- Anti-Terrorism/Force Protection Systems Effectiveness Model (SEM)
- AT/FP Afloat
- AT/FP Ashore
- Bandwidth Analysis Toolkit (BAT)
- Cyber Assassin (CA)
- Cyber Security System (CSS)
- Link-16 Model (LYNX)
- Mine Warfare Capabilities Evaluation Tool (MCET)
- Naval Simulation System (NSS)
- NSS Toolkit
- PED Utilization Model And Analyzer (PUMA)
- Target Input Generation Estimator (TIGER)
Airborne LIDAR False Alarm Reduction System (ALFARS)
The Airborne LIDAR False Alarm Reduction System (ALFARS) is an advanced CAD/CAC system that reduces the false positive load on operators from noisy 2-D and 3-D LIDAR images. ALFARS has been tested on a Navy LIDAR mine detection system to significantly reduce the operator load while maintaining high probability of detection. Mathematically, the expected error of a classifier given a training set of a specific size is equal to the sum of the squared bias and variance, plus a noise term. ALFARS uses an ensemble of many low bias / high variance classifiers that when combined, through a simple voting scheme, generate a meta-classifier with lower bias and variance, and therefore, lower error. ALFARS has a number of advanced features that measure the correlation, smoothness, etc. of the LIDAR images and then automatically chooses the best feature set.
ALFARS is still under development and seeks to advance the state-of-the-art classifier further as well as continue to develop additional and more robust features for LIDAR imaging.