AI Article Synopsis
- This paper introduces a new method for detecting drones using a convolutional neural network (CNN) and range-Doppler map images from FMCW radar, aiming to improve on current techniques.
- Current drone detection methods struggle with small or distant drones due to weak signal signatures, which can reduce their effectiveness.
- The proposed approach overlays multiple time-series images into one, resulting in significant improvements in detection accuracy based on experimental results with various drone sizes.
Article Abstract
This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range-Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range-Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN. The experimental results, using actual data for three different drone sizes, show significant performance improvements in drone detection accuracy compared to conventional methods.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398168 | PMC |
| http://dx.doi.org/10.3390/s24175805 | DOI Listing |
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