Automatic Target Recognition of Personnel and Vehicles from an Unmanned Aerial System Using Learning Algorithms

OBJECTIVE: Develop a system that can be integrated and deployed in a class 1 or class 2 Unmanned Aerial System (UAS) to automatically Detect, Recognize, Classify, Identify (DRCI) and target personnel and ground platforms or other targets of interest. The system should implement learning algorithms that provide operational flexibility by allowing the target set and DRCI taxonomy to be quickly adjusted and to operate in different environments. DESCRIPTION: The use of UASs in military applications is an area of increasing interest and growth. This coupled with the ongoing resurgence in the research, development, and implementation of different types of learning algorithms such as Artificial Neural Networks (ANNs) provide the potential to develop small, rugged, low cost, and flexible systems capable of Automatic Target Recognition (ATR) and other DRCI capabilities that can be integrated in class 1 or class 2 UASs. Implementation of a solution is expected to potentially require independent development in the areas of sensors, communication systems, and algorithms for DRCI and data integration.