17 research outputs found
Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks
In this paper, a cluster-based approach is used to address the distributed fusion estimation
problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of
random deception attacks. At each sampling time, measured outputs of the signal are provided by
a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local
processor which gathers the measured outputs of its sensors and, in turn, the local processors of all
clusters are connected with a global fusion center. The proposed cluster-based fusion estimation
structure involves two stages. First, every single sensor in a cluster transmits its observations to the
corresponding local processor, where least-squares local estimators are designed by an innovation
approach. During this transmission, deception attacks to the sensor measurements may be randomly
launched by an adversary, with known probabilities of success that may be different at each sensor.
In the second stage, the local estimators are sent to the fusion center, where they are combined
to generate the proposed fusion estimators. The covariance-based design of the distributed fusion
filtering and fixed-point smoothing algorithms does not require full knowledge of the signal evolution
model, but only the first and second order moments of the processes involved in the observation
model. Simulations are provided to illustrate the theoretical results and analyze the effect of the
attack success probability on the estimation performance.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de
Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks
This research was suported by the ``Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación'' of Spain and the European Regional Development Fund [grant number PID2021-124486NB-I00].Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.Ministerio de Ciencia e Innovación
MICINNEuropean Regional Development Fund
PID2021-124486NB-I00 ERDFAgencia Estatal de Investigación
AE
Unreliable networks with random parameter matrices and time-correlated noises: distributed estimation under deception attacks
This paper examines the distributed filtering and fixed-point smoothing problems for networked systems, considering random parameter matrices, time-correlated additive noises and random deception attacks. The proposed distributed estimation algorithms consist of two stages: the first stage creates intermediate estimators based on local and adjacent node measurements, while the second stage combines the intermediate estimators from neighboring sensors using least-squares matrix-weighted linear combinations. The major contributions and challenges lie in simultaneously considering various network-induced phenomena and providing a unified framework for systems with incomplete information. The algorithms are designed without specific structure assumptions and use a covariance-based estimation technique, which does not require knowledge of the evolution model of the signal being estimated. A numerical experiment demonstrates the applicability and effectiveness of the proposed algorithms, highlighting the impact of observation uncertainties and deception attacks on estimation accuracy
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Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives
National Natural Science Foundation of China under Grants 62103095, 61873058, 61873148 and 61933007; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA07; Royal Society of the UK; Alexander von Humboldt Foundation of Germany
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Centralized moving-horizon estimation for a class of nonlinear dynamical complex networks under event-triggered transmission scheme
Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.This article is concerned with the problem of event-triggered centralized moving-horizon state estimation for a class of nonlinear dynamical complex networks. An event-triggered scheme is employed to reduce unnecessary data transmissions between sensors and estimators, where the signal is transmitted only when certain condition is violated. By treating sector-bounded nonlinearities as certain sector-bounded uncertainties, the addressed centralized moving-horizon estimation problem is transformed into a regularized robust least-squares problem that can be effectively solved via existing convex optimization algorithms. Moreover, a sufficient condition is derived to guarantee the exponentially ultimate boundedness of the estimation error, and an upper bound of the estimation error is also presented. Finally, a numerical example is provided to demonstrate the feasibility and efficiency of the proposed estimator design method.National Natural Science Foundation of China. Grant Numbers: 61873148, 61933007, 62033008, 62073339, 62173343;
Natural Science Foundation of Shandong Province of China. Grant Number: ZR2020YQ49;
AHPU Youth Top-notch Talent Support Program of China. Grant Number: 2018BJRC009;
Natural Science Foundation of Anhui Province of China. Grant Number: 2108085MA07;
China Postdoctoral Science Foundation. Grant Number: 2018T110702;
Postdoctoral Special Innovation Foundation of Shandong Province of China. Grant Number: 201701015;
Royal Society of the UK;
Alexander von Humboldt Foundation of Germany
Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts
The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above