Instance-based novelty detection in passive sonar signals. (8th July 2022)

• Record Type: Journal Article
• Authors: Muniz, Victor Hugo Da Silva; Filho, João Baptista De Oliveira e Souza; Honorato, Eduardo Sperle
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 3(2022)
• Page Start: 161
• Page End: 171
• Publication Date: 2022-07-08
• Subjects: passive sonar -- novelty detection -- automatic classification systems -- instance learning -- kNN -- k-nearest neighbours; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In submarines, sonar operators have the main task of identifying potential threats, named as contacts in the military jargon. The principal tool exploited when dealing with such situations is the passive sonar system. Automatic contact classification models may relieve the huge sonar operator workload but require mechanisms capable of identifying any contact not considered during system development. This paper discusses the development of a hierarchical instance-based detector of unknown contact classes for passive sonar signals, focusing on practical strategies for its hyperparameter tuning and performance assessment. Experimental data exploited in system evaluation comprises the acoustic noise irradiated by 28 ships belonging to 8 classes. These ships were submitted to different operational conditions in several runs conducted in an acoustic range. The k NN algorithm has performed best, achieving a novelty detection rate of 78.0%, associated with an average known case identification rate of 95.0%, considering a five unknown class evaluation scenario.