Software-defined Methods for Robust Positioning Using Existing Wireless Infrastructure
Location-based services (LBS) and navigation has become ubiquitous in a mobile computing era for many applications such as commercial, medical, and military. An example of LBS is the Global Positioning System (GPS), which provides outdoor localization and can be found in many mobile devices nowadays. Still, there exists potential gaps in terms of robustness for continuous positioning. Positioning methods using existing wireless infrastructure suffer from malicious cyber-physical attacks, intentional or unintentional interference and jamming, and environmental effects such as multipath. Additionally, an absence of a robust indoor positioning system is desired. Therefore, this work addresses such gaps by exploiting software-defined radio (SDR) technologies combined with existing open-access wireless infrastructure. The need for instrumentation is addressed by fast-prototyping SDR receivers which can leverage research for robust positioning using real-time optimization methods. Two real-time SDR receivers based on LabVIEW fast-prototyping platform are developed and explored for robust positioning: (1) a real-time GPS SDR receiver; and (2) a Wireless Local Area Network (WLAN) SDR receiver.
For GPS infrastructure, two baseband level optimization techniques are presented: (a) a real-time reduced complexity minimum mean-square error (MMSE) GPS code optimization technique for interference mitigation; and (b) a least absolute shrinkage and selection operator (LASSO) based spoofing detector in the correlator level, namely the LASSO correlator. The MMSE detector achieves a bit-error rate (BER) gain of 105, and the LASSO correlator achieves a 0.3% detection error rate (DER) in nominal conditions when a spoofer-peak is present. Lastly, a third method is presented in the GPS navigation level: the Time Synchronization Attack Rejection and Mitigation based on sparse-domain (TSARM-S) technique. The method is based on clock data behavior changes in a higher-order derivative domain. The proposed method jointly estimates a dynamic solution for GPS timing and rejects behavior changes based on sparse events. TSARM-S detects and mitigates GPS clock attacks, with a correction root mean square error (RMSE) of 12 nsec.
Finally, this work presents SDR-Fi, a fingerprinting-based indoor positioning system (IPS) based on orthogonal frequency-division multiplexing (OFDM) channel state information (CSI) for model training and deep learning for classification. SDR-Fi achieves one-meter accuracy in complex indoor settings for WLAN infrastructure positioning.