Year: 2007
Type: dissertation
Abstract: A detailed analysis of autonomous navigation algorithms to achieve autonomous \nprecision landing is presented. The problem of integrated attitude determination \nand inertial navigation is solved. The theoretical results are applied and tested \nin three different applications. Optimality conditions for constrained quaternion \nestimation using the Kalman filter are derived. \nIt is common in spacecraft applications to separate the attitude determination \nfrom the inertial navigation system. While this approach has worked in the \npast, it inevitably degrades the navigation performance when the correlations between \nthe two systems are not correctly accounted for. It is shown how to optimally \ninclude an attitude determination algorithm into the Kalman filter. When the conditions \nto achieve optimality are not met, it is shown how to achieve sub-optimality \nby properly accounting for the correlation. \nThe traditional approach to inertial navigation is to employ the inertial measurement \nunit (IMU) outputs to propagate the estimated states forward in time, \nrather then use them to update the state. A detailed covariance analysis of deadreckoning \nMars entry navigation is performed. The contribution of various sources \nof IMU errors are explicitly accounted for and the filter performance is validated \nthrough Monte Carlo analysis. \nThe drawback of dead-reckoning is that this approach prevents the inertial \nmeasurements from reducing the uncertainty of the estimated states. While this \nshortcoming can be compensated by the availability of other measurements, it becomes \ncrucial when the IMU is the only sensor to provide measurements. Such a \nsituation arises, for example, during Mars atmospheric entry. In the second application \nof this work, IMU measurements from a NASA mission are processed in an \nextended Kalman filter, and the results are compared to dead-reckoning. It is shown \nthat is possible to reduce the uncertainty of the inertial states by filtering the IMU. \nThe final application is lunar descent to landing navigation. In this example \nthe IMU is filtered and the algorithms to include an attitude estimate into the \nKalman filter are tested. The design performance is confirmed by Monte Carlo \nanalysis.
Author: Renato Zanetti
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1896275511