Decentralized geolocation and optimal path planning using limited UAVs

A decentralized estimation architecture for determining an object's absolute position from relative position measurements, commonly called geolocation, is developed in this paper. Relative measurements are obtained from a two unmanned aerial vehicle (UAV) team with electronic support measure (ESM) sensors on board. One team combines their time of arrival (TOA) measurements forming one time difference of arrival measurement (TDOA) from an emitter's signal. Using an Extended Kalman Filter (EKF), pseudorange equations containing UAV positions and emitter position estimates are sequentially estimated to solve for absolute emitter positions. When N UAV teams are available, a decentralized EKF architecture is derived to optimally fuse estimates from N filters at the global fusion node. In addition, optimal trajectories for two UAVs are developed to minimize the covariance position errors. Weights are placed on the UAV motions, so minimum and maximum distances to the emitting object are restricted.