Team-optimal distributed MMSE estimation in general and tree networks

Sayin, Muhammed O.; Kozat, Süleyman S.; Baar, Tamer

doi:10.1016/j.dsp.2017.02.007

Cited by 2 publications

(4 citation statements)

References 27 publications

(44 reference statements)

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“…To this end, we work on the team-optimal estimation of dynamic parameters over distributed networks. We first use the framework of Sayin et al [29] to establish the model and the problem. Then, we introduce the efficient and optimal distributed learning (EODL) algorithm for the online estimation of dynamic parameters and prove that it is only applicable over certain network topologies.…”

Section: Related Workmentioning

confidence: 99%

“…Information coming from the agents in N (2) i and N (3) i can be accessed with a certain delay. through disclosure of local estimates and such performance cannot be achieved over cyclic networks [29] . We define the tree-networks as graph structures, where the vertices are connected with undirected edges without any cycles as shown in Fig.…”

Section: Optimal Estimation With the Disclosure Of Local Estimatesmentioning

confidence: 99%

“…We use the terminal cost function for measuring the team performance of different algorithms. The terminal cost is a function of the time horizon T and defined as [29]…”

Section: Simulationsmentioning

confidence: 99%

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Team-optimal online estimation of dynamic parameters over distributed tree networks

et al. 2019

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We study online parameter estimation over a distributed network, where the nodes in the network collaboratively estimate a dynamically evolving parameter using noisy observations. The nodes in the network are equipped with processing and communication capabilities and can share their observations or local estimates with their neighbors. The conventional distributed estimation algorithms cannot perform the team-optimal online estimation in the finite horizon global mean-square error sense (MSE). To this end, we present a team-optimal distributed estimation algorithm through the disclosure of local estimates for tracking an underlying dynamic parameter. We first show that the optimal estimation can be achieved through the diffusion of all the time stamped observations for any arbitrary network and prove that the team optimality through disclosure of local estimates is only possible for certain network topologies such as tree networks. We then derive an iterative algorithm to recursively calculate the combination weights of the disclosed information and construct the team-optimal estimate for each time step. Through series of simulations, we demonstrate the superior performance of the proposed algorithm with respect to the state-of-the-art diffusion distributed estimation algorithms regarding the convergence rate and the finite horizon MSE levels. We also show that while conventional distributed estimation schemes cannot track highly dynamic parameters, through optimal weight and estimate construction, the proposed algorithm presents a stable MSE performance.

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Section: Related Workmentioning

confidence: 99%

Section: Optimal Estimation With the Disclosure Of Local Estimatesmentioning

confidence: 99%

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Team-optimal online estimation of dynamic parameters over distributed tree networks

et al. 2019

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“…For example, [31] presented a novel team optimal decentralized estimation for a team of uninhabited aerial vehicles cooperating under communication imperfections. Sayin et al [32] studied the team optimal distributed estimation in general and tree networks. Afshari and Mahajan [33,34] provided the team optimal decentralized estimation of a linear stochastic process by reducing to the one-step predictor based on the common information.…”

Section: Introductionmentioning

confidence: 99%

Team optimal decentralized estimation by multiple agents with partial history sharing information structure

Liu

Zhang

2023

Asian Journal of Control

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In this paper, we study the team optimal decentralized estimation with partial history sharing information structure. There exist agents that have their own observations and share partial history observations to each other. The main contributions include two aspects: One is to give the iterative equations of the common estimation, which is the conditional expectation of state with respect to the common information for all agents and the innovation of the local information for each agent; the other is to provide the structure of the team optimal decentralized estimation, that is, the linear combination of the common estimation and the innovation of the local information. The novelty lies in that the estimation can be obtained at one time by defining an augmented state. In particular, the result is reduced to the well‐known centralized estimation when all the information is sharing for all the agents.

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Team-optimal distributed MMSE estimation in general and tree networks

Cited by 2 publications

References 27 publications

Team-optimal online estimation of dynamic parameters over distributed tree networks

Team-optimal online estimation of dynamic parameters over distributed tree networks

Team optimal decentralized estimation by multiple agents with partial history sharing information structure

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