Optimally Distributed Kalman Filtering with Data-Driven Communication

Dormann, Katharina; Noack, Benjamin; Hanebeck, Uwe D.

doi:10.3390/s18041034

Cited by 13 publications

(6 citation statements)

References 35 publications

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“…For effective resource planning, there is a need to consider joining distributed and centralized management, which anticipates the system's needs and regulates the entire network using Kalman filters. In [47], multisensor data fusion and distributed state estimation techniques that enable local processing of sensor data were the means of choice in order to minimize storage. In particular, a distributed implementation of the optimal Kalman filter was recently developed.…”

Section: An Overview Of Alternative Literature On Big Data Reducing Mmentioning

confidence: 99%

Intelligent Monitoring of Data Center Physical Infrastructure

Matko

Brezovec²,

Milanovič

2019

Applied Sciences

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This paper presents a new intelligent monitoring and event management method for data center physical infrastructure based on multilayer node event processing. The priorities of highly classified data centers are not only the maintenance of the highest level of reliability and availability of the operation, but also fast, specific event identification and energy consumption monitoring, which altogether improve the level of energy efficiency. The new method, using a tree node for each device, improves information about the events in a specific node. Its major advantage is that in the case of a large number of nodes, it considerably reduces the number of identified events (alarms/alerts) that might be the main cause. Fast event processing lowers data center physical infrastructure operating costs, improving at the same time the level of energy efficiency. The experimental results show that the use of tree nodes significantly reduces the number of unexpected events, the time needed for the main event identification, and the maintenance response time to events. By using event entities processing, multilayer nodes have a significant impact on the efficient operation of data center physical infrastructure.

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Section: An Overview Of Alternative Literature On Big Data Reducing Mmentioning

confidence: 99%

Intelligent Monitoring of Data Center Physical Infrastructure

Matko

Brezovec²,

Milanovič

2019

Applied Sciences

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“…Optimal fusion algorithms [ 22 , 23 ] can be designed if cross-correlations between the estimates are also known. They typically require the transmission of additional information [ 24 ] or specific communication strategies [ 25 , 26 ]. In the case where correlations are unknown, conservative fusion algorithms compute a bound on the actual but unknown error covariance matrix of the fusion results.…”

Section: Introductionmentioning

confidence: 99%

Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion

Funk

Noack

Hanebeck

2021

Sensors

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Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted. Compression of estimates and covariance matrices can endanger desirable properties like unbiasedness and may lead to unreliable fusion results. In this work, quantization methods for estimates and covariance matrices are presented and their usage with the optimal fusion formulas and covariance intersection is demonstrated. The proposed quantization methods significantly reduce the bandwidth required for data transmission while retaining unbiasedness and conservativeness of the considered fusion methods. Their performance is evaluated using simulations, showing their effectiveness even in the case of substantial data reduction.

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“…On the other hand, its implementation is very expensive in terms of data transmission; indeed, we require that all sensors can exchange their measurements. Such a limitation disappears by considering distributed filtering [1][2][3][4][5][6][7][8][9]. The key idea is that the communication among the nodes is limited.…”

Section: Introductionmentioning

confidence: 99%

Robust Distributed Kalman Filtering: On the Choice of the Local Tolerance

Emanuele

Gasparotto

Guerra

et al. 2020

Sensors

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We propose a distributed Kalman filter for a sensor network under model uncertainty. The distributed scheme is characterized by two communication stages in each time step: in the first stage, the local units exchange their observations and then they can compute their local estimate; in the final stage, the local units exchange their local estimate and compute the final estimate using a diffusion scheme. Each local estimate is computed in order to be optimal according to the least favorable model belonging to a prescribed local ambiguity set. The latter is a ball, in the Kullback–Liebler topology, about the corresponding nominal local model. We propose a strategy to compute the radius, called local tolerance, for each local ambiguity set in the sensor network, rather than keep it constant across the network. Finally, some numerical examples show the effectiveness of the proposed scheme.

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Optimally Distributed Kalman Filtering with Data-Driven Communication

Cited by 13 publications

References 35 publications

Intelligent Monitoring of Data Center Physical Infrastructure

Intelligent Monitoring of Data Center Physical Infrastructure

Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion

Robust Distributed Kalman Filtering: On the Choice of the Local Tolerance

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