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21 May 2021 Real-time location fingerprinting for mobile devices in an indoor prison setting
Nicolas Smith, Logan Smith, Surya Kodipaka, Ajaya Dahal, Bo Tang, John E. Ball, Maxwell Young
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Proceedings Volume 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX; 1175612 (2021) https://doi.org/10.1117/12.2587679
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Many correctional facilities suffer from the smuggling of cell phones and other wireless devices into prison walls. In order to locate these devices for confiscation, we must be able to map intercepted signals to indoor locations within a few meter radius. We chose to use cell phones of varying models and multiple low-cost software-defined radios for this task. The different types of cell phones provide us with a more robust dataset for location fingerprinting due to the different transmitter hardware in each. Furthermore, the SDRs allow us to easily receive the raw IQ data from WiFi signals while being more cost-efficient for smaller facilities. This raw data is collected from a harsh prison-like environment in a grid pattern and associated with the location they were captured. An advanced machine learning network uses the raw signals as input and locations as labels in order to map the signals to their respective locations. The accuracy of our system is then compared and discussed against prior works in this field. These studies often use values other than the raw IQ data such as channel state information and received signal strength indicator. Therefore, we augment our original input with each of these values and measure their effect on the system’s overall performance. The end result provides prisons with a tool capable of locating devices used in unauthorized zones for confiscation.
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Nicolas Smith, Logan Smith, Surya Kodipaka, Ajaya Dahal, Bo Tang, John E. Ball, and Maxwell Young "Real-time location fingerprinting for mobile devices in an indoor prison setting", Proc. SPIE 11756, Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, 1175612 (21 May 2021); https://doi.org/10.1117/12.2587679
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KEYWORDS
Mobile devices
Cell phones
Machine learning
Received signal strength
Transmitters
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