Suppressing Coupling and Stationary Clutters in FMCW Radars with Temporal Filtering

Liu, Jingtao; Gu, Changzhan; Zhang, Yueping; Mao, Jun‐Fa

doi:10.1109/apmc47863.2020.9331488

Cited by 9 publications

(1 citation statement)

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“…The IBDE method employs supervised learning which necessitates acquiring and organizing a sufficient amount of data for each label. Consequently, the issue of labeling has been raised, particularly regarding cost, leading to the investigation of various methods [ 8 , 9 , 10 ]. FMCW radar also encounters limitations owing to the labeling issue.…”

Section: Introductionmentioning

confidence: 99%

FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network

Cho,

Jung,

Lee

2023

Sensors

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This paper addresses the challenge of enhancing range precision in radar sensors through supervised learning. However, when the range precision surpasses the range resolution, it leads to a rapid increase in the number of labels, resulting in elevated learning costs. The removal of background noise in indoor environments is also crucial. In response, this study proposes a methodology aiming to increase range precision while mitigating the issue of a growing number of labels in supervised learning. Neural networks learned for a specific section are reused to minimize learning costs and maximize computational efficiency. Formulas and experiments confirmed that identical fractional multiple patterns in the frequency domain can be applied to analyze patterns in other FFT bin positions (representing different target positions). In conclusion, the results suggest that neural networks trained with the same data can be repurposed, enabling efficient hardware implementation.

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Section: Introductionmentioning

confidence: 99%