State fusion with unknown correlation: Ellipsoidal intersection

Sijs, Joris; Lazăr, Mircea; Bosch, P.P.J. van den

doi:10.1109/acc.2010.5531237

Cited by 58 publications

(49 citation statements)

References 8 publications

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“…The EI fusion method was proposed in [14] to handle the separation of the mutual information that might be included in the two local estimates. The update of the estimates is then only based on the exclusive information received to avoid incorrect and "over confident" estimates.…”

Section: Ellipsoidal Intersectionmentioning

confidence: 99%

QoS-Based Reputation Feedback Fusion under Unknown Correlation

Mehdi¹,

Bouguila²

2014

Adaptive and Intelligent Systems

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Abstract. Due to the flood of web services that offer similar functionalities, service consumers are left with a challenging selection decision. A popular approach to assist them with the service selection task is based on the reputation of web services. However, the propagation of reputation feedback in an open and distributed system of web services yield correlated reputation estimates. The existing web service reputation literature still lacks a system that handles the aggregation of reputation feedback under unknown correlation. To fill this gap, we employ two data fusion algorithms, the covariance intersection and ellipsoidal intersection, to aggregate QoS-based reputation feedback. Our experimental results endorse the advantageous capability and scalability of the proposed methods in aggregating reputation estimates, and show an enhanced performance when compared with the Kalman filter method.

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Section: Ellipsoidal Intersectionmentioning

confidence: 99%

QoS-Based Reputation Feedback Fusion under Unknown Correlation

Mehdi¹,

Bouguila²

2014

Adaptive and Intelligent Systems

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“…Some existing fusion methods are found in [63,52,51]. In this survey the ellipsoidal intersection fusion method of [52,51] is presented, since it results in algebraic expressions of the fusion formulas. In brief, ellipsoidal intersection derives an explicit characterization of the (unknown) correlation a priori to deriving algebraic fusion formulas that are based on the independent parts of p i (x[k]) and p j (x[k]).…”

Section: Nearest Neighboring Weightsmentioning

confidence: 99%

Low-Power Solutions for Wireless Passive Sensor Network Node Processor Architecture

Sai¹,

Ogirala²,

Mickle³

2012

Intelligent Sensor Networks

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Distributed solutions for signal processing techniques are important for establishing large-scale monitoring and control applications. They enable the deployment of scalable sensor networks for particular application areas. Typically, such networks consists of a large number of vulnerable components connected via unreliable communication links and are sometimes deployed in harsh environment. Therefore, dependability of sensor network is a challenging problem. An efficient and cost effective answer to this challenge is provided by employing runtime reconfiguration techniques that assure the integrity of the desired signal processing functionalities. Runtime reconfigurability has thorough impact both on system design, implementation, testing/validation and deployment. The presented research focuses on the widespreaded signal processing method known as state estimation with Kalman filtering in particular. To that extent, a number of distributed state estimation solutions that are suitable for networked systems in general are overviewed, after which robustness of the system is improved according to various runtime reconfiguration techniques.

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“…As such, the main contribution of this paper is a proof that a DKF where each node performs a local Kalman filter followed by the state-fusion method ellipsoidal intersection (EI) of [9], is a global covariance DKF. This proof consists of deriving asymptotic bounds of the full state-covariance for each node and showing that these bounds are a function of the global sensor data available within the sensor network.…”

Section: Introductionmentioning

confidence: 99%

A distributed Kalman filter with global covariance

Sijs

Lazăr

2011

Proceedings of the 2011 American Control Conference

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Most distributed Kalman filtering (DKF) algorithms for sensor networks calculate a local estimate of the global state-vector in each node. An important challenge within distributed estimation is that all sensors in the network contribute to the local estimate in each node. In this paper, a novel DKF algorithm is proposed with the goal of attaining the above property, which is denoted as global covariance. In the considered DKF set-up each node performs two steps iteratively, i.e., it runs a standard Kalman filter using local measurements and then fuses the resulting estimates with the ones received from its neighboring nodes. The distinguishing aspect of this set-up is a novel state-fusion method, i.e., ellipsoidal intersection (EI). The main contribution consists of a proof that the proposed DKF algorithm, in combination with EI for statefusion, enjoys the desired property under similar conditions that should hold for observability of standard Kalman filters. The advantages of developed DKF with respect to alternative DKF algorithms are illustrated for a benchmark example of cooperative adaptive cruise control.

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State fusion with unknown correlation: Ellipsoidal intersection

Cited by 58 publications

References 8 publications

QoS-Based Reputation Feedback Fusion under Unknown Correlation

QoS-Based Reputation Feedback Fusion under Unknown Correlation

Low-Power Solutions for Wireless Passive Sensor Network Node Processor Architecture

A distributed Kalman filter with global covariance

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