Affiliations:
ART (Advanced Radar Technology) Lab., Convergence Research Center for Future Automotive Technology, DGIST (Daegu Gyeongbuk Institute of Science and Technology), Hyeonpung-myeon, Dalseong-gun, Daegu, Republic of Korea
Correspondence:
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Corresponding author. Eugin Hyun, ART (Advanced Radar Technology) Lab., Convergence Research Center for Future Automotive Technology, DGIST (Daegu Gyeongbuk Institute of Science and Technology, 333 Techno Jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu, Republic of Korea. E-mail: [email protected].
Abstract: In this paper, we proposed a human-vehicle classification scheme using a Doppler spectrum distribution based on 2D Range-Doppler FMCW (Frequency Modulated Continuous Wave). Typically, because humans have non-rigid motion, multiple reflection points can appear on the Doppler spectrum. However, in the actual field, the Doppler spectrum distribution of a walking human is highly variable over time. Thus method using only this characteristic of the extended Doppler spectrum is limited with regard to human-vehicle classification. In order to improve the target classification performance, we designed two feature. The first is the Doppler spectrum extension features, which is expressed as the number of Doppler reflection points with magnitudes exceeding reference threshold. Next, we defined the Doppler spectrum variance feature, which is extracted as the difference the reflection points between two successive frames. We can determine how the Doppler spectrum expands with the first feature, and how the Doppler spectra change based on the second feature. To verify the proposed target classification scheme, we measured real data using a 24 GHz FMCW transceiver on an actual road with various scenarios of walking humans and moving vehicles. From an analysis of the results, we confirmed that the thresholds effectively classify humans and vehicles based on the two proposed features. Finally, we verified that the results of the proposed classification scheme using the two features were much better than those using the first feature alone.
