Multiple event-triggered H2/H∞ filtering for hybrid wired–wireless networked systems with random network-induced delays

Du, Dajun; Qi, Bo; Fei, Minrui; Peng, Chen

doi:10.1016/j.ins.2015.07.026

Cited by 62 publications

(20 citation statements)

References 38 publications

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“…By placing the series of estimates in ( 7 ) in the form of a deterministic trajectory function, ( 37 ) unifies the goals of STF as simply seeking a trajectory function that best explains the sensor data series in the time domain. To accommodate any a priori model information, F ( t ) may be specified as being a specific form of a few parameters or subject to certain constraints, denoted as F ( t; C ) where C denotes the set of the parameters or constraints.…”

Section: Discussionmentioning

confidence: 99%

“…• Non-cooperative/intractable object motion: constrained [28,35,41] /sparse [1] /circular [19] state sequence and maneuvers [14,27,43] , etc., • Unknown background and sensor uncertainties: noise characteristics [8] , detection probability [13] , clutter rate and detection profiles [31,46,49] , network-induced delay [7] , etc., and • Imperfect data: asynchronous sensor data [14,42] , intractable likelihood [16,33,37,44] , measurement random latency [48] , outlier [32] and continuous-time observation [12] , etc.…”

Section: Motivation and Outlinementioning

confidence: 99%

See 1 more Smart Citation

Clustering for filtering: Multi-object detection and estimation using multiple/massive sensors

Corchado

Sun

et al. 2017

Information Sciences

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Advanced multi-sensor systems are expected to combat the challenges that arise in object recognition and state estimation in harsh environments with poor or even no prior information, while bringing new challenges mainly related to data fusion and computational burden. Unlike the prevailing Markov-Bayes framework that is the basis of a large variety of stochastic filters and the approximate, we propose a clustering-based methodology for multi-sensor multi-object detection and estimation (MODE), named clustering for filtering (C4F), which abandons unrealistic assumptions with respect to the objects, background and sensors. Rather, based on cluster analysis of the input multi-sensor data, the C4F approach needs no prior knowledge about the latent objects (whether quantity or dynamics), can handle time-varying uncertainties regarding the background and sensors such as noises, clutter and misdetection, and does so computationally fast. This offers an inherently robust and computationally efficient alternative to conventional Markov-Bayes filters for dealing with the scenario with little prior knowledge but rich observation data. Simulations based on representative scenarios of both complete and little prior information have demonstrated the superiority of our C4F approach.

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Section: Discussionmentioning

confidence: 99%

Section: Motivation and Outlinementioning

confidence: 99%

Clustering for filtering: Multi-object detection and estimation using multiple/massive sensors

Corchado

Sun

et al. 2017

Information Sciences

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“…() However, in real‐world networks, the nodes typically have higher‐dimensional states, which are coupled via multidimensional channels. () In such situations, the controllability issue becomes more complicated and challenging.…”

Section: Introductionmentioning

confidence: 99%

Further on the controllability of networked MIMO LTI systems

Hao

Duan

Chen

2017

Intl J Robust & Nonlinear

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Further on the controllability of networked multiple-input-multiple-output systems, an efficient, necessary, and sufficient condition is derived, where the network topology is directed and weighted and the nodes are higher-dimensional linear time-invariant systems. The new condition is easier to verify, which explicitly shows the effects of the network topology, node-system dynamics, external control inputs, and inner interactions on the controllability of the whole networked system. For networked multiple-input-multiple-output systems in several specific topologies, the corresponding conditions are expressed more precisely. The effectiveness of the conditions is demonstrated through several examples.

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“…With the deep integration of electricity infrastructure and modern information and communication technology, a smart grid (SG) uses two-way flows of electricity and information to create a widely distributed automated energy delivery network [2][3][4][5][6][7][8][9][10], leading to the great improvement of the comprehensive level of automation and management. However, SG has been found vulnerable to cyberattacks as a large number of smart devices are deployed over unencrypted cyber communication environments [11][12][13][14][15]. Malicious cyberattacks are one type of the most popular cyberattacks, which may trigger the catastrophic damage to power supplies and widespread power outages [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Sparse False Data Injection Attack Method in Smart Grids with Incomplete Power Network Information

Zhong

et al. 2018

Complexity

Self Cite

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The paper investigates a novel sparse false data injection attack method in a smart grid (SG) with incomplete power network information. Most existing methods usually require the known complete power network information of SG. The main objective of this paper is to propose an effective sparse false data injection attack strategy under a more practical situation where attackers can only have incomplete power network information and limited attack resources to access the measurements. Firstly, according to the obtained measurements and power network information, some incomplete power network information is compensated by using the power flow equation approach. Then, the fault tolerance range of bad data detection (BDD) for the attack residual increment is estimated by calculating the detection threshold of the residual L2-norm test. Finally, an effective sparse imperfect strategy is proposed by converting the choice of measurements into a subset selection problem, which is solved by the locally regularized fast recursive (LRFR) algorithm to effectively improve the sparsity of attack vectors. Simulation results on an IEEE 30-bus system and a real distribution network system confirm the feasibility and effectiveness of the proposed new attack construction method.

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Multiple event-triggered H2/H∞ filtering for hybrid wired–wireless networked systems with random network-induced delays

Cited by 62 publications

References 38 publications

Clustering for filtering: Multi-object detection and estimation using multiple/massive sensors

Clustering for filtering: Multi-object detection and estimation using multiple/massive sensors

Further on the controllability of networked MIMO LTI systems

A Novel Sparse False Data Injection Attack Method in Smart Grids with Incomplete Power Network Information

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