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Artificial Intelligence Application for Air and Missile Defense Combat Identification, Planning and Weapon Assignment


TECHNOLOGY AREA(S): Info Systems, 

OBJECTIVE: Project Office Integrated Air & Missile Defense (IAMD) is the lead materiel developer of the Army’s Integrated Air & Missile Defense (AIAMD) Integrated Battle Command System (IBCS). IBCS will fuse multiple Sensors into a Single Integrated Air Picture for Air & Missile Defense engagement planning and execution. The objective of this SBIR submission is to develop a system architecture and algorithmic framework engine that supports artificial intelligence (AI)-based algorithms used to perform diverse functions such as Defense Design, Identification and Classification of tracks, Predictive Track Vectors, Sensor and Weapon Management, and other potential IAMD functions. 

DESCRIPTION: Advances in artificial intelligence (AI) and deep learning techniques, in conjunction with rapid growth in GPU hardware performance have opened up new possibilities for exploitation of AI to perform highly complex tasks with performance exceeding that of more traditional approaches. Potential applications within the Army Integrated Air and Missile Defense (IAMD) system include Identification and Classification of tracked objects, Defense Design, and Dynamic Planning and Tasking. To support AI / Deep Learning-based applications, the Army requires a robust, scalable architecture, framework and algorithm engine that can be utilized by multiple AI applications in an easily maintainable and extensible manner. The architecture and framework should include a combination of hardware and software that has a straightforward path of integration with current IAMD systems. The AI Engine should support integration and execution of multiple, simultaneous AI applications, as well as the ability to ingest, store, and process significant amounts of data. In addition to execution, the architecture, framework and engine should support training of the algorithms, which minimizes hardware and software costs as well as permits on-the-fly enhancements to the different applications. 

PHASE I: Develop a concept and initial prototype for a system architecture, framework, and algorithm engine. Demonstrate that the framework and engine will support AI-based functions such as Identification and Classification of tracks or others. Establish feasibility through evaluation of the framework via a study and/or use of simulation-based analysis. The Phase II effort will likely require secure access. The Phase I effort will not require access to classified information. 

PHASE II: Design, develop and deliver a prototype architecture, framework, and AI engine that demonstrates the capability to perform multiple AI-based functions, and is integrable with the current IAMD hardware and architecture. Perform the demonstration at a government defined facility. Prepare a Phase III development plan to transition the technology for the Army IAMD. 

PHASE III: Transition the Phase II product into a deployable capability to enter into detailed technical and operational testing. 


1: Vasudevan, Vijay. "TensorFlow: A system for Large-Scale Machine Learning." Usenix Associate, USENIX OSDI 2016 Conference, 2 November 2016.

KEYWORDS: Artificial Intelligence, Deep Learning; Identification; Classification, Defense Design, And Dynamic Planning, Automated Resource Assignment 

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