Towards Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR) is a paper by Paul A. Thomas, Gillian Marshall, David Faulkner, Philip Kent, Scott Page, Simon Islip, James Oldfield, Toby P. Breckon, Mikolaj E. Kundergorski, David J. Clark, Tim Styles, DSTL, Porton Down, QinetiQ, Cubica Technology, Durham University, Create Technologies Ltd, and AptCore Ltd.

Most land Intelligence, Surveillance, and Reconnaissance (ISR) assets, such as EO/IR cameras, primarily function as data collectors. The responsibility for understanding, decision-making, and sensor control falls on human operators, involving a significant cognitive load. Historically, any automation introduced into the system required creating specialized, centralized systems tailored to specific assets, targets, or environments. This approach resulted in complex, inflexible systems with limited interoperability.

This paper introduces the concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules can make independent low-level decisions to achieve higher-level objectives. They can seamlessly integrate, with minimal pre-configuration, into a High-Level Decision Making Module (HLDMM) through a middleware integration layer. This fusion of autonomy and interoperability presents challenges related to information fusion and asset management in an autonomous hierarchical system, which this work aims to address.

This paper presents the outcomes of a demonstration system called Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT). SAPIENT was tested in realistic base protection scenarios involving live sensors and targets. The SAPIENT system autonomously handled sensor cueing, intelligent data fusion, sensor tasking, target hand-off, and compensation for compromised sensors, all without human intervention. Importantly, it allowed for the rapid integration of ISR assets during system deployment rather than necessitating pre-design integration. The potential benefits encompass swift interoperability for coalition operations, reduced cognitive burden on operators for situation understanding, and autonomous sensor management in heterogeneous sensor systems.

Towards Sensor Modular Autonomy for Persistent Land Intelligence Surveillance and Reconnaissance (ISR) contains the following major sections:

  • Introduction
  • System Architecture
  • Autonomous Sensor Modules
  • High-Level Decision-Making Module
  • System Integration
  • Demonstration and Results
  • Conclusions and Future Work

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Authors– Paul A. Thomas, Gillian Marshall, David Faulkner, Philip Kent, Scott Page, Simon Islip, James Oldfield, Toby P. Breckon, Mikolaj E. Kundergorski, David J. Clark, Tim Styles, DSTL, Porton Down, QinetiQ, Cubica Technology, Durham University, Create Technologies Ltd, and AptCore Ltd.

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Post Image- Radar ASM engineering GUI showing a black and white video image of the scene; a “radar cone” display showing the radar beam, and detected plots & tracks, a range-Doppler map (objects further up the map being further away, objects moving toward the radar appear to the left of center and objects moving away from the radar appear to the right), a list of current tracks, and a plot of the classification over a moving time window (Image Credit: Authors)