Simulation of Human Micro-Doppler Signatures with Kinect Sensor

Abstract

The availability and access to real radar data collected for targets with a desired characteristic is often limited by monetary and practical resources, especially in the case of airborne radar. In such cases, the generation of accurate simulated radar data is critical to the successful design and testing of radar signal processing algorithms. In the case of human micro-Doppler research, simulations of the expected target signature are required for a wide parameter space, including height, weight, gender, range, angle and waveform. The applicability of kinematic models is limited to just walking, while the use of motion capture databases is restricted to the test subjects and scenarios recorded by a third-party. To enable the simulation of human micro-Doppler signatures at will, this work exploits the inexpensive Kinect sensor to generate human spectrograms of any motion and for any subject from skeleton tracking data. The simulated spectrograms generated are statistically compared with those generated from high quality motion capture data. It is shown that the Kinect spectrograms are of sufficient quality to be used in simulation and classification of human micro-Doppler.