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Feedback Beamforming in the Time Domain
Zvi Aharon Herscovici1, Israel Cohen1
1Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel.
Sensors (Basel, Switzerland)
|April 13, 2024
Summary
This study introduces a novel time-domain feedback beamformer for accurate real-time source localization, enhancing automation and AI by improving direction of arrival and signal range estimation even with low signal-to-noise ratios.
Area of Science:
- Signal Processing
- Acoustics
- Artificial Intelligence
Background:
- Real-time source localization is essential for advanced automation and AI applications.
- Low signal-to-noise ratio (SNR) and processing time constraints challenge localization accuracy.
- Existing methods may not meet the demanding requirements of high-end systems.
Purpose of the Study:
- To propose a new time-domain feedback-based beamformer architecture for real-time source localization.
- To estimate the direction of arrival (DOA) and signal range of reflective sources.
- To enhance localization precision through an integrated feedback mechanism.
Main Methods:
- Development of a novel time-domain feedback-based beamformer architecture.
- Estimation of direction of arrival (DOA) and signal range for reflective sources.
- Comparative analysis of the proposed method against conventional time-domain techniques.
Main Results:
- The proposed architecture effectively addresses real-time processing demands.
- The feedback mechanism significantly refines localization precision.
- Performance evaluation using peak-to-sidelobe ratio, mainlobe width, and directivity factor showed improved beamformer effectiveness.
Conclusions:
- The developed time-domain feedback beamformer offers a significant improvement in localization accuracy for real-time applications.
- This approach enhances the performance of automation and AI systems by overcoming SNR and processing time limitations.
- The feedback integration represents a key advancement in beamforming for precise source localization.
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