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Sensors 2018, 18(5), 1610; https://doi.org/10.3390/s18051610

A Framework of Covariance Projection on Constraint Manifold for Data Fusion

Intelligent Systems Research Institute, Sungkyunkwan University, Suwon, Gyeonggi-do 440-746, Korea
This paper is an extended version of the paper entitled “A general framework for data fusion and outlier removal in distributed sensor networks”, presented at IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Daegu, Korea, 16–18 November 2017.
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Received: 3 May 2018 / Revised: 14 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
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Abstract

A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information. The proposed method, referred to here as the Covariance Projection (CP) method, provides an unbiased and optimal solution in the sense of minimum mean square error (MMSE), if the projection is based on the minimum weighted distance on the constraint manifold. The proposed method not only offers a generalization of the conventional formula for handling constraints and data inconsistency, but also provides a new insight into data fusion in terms of a geometric-algebraic point of view. Simulation results are provided to show the effectiveness of the proposed method in handling constraints and data inconsistency. View Full-Text
Keywords: Bar-Shalom Campo; Covariance Projection method; data fusion; distributed architecture; Kalman filter; linear constraints; inconsistent data Bar-Shalom Campo; Covariance Projection method; data fusion; distributed architecture; Kalman filter; linear constraints; inconsistent data
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Abu Bakr, M.; Lee, S. A Framework of Covariance Projection on Constraint Manifold for Data Fusion . Sensors 2018, 18, 1610.

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