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High-Speed Detection of Moving Objects Using FMCW Radar Sensor
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AI
DOI：10.1109/LSENS.2026.3660746.png)
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Abstract
En 中文
Today, high-speed navigation has become crucial in air traffic and self-driving cars, where frequency-modulated continuous wave radars are widely used as low-cost range sensors. Such sensors allow calculating the range-Doppler map to increase the detection range for small drones. This map requires computing a 2-D fast Fourier transform (FFT) of the time-chirp matrix to find spectral peaks above the noise floor. Unfortunately, with every newly received chirp, the 2D-FFT must be recomputed, which is computationally expensive; moreover, it may cause performance degradation when detecting fast-moving objects. In this letter, we propose a low-cost algorithm that avoids large-size FFT calculations and instead calculates blockwise 2D-FFTs with further tracking of local maxima, thus lowering complexity and improving sensitivity of radar sensors in high-speed scenarios.
Keywords:
Chirp
Radar
Radar detection
Indexes
Fast Fourier transforms
Signal to noise ratio
Radar tracking
Radar cross-sections
Random variables
Radar signal processing
Sensor signal processing
detection
fast Fourier transform (FFT)
frequency-modulated continuous wave (FMCW)
RADAR
Journal
I
IF:
2.2
Papers:
323
Citations:
3.1K