No abstract

In static systems, state values are estimated using traditional least squares techniques based on a redundant set of measurements. Inaccuracies in measurement model parameter estimates can lead to significant errors in the state estimates. This paper describes a technique that considers these parameters in a modified least squares framework. It is also shown that this framework leads to the minimum variance solution. Both batch and sequential (recursive) least squares methods are described. One static system and one dynamic system are used as examples to show the benefits of the consider least squares methodology.

Two theoretical approaches for determining position using star observations are presented, the Attitude Matrix Solution and Inner Product Solution. Both approaches are designed for implementation in an autonomous Stellar Positioning System that uses the following measurement sources: an astronomical camera, a clock, and a set of two inclinometers. Before presenting the algorithms, the reference frames utilized throughout are defined. The two position estimation techniques are then individually presented, followed by a discussion of gravity model and geometric model refinements.

No abstract