Counter Improvised-Threat Simulation

The Counter Improvised-Threat Simulation program is dedicated to developing advanced technologies for detecting and neutralizing improvised explosive devices (IEDs). Managed by the Army Futures Command in collaboration with the Under Secretary of Defense for Research and Engineering and the Defense Threat Reduction Agency, its primary objective is to enhance the ability of deployed forces to accurately identify IEDs with minimal false alarms, thereby improving operational safety and efficiency. Additionally, it aims to mitigate the effects of IEDs while minimizing collateral damage.

A significant component involves developing standoff detection technologies. These include electro-optical, radar, light detection and ranging (LIDAR), and atomic magnetometer systems that can be integrated onto various platforms such as dismounted soldiers, ground vehicles, water-based systems, and aerial units. The goal is to achieve high probabilities of detecting IEDs across diverse environments while reducing false alarms from natural or man-made sources. The program also focuses on detecting electronic signatures of radio-controlled IEDs through advanced network techniques.

Another critical aspect is the development of technologies for IED neutralization, prevention, and mitigation. This includes directed energy sources, kinetic effectors, and protective encasement technologies designed to neutralize threats at a distance. The program explores robotic manipulation techniques to safely handle IEDs and aims to protect soldiers and equipment from potential explosions. By advancing these technologies, it seeks to maintain maneuver speeds while ensuring safety in various operational scenarios.

The program also emphasizes enabling technologies that support detection and prevention efforts through data science advancements. This includes sensor processing algorithms, data integration, threat forecasting, and autonomous maneuver capabilities. By leveraging machine learning techniques, it aims to improve threat prediction accuracy and reduce operator burden. The integration of artificial intelligence further enhances autonomous detection capabilities, optimizing sensor performance across multiple modalities in varying environments.