Optimal and efficient geolocation and path planning for unmanned aerial vehicles using uncertainty measures
Semper, Sean R.
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A general frame work for determining an object's absolute position from relative position measurements, commonly called geolocation, is developed in this dissertation. 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. Uncertainty metrics are derived for enhancing filter performance, allowing for a theoretical selection of guidance routines given operational requirements. When prior information is present then special stochastic approach is developed to include this information into the guidance routine. When the UAV heading angle contains errors, a newly derived a marginalized adaptive Gaussian sum propagator is used to estimate nonlinear UAV positions. Marginalizing the state-space places computational efforts on the nonlinear portions of the state-space and allows the linear portions to propagated using a linear Kalman Filter (KF). Combining new estimation methods allows one to deal with more complex scenarios and create robust architectures for passive geolocation solutions.