1,262 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
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
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)
On general systems with randomly occurring incomplete information
In the system and control community, the incomplete information is generally regarded as the results of (1) our limited knowledge in modelling real-world systems; and (2) the physical constraints on the devices for collecting, transmitting, storing and processing information.
In terms of system modelling, the incomplete information typically includes the parameter
uncertainties and norm-bounded non-linearities that occur with certain bounds. As for the
physical constraints, two well-known examples are the actuator/sensor saturation caused
by the limited power/altitude of the devices as well as the signal quantization caused by
limited bandwidth for signal propagation
Moving horizon estimation for networked systems with quantized measurements and packet dropouts
published_or_final_versio
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)
Variance-constrained filtering for uncertain stochastic systems with missing measurements
Copyright [2003] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this note, we consider a new filtering problem for linear uncertain discrete-time stochastic systems with missing measurements. The parameter uncertainties are allowed to be norm-bounded and enter into the state matrix. The system measurements may be unavailable (i.e., missing data) at any sample time, and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design a linear filter such that, for all admissible parameter uncertainties and all possible incomplete observations, the error state of the filtering process is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prescribed upper bound. It is shown that, the addressed filtering problem can effectively be solved in terms of the solutions of a couple of algebraic Riccati-like inequalities or linear matrix inequalities. The explicit expression of the desired robust filters is parameterized, and an illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed design approach
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)
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