Distributed and centralized fusion estimation from multiple sensors with Markovian delays

García-Ligero, María J.; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa

doi:10.1016/j.amc.2012.09.017

Cited by 28 publications

(12 citation statements)

References 14 publications

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“…Data fusion can be executed in either centralized or decentralized architectures. Whilst the first involves a common processor, and decentralized architectures consist of networks where each sensor has its own processing unit (Grime and Durrant-Whyte, 1994; Durrant-Whyte et al, 2001; Garcia-Ligero et al, 2012). On the one hand, centralized architectures need to be re-designed when changes in the sensing units take place, which implies costly and time-consuming development.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application

Zhang

Martínez

Clarke³

et al. 2019

Front. Neurorobot.

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This paper proposes a framework for uncertainty prediction in complex fusion networks, where signals become available sporadically. Assuming there is no information of the sensor characteristics available, a surrogated model of the sensor uncertainty is yielded directly from data through artificial neural networks. The strategy developed is applied to autonomous vehicle localization through odometry sensors (speed and orientation), so as to determine the location uncertainty in the trajectory. The results obtained allow for fusion of autonomous vehicle location measurements, and effective correction of the accumulated odometry error in most scenarios. The neural networks applicability and generalization capacity are proven, evidencing the suitability of the presented methodology for uncertainty estimation in non-linear and intractable processes.

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Section: Introductionmentioning

confidence: 99%

Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application

Zhang

Martínez

Clarke³

et al. 2019

Front. Neurorobot.

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“…During the past decade, the coordination problems of multi-agent systems have been a research focus attracting an increasing interest due primarily to their practical application insights in a variety of realms such as satellite formation control [3], collective behavior of flocking [22], attitude alignment among spacecraft [14], distributed estimation [7] and automated highway systems [2]. A critical issue in coordinated control problems is to design a distributed control protocol for communication behaviors and controller actuation based on the shared information (including graph topologies, real-time states of adjacent nodes and common control algorithms) in order to ensure that all the agents reach an agreement or collectively perform certain actions.…”

Section: Introductionmentioning

confidence: 99%

Event-Based $H_{\infty}$ Consensus Control of Multi-Agent Systems With Relative Output Feedback: The Finite-Horizon Case

Liu

Wang

et al. 2015

IEEE Trans. Automat. Contr.

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In this paper, the H∞ consensus control problem is investigated over a finite horizon for general discrete time-varying multi-agent systems subject to energy-bounded external disturbances. A decentralized estimation-based output feedback control protocol is put forward via the relative output measurements. A novel event-based mechanism is proposed for each intelligent agent to utilize the available information in order to decide when to broadcast messages and update control input. The aim of the problem addressed is to co-design the time-varying controller and estimator parameters such that the controlled multiagent systems achieve consensus with a disturbance attenuation level γ over a finite horizon [0, T ]. A constrained recursive Riccati difference equation approach is developed to derive the sufficient conditions under which the H∞ consensus performance is guaranteed in the framework of event-based scheme. Furthermore, the desired controller and estimator parameters can be iteratively computed by resorting to the MoorePenrose pseudo inverse. Finally, the effectiveness of the developed eventbased H∞ consensus control strategy is demonstrated in the numerical simulation.Index Terms-Multi-agent systems; event-based mechanism; H∞ consensus; Riccati difference equation; output feedback.

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“…Some centralised fusion estimation problem for multisensor systems were presented in [1,2], and the centralised fusion quadratic estimation problem for multisensor systems was addressed in [3]. Currently, there existed two commonly used centralised multisensor data fusion methods with adaptive unscented Kalman filter (UKF) [4] or ensemble Kalman filter [5] for process fault diagnosis including centralised measurement fusion and centralised state-vector fusion.…”

Section: Introductionmentioning

confidence: 99%

Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

Xing

Xia

2016

IET signal process.

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This study presents three non-linear centralised scaled unscented Kalman filter (SUKF) for multisensor data fusion algorithms, which are augmented measurements, measurements weighted and sequential filtering fusion. First, the accuracy analysis of extended Kalman filter (EKF) and SUKF is investigated in detail. Second, through comparing the error covariance traces and the absolute mean estimation errors of X and Y directions of centralised SUKF for multisensor data fusion algorithms with that of centralised EKF for multisensor data fusion algorithms, it can be remarked that the performance of centralised augmented measurements SUKF for multisensor data fusion algorithm is the best one among the six algorithms, which is to say that Algorithm (Iu) shows the best performance in accuracy. Finally, combining and synthetically analysing the running time of six algorithms, it illustrates that Algorithm (Iu) is optimal in comprehensive aspects among six algorithms.

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Distributed and centralized fusion estimation from multiple sensors with Markovian delays

Cited by 28 publications

References 14 publications

Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application

Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application

Event-Based $H_{\infty}$ Consensus Control of Multi-Agent Systems With Relative Output Feedback: The Finite-Horizon Case

Comparison of centralised scaled unscented Kalman filter and extended Kalman filter for multisensor data fusion architectures

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