Diffusion distributed Kalman filtering with adaptive weights

Cattivelli, Federico S.; Sayed, Ali H.

doi:10.1109/acssc.2009.5470006

Cited by 48 publications

(28 citation statements)

References 6 publications

(15 reference statements)

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“…This method uses an accuracy dependent consensus step in the standard KF steps. Different from the consensus approaches, now the diffusion strategy is widely used in the literature, where the estimates are linearly combined using a set of weights [33], [34]. This method is more practical when dealing with dynamic systems where new measurements must be processed in a timely manner instead of running consensus [35], [36].…”

Section: A Related Workmentioning

confidence: 99%

Distributed State Estimation Using RSC Coded Smart Grid Communications

Rana

2015

IEEE Access

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Recently, the renewable distributed energy resources (DERs) have become more and more popular due to carbonfree energy sources and environment-friendly electricity generation. Unfortunately, these power generation patterns are mostly intermittent in nature and distributed over the electrical grid which creates challenging problems in the reliability of the smart grid. Thus, smart grid has a strong requisite for an efficient communication infrastructure to facilitate estimating the DER states. In contrast to the traditional methods of centralised state estimation, we propose a distributed approach to microgrid state estimation based on the concatenated coding structure. In this framework, the DER state is treated as a dynamic outer code and the recursive systematic convolutional (RSC) code is seen as a concatenated inner code for protection and redundancy in the system states. Furthermore, in order to properly monitor the intermittent energy source from any place, this paper proposes a distributed state estimation method. Particularly, the outputs of the local state estimation are treated as measurements which are fed into the master fusion station. At the end, the global state estimation can be obtained by combining local state estimations with corresponding weighting factors. The weighting factors can be calculated by inspiring the covariance intersection method. The simulation results show that the proposed method is able to estimate the system state properly.

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Section: A Related Workmentioning

confidence: 99%

Distributed State Estimation Using RSC Coded Smart Grid Communications

Rana

2015

IEEE Access

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“…In these algorithms, measurements are exchanged among the neighboring sensors to generate local estimates, then by using consensus algorithms the fusion estimates are obtained. In [11] and [12], the local estimates, which are calculated by using a local Kalman filter, are fused by a convex combination. Two main objects of the aforementioned distributed fusion algorithms are improve local estimates and reduce the disagreement of the estimates among different sensors.…”

Section: Introductionmentioning

confidence: 99%

A Distributed sequential fusion algorithm over asynchronous sensor networks

Chu

et al. 2016

2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC)

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A distributed sequential fusion algorithm for decentralized asynchronous sensor networks is presented in this paper. Our distributed asynchronous sequential fusion algorithm can be divided into a local estimator and a fusion estimator. The local estimator utilize the local measurements to generate a local fusion estimates. And the local fusion estimates are fused by the fusion estimator to improve the local estimation performance. The proposed algorithm is operational for sensor networks with arbitrarily sampling rates and initial sampling time instants. The effectiveness of the proposed algorithm is validated by simulation results.

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“…In several practical applications, the consensus and sensing time steps are often equal [24]- [28], i.e., each node can communicate only once with its neighbouring nodes between two successive observations. Spurred by this consideration, the paper focuses on the design of a DPF implementation based on the diffusive strategies [29]- [32], which we refer to as the D/DPF. The main contribution of this paper is to incorporate diffusive fusion in the non-linear distributed estimation framework to eliminate the need of the consensus step.…”

Section: Introductionmentioning

confidence: 99%

Diffusive particle filtering for distributed multisensor estimation

Mohammad

Asif

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The paper proposes an on-line distributed implementation of the particle filter (DPF) for applications, where the sensing and consensus time scales are the same. We are motivated by state estimation problems in large, geographically-distributed agent/sensor networks, where bandwidth constraints limit the number of information transfers between neighbouring nodes. As an alternative to consensus strategies often used by the DPF, we propose a diffusive framework to eliminate the need of running the consensus step. In our Monte Carlo simulations, the proposed diffusion based DPF (D/DPF) outperforms the state-of-the-art consensus based DPF approaches in environments with limited bandwidth or/and intermittent connectivity.

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Diffusion distributed Kalman filtering with adaptive weights

Cited by 48 publications

References 6 publications

Distributed State Estimation Using RSC Coded Smart Grid Communications

Distributed State Estimation Using RSC Coded Smart Grid Communications

A Distributed sequential fusion algorithm over asynchronous sensor networks

Diffusive particle filtering for distributed multisensor estimation

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