Multiple Transmitter Localization under Time-Skewed Observations

Radio spectrum is a limited natural resource under a significant demand and thus, must be effectively monitored and protected from unauthorized access. Recently, there has been a significant interest in the use of inexpensive commodity-grade spectrum sensors for large-scale RF spectrum monitoring. These sensors being inexpensive can be deployed at much higher density, and thus, can provide much more accurate spectrum occupancy maps or intruder detection schemes. However, these sensors being inexpensive also have limited computing resources, and being independent and distributed can suffer from clock skew (i.e., their clocks may not be sufficiently synchronized). In this paper, we are interested in the problem of detection and localization of multiple intruders present simultaneously, in the above context of distributed sensors with limited resources and clock skew. The key challenge in addressing the intruder localization problem using sensors with clock skew is that it is very difficult to even derive an observation vector over sensors, for any (absolute) instant. In this work, we propose Group-Based Algorithm, a skew-aware multiple intruders localization method that essentially works by extracting observations across sensors for certain small sets of transmitters. Our results show that Group-Based Algorithm yields significant improvement of accuracy over relatively simpler approaches.