A Study on the bearing estimation of underwater target sound using small spaced hydrophone arrays

Abstract

The noise sources in the ocean can be classified into natural sounds caused by wind, waves, rain, and mammalian vocalizations, and anthropogenic sounds generated by human activity. These noise sources are variously present in broadband from low to high frequency. Generally, the shape of the noise generated in water varies depending on the characteristics of the noise source. A noise source such as a ship has a narrow band noise due to the reciprocating motion of the internal engine, and simultaneously produces broadband noise due to propeller-induced cavitation. There are also types of noise that occur instantaneously including a construction noise for offshore plant and vocalization of marine mammals. The problem of noise source detection and bearing estimation in water recently has received considerable attention due to their importance in military and civilian applications. A single omnidirectional hydrophone commonly has been used to measure only the magnitude of the noise generated in water. Therefore, in order to overcome the limitation of bearing estimation using a single hydrophone, several studies have been conducted to estimate the azimuth of the target located in water by connecting a number of hydrophones to the array. We developed a bearing estimation algorithm for detecting underwater targets using several shapes of hydrophone arrays placed in a small space. The proposed algorithm in this work is the method of estimating the target direction by combining the beam forming results of two or more arrays of N sensors arranged in the space. Acoustic experiments were conducted to verify the algorithm which can detect the direction of the sound source located at several known points and track the direction of the transmitted sound source attached to the starboard of vessel. The results show that the proposed method is capable of improving the accuracy for bearing estimation of underwater target (This research was funded by KIOST (PE99643, PG49550)).