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The applications of sonar and radar systems in underwater detection
- 1 Kings’ College London
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Abstract
The development of sonar and radar systems utilizing sound and radio waves, respectively, has evolved significantly over the past two centuries. Recent decades have witnessed remarkable progress in remote sensing using high-frequency Doppler radars for marine surface parameter analysis. Understanding animal behavior around sustainable ocean energy sources is crucial for mitigating potential risks linked to such installations. Increasing the Signal-to-Noise Ratio (SNR) enhances signal reception. A technique estimating noise in the detection coil's first half determines the transfer function between the reference and detection coils. This enables separation of the Ultra-Nuclear Magnetic Resonance signal from noise, preserving it using non-linear fitting. Sonar systems, using sound waves, detect underwater objects, map surfaces, and track marine life. Sound waves emitted by the transmitter are reflected back to calculate distances. Challenges in sonar systems include a blind zone caused by submerged transducers in shallow water. To improve, SNR enhancement is critical. Sonar's future may rely on AI-assisted signal processing. Radar systems, operating with radio waves, share features with sonar, focusing on object detection and ranging. Improvement avenues include 3D imaging and AI-enhanced signal processing. In conclusion, sonar and radar systems are crucial tools for underwater exploration and object detection. While challenges persist, innovative solutions and AI advancements promise enhanced capabilities for both systems.
Keywords
Sonar System, Radar System, Signal-to-noise Ratio
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Cite this article
Cai,C. (2024). The applications of sonar and radar systems in underwater detection. Applied and Computational Engineering,37,254-258.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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