Radar simulation of human micro-Doppler signature from video motion capture data

The availability of data sets on which signal processing techniques may be tested is critical to the development of human detection, identification, and classification algorithms. However, in many cases real radar data of the desired characteristics may be expensive or difficult to obtain. In this case, synthetic or simulated data is desired. Much of the simulated data used in publications is derived from the Boulic kinematic model. But, the Boulic model is only valid for walking and is not applicable to compute the micro-Doppler signatures of other human motions. The Carnegie Mellon University motion capture library includes data from a wide range of human activities and provides the time-varying position of body parts. In this work, this video motion capture data is used to generate the radar micro-Doppler signature for many human activities. Observations about the micro-Doppler signatures computed are also shared.