Cubica Presenting at SPIE DSS 2016

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September 22, 2016

We are delighted to be presenting at the SPIE Security and Defence conference, 26-29 September 2016 in Edinburgh. Our paper “Threat Assessment and Sensor Management in a Modular Architecture” will be presented in a special SAPIENT-focussed Networks of Autonomous Sensors session of the Counter-Terrorism, Crime-Fighting and Defence conference.

The paper presents a number of novel aspects of our SAPIENT High Level Decision Making Module, developed under funding from DSTL and Innovate UK. Read the abstract for our paper below.


Many existing asset/area protection systems, for example those deployed to protect critical national infrastructure, are comprised of multiple sensors such as EO/IR, radar, and PIDS, loosely integrated with a central Command and Control (C2) system. Whilst some sensors provide automatic event detection and C2 systems commonly provide rudimentary multi-sensor rule based alerting, the performance of such systems is limited by the lack of deep integration and autonomy. As a result, these systems have a high degree of operator burden. To address these challenges, an architectural concept termed “SAPIENT” was conceived. SAPIENT is based on multiple Autonomous Sensor Modules (ASMs) connected to a High-Level Decision Making Module that provides data fusion, situational awareness, alerting, and sensor management capability. The aim of the SAPIENT concept is to allow for the creation of a surveillance system, in a modular plug-and-play manner, that provides high levels of autonomy, threat detection performance, and reduced operator burden. This paper considers the challenges associated with developing an HLDMM aligned with the SAPIENT concept, through the discussion of the design of a realised HLDMM. Particular focus is drawn to how high levels of system level performance can be achieved whilst retaining modularity and flexibility. A number of key aspects of the HLDMM are presented, including an integrated threat assessment and sensor management framework, threat sequence matching, and ASM trust modelling. The results of real-world testing of the HLDMM, in conjunction with multiple Laser, Radar, and EO/IR sensors, in representative semi-urban environments, are presented.

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