Masters Theses

Keywords and Phrases

Fault Detection; Fusion; Multi-Sensor; Navigation


"Redundant sensor networks of inertial measurement units (IMUs) provide inherent robustness and redundancy to a navigation solution obtained by dead reckoning the fused accelerations and angular velocities sensed by the IMU. However, IMUs have been known to experience faults risking catastrophic mission failure creating large financial setbacks and an increased risk of human safety. Different fusion methods are analyzed for a multi-sensor network using cost effective IMUs, including direct averaging and covariance intersection. Simulations of a spacecraft in low Earth orbit are used to baseline a typical expensive IMU and compare the navigation solution obtained from a network of several low-cost IMUs from fused data. Robust on-board fault detection schemes are developed and analyzed for a multi-sensor distributed network specifically for IMUs.

Simulations of a spacecraft are used to baseline several cases of sensor failure in a distributed network undergoing fusion to produce an accurate navigation solution. The presented results exhibit a robust fault identification scheme that successfully removes a failing sensor from the fusion process while maintaining accurate navigation solutions. In the event of a temporary sensor failure, the fault detection algorithm recognizes the sensors' return to nominal operating conditions and processes its sensor data accordingly"--Abstract, page iii.


DeMars, Kyle J.

Committee Member(s)

Pernicka, Hank
Hosder, Serhat


Mechanical and Aerospace Engineering

Degree Name

M.S. in Aerospace Engineering


Missouri University of Science and Technology

Publication Date

Spring 2016


xiii, 144 pages

Note about bibliography

Includes bibliographic references (pages 142-143).


© 2016 Samuel J. Haberberger, All rights reserved.

Document Type

Thesis - Open Access

File Type




Library of Congress Subject Headings

Inertial navigation (Aeronautics)
-- Navigation -- Technological innovations
Multisensor data fusion

Thesis Number

T 10875

Electronic OCLC #



M.S. in Applied Mathematics with Statistics Emphasis