Abstract
Thesis is based on three different aspects of Kalman filtering.
>Kalman filters for navigation. Investigate the difference
between a Extended Kalman Filter and a Linearized Kalman Filter with feedback.
And show how different system models relate to these Kalman Filters when implemented in a filter.
>Smoothing. Investigate how much there is to be gained from
smoothing. We will only look at the fixed-interval smoother, using
the method of forward and backward filtering.
>The Covariance Analysis Program. Designing a program that handles
the theory of covariance analysis, which gives us the ability to compare
system model design and performance.