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Event-Based distributed adaptive Kalman filtering With Unknown Covariance of Process Noises

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第10期51卷 6170-6182页

作者： Mao, Jingyang Ding, Derui Dong, Hongli Ge, Xiaohua Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Northeast Petr Univ Inst Complex Syst & Adv Control Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Peoples R China

In this article, the distributed adaptive Kalman filtering is investigated for discrete-time stochastic nonlinear systems with gain perturbation as well as unknown covariance of process noises. For the adopted event-triggered communication scheduling, a distributed Kalman filter with an event timestamp is first constructed to effectively fuse the information from neighbors and itself while guaranteeing the unbiasedness. In light of stochastic analysis, the desired filter gain, achieving the suboptimality of filtering performance, is obtained recursively by solving two optimization issues with the form of Riccati-like difference equations. With the help of the fashionable weighted fusion conception combined with the well-known law of large numbers, a recursive estimation of process noise covariance is derived step by step and consequently suits for online computation. Finally, the effectiveness of the proposed filtering scheme is verified via a "lineland" system model.

关键词： Kalman filters Covariance matrices Perturbation methods Estimation Stochastic processes adaptive systems Recursive estimation distributed adaptive filtering event-triggered mechanism gain perturbation unknown covariance

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distributed adaptive filtering of α-Stable Signals

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IEEE SIGNAL PROCESSING LETTERS 2018年第10期25卷 1450-1454页

作者： Talebi, Sayed Pouria Werner, Stefan Mandic, Danilo P. Aalto Univ Dept Signal Proc & Acoust FI-02150 Espoo Finland Norwegian Univ Sci & Technol Dept Elect Syst NO-7491 Trondheim Norway Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

A cost-effective framework for distributed adaptive filtering of alpha-stable signals over sensor networks is proposed. First, the filtering paradigm of alpha-stable signals through multiple observations made over a network of sensors is revisited and an optimal solution is formulated. Then, an adaptive gradient descent based algorithm for distributed real-time filtering of alpha-stable signals via multiagent networks is derived. This not only provides an approximation of the formulated optimal solution, but also a cost-effective algorithm that scales with the size of the network. Moreover, performance of the derived algorithm is analyzed and convergence conditions are established.

关键词： alpha-stable random signals consensus fusion distributed adaptive filtering fractional differential sensor networks

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distributed MULTICHANNEL adaptive filtering

DISTRIBUTED MULTICHANNEL ADAPTIVE FILTERING

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European Signal Processing Conference

作者： Fernando G. Almeida Neto Vitor H. Nascimento Amanda de Paula University of Sao Paulo Federal Rural University of Pernambuco Cabo de Santo Agostinho

ISBN: (纸本)9781479988518

A new distributed multichannel technique is proposed for networks in which a different set of parameters is estimated by each node. The technique is proposed for non fully-connect topologies, so that nodes must store data and re-transmit information to other network elements. To reduce the amount of terms stored by each node, pre-computation of the data required by other elements is performed before the data sharing. The proposed method is adequate for implementation in networks with a large number of nodes, for which straightforward implementations would be prohibitive in terms of cost and memory.

关键词： distributed adaptive filtering distributed multichannel adaptive filtering Multichannel LMS

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Parameter estimation of the systems with irregularly missing data by using sequentially parallel distributed adaptive signal processing architecture

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ALEXANDRIA ENGINEERING JOURNAL 2023年 82卷 139-144页

作者： Raza, Hasan Abbasi, Waseem Aurangzeb, Khursheed Khan, Noor M. Anwar, Muhammad Shahid Alhussein, Musaed Hamdard Univ Dept Elect Engn Islamabad 44000 Pakistan Super Univ Dept Comp Sci & IT Lahore 54000 Pakistan King Saud Univ Coll Comp & Informat Sci Dept Comp Engn POB 51178 Riyadh 11543 Saudi Arabia Capital Univ Sci & Technol Dept Elect Engn Islamabad 44000 Pakistan Gachon Univ Dept AI & Software Seongnam si 13120 South Korea

This paper considers problems related to output error models for output data estimation and parameter identification in missing output data systems. In this regard, a new sequentially parallel distributed adaptive signal processing method with the implementation of a low complexity least squares algorithm is introduced to estimate the parameters of an auxiliary model relative to the original system, as well as handling irregularly missing output data in a stochastic framework. The validation of the proposed distributed architecture using the low complexity least squares algorithm is presented in terms of computational complexity and processing time. Measurement results show that the proposed architecture using the low complexity least squares approach provides fast convergence, parallel linear computational complexity, and significantly reduced processing time compared to the sequentially operated recursive least square (RLS) algorithm for parameter identification in missing output data systems. Finally, the effectiveness of the low complexity least squares algorithm is tested with a system example

关键词： Parameter estimation distributed adaptive filtering Missing output data Processing efficient parallel architecture

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Two-Layer Diffusion adaptive Filters Over Directed Markovian Switching Networks

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 3501-3506页

作者： Xie, Siyu Gan, Die Liu, Zhixin Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Zhongguancun Lab Beijing 100094 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100045 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 101408 Peoples R China

We consider the problem of distributed adaptive filtering in this letter, where a set of nodes in the network is required to estimate an unknown parameter of interest from noisy measurements cooperatively. Based on normalized least mean squares (NLMS) adaptive filters, we focus on a two-layer diffusion strategy to diffuse data more thoroughly. We propose and analyze the two-layer diffusion NLMS algorithm, where the communications between nodes in the network are described by directed Markovian switching graphs. The directed graphs are not only used for the combination of local estimates but also used for the adaptation step in the two-layer strategy. The stability results of the proposed two-layer diffusion adaptive filters are established under a general cooperative information assumption, without independence and stationarity signal conditions which were widely used in the literature. Moreover, the stability result indicates that even if any node cannot estimate the unknown parameter individually, the whole network can still fulfill the estimation task through communications. Simulation results also show that the proposed two-layer diffusion NLMS algorithm has better performance compared with the consensus one.

关键词： distributed adaptive filtering Markovian switching networks two-layer diffusion strategy stability

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Low communication parallel distributed adaptive signal processing (LC-PDASP) architecture for processing-inefficient platforms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第2期29卷 663-672页

作者： Raza, Hasan Hussain, Ghalib Khan, Noor M. Hamdard Univ Dept Elect Engn Fac Engn Islamabad Pakistan Capital Univ Sci & Technol Dept Elect Engn Islamabad Pakistan

In this paper, a low communication parallel distributed adaptive signal processing (LC-PDASP) architecture for a group of computationally incapable and inexpensive small platforms is introduced. The proposed architecture is capable of running computationally high adaptive filtering algorithms parallely with minimally low communication overhead. A recursive least square (RLS) adaptive algorithm based on the application of multiple-input multiple-output (MIMO) channel estimation is implemented on the proposed LC-PDASP architecture. Complexity and Communication burden of proposed LC-PDASP architecture are compared with that of conventional PDASP architecture. The comparative analysis shows that the proposed LC-PDASP architecture exhibits low computational complexity and provides an improvement more than of 85% reduced communication burden than the conventional PDASP architecture. Moreover, the proposed LC-PDASP architecture provides fast convergence performance in terms of mean square error (MSE) than the PDASP architecture.

关键词： MIMO channel estimation distributed adaptive filtering processing efficient parallel architecture

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From Smart to Intelligent Utility Meters in Natural Gas Distribution Networks 6

From Smart to Intelligent Utility Meters in Natural Gas Dist...

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6th IEEE Virtual World Forum on Internet of Things (IEEE WF-IoT)

作者： Matalkah, Ghaith Coyle, Edward J. Georgia Inst Technol Sch Elecetr & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781728155036

We propose a novel method for monitoring gas distribution networks (GDNs) using intelligent sensor nodes that can be integrated with existing smart gas meters (intelligent meters). The method aims at detecting and locating gas leaks in GDNs in real time. The intelligent meters leverage wireless connectivity in existing smart meters to implement this method collaboratively. The method comprises an active acoustic probing phase and a passive linear imaging phase. In the active acoustic phase, the intelligent meters collaboratively discover the topology of the monitored pipeline network using a novel acoustic pulse reflectometry technique. In the passive linear imaging phase, the intelligent meters use their knowledge of the pipeline network topology to create a linear image of the pipeline using the Time Exposure Acoustic (TEA) algorithm. The resulting image reveals the presence and locations of active gas leaks in the network. We present the theoretical basis of this new method and the results of its application in experimental settings.

关键词： Smart meter distributed leak detection gas distribution pipeline acoustic pulse reflectometry distributed adaptive filtering passive acoustic imaging

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COST-EFFECTIVE DIFFUSION KALMAN filtering WITH IMPLICIT MEASUREMENT EXCHANGES

COST-EFFECTIVE DIFFUSION KALMAN FILTERING WITH IMPLICIT MEAS...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Talebi, Sayed Pouria Kanna, Sithan Xia, Yili Mandic, Danilo P. Imperial Coll London Elect & Elect Engn Dept London SW7 2AZ England Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509041176

A resource effective extension to the class of distributed real-time diffusion Kalman filters is proposed. The proposed scheme removes the need to share measurement variables explicitly, by sharing only the state estimates and state error covariance matrices which implicitly contain the information about the measurements, observations matrices, and noise covariance matrices. The proposed distributed Kalman filter is quiet general, and its performance is comparable to that of existing diffusion based schemes, while having lower communication and computational requirements per-iteration compared to current distributed Kalman filtering algorithms.

关键词： Kalman filter distributed estimation diffusion strategy distributed adaptive filtering sensor networks

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Cost-effective diffusion Kalman filtering with implicit measurement exchanges

Cost-effective diffusion Kalman filtering with implicit meas...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sayed Pouria Talebi Sithan Kanna Yili Xia Danilo P. Mandic Electrical and Electronic Engineering Department Imperial College London SW7 2AZ UK The School of Information Science and Engineering Southeast University Nanjing 210096 China

ISBN: (纸本)9781509041183

关键词： Kalman filter distributed estimation diffusion strategy distributed adaptive filtering sensor networks Kalman filters Kalman filtering Sensor networks Covariance matrix diffusion Distribute Implicit

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A distributed Quaternion Kalman Filter With Applications to Fly-by-Wire Systems

A Distributed Quaternion Kalman Filter With Applications to ...

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IEEE International Conference on Digital Signal Processing (DSP)

作者： Talebi, Sayed Pouria Kanna, Sithan Xia, Yili Mandic, Danilo P. Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509041657

The introduction of automated flight control and management systems have made possible aircraft designs that sacrifice aerodynamic stability in order to incorporate stealth technology into their shape, operate more efficiently, and are highly manoeuvrable. Therefore, modern flight management systems are reliant on multiple redundant sensors to monitor and control the rotations of the aircraft. To this end, a novel distributed quaternion Kalman filtering algorithm is developed for tracking the rotation and orientation of an aircraft in the three-dimensional space. The algorithm is developed so as to distribute computation among the sensors in a manner that forces them to consent to a unique solution while being robust to sensor and link failure, a desirable characteristic for flight management systems. In addition, the underlying quaternion valued state space model allows to avoid problems associated with gimbal lock. The performance of the developed algorithm is verified through simulations.

关键词： Quaternion-valued adaptive signal processing quaternion-valued state space modeling distributed adaptive filtering fly-by-wire

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