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Published on: October 31, 2011
Direction of arrival estimation with a vector intensity hydrophone array towed by an autonomous surface vehicle
Davis Rider1, Laurent Grare2, Luc Lenain2
1Georgia Institute of Technology, Atlanta, Georgia 30306, USA.
A compact tetrahedron hydrophone array achieves low-frequency direction of arrival (DOA) finding, matching vector sensor performance. This towed array successfully determined the bearing of deep-water acoustic transmissions over long distances.
Area of Science:
- Ocean acoustics
- Array signal processing
- Underwater navigation
Background:
- Direction of Arrival (DOA) finding is crucial for underwater acoustic localization.
- Traditional methods often rely on bulky or less sensitive sensors.
- Vector sensors offer directional information but can be complex and expensive.
Purpose of the Study:
- To demonstrate a compact hydrophone array emulating vector sensor DOA performance.
- To assess the array's capability in determining horizontal DOA of low-frequency signals.
- To validate DOA finding performance in a deep-water, long-range experimental setting.
Main Methods:
- Development of a 33-cm, four-element, tetrahedron-shaped hydrophone array.
- Deployment of the array from an autonomous surface vehicle in deep water.
- Application of pressure gradient-based methodology for DOA estimation.
- Utilizing both coherent and incoherent DOA finding techniques.
Main Results:
- The tetrahedron hydrophone array successfully emulated low-frequency DOA finding performance.
- Horizontal DOA (bearing) was determined for low-frequency (210-310 Hz) transmissions.
- Effective DOA determination was achieved for sources at ~1100 m depth and up to 176 km range.
- Consistent DOA values were obtained for signal-to-noise ratios above 11 dB.
Conclusions:
- The compact tetrahedron hydrophone array is a viable alternative to vector sensors for low-frequency DOA finding.
- The demonstrated system offers effective long-range acoustic source localization in deep-water environments.
- The pressure gradient-based methodology provides robust performance across various signal-to-noise ratios.
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