The optimality for the distributed Kalman filtering fusion with feedback

Zhu, Yunmin; Zhou, You; Zhao, Juan; Zhang, Keshu; Li, X.Rong

doi:10.1016/s0005-1098(01)00074-7

Cited by 158 publications

(95 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A consensus step (Section 3.1) is introduced to provide consistency in the local estimates. As in [3,4], we model the local posterior with a Gaussian but develop consensus-based distributed counterpart of the optimal decentralized fusion rule [8]. We go one step further and use such Gaussian approximations in the context of local UPF as the proposal distribution.…”

Section: Consensus-based Distributed Upfmentioning

confidence: 99%

Consensus-based distributed unscented particle filter

Mohammadi

Asif

2011

2011 IEEE Statistical Signal Processing Workshop (SSP)

View full text Add to dashboard Cite

In this paper, we propose a consensus-based, distributed implementation of the unscented particle filter (CD/UPF) that extends the distributed Kalman filtering framework to non-linear, distributed dynamical systems with non-Gaussian excitations. Compared to the existing distributed implementations of the particle filter, the CD/UPF offers two advantages. First, it uses all available local observations including the most recent ones in deriving the proposal distribution. Second, computation of global estimates from local estimates during the consensus step is based on an optimal fusion rule. In our bearingonly tracking simulations, the performance of the proposed CD/UPF is virtually indistinguishable from its centralized counterpart.

show abstract

Section: Consensus-based Distributed Upfmentioning

confidence: 99%

Consensus-based distributed unscented particle filter

Mohammadi

Asif

2011

2011 IEEE Statistical Signal Processing Workshop (SSP)

View full text Add to dashboard Cite

show abstract

“…In majority papers autonomous systems [1,2,3,6,12] are considered. A control, if introduced, is a known input [5] or depends on local state estimate [7], only.…”

Section: Introductionmentioning

confidence: 99%

State estimation in a decentralized discrete time LQG control for a multisensor system

Duda

2017

Archives of Control Sciences

View full text Add to dashboard Cite

In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes.In the paper, contrary to this, the controls are not transmitted between nodes. It leads to nonconventional filtration because the controls from other nodes are treated as random variables for each node. The cost for the additional reduced transmission is an increased filter computation at each node.

show abstract

“…Finally, the fusion center optimally estimates the object by using all received local estimates. Recently, to overcome the disadvantages of the centralized estimation, various decentralized and parallel versions of the standard Kalman filters have been proposed for linear dynamic systems with a multisensor environment [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Decentralized Moving Average Filtering with Uncertainties

Song¹

2016

Journal of Sensor Science and Technology

View full text Add to dashboard Cite

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

show abstract

The optimality for the distributed Kalman filtering fusion with feedback

Cited by 158 publications

References 8 publications

Consensus-based distributed unscented particle filter

Consensus-based distributed unscented particle filter

State estimation in a decentralized discrete time LQG control for a multisensor system

Decentralized Moving Average Filtering with Uncertainties

Contact Info

Product

Resources

About