Single-Filter Finite Fault Detection and Exclusion Methodology for Real-Time Validation of Plug-and-Play Sensors

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 designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with well-understood 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 key component-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 proposed algorithm combines a residual-based test statistic with the partial update formulation of the Kalman-Schmidt filter to provide a reliable method for sensor model validation that protects the integrity of the navigation solution during the validation process, all using only a single existing filter. The performance of the proposed method is validated against traditional fault detection and exclusion methods (such as normalized solution separation and conventional residual sequence monitoring) using Monte-Carlo simulations in a 2D non-Global Positioning System navigation problem with a plug-and-play position sensor.