Decentralized data fusion with inverse covariance intersection

Noack, Benjamin; Sijs, Joris; Reinhardt, Marc; Hanebeck, Uwe D.

doi:10.1016/j.automatica.2017.01.019

Cited by 182 publications

(107 citation statements)

References 10 publications

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“…For example, the measurement update step of set-membership filter always involves the intersection of predicted ellipsoid and measurement ellipsoid [4]. In the multisensor estimation fusion setting, each sensor sends the local estimated ellipsoid to the fusion center and cross-correlation is unknown, then we hope to derive an optimal ellipsoid to contain the intersection of local ellipsoids [29], which can improve the accuracy of estimation.…”

Section: Problem Statementmentioning

confidence: 99%

On equivalence of major relaxation methods for minimum ellipsoid covering intersection of ellipsoids

2019

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Section: Problem Statementmentioning

confidence: 99%

On equivalence of major relaxation methods for minimum ellipsoid covering intersection of ellipsoids

2019

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“…The idea with SF is to approximately decouple the fused estimates, and only pick information from one of the estimates in each direction for the fused result. SF has no known consistency guarantees, and examples where it is inconsistent are known [11]. However, it can still be considered an attractive alternative for T2TF [12].…”

Section: Track-to-track Fusionmentioning

confidence: 99%

“…Covariance Intersection (ICI, [10,11]): The relatively recent ICI method is designed to overestimate and remove any common information from the fused estimate. Assumptions are made about correlations, which allows ICI to be less conservative than CI.…”

Section: ) Inversementioning

confidence: 99%

“…It also provides the same covariance matrix as the largest ellipsoid method [1], but gives a different mean estimate. A recent and promising method is the inverse covariance intersection (ICI, [10,11]) method, which provides a middle way, providing a less conservative estimate than CI, while being able to guarantee consistency under quite general assumptions.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Tracking in Sensor Networks with Varying Coverage

Nygårds

Deleskog

Hendeby

2018

2018 21st International Conference on Information Fusion (FUSION)

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The number of sensors used in tracking scenarios is constantly increasing, this puts high demands on the tracking methods to handle these data streams. Central processing (ideally optimal) puts high demands on the central node, is sensitive to inaccurate sensor parameters, and suffers from the single point of failure problem. Decentralizing the tracking can improve this, but may give considerable performance loss. The newly presented inverse covariance intersection method, proven to be consistent, even under unknown track cross-correlations, is benchmarked against alternatives. Different track-to-track methods, including smoothed association over a window, are compared. A scenario with objects tracked in multiple cameras, not necessarily optimized for tracking, are used to give realism to the evaluations.

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“…Generally, the physical processes are modeled by linear or nonlinear dynamical systems, while the disturbance noises in multi-sensor fusion systems are considered as Gaussian or non-Gaussian disturbances. When considering the Gaussian white noise with known covariances, there mainly exist three different distributed fusion estimation methods: i) Optimal distributed fusion estimation strategies [6]- [8]; ii) Suboptimal distributed weighted fusion estimation methods [9]- [11]; iii) Suboptimal distributed covariance intersection fusion estimation methods [12]- [15]. Notice that the assumption of Gaussian white noises may not be satisfied in practical systems, particularly, the accurate covariances may not be obtained in practical applications.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem

Chen

et al. 2019

IEEE Trans. Automat. Contr.

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In this paper, we study the distributed fusion estimation problem for linear time-varying systems and nonlinear systems with bounded noises, where the addressed noises do not provide any statistical information, and are unknown but bounded. When considering linear time-varying fusion systems with bounded noises, a new local Kalman-like estimator is designed such that the square error of the estimator is bounded as time goes to ∞. A novel constructive method is proposed to find an upper bound of fusion estimation error, then a convex optimization problem on the design of an optimal weighting fusion criterion is established in terms of linear matrix inequalities, which can be solved by standard software packages. Furthermore, according to the design method of linear timevarying fusion systems, each local nonlinear estimator is derived for nonlinear systems with bounded noises by using Taylor series expansion, and a corresponding distributed fusion criterion is obtained by solving a convex optimization problem. Finally, target tracking system and localization of a mobile robot are given to show the advantages and effectiveness of the proposed methods.

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Decentralized data fusion with inverse covariance intersection

Cited by 182 publications

References 10 publications

On equivalence of major relaxation methods for minimum ellipsoid covering intersection of ellipsoids

On equivalence of major relaxation methods for minimum ellipsoid covering intersection of ellipsoids

Decentralized Tracking in Sensor Networks with Varying Coverage

A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem

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