FMCW Radar Signal Processing Pipeline for Aircraft Marshalling Signals Classification

Jim Vermunt, Federico Corradi

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Abstract

We present a methodology for obtaining a low memory and low computational load for a radar signal processing pipeline of a Frequency-Modulated Continuous-Wave (FMCW) radar tailored to embedded systems. Our analysis covers the entire radar signal processing pipeline, including radar signal conditioning, feature extraction, and neural network models, including pruning and quantization. We focus on a complex dataset of aircraft marshalling signals captured from an ultra-low-power, single-input, single-output (SISO) FMCW radar. With our methodology, we achieve a new state-of-the-art classification accuracy of 71.4\%, marking a significant improvement of over six percentage points while maintaining a reduced memory footprint and minimal computing requirements. This advancement contributes to developing efficient radar signal processing systems for resource-constrained applications and represents a crucial step toward realizing highly efficient and accurate radar-based gesture recognition systems.
Original languageEnglish
Title of host publication2024 21st European Radar Conference, EuRAD 2024
PublisherInstitute of Electrical and Electronics Engineers
Pages457-460
Number of pages4
ISBN (Electronic)978-2-87487-079-8
DOIs
Publication statusPublished - 4 Nov 2024
Event21st European Radar Conference, EuRAD 2024 - Paris, France
Duration: 25 Sept 202427 Sept 2024
Conference number: 21
https://www.eumweek.com/

Conference

Conference21st European Radar Conference, EuRAD 2024
Abbreviated titleEuRAD 2024
Country/TerritoryFrance
CityParis
Period25/09/2427/09/24
Internet address

Funding

This work has been funded by the Dutch Organization for Scientific Research (NWO) with Grant OCENWM.22.331.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk OnderzoekOCENWM.22.331

    Keywords

    • Gesture Recognition
    • Human Activity Monitoring
    • Deep Learning Methods
    • Radar signal processing
    • FMCW Radar
    • Radar
    • Embedded Device
    • Aircraft Marshalling Signals
    • Deep Learning
    • Human Gesture Classification

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