Human Detection Using Doppler Radar Based on Physical Characteristics of Targets

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.