Passive Bearing Estimation Using a 2-D Acoustic Vector Sensor Mounted on a Hybrid Autonomous Underwater Vehicle

Abstract

The bearing estimation problem of low-frequency underwater acoustic sources is of paramount importance in antisubmarine warfare and passive acoustic monitoring applications. Typical solutions require manned vessels and towed arrays characterized by high costs, risks, and deployment difficulties. An alternative solution for such applications is represented by acoustic vector sensors (AVSs), which are lightweight, compact, and moderate in cost, have promising performance in terms of bearing discrimination and the potential to operate from autonomous underwater platforms. This article presents an experimental assessment of a 2-D AVS derived from a sonobuoy and mounted on a hybrid autonomous underwater vehicle (AUV) for estimating the bearing of underwater sources. The sensor model, its technical characteristics, the installation onboard the vehicle, and the implementation of the passive signal processing chain are reported. Three different algorithms for bearing estimation have been tested on real data: one based on a time-domain representation, one based on the frequency domain, and one relying on joint time–frequency representation. Experimentation and data are presented, first from very shallow waters within the Italian Navy base of CSSN, La Spezia, and then from the Tyrrhenian Sea, in the context of a multiplatform exercise led by the NATO S&TO CMRE. Using an acoustic navigation system as ground truth, these experiments allow the assessment of bearing estimation capabilities in both AUV (by using propeller and pump jets) and gliding navigation modes. While the results of all the three processing methods are at least par to those recently reported in similar experiments with different vector sensors and processing, the time–frequency-domain method can identify multiple sources without prior knowledge of the source signal waveform or bandwidth.