The Hypothesizing Distributed Kalman Filter

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

doi:10.1109/mfi.2012.6343017

Cited by 26 publications

(19 citation statements)

References 12 publications

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“…In this paper the rescaled notation which was introduced in [21] is used, hence, the central fusion of the local parameters as in (2) and (3) has the following form…”

Section: Distributed Kalman Filtermentioning

confidence: 99%

“…, R S k } are known at each distributed processor. As this is not realistic in most practical applications, measurement model hypotheses were introduced by M. Reinhardt et al in [21], [22] which will be summarized in the following sections.…”

Section: Distributed Kalman Filtermentioning

confidence: 99%

“…At first, the debiasing methodology for the global estimate which is also called Hypothesizing Distributed Kalman Filter from [21] will be summarized. Then, the covariance debiasing from [1] will be presented.…”

Section: Debiasing the Distributed Kalman Filtermentioning

confidence: 99%

“…However, in both scenarios a degradation in performance can be seen, which is due to the bias in the covariance estimates. The bias in the fused state estimate can be compensated using a methodology published by Reinhardt et al in [21], [22] whereby an additional covariance correction matrix is also transmitted to the fusion centre. However, this method does not allow for a consistent global covariance to be reconstructed.…”

Section: Introductionmentioning

confidence: 99%

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On how the distributed Kalman filter is related to the federated Kalman filter

Govaers

Charlish

Koch

2014

2014 IEEE Aerospace Conference

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In this paper, a direct connection between the covariance debiasing methodology for the distributed Kalman (DKF) filter in [1] and the federated Kalman filter is shown. In particular, it can be seen that for a unique choice of the information gain hypothesis of the DKF, the covariance debiasing becomes equivalent to the federated Kalman filter. As the complexity of the covariance calculation for the federated Kalman filter is rather low, a hybrid solution is proposed. A numerical evaluation presents two different scenarios where the state estimate of the distributed Kalman filter outperforms the federated Kalman filter in terms of accuracy. The first scenario is using linear Gaussian noise on position measurements whereas in the second scenario a distributed radar application is shown

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“…In this paper the rescaled notation which was introduced in [21] is used, hence, the central fusion of the local parameters as in (2) and (3) has the following form…”

Section: Distributed Kalman Filtermentioning

confidence: 99%

Section: Distributed Kalman Filtermentioning

confidence: 99%

Section: Debiasing the Distributed Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On how the distributed Kalman filter is related to the federated Kalman filter

Govaers

Charlish

Koch

2014

2014 IEEE Aerospace Conference

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“…The local estimates have to be fused in the data sink to obtain a consistent estimate, which is optimal and equal to the result of a centralized Kalman filter. Beside this article, several extensions of this filter have been proposed—assumptions on the available information can be relaxed [ 24 , 25 ], it can be implemented in information form [ 26 ], or combinations with ellipsoidal state estimation are possible [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Optimally Distributed Kalman Filtering with Data-Driven Communication

Dormann

Noack

Hanebeck

2018

Sensors

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For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested. In this article, different extensions of the optimally distributed Kalman filter are proposed that employ data-driven transmission schemes in order to reduce communication expenses. As a first relaxation of the full-rate communication scheme, it can be shown that each node only has to transmit every second time step without endangering consistency of the fusion result. Also, two data-driven algorithms are introduced that even allow for lower transmission rates, and bounds are derived to guarantee consistent fusion results. Simulations demonstrate that the data-driven distributed filtering schemes can outperform a centralized Kalman filter that requires each measurement to be sent to the center node.

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A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

Kim,

Lee,

Lee

2024

Electronics Letters

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This letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting‐edge SLAM method, LIO‐SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature‐independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state‐of‐the‐art 3D SLAM technique. The convergence methods, namely “dynamic weight assignment” and “winner takes all”, are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. In the future, this research will focus on automating crop cultivation and harvesting by integrating the proposed system with robot arm control.

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The Hypothesizing Distributed Kalman Filter

Cited by 26 publications

References 12 publications

On how the distributed Kalman filter is related to the federated Kalman filter

On how the distributed Kalman filter is related to the federated Kalman filter

Optimally Distributed Kalman Filtering with Data-Driven Communication

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

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