85 research outputs found
A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem
Disturbance noises are always bounded in a practical system, while fusion
estimation is to best utilize multiple sensor data containing noises for the
purpose of estimating a quantity--a parameter or process. However, few results
are focused on the information fusion estimation problem under bounded noises.
In this paper, we study the distributed fusion estimation problem for linear
time-varying systems and nonlinear systems with bounded noises, where the
addressed noises do not provide any statistical information, and are unknown
but bounded. When considering linear time-varying fusion systems with bounded
noises, a new local Kalman-like estimator is designed such that the square
error of the estimator is bounded as time goes to . A novel
constructive method is proposed to find an upper bound of fusion estimation
error, then a convex optimization problem on the design of an optimal weighting
fusion criterion is established in terms of linear matrix inequalities, which
can be solved by standard software packages. Furthermore, according to the
design method of linear time-varying fusion systems, each local nonlinear
estimator is derived for nonlinear systems with bounded noises by using Taylor
series expansion, and a corresponding distributed fusion criterion is obtained
by solving a convex optimization problem. Finally, target tracking system and
localization of a mobile robot are given to show the advantages and
effectiveness of the proposed methods.Comment: 9 pages, 3 figure
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
Information fusion algorithms for state estimation in multi-sensor systems with correlated missing measurements
In this paper, centralized and distributed fusion estimation problems in linear discrete-time stochastic systems with missing observations coming from multiple sensors are addressed. At each sensor, the Bernoulli random variables describing the phenomenon of missing observations are assumed to be correlated at instants that differ m units of time. By using an innovation approach, recursive linear filtering and fixed-point smoothing algorithms for the centralized fusion problem are derived in the least-squares sense. The distributed fusion estimation problem is addressed based on the distributed fusion criterion weighted by matrices in the linear minimum variance sense. For each sensor subsystem, local least-squares linear filtering and fixed-point smoothing estimators are given and the estimation error cross-covariance matrices between any two sensors are derived to obtain the distributed fusion estimators. The performance of the proposed estimators is illustrated by numerical simulation examples where scalar and two-dimensional signals are estimated from missing observations coming from two sensors, and the estimation accuracy is analyzed for different missing probabilities and different values of m.Ministerio de Ciencia e InnovaciĂłn (Programa FPU and Grant No. MTM2011-24718
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)
Distributed Fusion Estimation for Multisensor Multirate Systems with Stochastic Observation Multiplicative Noises
This paper studies the fusion estimation problem of a class of multisensor multirate systems with observation multiplicative noises. The dynamic system is sampled uniformly. Sampling period of each sensor is uniform and the integer multiple of the state update period. Moreover, different sensors have the different sampling rates and observations of sensors are subject to the stochastic uncertainties of multiplicative noises. At first, local filters at the observation sampling points are obtained based on the observations of each sensor. Further, local estimators at the state update points are obtained by predictions of local filters at the observation sampling points. They have the reduced computational cost and a good real-time property. Then, the cross-covariance matrices between any two local estimators are derived at the state update points. At last, using the matrix weighted optimal fusion estimation algorithm in the linear minimum variance sense, the distributed optimal fusion estimator is obtained based on the local estimators and the cross-covariance matrices. An example shows the effectiveness of the proposed algorithms
Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints
This paper studies the event-triggered distributed fusion estimation problems
for a class of nonlinear networked multisensor fusion systems without noise
statistical characteristics. When considering the limited resource problems of
two kinds of communication channels (i.e., sensor-to-remote estimator channel
and smart sensor-to-fusion center channel), an event-triggered strategy and a
dimensionality reduction strategy are introduced in a unified networked
framework to lighten the communication burden. Then, two kinds of compensation
strategies in terms of a unified model are designed to restructure the
untransmitted information, and the local/fusion estimators are proposed based
on the compensation information. Furthermore, the linearization errors caused
by the Taylor expansion are modeled by the state-dependent matrices with
uncertain parameters when establishing estimation error systems, and then
different robust recursive optimization problems are constructed to determine
the estimator gains and the fusion criteria. Meanwhile, the stability
conditions are also proposed such that the square errors of the designed
nonlinear estimators are bounded. Finally, a vehicle localization system is
employed to demonstrate the effectiveness and advantages of the proposed
methods.Comment: 15 pages,9 figures. The first draft was completed in June 2021, and
this is the revised versio
Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks
In this paper, a cluster-based approach is used to address the distributed fusion estimation
problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of
random deception attacks. At each sampling time, measured outputs of the signal are provided by
a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local
processor which gathers the measured outputs of its sensors and, in turn, the local processors of all
clusters are connected with a global fusion center. The proposed cluster-based fusion estimation
structure involves two stages. First, every single sensor in a cluster transmits its observations to the
corresponding local processor, where least-squares local estimators are designed by an innovation
approach. During this transmission, deception attacks to the sensor measurements may be randomly
launched by an adversary, with known probabilities of success that may be different at each sensor.
In the second stage, the local estimators are sent to the fusion center, where they are combined
to generate the proposed fusion estimators. The covariance-based design of the distributed fusion
filtering and fixed-point smoothing algorithms does not require full knowledge of the signal evolution
model, but only the first and second order moments of the processes involved in the observation
model. Simulations are provided to illustrate the theoretical results and analyze the effect of the
attack success probability on the estimation performance.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)
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
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