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FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network
Homin Cho1,2, Yunho Jung3,4, Seongjoo Lee1,2
1Department of Semiconductor Systems Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2024
Summary
This study enhances radar range precision using supervised learning. Reusing neural networks minimizes costs and improves efficiency, even with increased labels, for better indoor radar sensing.
Area of Science:
- Radar Systems Engineering
- Machine Learning Applications
- Signal Processing
Background:
- Supervised learning enhances radar range precision but faces challenges with increased labels and learning costs when precision exceeds resolution.
- Background noise removal is critical for indoor radar sensing applications.
Purpose of the Study:
- To propose a methodology for increasing radar range precision while mitigating the escalating number of labels in supervised learning.
- To reduce learning costs and maximize computational efficiency in radar systems.
Main Methods:
- Utilizing neural networks trained for specific sections and reusing them for other applications.
- Applying fractional multiple patterns in the frequency domain for pattern analysis across different FFT bin positions.
Main Results:
- Demonstrated that identical fractional multiple patterns in the frequency domain can analyze patterns at various FFT bin positions.
- Confirmed that neural networks trained on the same data can be repurposed effectively.
Conclusions:
- The proposed methodology successfully increases range precision while managing label proliferation in supervised learning.
- Repurposing trained neural networks enables efficient hardware implementation for radar systems, reducing computational overhead.
