Human Detection Using Doppler Radar Based on Physical Characteristics of Targets

• Published in: IEEE geoscience and remote sensing letters Vol. 12; no. 2; pp. 289 - 293
• Main Authors: Kim, Youngwook, Ha, Sungjae, Kwon, Jihoon
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Animals; Bicycles; Doppler effect; Doppler radar; Feature extraction; human detection; Human subjects; micro-Doppler; phase unwrapping; Radar; Spectrogram; support vector machine (SVM); Support vector machines; target classification; micro-Doppler; target classification; Doppler radar; human detection; support vector machine (SVM); phase unwrapping
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2014.2336231

In this letter, we propose a method for detecting a human subject using Doppler radar by investigating the physical characteristics of targets. Human detection has a number of applications in security, surveillance, and search-and-rescue operations. To classify a target from the Doppler signal, several features related to the physical characteristics of a target are extracted from a spectrogram. The features include the frequency of the limb motion, stride, bandwidth of the Doppler signal, and distribution of the signal strength in a spectrogram. The main contribution of this letter is the use of stride information of a target for the classification. Owing to the different lengths of legs and kinematic signatures of the target species, a human subject occupies a unique space in the domain of the stride and the frequency of limb motion. To verify the proposed method, we investigated humans, dogs, bicycles, and vehicles using the developed continuous-wave Doppler radar. The human subject is identified by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 96% with fourfold cross validation.