RF Emitter geolocation using PDOA algorithms and UAVs - A strategy from emitter detection to location prediction

Abstract

In this thesis, I explored strategies for locating an RF emitter. Expanding on an idea conceived at Norwegian Defence Research Establishment (FFI), of using small, cheap RSS sensors and Unmanned Aerial Vehicles (UAVs) to search for unknown RF emitters. Cheap and simple, will in most cases, mean that some property of the system suffers, compared to more complicated and expensive systems. This thesis attempts to circumvent these issues by using multiple sensors instead of one single larger sensor.

How to best organize and use multiple sensors in a distributed autonomous con- text is a problem that is complicated, if not impossible, to solve analytically. Applying artificial intelligence methods to this problem allows for finding good solutions and strategies while maintaining computational feasibility. The results of this work outline a strategy from emitter-detection to location-prediction, in- cluding analysis of trade-offs between accuracy and resource consumption. The strategy presented here may be implemented in a functional real-world demon- stration platform, with few modifications, and provides the ground-work for a cheap, fully autonomous, distributed UAV system for locating unknown RF emit- ters.

I have found that the marginal gain from adding more UAVs decrease faster than that from adding more steps (time) per UAV. Furthermore, it is important to avoid ambiguities. Ambiguities present two or more locations which cannot be distinguished without a carefully selected formation. Finally, it may not be possible to optimize this problem fully with the computational capacity available today. This leads to developing good heuristics, approximate solutions, that provide sufficient performance. A few such heuristics are presented here, most notably using an attraction force to model optimized behaviour.