2,712 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
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally,
conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002
and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140
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 EconomĂa y Competitividad
and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014-
52291-P, MTM2017-84199-P)
Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission
This paper is concerned with the optimal fusion estimation problem in networked stochastic systems with bounded random delays and packet dropouts, which unavoidably occur during the data transmission in the network. The measured outputs from each sensor are perturbed by random parameter matrices and white additive noises, which are cross-correlated between the different sensors. Least-squares fusion linear estimators including filter, predictor and fixed-point smoother, as well as the corresponding estimation error covariance matrices are designed via the innovation analysis approach. The proposed recursive algorithms depend on the delay probabilities at each sampling time, but do not to need to know if a particular measurement is delayed or not. Moreover, the knowledge of the signal evolution model is not required, as the algorithms need only the first and second order moments of the processes involved. Some of the practical situations covered by the proposed system model with random parameter matrices are analyzed and the influence of the delays in the estimation accuracy are examined in a numerical example.This research is supported by the âMinisterio de EconomĂa y Competitividadâ and âFondo
Europeo de Desarrollo Regionalâ FEDER (Grant No. MTM2014-52291-P)
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Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects
In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multi-objective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted
Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission
In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.This research is supported by Ministerio de EconomĂa y Competitividad and Fondo Europeo de Desarrollo Regional FEDER (grant No. MTM2014-52291-P)
On Distributed Linear Estimation With Observation Model Uncertainties
We consider distributed estimation of a Gaussian source in a heterogenous
bandwidth constrained sensor network, where the source is corrupted by
independent multiplicative and additive observation noises, with incomplete
statistical knowledge of the multiplicative noise. For multi-bit quantizers, we
derive the closed-form mean-square-error (MSE) expression for the linear
minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous
communication channels, we propose several rate allocation methods named as
longest root to leaf path, greedy and integer relaxation to (i) minimize the
MSE given a network bandwidth constraint, and (ii) minimize the required
network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao
lower bound (CRLB) and compare the MSE performance of our proposed methods
against the CRLB. Our results corroborate that, for low power multiplicative
observation noises and adequate network bandwidth, the gaps between the MSE of
our proposed methods and the CRLB are negligible, while the performance of
other methods like individual rate allocation and uniform is not satisfactory
Handling Out-of-Sequence Data: Kalman Filter Methods or Statistical Imputation?
The issue of handling sensor measurements data over single and multiple lag delays also known as outof-sequence measurement (OOSM) has been considered. It is argued that this problem can also be addressed using model-based imputation strategies and their application in comparison to Kalman filter (KF)-based approaches for a multi-sensor tracking prediction problem has also been demonstrated. The effectiveness of two model-based imputation procedures against five OOSM methods was investigated in Monte Carlo simulation experiments. The delayed measurements were either incorporated (or fused) at the time these were finally available (using OOSM methods) or imputed in a random way with higher probability of delays for multiple lags and lower probability of delays for a single lag (using single or multiple imputation). For single lag, estimates of target tracking computed from the observed data and those based on a data set in which the delayed measurements were imputed were equally unbiased; however, the KF estimates obtained using the Bayesian framework (BF-KF) were more precise. When the measurements were delayed in a multiple lag fashion, there were significant differences in bias or precision between multiple imputation (MI) and OOSM methods, with the former exhibiting a superior performance at nearly all levels of probability of measurement delay and range of manoeuvring indices. Researchers working on sensor data are encouraged to take advantage of software to implement delayed measurements using MI, as estimates of tracking are more precise and less biased in the presence of delayed multi-sensor data than those derived from an observed data analysis approach.Defence Science Journal, 2010, 60(1), pp.87-99, DOI:http://dx.doi.org/10.14429/dsj.60.11
Optimized state estimation for nonlinear dynamical networks subject to fading measurements and stochastic coupling strength: An event-triggered communication mechanism
summary:This paper is concerned with the design of event-based state estimation algorithm for nonlinear complex networks with fading measurements and stochastic coupling strength. The event-based communication protocol is employed to save energy and enhance the network transmission efficiency, where the changeable event-triggered threshold is adopted to adjust the data transmission frequency. The phenomenon of fading measurements is described by a series of random variables obeying certain probability distribution. The aim of the paper is to propose a new recursive event-based state estimation strategy such that, for the admissible linearization error, fading measurements and stochastic coupling strength, a minimum upper bound of estimation error covariance is given by designing the estimator gain. Furthermore, the monotonicity relationship between the trace of the upper bound of estimation error covariance and the fading probability is pointed out from the theoretical aspect. Finally, a simulation example is used to show the effectiveness of developed state estimation algorithm
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 sensorâs 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 EconomĂa, Industria y Competitividad, Agencia Estatal de
InvestigaciĂłn and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
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