In this demo by Qualcomm AI Research, we showcase passive positioning using radio frequency, otherwise known as RF sensing, which is useful for a variety of tasks such as intrusion detection, asset tracking, and smart energy usage. Unlike cameras, it works in all lighting conditions and safeguards privacy since it does not capture visual data. Existing machine learning solutions are often demonstrated on small floor plans within controlled-lab environments and require labels for supervised learning. Our new machine learning-based methods work on large floor plans and only require weakly labeled training data long with a floor plan. Using three to four access points, our methods demonstrate about 1 meter accuracy in multi-room, multi-floor environments with a 150 to 400 square meter floor surface area. This demo shows what can be achieved with our two technologies, OT IsoMap and WiCluster, paving the way for passive positioning solutions that can be easily implemented in smart homes, company buildings, and cities.
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