This work aims to tackle the problem of GNSS-denied scenarios, which occur when Inertial Navigation Systems (INS) encounter drift problems over time and standalone GNSS fails. The most significant developments are a fourfold improvement in time synchronization, the use of dual-layer KF + MHE (Moving Horizon Estimation), the optimization of multi-sensor redundancy (MSR) and PSNR, and the integration of many DME stations into a tightly linked GNSS/INS system. The Kalman Filter mixes INS predictions with unprocessed GNSS data directly, improving availability in case of GNSS outages. Multi-Sensor Redundancy (MSR) systems include odometers and lidar to mitigate INS drift in the absence of GNSS/DME. By using PSNR weighting, one may increase interference resistance and decrease interference. Quadruple time synchronization guarantees that all sensor data are precisely matched in terms of time prior to fusion. Thanks to the real-time position/velocity estimates given by the dual-layer KF + MHE, the distance-traveled error is maintained at 0.1% even during lengthy GNSS outages. The performance findings demonstrate that the 3D accuracy is 40–60% higher than loosely coupled systems, with a 1 cm RMS inaccuracy and a 0.5 m drift after 10 minutes without GNSS. Potential future additions include an RF-Geolocation Mesh for cooperative positioning using nearby radios and Visual Positioning System (VPS) cameras for landmark-based navigation. Applications that depend on this include autonomous cars, UAVs, military navigation, indoor/city navigation, and defense systems that are resistant to jamming and spoofing.