Multi-sensor fusion for robust target tracking in the simultaneous presence of set-membership and stochastic Gaussian uncertainties

This study proposes a novel data fusion algorithm in sensor networks with simultaneous presence of set-membership and stochastic Gaussian measurement uncertainties. The proposed method is grounded in the marriage of ellipsoidal calculus theory and data compression algorithm. The point-valued measure...

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Vydáno v:	IET radar, sonar & navigation Ročník 11; číslo 4; s. 621 - 628
Hlavní autoři:	Lu, Kelin, Zhou, Rui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.04.2017
Témata:	Algorithms calculus data compression data compression algorithm Data fusion data fusion algorithm ellipsoidal calculus theory estimation set estimation theory Gaussian processes Kalman filters mean square error multisensor fusion Networks optimal Kalman gain point‐valued measurement Research Article robust target tracking sensor fusion sensor networks set theory set‐membership set‐valued measurement Sonar stochastic Gaussian measurement uncertainty stochastic processes Stochasticity Target tracking target tracking problem Tracking Uncertainty upper bound minimization data compression multisensor fusion point-valued measurement set-membership estimation theory sensor fusion mean square error sensor networks set theory calculus target tracking problem set-valued measurement stochastic Gaussian measurement uncertainty data fusion algorithm optimal Kalman gain target tracking Gaussian processes estimation set robust target tracking ellipsoidal calculus theory data compression algorithm stochastic processes Kalman filters upper bound minimization
ISSN:	1751-8784, 1751-8792
On-line přístup:	Získat plný text
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