Paper
5 October 2007 Algorithms and evaluation framework for uninhabited vehicles
Author Affiliations +
Proceedings Volume 6736, Unmanned/Unattended Sensors and Sensor Networks IV; 673607 (2007) https://doi.org/10.1117/12.738031
Event: Optics/Photonics in Security and Defence, 2007, Florence, Italy
Abstract
The nature of co-operating Uninhabited Vehicle (UV) systems is such that performance enhancements are likely to be a result of greatly increased system complexity. Complexity emerges through the interaction of multiple autonomous UVs reacting to their current surroundings. This complexity presents a fundamental challenge to the specification, design and evaluation of such systems, and drives the need for new approaches to the systems engineering. For applications involving multiple autonomous UVs, research into collective and emergent behaviour offers potential benefits in terms of improved system performance and the utilisation of individual UVs with lower processing complexity. This paper reports on the development of a new simulation framework that addresses the systems engineering issues and allows novel algorithms to be created and assessed. Examples are given of how the framework has been used to develop and assess the performance of individual and multiple UVs, as well as unattended ground sensors. Furthermore, a variety of novel algorithms developed using the framework are described and example results are provided. These include co-operative UV missions requiring improved detection performance and the improved management of unattended ground sensors to minimise power usage.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
C. Angell and M. Bernhardt "Algorithms and evaluation framework for uninhabited vehicles", Proc. SPIE 6736, Unmanned/Unattended Sensors and Sensor Networks IV, 673607 (5 October 2007); https://doi.org/10.1117/12.738031
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KEYWORDS
Sensors

Algorithm development

Detection and tracking algorithms

Monte Carlo methods

Computer simulations

Data fusion

Target detection

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