Characterization of the optimum of a quadratic program with convex constraints. Application to sensor data fusion

We analyse theoretically a maximisation quadratic program which can arise in multi-target/multi-sensor area. The goal is to find the point x which minimizes the quadratic distance between x and a given point y. This optimum must lie in a convex constrained region defined by linear inequalities. We p...

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Published inFUSION 2002 : proceedings of the Fifth International Conference on Information Fusion : July 8-11, 2002, Loews Annapolis Hotel, Annapolis, Maryland, USA Vol. 2; pp. 1486 - 1491 vol.2
Main Authors Musso, C., Dodin, P.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 2002
Subjects
Context
H infinity control
Linear matrix inequalities
Linear programming
Quadratic programming
Resource management
Sensor fusion
Sensor phenomena and characterization
Vectors
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Summary:We analyse theoretically a maximisation quadratic program which can arise in multi-target/multi-sensor area. The goal is to find the point x which minimizes the quadratic distance between x and a given point y. This optimum must lie in a convex constrained region defined by linear inequalities. We present a characterisation of this optimum in a compact dual form. This optimisation framework can be helpful, for example, in muti-objective programming like decentralized resource allocation.
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ISBN:9780972184410
0972184414
DOI:10.1109/ICIF.2002.1020991