Signal Expansion Method in Indoor FMCW Radar Systems for Improving Range Resolution

Area of Science:

• Electrical Engineering
• Sensor Technology
• Signal Processing

Background:

• Frequency Modulated Continuous Wave (FMCW) radar is crucial for autonomous systems like vehicles and drones.
• Current high-performance FMCW radar systems are expensive due to large modulation bandwidths and high operating frequencies (above 77 GHz).
• There is a need for cost-effective FMCW radar solutions with improved range resolution for applications like indoor motion detection and autonomous drones.

Purpose of the Study:

• To enhance the range resolution of FMCW radar beyond limitations imposed by modulation bandwidth.
• To develop novel algorithms that improve the accuracy of object detection in FMCW radar systems.
• To provide a cost-effective solution for precise sensing in autonomous applications.

Main Methods:

• Proposed Adaptive Mirror Padding and Phase Correction Padding algorithms to extend the beat frequency signal in the time domain.
• Evaluated algorithm performance against existing methods like Zero Padding and Mirror Padding using metrics such as Range RMSE, ρs (side lobe to main lobe ratio), and OS CFAR (Ordered Statistics Constant False Alarm Rate).
• Validated the proposed algorithms using both MATLAB simulations and an actual FMCW radar system.

Main Results:

• The proposed algorithms demonstrated comparable Range RMSE to existing methods.
• Significant improvements were observed in ρs: approximately 3x for Adaptive Mirror Padding and 6x for Phase Correction Padding compared to existing algorithms.
• Accurate detection rates improved: ~10% for Adaptive Mirror Padding and ~20% for Phase Correction Padding in single-target scenarios using OS CFAR.
• Phase Correction Padding showed a ~20% improvement in multi-target scenarios using OS CFAR.
• Algorithm performance was consistent across both simulation and real-world radar experiments.

Conclusions:

• Adaptive Mirror Padding and Phase Correction Padding effectively improve FMCW radar range resolution and detection accuracy without requiring higher bandwidths.
• These algorithms offer a cost-effective alternative to expensive high-frequency radars for precise sensing in autonomous applications.
• The validated performance in both simulation and real-world environments confirms the practical applicability of the proposed methods for autonomous driving and drone technologies.