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. This research aims to provide all-source multi-sensor resiliency, assurance, and integrity through an autonomous sensor management framework. The proposed framework dynamically places each sensor in an all-source system into one of four modes: monitoring, validation, calibration, and remodeling. Each mode contains specific and novel realtime processes that affect how a navigation system responds to sensor measurements. The monitoring mode is driven by a novel sensor-agnostic fault detection, exclusion, and integrity monitoring method that minimizes the assumptions on the fault type, all-source sensor composition, and the number of faulty sensors. The validation mode provides a novel method for the online validation of sensors which have questionable sensor models, in a fault-agnostic and sensor-agnostic manner, and without compromising the ongoing navigation solution in the process. The remaining two modes, calibration and remodeling, generalize and integrate online calibration and model identification processes to provide autonomous and dynamic estimation of candidate model functions and their parameters, which when paired with the monitoring and validation processes, directly enable resilient, self-correcting, plug-and-play open architecture navigation systems
