Optimization Techniques for Robust Positioning, Timing, and Security

Localization has transformed current technologies to provide diverse applications in content delivery, monitoring, entertainments, military, and security. Likewise, future applications strongly drive the development of location determination technologies. However, localization systems still suffer from low localization accuracies, environment peculiarities, and insecurity to unintentional system jamming or intentional attacks. Hence, this work addresses these current challenges and presents robust optimization techniques for localization in three distinct settings. More specifically, it is illustrated that the optimization techniques can be employed to render robust localization systems. The settings under study are: 1) Indoor localization using Wireless Local Area Networks (WLANs); 2) Collaborative multi-agent localization in hostile environments; 3) Spoofing detection and mitigation in Global Positioning Systems (GPSs).

While WLAN has been initially designed for wireless networking and not positioning, it has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. Notwithstanding, WLAN fingerprinting faces several challenges and hence, in this work, our goal is to overview these challenges, corresponding state-of-the-art approaches, and propose solutions to overcome these challenges. This work discusses conventional localization schemes, state-of-theart approaches, practical deployment challenges and then we propose our novel solutions. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with vsignificantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods. Our results depict illustrative evaluation of the approaches in the literature and guide to future improvement opportunities.

Secondly, this work addresses the problem of anchor-free multi-agent collaborative localization in two-tier coarse-refine setting. We discuss three different coarse localization schemes: 1) Intuitive Coarse Localization (ICL), 2) Multidimensional Scaling (MDS) and 3) Semidefinite Programming (SDP). Then, a unified set of sequential and parallel least squares (LS) techniques are applied to refine these coarse estimates. Our numerical results demonstrate that (a) performance of MDS and SDP methods is further improved by LS techniques; (b) the LS techniques reach the same accuracy level for all coarse localization techniques. An outlier detection procedure is also introduced for localization in the presence of outliers. The performance gains are observed through the simulated tests and yield important insights to practitioners.

Finally, this work introduces the Time Synchronization Attack Rejection and Mitigation (TSARM) technique for Time Synchronization Attacks (TSAs) over the Global Positioning System (GPS). The technique estimates the clock bias and drift of the GPS receiver along with the possible attack contrary to previous approaches. Having estimated the time instants of the attack, the clock bias and drift of the receiver are corrected. The proposed technique is computationally efficient and can be easily implemented in real time, in a fashion complementary to standard algorithms for position, velocity, and time estimation in off-the-shelf receivers. The performance of this technique is evaluated on a set of collected data from a real GPS receiver. Our method renders excellent time recovery consistent with the application requirements. The numerical results demonstrate that the TSARM technique outperforms competing approaches in the literature.