional Neural Networks (CNNs). This study evaluated the accuracy of seven different ML techniques for ship classification using SoNaR data. The primary objective was to develop and assess an ML model that integrates real characterization data from static and moving ships. We employed eXtreme Gradient Boost (XGBoost) and Linear Discriminant Analysis (LDA) to model this scenario and compare their performances with a Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and ridge regression [13; 14; 15]. Data preprocessing could be conducted with signal compression using Principal Component Analysis (PCA) [16]. The main contributions of this study are summarized as follows:  
* A comparative evaluation of seven machine learning algorithms for ship classification using real-world passive SoNaR data. * The inclusion of LDA and XGBoost, which were not previously applied in this context, as strong candidates for real-time, accurate classification. * A detailed investigation into the effect of preprocessing techniques--such as decimation, TPSW, and PCA--on the models' performances. * The construction of a balanced dataset from real static and dynamic operations that involved 12 ships of the Brazilian Navy. The remainder of this paper is organized as follows: Section 2 presents the preprocessing pipeline and SoNaR signal transformations. Section 3 details the machine learning models employed. Section 4 outlines the experimental setup and evaluation methodology. Section 5 discusses the results, and Section 6 concludes with final remarks and future directions. Decimation techniques may be used on the raw data to optimize the system performance. Our aim was to evaluate the effectiveness and robustness of the evaluated algorithms in static and dynamic contexts, focusing on learning and identifying significant patterns and enhancing the prediction accuracy for real-world measurements. The resulting classifier was trained and evaluated using target records acquired during several data collection expeditions, one for each vessel, conducted by the Brazilian Navy at the Brazilian Operating Systems Support Center (CASOP) in the Arraial do Cabo literal area. The dataset consisted of acoustic noise signals corresponding to 12 ships in a 1500-meter-long shallow water route in a controlled environment.