Sensor Placement Strategies for Target Localization via 3-D TOA Measurements in Underwater Acoustic Sensor Networks

Area of Science:

• Oceanography
• Acoustics
• Sensor Networks

Background:

• Underwater acoustic sensor networks (UASNs) face challenges in accurate target localization due to idealized models.
• Existing methods often overestimate performance by not accounting for complex underwater acoustic propagation.

Purpose of the Study:

• To develop an optimal sensor placement strategy for 3-D time-of-arrival (TOA) based localization in UASNs.
• To enhance localization accuracy and robustness by incorporating realistic environmental factors.

Main Methods:

• Derived an exact acoustic propagation time and TOA measurement variance using a non-linear ray acoustic model.
• Developed a realistic 3-D TOA measurement model considering depth-dependent sound speed profiles (SSP) and heterogeneous noise.
• Proposed a MinMax k-Means algorithm to minimize the average trace of the Cramér-Rao lower bound (CRLB) for sensor configuration.

Main Results:

• The proposed sensor placement strategy significantly improves localization accuracy compared to conventional methods.
• Demonstrated enhanced robustness in complex underwater environments through extensive numerical simulations.
• Validated the effectiveness of the non-linear ray acoustic model and MinMax k-Means algorithm.

Conclusions:

• The developed sensor placement strategy provides a more realistic and effective approach for 3-D TOA-based localization in UASNs.
• Accurate modeling of acoustic propagation and environmental noise is crucial for reliable underwater localization.
• The MinMax k-Means algorithm offers an efficient solution for optimizing sensor configurations in challenging acoustic environments.