Adaptive focusing for wideband beamforming in multipath environments

Area of Science:

• Underwater acoustics
• Signal processing
• Sonar systems

Background:

• Achieving high time-bandwidth product is crucial for low signal-to-noise ratio (SNR) target detection and localization in complex multipath environments.
• Dynamic environments and platform maneuvers often limit the achievable time-bandwidth product.

Purpose of the Study:

• Introduce data-driven wideband focusing methods for passive sonar.
• Optimize parameterized unitary matrices to align signal subspaces across the frequency band.
• Overcome limitations of traditional model-based approaches in complex environments.

Main Methods:

• Develop data-driven wideband focusing techniques for passive sonar.
• Minimize the log-determinant of the wideband covariance to ensure data coherence and preserve SNR.
• Propose two adaptive methods: a fully adaptive and a partially adaptive approach for noise robustness.

Main Results:

• Simulations in a shallow-water waveguide demonstrate superior performance of data-driven focusing over model-based methods.
• Validation using SWellEx-96 S59 event data confirms improved tonal target detection and localization.
• Methods effectively handle strong wideband interference in complex multipath scenarios.

Conclusions:

• Data-driven focusing methods offer a flexible and robust alternative to model-based approaches for passive sonar.
• The proposed techniques successfully enhance SNR and improve target detection and localization capabilities.
• These methods are vital for advancing passive sonar performance in challenging acoustic conditions.