Navigation and positioning system in GPS-denied environments using quantum-inspired and adaptive sensor frameworks

A navigation method for operation in GPS-denied environments, the navigation method comprising:

• representing and maintaining multiple position hypothesis using wavefunction-like expansions and amplitude-based filtering;
• defining, managing, and dynamically updating software-based anchor references for position triangulation and drift correction;
• receiving solar, geomagnetic, and environmental data, and proactively adjusting sensor weighting and fallback modes based on forecasted disturbances;
• employing quantum-algebraic key generation and location-derived ephemeral keys to secure sensor data and inter-device transmissions;
• implementing an AR overlay for visualizing and editing virtual anchor point (VAP) reference;
• and
• monitoring one or more physiological signals of an operator and dynamically adapting the AR overlay based on estimated cognitive load;
• wherein the act of representing multiple position hypothesis is performed in conjunction with the updated virtual anchor points to improve positional accuracy in the absence of GPS;
• and
• wherein the cryptographic securing of sensor data is based on location-derived ephemeral key generation.

A method of optimizing anchor references in a GPS-denied navigation system, the method comprising:

• collecting multi-sensor data from at least one inertial sensor and one auxiliary sensor selected from the group consisting of:
• magnetometer, LiDAR, camera, radar, barometer, and fractal antenna signals;
• representing sensor and geospatial states in a quantum-inspired state space, wherein amplitude updates are computed based on sensor error metrics;
• applying a bio-inspired or artificial intelligence algorithm selected from the group consisting of reinforcement learning, genetic evolution, and swarm intelligence to reposition or reweight virtual anchor points in near real time;
• integrating the updated anchor references into a quantum-inspired sensor fusion process to reduce sensor drift and improve positional accuracy under changing environmental conditions;
• and
• optionally deploying at least part of the quantum-inspired sensor fusion process on an edge device with constrained computational resources, using approximate wavefunction expansions or selective amplitude discarding to maintain real-time responsiveness;
• wherein the repositioning or reweighting of anchor references is refined through a combination of bio-inspired heuristics and reinforcement learning models, and
• wherein the quantum-inspired fusion process conforms to a Hilbert-space-based representation of sensor and location states.