![]() This information is vital for a wide range of tasks such as collision avoidance, Aid to Navigation (AToN) entries, avoiding illegal activities and seeking emergency assistance etc. The classification trials revealed a high accuracy of 94.7%.Ī highly desirable capability by a vessel in an open sea is the ability to achieve awareness about marine traffic in its surroundings and map ocean depth using its hydroacoustic instrumentation onboard. The proposed analysis and classification techniques were assessed through trials with 877 real acoustic signatures recorded under varying conditions of ship’s speed and sea state. Thirdly, a novel set of reference parameters are provided that aid classification into categories of large merchant ship type 1, large merchant ship type 2, large merchant ship type 3, medium merchant ship, oiler, car carrier, cruise ship, fishing boat and fishing trawler. Secondly, this paper presents a classification framework that uses the features extracted from DEMON spectra and compares them with a reference set. Firstly, some novel DEMON spectra analysis techniques are proposed to estimate a water vessel’s number of shafts, speed, and relative course. This paper makes three novel contributions in this area. Parameters such as number of shafts and vessel course and speed can effectively aid the vessel classification process. Reported research mostly focuses on SRPM and NOB. To make the process systematic, calculation of the parameters of the received acoustic samples can be visually analyzed using Detection of Envelope Modulation on Noise (DEMON) spectra. Empirical knowledge comes with experience, and the manual process is prone to human error. Based on this information vessel classification is performed. Expert sonar operators use their empirical knowledge to estimate a vessel’s SRPM and NOB. The valuable information that can be extracted from the recorded acoustic signature includes shaft revolutions per minute (SRPM), number of blades (NOB), number of shafts, course and speed etc. The raw sonar data consisting of the acoustic signatures is generally observed manually by sonar operators for suggesting class of query vessel. One of the typical signatures emitted by a vessel is its acoustic measurements. Vessels in vicinity can be identified using their signatures. ![]() ![]() Identifying marine traffic in surroundings is an important task for vessels in an open sea. A classification framework as well as a set of reference parameters for comparison are put forth. This paper presents some novel methods to estimate a vessel’s number of shafts, course, speed and classify it using the underwater acoustic noise it generates.
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