Decentralised data fusion with exponentials of polynomials

Decentralised data fusion with exponentials of polynomials

We demonstrate applicability of a general class of multivariate probability density functions of the form e -P(x) , where P(x) is an elliptic polynomial, to decentralised data fusion tasks. In particular, we derive an extension to the covariance Intersect algorithm for this class of distributions an...

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Hauptverfasser: Tonkes, B., Blair, A.D.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Bayesian methods
Intelligent robots
Particle filters
Polynomials
Probability distribution
Robot sensing systems
Sensor phenomena and characterization
Uncertainty
USA Councils
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description We demonstrate applicability of a general class of multivariate probability density functions of the form e -P(x) , where P(x) is an elliptic polynomial, to decentralised data fusion tasks. In particular, we derive an extension to the covariance Intersect algorithm for this class of distributions and demonstrate the necessary operations - diffusion, multiplication and linear transformation - for Bayesian operations. A simulated target tracking application demonstrates the use of these operations in a decentralised scenario, employing range-only sensing to show their generality beyond Gaussian representations.
doi_str_mv 10.1109/IROS.2007.4399072
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subjects Bayesian methods
Intelligent robots
Particle filters
Polynomials
Probability distribution
Robot sensing systems
Sensor phenomena and characterization
Uncertainty
USA Councils
title Decentralised data fusion with exponentials of polynomials
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