A detailed analysis of autonomous navigation algorithms to achieve autonomous
precision landing is presented. The problem of integrated attitude determination
and inertial navigation is solved. The theoretical results are applied and tested
in three different applications. Optimality conditions for constrained quaternion
estimation using the Kalman filter are derived.
It is common in spacecraft applications to separate the attitude determination
from the inertial navigation system. While this approach has worked in the
past, it inevitably degrades the navigation performance when the correlations between
the two systems are not correctly accounted for. It is shown how to optimally
include an attitude determination algorithm into the Kalman filter. When the conditions
to achieve optimality are not met, it is shown how to achieve sub-optimality
by properly accounting for the correlation.
The traditional approach to inertial navigation is to employ the inertial measurement
unit (IMU) outputs to propagate the estimated states forward in time,
rather then use them to update the state. A detailed covariance analysis of deadreckoning
Mars entry navigation is performed. The contribution of various sources
of IMU errors are explicitly accounted for and the filter performance is validated
through Monte Carlo analysis.
The drawback of dead-reckoning is that this approach prevents the inertial
measurements from reducing the uncertainty of the estimated states. While this
shortcoming can be compensated by the availability of other measurements, it becomes
crucial when the IMU is the only sensor to provide measurements. Such a
situation arises, for example, during Mars atmospheric entry. In the second application
of this work, IMU measurements from a NASA mission are processed in an
extended Kalman filter, and the results are compared to dead-reckoning. It is shown
that is possible to reduce the uncertainty of the inertial states by filtering the IMU.
The final application is lunar descent to landing navigation. In this example
the IMU is filtered and the algorithms to include an attitude estimate into the
Kalman filter are tested. The design performance is confirmed by Monte Carlo
analysis.Aerospace Engineering and Engineering Mechanic