Residual-based multi-filter methodology for all-source fault detection, exclusion, and performance monitoring

Abstract

All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have constrained faults to biases and designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with valid measurement models, none of which are guaranteed for all-source systems. As part of an overall all-source assured or resilient navigation objective, this research contributes a fault- and sensor-agnostic fault detection and exclusion method that can provide the user with performance guarantees without constraining the statistical distribution of the fault. The proposed method is compared against normalized solution separation approaches using Monte-Carlo simulations in a 2D non-GPS navigation problem.