Array invariant-based ranging of a source of opportunity

Area of Science:

• Underwater acoustics
• Signal processing
• Geophysical exploration

Background:

• Ship noise can be random and anisotropic, posing challenges for tracking.
• Ray-based blind deconvolution (RBD) can estimate the Green's function from ship noise.
• Array invariant (AI) is effective for robust source-range estimation in shallow water.

Purpose of the Study:

• Investigate the feasibility of tracking ships using RBD and AI.
• Combine RBD and AI for enhanced ship localization and tracking in shallow water.
• Evaluate the accuracy of the combined methods for a ship of opportunity.

Main Methods:

• Utilized ray-based blind deconvolution (RBD) to estimate the Green's function.
• Applied array invariant (AI) to the estimated Green's function.
• Employed a 16-element, 56-m long vertical array in ~100-m deep shallow water.
• Focused on ship noise within the 200-900 Hz frequency band.

Main Results:

• Successfully localized and tracked a ship of opportunity.
• Achieved a relative range error with a 5% standard deviation.
• Demonstrated tracking along a path at ranges of 1.8-3.4 km.
• Exploited multipath arrivals separated in beam angle and travel time.

Conclusions:

• The combination of RBD and AI is a feasible and effective method for shallow water ship tracking.
• Accurate ship localization and tracking can be achieved using ambient noise from a ship of opportunity.
• This approach offers a robust solution for underwater acoustic surveillance and navigation.