ArticleSensors (Basel, Switzerland)2020
Doppler-Spectrum Feature-Based Human-Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Hybrid Millimeter-Wave Radar-Ultrasonic Fusion System for Robust Human Activity Recognition with Attention-Enhanced Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Enhanced efficiency and security in cross-chain transmission of blockchain internet of ports through multi-feature-based joint learning.Scientific reports · 2025Article
- Fully Integrated 24-GHz 1TX-2RX Transceiver for Compact FMCW Radar Applications.Sensors (Basel, Switzerland) · 2024Article
- A Passive RF Testbed for Human Posture Classification in FM Radio Bands.Sensors (Basel, Switzerland) · 2023Article
- Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Sensors and System for Vehicle Navigation.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we propose a Doppler-spectrum feature-based human-vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.