Paper
13 July 2000 Decentralized track fusion in dynamic networks
David Nicholson, Rob H. Deaves
Author Affiliations +
Abstract
Decentralized systems merit a detailed analysis in view of the potential advantages that they offer. These include significant improvements in fault tolerance, modularity and scalability. Such attributes are required by a number of systems that are currently being planned within the defence and civil aerosense sectors. A recognized difficulty with the decentralized network architecture is the potential it creates for redundant data to proliferate as a result of cyclic information flows. This can lead to estimation biases and divergence. Solutions which require the network information sources to be tagged in some way are not generally possible without relaxing some of the constraints on which the decentralized paradigm is founded. This paper consequently investigates a different approach. Specifically, it examines the application of the Covariance Intersection (CI) data fusion technique. CI is relevant to the redundant data problem because it guarantees consistent estimates without requiring correlations to be maintained. The estimation performance of CI is compared here, with respect to a restricted Kalman approach, for a dynamic multi-platform network example. It is concluded that a hybrid CI/Kalman approach offers the best solution, since it exploits known independent information and unknown correlated information without having to relax the decentralized constraints.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Nicholson and Rob H. Deaves "Decentralized track fusion in dynamic networks", Proc. SPIE 4048, Signal and Data Processing of Small Targets 2000, (13 July 2000); https://doi.org/10.1117/12.391998
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Cited by 10 scholarly publications.
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KEYWORDS
Laser induced damage

Sensors

Data fusion

Network architectures

Grazing incidence

Detection and tracking algorithms

Algorithm development

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