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

Description:

TECHNOLOGY AREA(S): Electronics 

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. Additional development in the areas of payload integration and Human-Machine Interface (HMI) may be required to develop a complete system solution. One of the desired characteristics of the system is to use the flexibility afforded by the learning algorithms to allow for the quick adjustment of the target set or the taxonomy of the target set DRCI categories or classes. This could allow for the expansion of the system into a Homeland Security environment. 

PHASE I: Conduct an assessment of the key components of a complete objective payload system constrained by the Size Weight and Power (SWAP) payload restrictions of a class 1 or class 2 UAS. Systems Engineering concepts and methodologies may be incorporated in this assessment. It is anticipated that this will require, at a minimum, an assessment of the sensor suite, learning algorithms, and communications system. The assessment should define requirements for the complete system and flow down those requirements to the sub-component level. Conduct a laboratory demonstration of the learning algorithms for the DRCI of the target set and the ability to quickly adjust to target set changes or to operator-selected DRCI taxonomy. 

PHASE II: Demonstrate a complete payload system at a Technology Readiness Level (TRL) 5 or higher operating in real time. On-flight operation can be simulated. Complete a feasibility assessment addressing all engineering and integration issues related to the development of the objective system fully integrated in a UAS capable of detecting, recognizing, classifying, identifying and providing targeting data to lethality systems. Conduct a sensitivity analysis of the system capabilities against the payload SWAP restrictions to inform decisions on matching payloads to specific UAS platforms and missions. 

PHASE III: Develop, integrate and demonstrate a payload operating in real time while on-flight in a number of different environmental conditions and providing functionality at tactically relevant ranges to a TRL 7. Demonstrate the ability to quickly adjust the target set and DRCI taxonomy as selected by the operator. Demonstrate a single operator interface to command-and-control the payload. Demonstrate the potential to use in military and homeland defense missions and environments. 

REFERENCES: 

1: John P. Abizaid and Rosa Brooks, Recommendations and Report of the Task Force on US Drone Policy (Washington, DC: The Stimson Center, 2014).

2:  Y. Bengio, "Springtime for AI: the rise of deep learning," Scientific American, June 2016.

3:  Department of Defense, Joint Operational Access Concept ( JOAC), Department of Defense website, 17 January 2012.

4:  M. T. Hagan, H. B. Demuth, M. Hudson Beale and O. De Jesus, Neural Networks Design, 2nd ed., Lexington, KY, published by Martin Hagan, 2016.

5:  J. Heaton, Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks, St. Louis, MO, Heaton Research, Inc, 2015.

6:  S. Samarasinghe, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition," Boca Raton, FL, Auerbach Publications, 2007.

7:  Yasmin Tadjdeh, "Small UAV Demand by U.S. Army Ebbs as Overseas Market Surging," National Defense Magazine website, September 2013.

8:  D. S. Touretzky and D. A. Pomerlau, "What’s hidden in the hidden layers?" BYTE Magazine, pp. 227-233, August 1989.

9:  Robert O. Work and Shawn Brimley, 20YY: Preparing for War in the Robotic Age (Washington DC: Center for a New American Security, January 2014), 7.

10:  Tedesco, Matthew T. "Countering the Unmanned Aircraft Systems Threat", Military Review, November-December 2015, http://usacac.army.mil/CAC2/MilitaryReview/Archives/English/MilitaryReview_20151231_art012.pdf

KEYWORDS: Learning Algorithms, Artificial Neural Networks (ANNs), Automatic Target Recognition (ATR), Target Detection, Target Classification, Target Identification, Unmanned Air System (UAS), Targeting 

CONTACT(S): 

Ramon Llanos 

(973) 724-5866 

ramon.r.llanos.civ@mail.mil 

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