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
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Popis
Shrnutí: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 measurement and the set-valued measurement are compressed into a uniform framework during the estimation. An optimal Kalman gain is obtained that minimises the upper bound of the mean square error of the estimation set. The proposed algorithm is applied to the target tracking problem and the estimation results show that the proposed algorithm improves the tracking performance.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2016.0198