Assured Integrated Mechanization of Positioning, Navigation and Timing (AIM-PNT) System

Recent advancements in inertial sensor technology, pervasive communication signals of opportunity (SOOP), availability of quality databases of Earth’s anomalies as well as non-linear estimation techniques have created an opportunity to enable assured PNT capabilities without reliance on GPS/GNSS. To utilize this opportunity, QuNav proposes to develop an Assured Integrated Mechanization of PNT (AIM-PNT). AIM-PNT integrates three key enabling technologies with mutually complementary benefitsinto a joint system architecture:    · Inertial Navigation System (INS): INS supports a completely self-contained navigation without relying on external sources. Yet, inertial accuracy will still eventually degrade to unacceptable levels. Hence, AIM-PNT uses INS as a core sensor and applies the other two enabling technologies in order to provide aiding observations to estimate INS error states and adjust navigation outputs thus mitigating the output drift.  · Integration with Low Earth Orbit (LEO) SOOP: To fully utilize the potential advantages of LEO signals, the proposed AIM-PNT system will incorporate an innovative approach of a software-defined universal LEO PNT receiver (SUPER) toward a multiple-constellations, hybrid signals (non-cooperative and cooperative), and integrated (with an INS) LEO PNT solution capability.  · Map-Matching of Earth Fields’ Anomalies: The use of LEO SOOP enhances the overall frequency diversity and received signal power thus improving the PNT resilience in contested environments. Yet, these signals are still susceptible to similar fundamental shortcomings as GNSS (i.e., unintentional interference, jamming and spoofing). To further enhance the PNT resilience, AIM-PNT augments the system architecture with aiding from Earth’s anomaly maps. To address the challenge of low-resolution maps, we propose to employ analytic inversion of Earth’s field anomalies into discrete sources for SLAM. By applying the inversion approach, the sources responsible for the anomalies in a small region (local scale and fine resolution) can be estimated accurately and then registered upward to fill the details of large scale and coarse resolution maps.  The sensor-fusion architecture utilizes a partitioned estimation scheme with separated processing of (i) linear measurements via an extended Kalman filter (EKF), and (ii) non-linear measurements via a Bayesian particle filter. A marginalized particle filter methodology is applied to enable a computationally-scalable joint processing. In Phase I, technical feasibility and performance characteristics will be demonstrated and evaluated using a combination of simulated and experimental data.  To comply with Modular Open System Architecture (MOSA), input sensor messages and output navigation messages will be formulated using ASPN 2.2 format. This way, the AIM-PNT capability can be directly inserted into a Common Application Space of EGI-M.