Predictive Image Navigation Aiding Against GPS Spoofing

The patent introduces an intermediary system (predictive navigation system with machine learning model) that mediates between the vulnerable GPS/FMV signals and the navigation decision-making process. This intermediary generates predictive images and detects signal anomalies, providing an additional layer of verification that reduces vulnerability to spoofing and jamming while maintaining navigation accuracy.

The system implements feedback mechanisms where the machine learning model continuously compares predicted images with actual sensor inputs, detects discrepancies caused by spoofing or jamming, and adjusts navigation decisions accordingly. This feedback loop enables the system to identify and reject manipulated signals while maintaining accurate navigation.

The system creates copies of expected navigation data through the machine learning model's predictive images. By generating synthetic representations of what the environment should look like based on historical and sensor data, the system can compare these copies with actual sensor inputs to detect spoofing or jamming, thereby improving reliability without requiring completely new hardware.

The machine learning model serves multiple functions: it generates predictive images for navigation, detects spoofing and jamming, and adapts to changing environments. This multi-functionality reduces the need for separate specialized systems, thereby limiting the increase in device complexity while achieving improved resistance to adversarial conditions.

The system performs preliminary actions by pre-processing sensor data and pre-generating predictive images using the machine learning model before critical navigation decisions are required. This allows computational-intensive tasks to be completed in advance, reducing real-time processing requirements and enabling faster response to changing environmental conditions in contested environments.

The system dynamically adjusts its processing requirements based on environmental conditions and mission criticality. In less contested environments, processing can be less intensive, while in highly contested environments where spoofing and jamming are detected, the system increases computational resources allocated to predictive image generation and anomaly detection, thereby optimizing the balance between adaptability and processing time.