A multi-task learning deep complex network for shallow-water source ranging in the complex environment
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a multi-task learning deep complex network (MTL-DCN) for accurate underwater acoustic source ranging. The MTL-DCN effectively addresses environmental mismatches caused by internal solitary waves (ISWs), improving performance over existing methods.
Area Of Science
- Ocean Acoustics
- Signal Processing
- Machine Learning
Background
- Internal solitary waves (ISWs) in shallow water cause environmental mismatches, degrading underwater acoustic ranging performance.
- Sound speed profile (SSP) mismatches are a primary factor in performance degradation for single-task learning deep complex networks (STL-DCN) and conventional matched-field processing (CMFP).
Purpose Of The Study
- To develop an advanced ranging algorithm capable of mitigating environmental mismatches in shallow water acoustics.
- To enhance the accuracy of underwater acoustic source localization in dynamic environments influenced by ISWs.
Main Methods
- A multi-task learning deep complex network (MTL-DCN) was proposed, integrating an adaptive weighted multi-task learning mechanism.
- The MTL-DCN simultaneously estimates underwater acoustic source range and the sound speed profile (SSP) along the propagation path.
- The algorithm was validated using simulation analysis and experimental data from the South China Sea.
Main Results
- Simulation analysis confirmed SSP mismatch as the key factor degrading ranging performance.
- The MTL-DCN demonstrated superior representation and processing of complex acoustic data with phase relationships compared to STL-DCN and CMFP.
- Experimental results showed significantly improved range estimation accuracy using the MTL-DCN.
Conclusions
- The proposed MTL-DCN algorithm reliably estimates underwater acoustic source ranges in complex, fluctuating environments influenced by ISWs.
- The adaptive weighted multi-task learning approach effectively addresses environmental mismatches, offering a robust solution for shallow water acoustics.
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