433 research outputs found
Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey
Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
On general systems with network-enhanced complexities
In recent years, the study of networked control systems (NCSs) has gradually become an active research area due to the advantages of using networked media in many aspects such as the ease of maintenance and installation, the large flexibility and the low cost. It is well known that the devices in networks are mutually connected via communication cables that are of limited capacity. Therefore, some network-induced phenomena have inevitably emerged in the areas of signal processing and control engineering. These phenomena include, but are not limited to, network-induced communication delays, missing data, signal quantization, saturations, and channel fading. It is of great importance to understand how these phenomena influence the closed-loop stability and performance properties
Networked fusion estimation with multiple uncertainties and time-correlated channel noise
This paper is concerned with the fusion filtering and fixed-point smoothing problems for a class of networked
systems with multiple random uncertainties in both the sensor outputs and the transmission connections. To deal
with this kind of systems, random parameter matrices are considered in the mathematical models of both the
sensor measurements and the data available after transmission. The additive noise in the transmission channel
from each sensor is assumed to be sequentially time-correlated. By using the time-differencing approach, the
available measurements are transformed into an equivalent set of observations that do not depend on the timecorrelated
noise. The innovation approach is then applied to obtain recursive distributed and centralized fusion
estimation algorithms for the filtering and fixed-point smoothing estimators of the signal based on the transformed
measurements, which are equal to the estimators based on the original ones. The derivation of the algorithms
does not require the knowledge of the signal evolution model, but only the mean and covariance functions of
the processes involved (covariance information). A simulation example illustrates the utility and effectiveness of
the proposed fusion estimation algorithms, as well as the applicability of the current model to deal with different
network-induced random phenomena.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 problem in discrete-time stochastic systems with random parameter matrices
This paper addresses the least-squares quadratic filtering problem in
discrete-time stochastic systems with random parameter matrices in both
the state and measurement equations. Defining a suitable augmented system, this problem is reduced to the least-squares linear filtering problem
of the augmented state based on the augmented observations. Under the
assumption that the moments, up to the fourth-order one, of the original
state and measurement vectors are known, a recursive algorithm for the optimal linear filter of the augmented state is designed, from which the optimal
quadratic filter of the original state is obtained. As a particular case, the
proposed results are applied to multi-sensor systems with state-dependent
multiplicative noise and fading measurements and, finally, a numerical simulation example illustrates the performance of the proposed quadratic filter
in comparison with the linear one and also with other filters in the existing
literature.Ministerio de Economía y Competitividad (Grant No. MTM2014-52291-P and FPU programme
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)
Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels
The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigaciónand Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays
This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2013 IEEE.This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.This work was supported in part by the National Natural Science Foundation of China by Grants 61273156, 61028008, 60825303, 61104125, and 11271103, National 973 Project by Grant 2009CB320600, the Fok Ying Tung Education Fund by Grant 111064, the Special Fund for the Author of National Excellent Doctoral Dissertation of China by Grant 2007B4, the State Key Laboratory of Integrated Automation for the Process Industry (Northeastern University) of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. by Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal
This paper focuses on the least-squares linear fusion filter design
for discrete-time stochastic signals from multisensor measurements
perturbed not only by additive noise, but also by different uncertainties
that can be comprehensively modeled by random parameter
matrices. The additive noises from the different sensors are assumed
to be cross-correlated at the same time step and correlated with
the signal at the same and subsequent time steps. A covariancebased
approach is used to derive easily implementable recursive
filtering algorithms under the centralized, distributed and sequential
fusion architectures. Although centralized and sequential estimators
both have the same accuracy, the evaluation of their computational
complexity reveals that the sequential filter can provide a significant
reduction of computational cost over the centralized one. The
accuracy of the proposed fusion filters is explored by a simulation
example, where observation matrices with random parameters are
used to describe different kinds of sensor uncertainties.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal
de Investigación and Fondo Europeo de Desarrollo Regional FEDER [grant number MTM2017-
84199-P]
Two Compensation Strategies for Optimal Estimation in Sensor Networks with Random Matrices, Time-Correlated Noises, Deception Attacks and Packet Losses
Due to its great importance in several applied and theoretical fields, the signal estimation
problem in multisensor systems has grown into a significant research area. Networked systems are
known to suffer random flaws, which, if not appropriately addressed, can deteriorate the performance
of the estimators substantially. Thus, the development of estimation algorithms accounting for these
random phenomena has received a lot of research attention. In this paper, the centralized fusion linear
estimation problem is discussed under the assumption that the sensor measurements are affected
by random parameter matrices, perturbed by time-correlated additive noises, exposed to random
deception attacks and subject to random packet dropouts during transmission. A covariance-based
methodology and two compensation strategies based on measurement prediction are used to design
recursive filtering and fixed-point smoothing algorithms. The measurement differencing method—
typically used to deal with the measurement noise time-correlation—is unsuccessful for these kinds of
systems with packet losses because some sensor measurements are randomly lost and, consequently,
cannot be processed. Therefore, we adopt an alternative approach based on the direct estimation of
the measurement noises and the innovation technique. The two proposed compensation scenarios
are contrasted through a simulation example, in which the effect of the different uncertainties on the
estimation accuracy is also evaluated.Ministerio de Ciencia e Innovacion, Agencia Estatal de InvestigacionEuropean Commission PID2021-124486NB-I0
Networked distributed fusion estimation under uncertain outputs with random transmission delays, packet losses and multi-packet processing
This paper investigates the distributed fusion estimation problem for networked systems whose mul- tisensor measured outputs involve uncertainties modelled by random parameter matrices. Each sensor transmits its measured outputs to a local processor over different communication channels and random failures –one-step delays and packet dropouts–are assumed to occur during the transmission. White sequences of Bernoulli random variables with different probabilities are introduced to describe the ob- servations that are used to update the estimators at each sampling time. Due to the transmission failures, each local processor may receive either one or two data packets, or even nothing and, when the current measurement does not arrive on time, its predictor is used in the design of the estimators to compensate the lack of updated information. By using an innovation approach, local least-squares linear estimators (filter and fixed-point smoother) are obtained at the individual local processors, without requiring the signal evolution model. From these local estimators, distributed fusion filtering and smoothing estimators weighted by matrices are obtained in a unified way, by applying the least-squares criterion. A simula- tion study is presented to examine the performance of the estimators and the influence that both sensor uncertainties and transmission failures have on the estimation accuracy.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)
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