Positioning, Navigation, And Timing Trust Inference Engine

Critical infrastructure users need to infer the assurance of position, velocity, and time (PVT) estimates. Such an inference performed by positioning, navigation, and timing (PNT) platforms considers multiple sources of information, such as sensor inputs, anti-spoofing (A-S) techniques, situational awareness (SA) information, and other auxiliary sources ( such as network data). The challenge is to fuse trust assumptions and assessments of these sources into useful assurance metrics. We present PNTTING, a PNT Trust Inference Engine that facilitates trust fusion according to probabilistic models with rigorous semantics. PNTTING's architecture comprises inputs, processing, and outputs. Inputs include measurements collected while the platform is being used in the field or via recorded scenarios. Inputs also include a priori trust assumptions of measurements. Processing relies on data models and probabilistic procedures developed by assurance model designers and PNT engineers. Finally, the outputs are derived from inference tasks, which describe probability queries related to assessing assurance. We present results of a PNTTING implementation using illustrative scenarios. This implementation identifies several challenges to realizing PNTTING in practice. These challenges include the need for realistic test vectors/scenarios, models for the trust assumptions, and a capable and extensible computational framework. Even with those challenges, our implementation of PNTTING shows the potential of creating a PNT platform that includes inference mechanisms of trust with a wide range of inputs and conditions. This approach enables evaluating the effectiveness of PNT platforms and creating assurance metrics during operation.