A Framework for Collaborative All-Source Navigation With Fault Detection and Exclusion

The 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 multifilter residual monitoring approach for fault detection and exclusion, which is augmented with an additional “observability” extended Kalman filter sublayer for resilience. We monitor the
a posteriori
state covariances in this sublayer to provide intrinsic awareness when navigation state observability assumptions required for integrity are in danger. The framework leverages this to selectively augment with offboard information and preserve resilience. By maintaining split parallel
collaborative
and
proprioceptive
instances and employing the “stingy collaboration” technique, we are able to maximize efficient use of network resources, limit the propagation of unknown corruption to a single donor, and maintain consistent collaborative navigation without fear of double-counting in a scalable processing footprint. Last, 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.