An approximate factorization approach to multi-jammer location and range estimation from peer-to-peer connectivity measurements

The localization (i.e., location and range) of communication jammers in an area of operation of mobile agents is considered. The jammers are operated by adversaries to disrupt/jam communications among mobile agents to degrade mission performance. In comparison with prior results, we address estimation of jammer locations and ranges based purely on observed time series of peer-to-peer agent communication connectivities and corresponding agent locations. Jamming effects and communication connectivity are characterized by stochastic models. The agents are not assumed to have any additional sensors to measure received signal strength or to have any visual or other perception to “see” jammers. The fundamental algorithmic challenge is the inherent empirical ambiguity as to which agent was jammed when a loss of communication is detected. For this purpose, we develop an algorithmic framework based on approximate factorization, spatial local density-based filtering, and maximum likelihood estimation. We show efficacy of the proposed approach through simulation studies with large numbers of mobile agents in multiple simulated scenarios.