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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. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an 'extended degree of scattering points' and a 'different degree of scattering points' to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the 'presence of vital signs', which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]