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Method for Improving Range Resolution of Indoor FMCW Radar Systems Using DNN.

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  • 1Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea.

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This study introduces a novel two-step algorithm using multiple low-cost Frequency-Modulated Continuous Wave (FMCW) RADARs for enhanced object detection. The method achieves high-distance resolution cost-effectively, improving upon existing technologies.

Keywords:
OS-CFARdeep neural networkfrequency modulated continuous wave RADARroot mean square error

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Object detection is crucial for various applications, with Frequency-Modulated Continuous Wave (FMCW) RADAR being a key technology.
  • Achieving high-distance resolution in FMCW RADAR typically necessitates high-modulation bandwidth, leading to prohibitive costs.
  • Existing methods face limitations in cost-effective high-resolution object detection.

Purpose of the Study:

  • To propose a novel, cost-effective two-step algorithm for high-distance resolution object detection using multiple low-cost FMCW RADARs.
  • To enhance object localization accuracy by inferring sectors and then measuring position within a grid structure.
  • To enable reliable multi-target detection and improve overall performance compared to existing algorithms.

Main Methods:

  • A two-step algorithm employing Deep Neural Networks (DNN) with multiple low-cost FMCW RADARs.
  • Sector inference based on distance measurements from individual RADARs, followed by grid-based position measurement.
  • Integration of a Gaussian filter and Ordered-Statistic Constant False Alarm Rate (OS-CFAR) for multi-target detection.

Main Results:

  • The proposed algorithm significantly improves distance resolution beyond the modulation bandwidth limitations.
  • Demonstrated up to a five-fold performance improvement compared to previous methods at similar complexity.
  • Achieved Root Mean Square Errors (RMSEs) of 0.3542 m (simulation) and 0.41002 m (actual measurement) for single targets, and 0.548265 m (simulation) and 0.762542 m (actual measurement) for multiple targets.

Conclusions:

  • The developed two-step algorithm effectively enhances object detection capabilities using affordable FMCW RADAR arrays.
  • The method offers a significant advancement in achieving high-distance resolution and multi-target detection cost-effectively.
  • Validated through simulations and real-world measurements, confirming its practical applicability in RADAR systems.