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Synchronization-Free Underwater Acoustic Localization for Autonomous Platforms: A Neural Network TDOA Approach and the Role of Receiver Geometry

Yiğit Mahmutoğlu
July 20, 2026
Published Date

Research Abstract & Technology Focus

Accurate underwater acoustic localization is a key enabling capability for autonomous underwater vehicles and underwater drones, which cannot rely on satellite positioning while submerged and therefore depend on acoustic methods to determine their position. Localization based on time-of-arrival (TOA) measurements requires precise time synchronization between the source and the receivers, which is difficult to maintain in practical deployments. The time-difference-of-arrival (TDOA) representation removes this requirement but discards part of the absolute timing information, reducing localization accuracy. This study investigates a physics-based feedforward multilayer perceptron (FF-MLP) framework for two-dimensional range–depth underwater localization that learns directly from the arrival-time structure induced by sound-speed variability and multipath, with the receiver-array geometry treated as a central design variable for improving synchronization-free TDOA localization. Using multi-receiver arrival times generated with the BELLHOP beam-tracing model under a representative Mediterranean underwater environment, synchronous TOA, biased TOA, and TDOA measurement representations are compared on a common footing, and the effects of the receiver depth distribution, the number of receivers, and the reference-receiver position are systematically examined through Monte Carlo evaluation. The results show that the receiver-array geometry, rather than the measurement representation alone, is decisive for TDOA-based localization: with an appropriately designed geometry, synchronization-free TDOA localization achieves a median two-dimensional RMSE of 11.28 m, approaching the accuracy attainable with synchronous TOA, which requires precise time synchronization. These findings indicate that careful receiver-geometry design can make synchronization-free TDOA a practical alternative to synchronous TOA for the acoustic localization of autonomous underwater vehicles.
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What is the core focus of the research titled 'Synchronization-Free Underwater Acoustic Localization for Autonomous Platforms: A Neural Network TDOA Approach and the Role of Receiver Geometry'?

This literature focuses on: Accurate underwater acoustic localization is a key enabling capability for autonomous underwater vehicles and underwater drones, which cannot rely on satellite positioning while submerged and therefore depend on acoustic methods to determine their...

What other academic literature is closely related to 'Synchronization-Free Underwater Acoustic Localization for Autonomous Platforms: A Neural Network TDOA Approach and the Role of Receiver Geometry'?

Yes, highly correlated activity was mapped. An entry titled 'Multi-Source Sensor Fusion Localization Method for Autonomous Underwater Vehicles Based on Deep Learning' discusses this: Autonomous Underwater Vehicles (AUVs) are increasingly used in deep-sea exploration, environmental monitoring, and marine engineering. Their operat...

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