Efficient Localization of Multiple Intruders in Shared Spectrum System

We address the problem of localizing multiple intruders (unauthorized transmitters) using a distributed set of sensors in the context of a shared spectrum system. In contrast to single transmitter localization, multiple transmitter localization (MTL) has not been thoroughly studied. In shared spectrum systems, it is important to be able to localize simultaneously present multiple intruders to effectively protect a shared spectrum from malware-based, jamming, or other multi-device unauthorized-usage attacks. The key challenge in solving the MTL problem comes from the need to separate an aggregated signal received from multiple intruders into separate signals from individual intruders. Furthermore, in a shared spectrum paradigm, presence of an evolving set of authorized users (e.g., primary and secondary users) adds to the challenge. In this paper, we propose an efficient algorithm for the MTL problem based on the hypothesis-based Bayesian approach called MAP. Direct application of the MAP approach to the MTL problem incurs prohibitive computational and training cost. In this work, we develop optimized techniques based on MAP with significantly improved computational and training costs. In particular, we develop a novel interpolation method, ILDW, which helps minimize the training cost. We generalize our techniques via online-learning to the setting wherein there may be a set of dynamically-changing authorized users present in the background. We evaluate our developed techniques on large-scale simulations as well as on small-scale indoor and outdoor testbeds. Our experiments demonstrate that our technique outperforms the prior approaches by significant margins, i.e., error up to 74% less in large-scale simulations and 30% less in real-world testbeds.