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High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation.

Bong-Seok Kim1, Youngseok Jin1, Jonghun Lee1,2

  • 1Division of Automotive Technology, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea.

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|July 2, 2021
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Summary
This summary is machine-generated.

This study introduces a high-efficiency super-resolution algorithm for frequency-modulated continuous-wave (FMCW) radar. The novel method reduces computational complexity by adaptively selecting samples, achieving performance similar to MUSIC without degradation.

Keywords:
FMCW radarMUSIClow complexitysuper-resolution

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing

Background:

  • Frequency-modulated continuous-wave (FMCW) radar systems often use a fixed number of samples, determined by maximum detectable distance, which can be inefficient for closer targets.
  • Conventional super-resolution algorithms, like MUSIC, require significant computational resources.

Purpose of the Study:

  • To propose a high-efficiency super-resolution algorithm for FMCW radar that reduces computational complexity.
  • To maintain or improve performance compared to existing super-resolution techniques.

Main Methods:

  • Developed a novel FMCW radar algorithm utilizing Fast Fourier Transform (FFT) for coarse target range estimation.
  • Adaptively selects a reduced number of samples based on FFT-estimated ranges for super-resolution processing.
  • Replaces the maximum sample count with the reduced sample count for super-resolution input.

Main Results:

  • The proposed algorithm achieves performance comparable to the Multiple Signal Classification (MUSIC) algorithm.
  • Demonstrates an average complexity reduction of 88% compared to the conventional MUSIC algorithm.
  • Validated through simulations and practical experimental results.

Conclusions:

  • The proposed algorithm offers a significant reduction in computational complexity for FMCW radar super-resolution.
  • It achieves similar high performance to conventional methods, making it suitable for practical applications.
  • Adaptive sample selection is key to improving efficiency without compromising accuracy.