Non-Obtrusive Detection of Concealed Metallic Objects Using Commodity WiFi Radios

In light of increasing interest in detection of concealed metallic weapons, there is a great need to have robust and non-obtrusive metal detection systems with large coverage areas. Conventional systems based on electromagnetic induction or X- rays are effective, but have small coverage areas in addition to requiring costly infrastructure. In this paper, we explore the use of ubiquitously present WiFi signals for non-obtrusive detection of concealed metal objects. For the purpose, we build a prototype system consisting of a single- antenna commodity WiFi radio as a transmitter, and two multi-antenna radios as receivers placed in an indoor environment of approximately 42 ft by 39 ft. We conduct extensive experiments with subjects walking through the setup with (or without) a sheet of metal placed around their chests. We use the channel state-information collected from the receivers to train a deep convolutional neural network, and find that the proposed system can differentiate between the metal and non-metal cases with an average accuracy of 86.44.