312 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
Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances
A direct approach of designing weighted fusion robust steady-state Kalman filters with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule, the six robust weighted fusion steady-state Kalman filters are designed based on the worst-case conservative system with the conservative upper bounds of noise variances. The actual filtering error variances of each fuser are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. A Lyapunov equation method for robustness analysis is proposed. Their robust accuracy relations are proved. A simulation example verifies their robustness and accuracy relations
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 âone-step delays and packet dropoutsâare 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 EconomĂa, Industria y Competitividad, Agencia Estatal de InvestigaciĂłn and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
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)
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 EconomĂa, Industria y Competitividad, Agencia Estatal de InvestigaciĂłn and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
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 EconomĂa, Industria y Competitividad, Agencia Estatal
de InvestigaciĂłn and Fondo Europeo de Desarrollo Regional FEDER [grant number MTM2017-
84199-P]
Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays
This paper investigates the distributed fusion estimation of a signal for a class of multi-sensor
systems with random uncertainties both in the sensor outputs and during the transmission connections.
The measured outputs are assumed to be affected by multiplicative noises, which degrade the signal,
and delays may occur during transmission. These uncertainties are commonly described by means of
independent Bernoulli random variables. In the present paper, the model is generalised in two directions:
(i) at each sensor, the degradation in the measurements is modelled by sequences of random variables
with arbitrary distribution over the interval [0, 1]; (ii) transmission delays are described using three-state
homogeneous Markov chains (Markovian delays), thus modelling dependence at different sampling
times. Assuming that the measurement noises are correlated and cross-correlated at both simultaneous
and consecutive sampling times, and that the evolution of the signal process is unknown, we address the
problem of signal estimation in terms of covariances, using the following distributed fusion method. First,
the local filtering and fixed-point smoothing algorithms are obtained by an innovation approach. Then,
the corresponding distributed fusion estimators are obtained as a matrix-weighted linear combination
of the local ones, using the mean squared error as the criterion of optimality. Finally, the efficiency of
the algorithms obtained, measured by estimation error covariance matrices, is shown by a numerical
simulation example.Ministerio de EconomĂa, Industria y CompetitividadEuropean Union (EU)
MTM2017-84199-PAgencia Estatal de InvestigaciĂł
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)
Recommended from our members
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
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
- âŠ