660 research outputs found
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)
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 methodâ
typically used to deal with the measurement noise time-correlationâis 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 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 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 EconomĂa, Industria y Competitividad, Agencia Estatal de InvestigaciĂłnand Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Covariance-based least-squares filtering algorithm under Markovian measurement delays
This paper addresses the least-squares linear filtering problem of signals
from measurements which may be randomly delayed by one or two sampling
times. The delays are modelled by a homogeneous discrete-time
Markov chain to capture the dependence between them. Assuming that
the evolution equation generating the signal is not available and that only
the first- and second-order moments of the processes involved in the observation
model are known, a recursive filtering algorithm is derived using an
innovation approach. Recursive formulas for the filtering error variances are
also obtained to measure the precision of the proposed estimators.This research is supported by Ministerio de EconomĂa y Competitividad and Fondo Europeo de Desarrollo Regional
FEDER (grant no. MTM2014-52291-P)
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)
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
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]
- âŠ