The use of Unmanned Aerial Vehicles (UAV) has exploded over the last decade with the constant need to reduce costs while maintaining capability. Despite the relentless development of electronics and battery technology there is a sustained need to reduce the size and weight of the on-board systems to free-up payload capacity. One method of reducing the energy storage requirement of UAVs is to utilise naturally occurring sources of energy found in the atmosphere. This thesis explores the use of static and semi-dynamic soaring to extract energy from naturally occurring shallow layer cumulus convection to improve range, endurance and average speed. A simulation model of an X-Models XCalibur electric motor-glider is used in combination with a refined 4D parametric atmospheric model to simulate soaring flight. The parametric atmospheric model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation. For micro to small UAVs to be able to track the desired trajectories a highly accurate Attitude Heading Reference System (AHRS) is needed. Detailed analysis of the practical implementation of advanced attitude determination is used to enable optimal execution of the trajectories generated. The new attitude determination methods are compared to existing Kalman and complimentary type filters. Analysis shows the methods developed are capable of providing accurate attitude determination with extremely low computational requirements, even during extreme manoeuvring. The new AHRS techniques reduce the need for powerful on-board microprocessors. This new AHRS technique is used as a foundation to develop a robust navigation filter capable of providing improved drift performance, over traditional filters, in the temporary absence of global navigation satellite information. All these algorithms have been verified by flight tests using a mixture of manned and unmanned aerial vehicles and avionics developed specifically for this thesis.

This paper describes, the development of a sensor fusion algorithm-based Kalman lter ar- chitecture, in combination with a low cost Inertial Measurement Unit (IMU) for an Attitude Heading Reference System (AHRS). A low cost IMU takes advantage of the use of MEMS technology enabling cheap, compact, low grade sensors. The use of low cost IMUs is primar- ily targeted towards Unmanned Aerial Vehicle (UAV) applications due to the requirements for small package size, light weight, and low energy consumption. The high dynamics nature of smaller airframes, coupled with the typical vibration induced noise of UAVs require an e cient, reliable, and robust AHRS for vehicle control. To eliminate the singularities at 90 on the pitch and roll axes, and to keep the computational e ciency high, quaternions are used for state attitude representation.