Distributed Robust Dimensionality Reduction Fusion Estimation Under DoS Attacks and Uncertain Covariances

Xu, Meng; Zhang, Yuchen; Zhang, Dongmei; Chen, Bin

doi:10.1109/access.2021.3050198

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Chen et al [8] designed a distributed resilient filter for power systems exposed to DoS attacks and gain perturbations. Similarly, the distributed robust fusion estimation problem for systems under DoS attacks and uncertain covariances was studied by [9]. For replay attacks, [10] designed a recursive distributed Kalman fusion estimator focused on the linear minimum variance, for bandwidth-constrained cyber-physical systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks

2022

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This paper focuses on the distributed fusion estimation problem in which a signal transmitted over wireless sensor networks is subject to deception attacks and random delays. We assume that each sensor can suffer attacks that may corrupt and/or modify the output measurements. In addition, communication failures between sensors and their local processors can delay the receipt of processed measurements. The randomness of attacks and transmission delays is modelled by different Bernoulli random variables with known probabilities of success. According to these characteristics of the sensor networks and assuming that the measurement noises are cross-correlated at the same time step between sensors and are also correlated with the signal at the same and subsequent time steps, we derive a fusion estimation algorithm, including prediction and filtering, using the distributed fusion method. First, for each sensor, the local least-squares linear prediction and filtering algorithm are derived, using a covariance-based approach. Then, the distributed fusion predictor and the corresponding filter are obtained as the matrix-weighted linear combination of corresponding local estimators, checking that the mean squared error is minimised. A simulation example is then given to illustrate the effectiveness of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks

2022

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“…Under the network DoS attack, the event-triggered security estimation problems of sensor networks have been addressed in [ 9 , 10 , 11 ], whereas the distributed dimensional-reduction fusion estimators have been designed in [ 12 , 13 ]. In [ 14 ], the optimal DoS attack scheduling problem from the attacker’s perspective has been studied.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks

Sun

2022

Sensors

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In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear minimum variance criterion is presented based on compressed measurement data. It has the same estimation accuracy as and lower computational cost than that based on uncompressed measurement data. When the attack rates and noise variances of the stochastic deception attack signals are unknown, a correlation function method is employed to identify them. Then, a distributed self-tuning filter is obtained by substituting the identified results into the distributed optimal filtering algorithm. The convergence of the presented algorithms is analyzed. A simulation example verifies the effectiveness of the proposed algorithms.

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“…Notice that, whether the above single-sensor tracking estimation methods, or multi-sensor fusion tracking methods [19]- [22], the system uncertainty should be effectively handled. Particularly, for the most existing distributed estimation works, a saturated-constrained distributed filtering algorithm was developed in [23] under uncertain missing probabilities, and the boundedness and monotonicity were also discussed.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fusion Tracking Estimation Under Range-Only Measurement

2022

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This paper is concerned with the distributed fusion estimation problem of range-only target tracking system with unknown but bounded noises, where the linear and nonlinear motion models are both considered. Particularly, a kind of nonlinear transformation is used to convert the nonlinear distance measurement model into a linear one, which eliminates the corresponding linearization errors in the design of estimation error system. In spite of the transformed measurement noise becomes more complicated, while it is still bounded. Moreover, for the nonlinear target motion model, the state linearization error caused by the Taylor expansion is modeled by the state dependent matrix with uncertainty bounded matrix. In this case, based on the bounded recursive optimization algorithm, two kinds of convex optimization problems are established to determine the gains of the local/fusion estimators, and the stability of the designed estimators also can be guaranteed. Finally, two different range-only target tracking systems are presented to show the effectiveness and advantages of the proposed methods.

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Distributed Robust Dimensionality Reduction Fusion Estimation Under DoS Attacks and Uncertain Covariances

Cited by 8 publications

References 44 publications

Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks

Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks

Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks

Distributed Fusion Tracking Estimation Under Range-Only Measurement

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