RF Emitter geolocation using PDOA algorithms and UAVs - A strategy from emitter detection to location prediction
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In this thesis, I explored strategies for locating an RF emitter. Expanding onan idea conceived at Norwegian Defence Research Establishment (FFI), of usingsmall, cheap RSS sensors and Unmanned Aerial Vehicles (UAVs) to search forunknown RF emitters. Cheap and simple, will in most cases, mean that someproperty of the system suffers, compared to more complicated and expensivesystems. This thesis attempts to circumvent these issues by using multiple sensorsinstead 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 goodsolutions and strategies while maintaining computational feasibility. The resultsof this work outline a strategy from emitter-detection to location-prediction, in-cluding analysis of trade-offs between accuracy and resource consumption. Thestrategy presented here may be implemented in a functional real-world demon-stration platform, with few modifications, and provides the ground-work for acheap, fully autonomous, distributed UAV system for locating unknown RF emit-ters. I have found that the marginal gain from adding more UAVs decrease fasterthan that from adding more steps (time) per UAV. Furthermore, it is importantto avoid ambiguities. Ambiguities present two or more locations which cannotbe distinguished without a carefully selected formation. Finally, it may not bepossible to optimize this problem fully with the computational capacity availabletoday. This leads to developing good heuristics, approximate solutions, thatprovide sufficient performance. A few such heuristics are presented here, mostnotably using an attraction force to model optimized behaviour.