Data Fusion for Space Object Classification using Human-Quality Artificial Intelligence Technique
In a recent effort, Stottler Henke used low-level pixel processing techniques to process actual infrared and radar images for better correlation, classification, and lethality discrimination for ballistic missile defense targets in order to better utilize disparate sensor data in missile defense to perform improved correlations and classification.
The sensor data fusion system we developed used artificial intelligence (AI) techniques and the concept of extracting features from raw sensor data and reasoning about those features and their hypothetical association with hypothesized objects.
Specifically, this project accomplishes this by using artificial intelligence techniques to implement human-quality reasoning on sparse, physics-based object features extracted from sensor data. By performing human-quality reasoning, the system can perform superiorly to other systems utilizing the same sensor data. The techniques and software were proven with actual X-band and IR sensor data for relevant IR/Radar targets and achieved excellent performance results on this real data.