Self-localization of mobile underwater vector sensor platforms using a source of opportunity

Area of Science:

• Oceanography
• Acoustics
• Robotics

Background:

• Distributed underwater sensing arrays require precise mobile sensor positioning.
• Traditional positioning relies on active acoustics, adding complexity.
• Passive self-localization using ambient acoustics offers a simpler alternative.

Purpose of the Study:

• Introduce a novel, passive self-localization method for mobile underwater vector sensor platforms.
• Simplify underwater sensor positioning by avoiding active acoustic sources.
• Enhance robustness against sensor errors through a generalized method.

Main Methods:

• Developed a linear least-square formulation for self-localization.
• Utilized acoustic recordings from distant surface vessels.
• Integrated data from time-synchronized vector sensors and inertial navigation systems (INS).

Main Results:

• Successfully demonstrated a passive self-localization method for two vector sensor platforms.
• The method uses readily available acoustic sources of opportunity.
• Experimental validation with drifting buoys confirmed robustness.

Conclusions:

• The proposed linear least-square method offers a simpler and more robust approach to underwater sensor self-localization.
• This passive method reduces hardware and operational complexity compared to active acoustic systems.
• Generalization to multiple vector sensor pairs enhances reliability, particularly with potential INS errors.