225 research outputs found
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Distributed Kalman Filters over Wireless Sensor Networks: Data Fusion, Consensus, and Time-Varying Topologies
Kalman filtering is a widely used recursive algorithm for optimal state estimation of linear stochastic dynamic systems. The recent advances of wireless sensor networks (WSNs) provide the technology to monitor and control physical processes with a high degree of temporal and spatial granularity. Several important problems concerning Kalman filtering over WSNs are addressed in this dissertation. First we study data fusion Kalman filtering for discrete-time linear time-invariant (LTI) systems over WSNs, assuming the existence of a data fusion center that receives observations from distributed sensor nodes and estimates the state of the target system in the presence of data packet drops. We focus on the single sensor node case and show that the critical data arrival rate of the Bernoulli channel can be computed by solving a simple linear matrix inequality problem. Then a more general scenario is considered where multiple sensor nodes are employed. We derive the stationary Kalman filter that minimizes the average error variance under a TCP-like protocol. The stability margin is adopted to tackle the stability issue. Second we study distributed Kalman filtering for LTI systems over WSNs, where each sensor node is required to locally estimate the state in a collaborative manner with its neighbors in the presence of data packet drops. The stationary distributed Kalman filter (DKF) that minimizes the local average error variance is derived. Building on the stationary DKF, we propose Kalman consensus filter for the consensus of different local estimates. The upper bound for the consensus coefficient is computed to ensure the mean square stability of the error dynamics. Finally we focus on time-varying topology. The solution to state consensus control for discrete-time homogeneous multi-agent systems over deterministic time-varying feedback topology is provided, generalizing the existing results. Then we study distributed state estimation over WSNs with time-varying communication topology. Under the uniform observability, each sensor node can closely track the dynamic state by using only its own observation, plus information exchanged with its neighbors, and carrying out local computation
A new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noises
This paper is concerned with the distributed filtering problem for a class of discrete-time stochastic systems over
a sensor network with a given topology. The system presents the following main features: (i) random parameter
matrices in both the state and observation equations are considered; and (ii) the process and measurement noises
are one-step autocorrelated and two-step cross-correlated. The state estimation is performed in two stages. At the
first stage, through an innovation approach, intermediate distributed least-squares linear filtering estimators are
obtained at each sensor node by processing available output measurements not only from the sensor itself but
also from its neighboring sensors according to the network topology. At the second stage, noting that at each
sampling time not only the measurement but also an intermediate estimator is available at each sensor, attention
is focused on the design of distributed filtering estimators as the least-squares matrix-weighted linear combination
of the intermediate estimators within its neighborhood. The accuracy of both intermediate and distributed
estimators, which is measured by the error covariance matrices, is examined by a numerical simulation
example where a four-sensor network is considered. The example illustrates the applicability of the proposed
results to a linear networked system with state-dependent multiplicative noise and different network-induced
stochastic uncertainties in the measurements; more specifically, sensor gain degradation, missing measurements
and multiplicative observation noises are considered as particular cases of the proposed observation model.This research is supported by Ministerio de Economía y Competitividad
and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014-
52291-P, MTM2017-84199-P)
Linear Estimation in Interconnected Sensor Systems with Information Constraints
A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed
Linear Estimation in Interconnected Sensor Systems with Information Constraints
A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed
State Estimation Fusion for Linear Microgrids over an Unreliable Network
Microgrids should be continuously monitored in order to maintain suitable voltages over
time. Microgrids are mainly monitored remotely, and their measurement data transmitted through
lossy communication networks are vulnerable to cyberattacks and packet loss. The current study
leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of
estimation fusion using various machine-learning (ML) regression methods as data fusion methods
by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid
in order to achieve more accurate and reliable state estimates. This unreliability in measurements
is because they are received through a lossy communication network that incorporates packet loss
and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent
ordered weighted averaging (DOWA) operators are also employed for further comparisons.
The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML
regression methods through the RMSE, MAE and R-squared indices under the condition of missing
and manipulated measurements. In general, the results obtained by the Random Forest regression
method were more accurate than those of other methods.This research was partially funded by public research projects of Spanish Ministry of
Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/
501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual
Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17
Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions
This paper is concerned with the least-squares linear centralized estimation problem
in multi-sensor network systems from measured outputs with uncertainties modeled by random
parameter matrices. These measurements are transmitted to a central processor over different
communication channels, and owing to the unreliability of the network, random one-step delays and
packet dropouts are assumed to occur during the transmissions. In order to avoid network congestion,
at each sampling time, each sensor’s data packet is transmitted just once, but due to the uncertainty
of the transmissions, the processing center may receive either one packet, two packets, or nothing.
Different white sequences of Bernoulli random variables are introduced to describe the observations
used to update the estimators at each sampling time. To address the centralized estimation problem,
augmented observation vectors are defined by accumulating the raw measurements from the different
sensors, and when the current measurement of a sensor does not arrive on time, the corresponding
component of the augmented measured output predictor is used as compensation in the estimator
design. Through an innovation approach, centralized fusion estimators, including predictors, filters,
and smoothers are obtained by recursive algorithms without requiring the signal evolution model.
A numerical example is presented to show how uncertain systems with state-dependent multiplicative
noise can be covered by the proposed model and how the estimation accuracy is influenced by both
sensor uncertainties and transmission failures.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)
Networked signal and information processing
The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources
Networked Signal and Information Processing
The article reviews significant advances in networked signal and information
processing, which have enabled in the last 25 years extending decision making
and inference, optimization, control, and learning to the increasingly
ubiquitous environments of distributed agents. As these interacting agents
cooperate, new collective behaviors emerge from local decisions and actions.
Moreover, and significantly, theory and applications show that networked
agents, through cooperation and sharing, are able to match the performance of
cloud or federated solutions, while offering the potential for improved
privacy, increasing resilience, and saving resources
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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