Complementarity and Fusion of FMCW and Wi-Fi Passive Radars for Pedestrian Flow Monitoring

This study explores the complementarity and fusion of two sensing technologies for pedestrian flow estimation: ubiquitous Wi-Fi based passive radar (WPR) and deployable frequency-modulated continuous wave (FMCW) active radar, both combined with a convolutional neural network (CNN) for postprocessing. Wi-Fi signals, already widespread in many environments, enable passive, wide-area motion sensing without requiring additional infrastructure. FMCW radars, by contrast, offer high-resolution range-Doppler measurements and can be selectively deployed in target locations, such as pedestrian streets. We begin by individually evaluating the performance of FMCW and Wi-Fi passive radar systems in estimating pedestrians flow, highlighting their respective strengths, limitations, and complementarity. To further improve the system performance, we propose a fusion approach at both the decision and feature levels. For decision-level fusion, we implement majority voting and probability averaging strategies to combine the predictions from both radars. For feature-level fusion, we extract features from both radar systems using CNNs and merge them before classification. Our experimental results, from measurements collected on the same scene by the two radars, show that the fusion approaches significantly enhance the flow estimation accuracy compared to using either radar system alone. The feature-level fusion method, in particular, demonstrates superior performance by effectively integrating the spatial and motion information captured by both radar types. This work demonstrates the value of hybrid sensing systems that combine opportunistic and purpose-built technologies for reliable pedestrian counting and flow estimation in diverse urban scenarios and provides a robust framework for future developments in multisensor data fusion.