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.

考虑了移动智能体作业区域内通信干扰器的定位（即位置和范围）。干扰器由敌方操作，用于破坏/干扰移动智能体之间的通信，从而降低任务性能。与先前的结果相比，我们仅基于观察到的对等智能体通信连接性的时间序列以及相应的智能体位置来估计干扰器的位置和范围。干扰效应和通信连接性通过随机模型来表征。不假定智能体具有任何额外的传感器来测量接收信号强度，也不假定其具有任何视觉或其他感知能力来“看到”干扰器。基本的算法挑战在于，当检测到通信丢失时，关于哪个智能体被干扰存在固有的经验模糊性。为此，我们开发了一个基于近似分解、基于空间局部密度的滤波以及最大似然估计的算法框架。我们通过在多个模拟场景中对大量移动智能体进行模拟研究，展示了所提出方法的有效性。