Fusion Technology and Systems Design Challenges for the Counter-Small UAS Threat

Abstract

The employment of Small Unmanned Aerial Systems (sUAS) poses an emerging threat to the military and to the civilian infrastructure, and countering these sUAS operations requires addressing challenges that are unique to this problem space. Technologies for addressing these threats, to include Data and Information Fusion technology, are also being challenged. There are several factors that make the challenge of dealing with the threat of sUAS very challenging. One is that the threat envelope for sUAS ranges from manufactured clutter to the delivery of a wide range of lethal weapons. Another is that they are, in the context of currently-deployed technology and technology under development, very difficult to reliably detect, track, and identify even when resolvable by surveillance sensors; many papers use the terms Low, Small, and Slow (LSS) to describe this threat category. The LSS category is distinguished from the sUAS category in that, in terms of the usual five-Group UAS categorization, sUASs fall in the Group 1 and 2 class, and LSSs extend to include Groups 1 to 35. At least two other factors widen their threat envelope for both of these classes: rapidly advancing design and development technologies for sUASs, e.g., stealthy materials (sUASs are already a reasonably non-attributable threat), and new methods of propulsion, as well as autonomous operation, and the ability to exploit massing effects via clustering and or swarming. This paper will provide a systems-engineering overview of the varying impacts of this wide threat envelope on the design, development, and testing of Data Fusion (DF)-centric counter-sUAS (CsUAS) systems. The paper first provides a literature survey of recent and current DF-based R and D across the Levels of Fusion as described in the JDL Data Fusion Reference Model, and across the dimensions of the threat envelope. Importantly, we note the existence of a wide variety of commercial systems that claim DF capabilities.

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Document Details

Document Type
Technical Report
Publication Date
Dec 14, 2021
Accession Number
AD1173767

Entities

People

  • Ali K. Raz
  • Carl Timothy Kelley
  • Christopher N. Bowman
  • James Llinas
  • Nathan Thomas
  • Todd Denniston

Organizations

  • George Mason University
  • Naval Surface Warfare Center
  • University at Buffalo

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acoustic Detectors
  • Aircrafts
  • Cognitive Systems Engineering
  • Control Systems
  • Data Fusion
  • Detection
  • Detectors
  • Kalman Filters
  • Machine Learning
  • Neural Networks
  • Ontologies
  • Operations Research
  • Recognition
  • Sensor Networks
  • Signal Processing
  • Small Unmanned Aerial Systems
  • Systems Engineering
  • Target Recognition
  • Target Tracking
  • Threat Evaluation
  • Unmanned Aerial Systems
  • Unmanned Aerial Vehicles

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design

Technology Areas

  • Autonomy
  • Autonomy - UAVs
  • Space