Utilizing the Euler-Rodrigues symmetric parameters (attitude quaternion) to describe vehicle orientation, we develop a multiplicative, nonlinear (extended) variation of the Kalman filter (MEKF) to fuse data from low-cost sensors. The sensor suite is comprised of gyroscopes, accelerometers, and a GPS receiver. In contrast to the common approach of using the complete vehicle attitude as the quantities to be estimated, our filter states consist of the three components of an attitude error vector. In parallel with the time update of the attitude error estimate, we utilize the gyroscope measurements for the time propagation of the attitude quaternion. The accelerometer and the GPS sensors are used independently for the measurement update portion of the Kalman filter. For both sensors, a vector arithmetic approach is used to determine the attitude error vector. Following each measurement update, a multiplicative reset operation moves the attitude error information from the filter state into the attitude estimate. This reset operation utilizes quaternion algebra to implicitly maintain the unity-norm constraint. We demonstrate the effectiveness of our attitude estimation algorithm through flight simulations and flight tests of aggressive maneuvers such as loops and small-radius circles. We implement an approach to aerobatic maneuvering for miniature air vehicles (MAVs) using time-parameterized attitude trajectory generation and an associated attitude tracking control law. We designed two methodologies, polynomial and trigonometric, for creating functions that specify pitch and roll angles as a function of time. For both approaches, the functions are constrained by the maneuver boundary conditions of aircraft position and velocity. We construct a trajectory tracking feedback control law to regulate aircraft orientation throughout the maneuvers. The trajectory generation algorithm was used to construct several maneuvers and trajectory tracking control law successfully executed the maneuvers in the flight simulator. In addition to the simulation results, MAV flight tests verified the performance of the maneuver generation and control. To achieve obstacle avoidance maneuvering, the time parameterized trajectories were converted to spatially parameterized paths, which allowed for inertial reference frame position error to be included in the control law feedback loop. We develop a novel method to achieve the spatial parameterization using a prediction and correction approach. Additionally, the first derivative of position of the desired path is modified using a corrective parameter scheme prior to being used in the control. Using the path position error and the corrected derivative, we utilize a unit-norm quaternion framework to implement a proportional-derivative (PD) control law. This control law was demonstrated in simulation and hardware on maneuvers designed specifically to avoid obstacles, namely the Immelmann and the Close-Q, as well as a basic loop.