Gossip-Based Distributed Tracking in Networks of Heterogeneous Agents

Ma, Kangjian; Wu, Shaochuan; Wei, Yanbo; Zhang, Wenbin

doi:10.1109/lcomm.2016.2637889

Cited by 15 publications

(16 citation statements)

References 10 publications

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“…As the result of serial operation, these algorithms require special communication topology which should be sequentially connected as a ring/chain. Gossip algorithm is found as popular in the fusion process of distributed estimators as well [11], [12]. At every round of iteration, the sensor, either deterministically or randomly, selects one node in its neighborhood, with which its local information is fused.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Implementation of Steady-State Kalman Filter

Yan¹,

Xu²,

Mo³

et al. 2021

Preprint

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This paper studies the distributed state estimation in sensor network, where m sensors are deployed to infer the n-dimensional state of a linear time-invariant (LTI) Gaussian system. By a lossless decomposition of optimal steady-state Kalman filter, we show that the problem of distributed estimation can be reformulated as synchronization of homogeneous linear systems. Based on such decomposition, a distributed estimator is proposed, where each sensor node runs a local filter using only its own measurement and fuses the local estimate of each node with a consensus algorithm. We show that the average of the estimate from all sensors coincides with the optimal Kalman estimate. Numerical examples are provided in the end to illustrate the performance of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

A Distributed Implementation of Steady-State Kalman Filter

Yan¹,

Xu²,

Mo³

et al. 2021

Preprint

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show abstract

“…In view of (23), it is not difficult to verify that (S, 1 n ) is also controllable. Hence, by the choice of ζ, there exists P > 0 that solves (41). Together with (40), it holds that…”

Section: A Synchronization Of Local Statesmentioning

confidence: 97%

“…This section is devoted to a distributed implementation of the Kalman filter with reduced communication among sensors. The traditional approaches [9], [10], [12]- [17], [19], [41]- [45] require that each sensor node broadcasts its local information to neighbors at least once during the sampling interval. This inevitably causes a large number of data transmission, which leads to an increased communication burden and a shortened lifetime of the sensor network.…”

Section: An Event-based Distributed Implementation Of Kalman Filtermentioning

confidence: 99%

A Framework for Distributed Estimation with Reduced Communication via Event-Based Strategies

Yan¹,

Mo²,

Ishii³

2022

Preprint

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In this paper, we consider the problem of distributed estimation in a sensor network, where multiple sensors are deployed to estimate the state of a linear time-invariant (LTI) Gaussian system. By losslessly decomposing the Kalman filter, a framework of event-based distributed estimation is developed, where each sensor node runs a local filter using solely its own measurement, alongside with an event-based synchronization algorithm to fuse the neighboring information. One novelty of the proposed framework is that it decouples the local filter from synchronization process. By doing so, we prove that a general class of triggering strategies can be applied in our framework, which yields stable distributed estimators under the minimal requirements of network connectivity and collective system observability. Moreover, the developed results can be generalized to achieve a distributed implementation of any Luenberger observer. By solving a semi-definite programming (SDP), we further present a low-rank estimator design to obtain the optimal gain of Luenberger observer such that the distributed estimation is realized under the constraint of message size. Therefore, as compared with existing works, the proposed algorithm enjoys lower data size for each transmission. Numerical examples are finally provided to demonstrate the proposed methods.

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“…To handle local unobservability, a finite-time consensus-based distributed estimator is proposed in [14], which is based on the max-consensus technique. In [15,16], the authors propose a gossip-based algorithm that adopts a random communication strategy to achieve consensus. In contrast to consensus-based algorithms, a lower bandwidth can also guarantee the consensus of gossip-based algorithms with the cost of a slow convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

Zhang

Shen

2020

Sensors

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Estimation accuracy is the core performance index of sensor networks. In this study, a kind of distributed Kalman filter based on the non-repeated diffusion strategy is proposed in order to improve the estimation accuracy of sensor networks. The algorithm is applied to the state estimation of distributed sensor networks. In this sensor network, each node only exchanges information with adjacent nodes. Compared with existing diffusion-based distributed Kalman filters, the algorithm in this study improves the estimation accuracy of the networks. Meanwhile, a single-target tracking simulation is performed to analyze and verify the performance of the algorithm. Finally, by discussion, it is proved that the algorithm exhibits good all-round performance, not only regarding estimation accuracy.

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Gossip-Based Distributed Tracking in Networks of Heterogeneous Agents

Cited by 15 publications

References 10 publications

A Distributed Implementation of Steady-State Kalman Filter

A Distributed Implementation of Steady-State Kalman Filter

A Framework for Distributed Estimation with Reduced Communication via Event-Based Strategies

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

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