Distributed Cyclic Spectrum Feature-Based Modulation Classification

2008 ◽  
Author(s):  
William C. Headley ◽  
Jesse D. Reed ◽  
Claudio R. C. M. da Silva
Keyword(s):  
Modulation Classification ◽  
Spectrum Feature ◽  
Feature Based ◽  
Cyclic Spectrum

scholarly journals Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2001 ◽  
Author(s):  
Eugin Hyun ◽  
YoungSeok Jin
Keyword(s):  
Classification Scheme ◽  
Vehicle Classification ◽  
Doppler Spectrum ◽  
Support Vector ◽  
Radar Sensor ◽  
Moving Vehicle ◽  
Fmcw Radar ◽  
Wave Radar ◽  
Spectrum Feature ◽  
Feature Based

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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