There is growing interest in human tracking, human activity recognition (HAR), and person identification by reusing wireless signals transmitted by existing communication devices, such as WiFi and cellular radio transceivers.
Radiofrequency sensing systems have emerged as viable alternatives to cameras for environmental monitoring.
In particular, a well-established method for detecting and classifying human movements using reflected radio signals is the timefrequency analysis of the small-scale Doppler effect, termed micro-Doppler (mD) caused by different parts of the body, which requires
sampling density and regularly spaced channel impulse response (CIR).
The proposed method addresses the problem of enabling integrated sensing and communication in realistic communication systems, reusing existing communication traffic for sensing as much as possible and thus introducing only a minimal amount of additional overhead.