Synthesis of Passive Human Radio Frequency Signatures via Generative Adversarial Network

Human occupancy in an enclosed space can cause variation of the passive radio frequency (RF) spectrum. To assess the RF spectrum variation, a cognitive radio (CR) based human occupancy detection (CRHOD) method successfully determines presence of people. However, a wireless environment can be easily interfered by jamming signals or by replaying recorded samples. Hence, the knowledge of the RF environment is a critical aspect of a passive RF signals-based security monitoring system. Instead of retraining detectors with newly collected data, future systems can adapt to a new environment by predicting the RF signatures with human occupancy given the baseline spectrum of the environment measured without human occupancy. Synthesizing RF signatures of human occupancy is a challenging research area due to the lack of prior knowledge of how a human body alters the RF data. A human RF signatures generation system via conditional generative adversarial networks (GAN) is proposed in this paper to synthesize spectrum with human occupancy using the baseline spectrum at the area of interest. First, the trained human RF signatures GAN (HSGAN) model synthesizes passive RF signals with human occupancy via the baseline spectrum without human occupancy collected in the enclosed space. Second, the trained HSGAN model predicts the human RF signatures in the enclosed space at a new location using the HSGAN model trained in other locations. Lastly, the HSGAN model is quantitatively evaluated via two classifiers including a convolutional neural network (CNN) model and a k-nearest neighbors (KNN) classifier for the quality of the synthesized spectrum. In addition, a 99.5% correlation between synthesize human RF signatures and real human RF signatures results from the HSGAN.