Date of Award

9-2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Systems Engineering and Management

First Advisor

Robert C. Leishman, PhD

Abstract

The novel ARMAS-SOM framework fuses collaborative all-source sensor information in a resilient manner with fault detection, exclusion, and integrity solutions recognizable to a GNSS user. This framework uses a multi-filter residual monitoring approach for fault detection and exclusion and is augmented with an additional "observability" EKF sub-layer for resilience. We monitor the a posteriori state covariances in this sub-layer to provide intrinsic awareness when navigation state observability assumptions required for integrity are in danger. This is used to selectively augment the framework with offboard information to preserve resilience. By maintaining split parallel collaborative and proprioceptive estimation instances and employing a novel "stingy collaboration" technique, we are able to limit the propagation of unknown corruption to a single collaborative donor, facilitate seamless recovery from collaborative corruption, and maintain consistent navigation without fear of double-counting in a scalable processing footprint. Lastly, we preserve the ability to return to autonomy and are able to use the same intrinsic awareness to notify the user when it is safe to do so.

AFIT Designator

AFIT-ENV-DS-21-S-065

DTIC Accession Number

AD1150366

Download

Share

COinS