The Autonomous and Resilient Management of All-source Sensors (ARMAS) framework monitors residual-space test statistics across unique sensor-exclusion banks of filters, (known as subfilters) to provide a resilient, fault-resistant all-source navigation architecture with assurance. A critical assumption of this architecture, demonstrated in this paper, is fully overlapping state observability across all subfilters. All-source sensors, particularly those that only provide partial state information (altimeters, TDoA, AOB, etc.) do not intrinsically meet this requirement.
This paper presents a novel method to monitor real-time overlapping position state observability and introduces an "observability bank" within the ARMAS framework, known as Stable Observability Monitoring (SOM). SOM uses real-time stability analysis to provide an intrinsic awareness to ARMAS of the capabilities of the fault detection and exclusion (FDE) functionality. We define the ability to maintain consistent all-source FDE to recover failed sensors as navigation resilience. A resilient FDE capability then is one that is "aware" of when it requires more sensor information to protect the consistency of the FDE and integrity functions from corruption. SOM is the first demonstration of such a system, for all-source sensors, that the authors are aware.
A multi-agent 3D environment simulating both GNSS and position and velocity alternative navigation sensors was created and individual GNSS pseudorange sensor anomalies are utilized to demonstrate the capabilities of the novel algorithm. This paper demonstrates that SOM seamlessly integrates within the ARMAS framework, provides timely prompts to augment with new sensor information from other agents, and indicates when framework stability and preservation of all-source navigation integrity are achieved.
Gipson, Jonathon S. and Leishman, Robert C., "Resilience for Multi-filter All-source Navigation Framework with Integrity" (2021). Faculty Publications. 771.