Typical algorithms for processing unresolved space imagery from optical systems make broad assumptions about the expected behavior of the sensors during collection. While these techniques are often successful at data reduction for a particular mission, they rarely extend to sensors in different operating modes. Such specialized techniques therefore reduce the number of sensors able to contribute imagery. By approaching this problem with analysis of the fundamental dynamic equations and geometry at play, we can gain a deeper understanding into the behavior of both stars and space objects viewed through optical sensors. This type of analysis has the potential to enable data collection from a wider variety of sensors, increasing both the quantity and quality of data available for space object catalog maintenance. This dissertation will explore the implications of this approach to unresolved data processing. Sensor-level motion descriptions will be derived and applied to the problem of space object discrimination and tracking. Results of this processing pipeline as applied to both simulated and real optical data will be presented. / Ph. D.

This thesis investigates using adaptive estimation techniques to determine unknown model parameters such as size and surface material reflectivity, while estimating position, velocity, attitude, and attitude rates of a resident space object. This work focuses on the application of these methods to the space situational awareness problem. This thesis proposes a unique method of implementing a top-level gating network in a dual-layer hierarchical mixture of experts. In addition it proposes a decaying learning parameter for use in both the single layer mixture of experts and the dual-layer hierarchical mixture of experts. Both a single layer mixture of experts and dual-layer hierarchical mixture of experts are compared to the multiple model adaptive estimation in estimating resident space object parameters such as size and reflectivity. The hierarchical mixture of experts consists of macromodes. Each macromode can estimate a different parameter in parallel. Each macromode is a single layer mixture of experts with unscented Kalman filters used as the experts. A gating network in each macromode determines a gating weight which is used as a hypothesis tester. Then the output of the macromode gating weights go to a top level gating weight to determine which macromode contains the most probable model. The measurements consist of astrometric and photometric data from non-resolved observations of the target gathered via a telescope with a charge coupled device camera. Each filter receives the same measurement sequence. The apparent magnitude measurement model consists of the Ashikhmin Shirley bidirectional reflectance distribution function. The measurements, process models, and the additional shape, mass, and inertia characteristics allow the algorithm to predict the state and select the most probable fit to the size and reflectance characteristics based on the statistics of the measurement residuals and innovation covariance. A simulation code is developed to test these adaptive estimation techniques. The feasibility of these methods will be demonstrated in this thesis.

Aerial recovery of autonomous micro air vehicles (MAVs) presents many unique challenges due to the difference in size and speed of the recovery vehicle and MAV. This thesis presents algorithms to enable an autonomous MAV to estimate the orbit of a recovery vehicle and track the orbit until the final docking phase. Methods for estimating ellipses that are rotated out of the x − y plane are developed and demonstrated through simulation. These algorithms are shown to be robust to noise and stable numerically. Parameter update methods that are computationally inexpensive, such as recursive least squares and Kalman filtering, are discussed and simulated. A discussion is given of orbit tracking algorithms for circular orbits, and these methods are expanded to include elliptical orbits. These algorithms enable the MAV to track the recovery vehicle's orbit, based on a vector field approach. The tracking algorithms are divided into lateral and longitudinal controllers that allow for tracking of inclined orbits. Finally, the hardware and software setup for live flight tests is discussed. Flight test results are given that validate the functionality of the orbit estimation and orbit tracking algorithms.