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This work presents a simulation framework to generate human micro-Dopplers inWiFi based passive radar scenarios, wherein we simulate IEEE 802.11g complaintWiFi transmissions using MATLAB's WLAN toolbox and human animation modelsderived from a marker-based motion capture system. We integrate WiFitransmission signals with the human animation data to generate themicro-Doppler features that incorporate the diversity of human motioncharacteristics, and the sensor parameters. In this paper, we consider fivehuman activities. We uniformly benchmark the classification performance ofmultiple machine learning and deep learning models against a common dataset.Further, we validate the classification performance using the real radar datacaptured simultaneously with the motion capture system. We present experimentalresults using simulations and measurements demonstrating good classificationaccuracy of $\geq$ 95\% and $\approx$ 90\%, respectively.

SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition