Resilient Collaborative All-source Navigation

Abstract

The Autonomous and Resilient Management of All-source Sensors with Stable Observability Monitoring (ARMAS-SOM) framework fuses collaborative all-source sensor information in a resilient manner with fault detection, exclusion, and integrity solutions recognizable to a Global Navigation Satellite System (GNSS) user. This framework uses a multi-filter residual monitoring approach for fault detection and exclusion which is augmented with an additional "observability" Extended Kalman Filter (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. The framework leverages this to selectively augment with offboard information and preserve resilience. By maintaining split parallel collaborative and proprioceptive frameworks and employing a novel "stingy collaboration" technique, we are able maximize efficient use of network resources, limit the propagation of unknown corruption to a single donor, prioritize high fidelity donors, and maintain consistent collaborative 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.