Continuous Human Activity Classification With Unscented Kalman Filter Tracking Using FMCW Radar

Short-range compact radar systems offer attractive modality for localization and tracking of human targets in indoor and outdoor environments for industrial and consumer applications. Micro-Doppler radar reflections from human targets can be sensed and used for human activity classification, which has applications in human-computer interaction and health assessment among others. Traditionally, the detected human targets' location are tracked and its micro-Doppler spectrogram extracted for further activity classification of the human target. In this letter, we propose a novel integrated human localization and activity classification using unscented Kalman filter and demonstrate our results using a short-range 60-GHz frequency modulated continuous wave radar. The proposed solution is shown to result in an improved classification accuracy with the capability of providing uncertainty with associated classification probabilities and, thus, is a simple mechanism to achieve Bayesian classification.