Human detection algorithm for Doppler radar using prediction error in autoregressive model

Using a Doppler radar as a motion sensor is promising for monitoring human daily activities. Various detection algorithms for human activities and vital signs have been proposed; however, most require a controlled environment and do not allow for the presence of moving objects in the sensing area. In actuality, motor-actuated appliances such as electric fans and heaters are often noise sources in homes. The velocities of such appliances overlap with those of a person and can cause a Doppler effect even without human presence. We resolve this issue in the current study by analyzing motion regularity. To detect the irregular motions that characterize a person's presence, we utilize the prediction errors from an autoregressive model. A performance evaluation of the proposed algorithm shows that high accuracy is achieved for human detection under disturbances caused by electric appliance motions. Moreover, our method outperforms the conventional frequency-based method that uses power spectra of the Doppler signal.