Heterogeneous state estimation in dynamic networked systems

Sijs, Joris; Bosch, Paul

doi:10.1049/iet-cta.2014.0811

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…Among the many works on this subject are [7]- [15]. We will describe these works in detail in Section III.…”

Section: A Motivationmentioning

confidence: 99%

“…A survey on different fusion rules which provide stable observers is given in [15], which provides a state fusion approach to distributed Kalman filtering instead of a consensus-based approach. The paper also derives a stability condition which guarantees that the covariance of the estimates is uniformly upper bounded on all the sensor nodes where the estimator is running.…”

Section: A Distributed Kalman Filteringmentioning

confidence: 99%

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A design method for distributed luenberger observers

Rego

Aguiar

Pascoal

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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“…Among the many works on this subject are [7]- [15]. We will describe these works in detail in Section III.…”

Section: A Motivationmentioning

confidence: 99%

Section: A Distributed Kalman Filteringmentioning

confidence: 99%

A design method for distributed luenberger observers

Rego

Aguiar

Pascoal

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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“…iS − U j = I n x holds, which is equivalent to T X i T when T follows (11). (ii) The alternative eigenvalue decomposition in (10), i.e., denoted with eig * (·), gives that T U j 0 0 0 T =D U j .…”

Section: A State Fusion With Unknown Correlationsmentioning

confidence: 99%

“…Each node computes a local estimate of the process' state by combining its own measurement with data received from neighboring nodes, which could be either their local measurement or their local state estimation result (as computed by the neighbor). See, for example, solutions on distributed state estimation proposed in [9], [1], [8], [11] and the illustrative networked system in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

An empirical method to fuse partially overlapping state vectors for distributed state estimation

Sijs

Hanebeck

Noack

2013

2013 European Control Conference (ECC)

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State fusion is a method for merging multiple estimates of the same state into a single fused estimate. Dealing with multiple estimates is one of the main concerns in distributed state estimation, where an estimated value of the desired state vector is computed in each node of a networked system. Most solutions for distributed state estimation currently available assume that every node computes an estimate of the (same) global state vector. This assumption is impractical for systems observing large-area processes, due to the sheer size of the process state. A feasible solution is one where each node estimates a part of the global state vector, allowing different nodes in the network to have overlapping state elements. Although such an approach should be accompanied by a corresponding state fusion method, existing solutions cannot be employed as they merely consider fusion of two different estimates with equal state representations. Therefore, an empirical solution is presented for fusing two state estimates that have partially overlapping state elements. A justification of the proposed fusion method is presented, along with an illustrative case study for observing the temperature profile of a large rod, though a formal derivation is future research.

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“…For example, in [26], heterogeneity in a multi‐agent system is caused due to non‐identical speeds of the agents and in [27] due to non‐identical inertias. The problem of heterogeneous state estimation in dynamic networked systems has been addressed in [28] and for a class of neural networks in [29] with time‐varying delay. Besides, heterogeneity in terms of different dynamics, consisting of mixed first‐order and second‐order integrator agents, is studied in [30].…”

Section: Introductionmentioning

confidence: 99%