scholarly journals Adaptive Kalman Estimation in Target Tracking Mixed with Random One-Step Delays, Stochastic-Bias Measurements, and Missing Measurements

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Sujuan Chen ◽  
Yinya Li ◽  
Guoqing Qi ◽  
Andong Sheng
Keyword(s):  
Target Tracking ◽  
Discrete Time ◽  
Adaptive Filter ◽  
Stochastic Systems ◽  
Tracking System ◽  
Random Variables ◽  
Design Approach ◽  
Missing Measurements ◽  
One Step ◽  
Mixed Uncertainties

The objective of this paper is concerned with the estimation problem for linear discrete-time stochastic systems with mixed uncertainties involving random one-step sensor delay, stochastic-bias measurements, and missing measurements. Three Bernoulli distributed random variables are employed to describe the uncertainties. All the three uncertainties in the measurement have certain probability of occurrence in the target tracking system. And then, an adaptive Kalman estimation is proposed to deal with this problem. The adaptive filter gains can be obtained in terms of solutions to a set of recursive discrete-time Riccati equations. Examples in three scenarios of target tracking are exploited to show the effectiveness of the proposed design approach.

On Nonlinear $H_{\infty }$ Filtering for Discrete-Time Stochastic Systems With Missing Measurements

2008 ◽  
Vol 53 (9) ◽  
pp. 2170-2180 ◽  
Author(s):  
Bo Shen ◽  
Zidong Wang ◽  
Huisheng Shu ◽  
Guoliang Wei
Keyword(s):  
Discrete Time ◽  
Stochastic Systems ◽  
Missing Measurements

scholarly journals Probability-Dependent Static Output Feedback Control for Discrete-Time Nonlinear Stochastic Systems with Missing Measurements

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Wangyan Li ◽  
Guoliang Wei ◽  
Licheng Wang
Keyword(s):  
Discrete Time ◽  
Output Feedback ◽  
Stochastic Systems ◽  
Output Feedback Control ◽  
Upper And Lower Bounds ◽  
Static Output Feedback ◽  
Missing Measurements ◽  
Design Procedures ◽  
Static Output Feedback Control ◽  
Static Output

This paper is devoted to the problems of gain-scheduled control for a class of discrete-time stochastic systems with infinite-distributed delays and missing measurements by utilizing probability-dependent Lyapunov functional. The missing-measurement phenomenon is assumed to occur in a random way, and the missing probability is time varying with securable upper and lower bounds that can be measured in real time. The purpose is to design a static output feedback controller with scheduled gains such that, for the admissible random missing measurements, time delays, and noises, the closed-loop system is exponentially mean-square stable. At last, a simulation example is exploited to illustrate the effectiveness of the proposed design procedures.

scholarly journals Optimal Fusion Filtering in Multisensor Stochastic Systems with Missing Measurements and Correlated Noises

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
R. Caballero-Águila ◽  
I. García-Garrido ◽  
J. Linares-Pérez
Keyword(s):  
Stochastic Systems ◽  
Linear Filter ◽  
Missing Measurements ◽  
Process Noise ◽  
Estimation Algorithms ◽  
Optimal Linear ◽  
One Step ◽  
Optimal Fusion ◽  
Innovation Approach ◽  
Correlated Noises

The optimal least-squares linear estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems with missing measurements and autocorrelated and cross-correlated noises. The stochastic uncertainties in the measurements coming from each sensor (missing measurements) are described by scalar random variables with arbitrary discrete probability distribution over the interval[0,1]; hence, at each single sensor the information might be partially missed and the different sensors may have different missing probabilities. The noise correlation assumptions considered are (i) the process noise and all the sensor noises are one-step autocorrelated; (ii) different sensor noises are one-step cross-correlated; and (iii) the process noise and each sensor noise are two-step cross-correlated. Under these assumptions and by an innovation approach, recursive algorithms for the optimal linear filter are derived by using the two basic estimation fusion structures; more specifically, both centralized and distributed fusion estimation algorithms are proposed. The accuracy of these estimators is measured by their error covariance matrices, which allow us to compare their performance in a numerical simulation example that illustrates the feasibility of the proposed filtering algorithms and shows a comparison with other existing filters.

scholarly journals Rumor Processes on N

2011 ◽  
Vol 48 (3) ◽  
pp. 624-636 ◽  
Author(s):  
Valdivino V. Junior ◽  
Fábio P. Machado ◽  
Mauricio Zuluaga
Keyword(s):  
Discrete Time ◽  
Stochastic Systems ◽  
Random Variables ◽  
Modeling Processes ◽  
Speaking Up ◽  
Rumor Spreading ◽  
Infinite Set

We study four discrete-time stochastic systems on N, modeling processes of rumor spreading. The involved individuals can either have an active or a passive role, speaking up or asking for the rumor. The appetite for spreading or hearing the rumor is represented by a set of random variables whose distributions may depend on the individuals. Our goal is to understand - based on the distribution of the random variables - whether the probability of having an infinite set of individuals knowing the rumor is positive or not.

scholarly journals Rumor Processes on N

2011 ◽  
Vol 48 (03) ◽  
pp. 624-636 ◽  
Author(s):  
Valdivino V. Junior ◽  
Fábio P. Machado ◽  
Mauricio Zuluaga
Keyword(s):  
Discrete Time ◽  
Stochastic Systems ◽  
Random Variables ◽  
Modeling Processes ◽  
Speaking Up ◽  
Rumor Spreading ◽  
Infinite Set

We study four discrete-time stochastic systems on N, modeling processes of rumor spreading. The involved individuals can either have an active or a passive role, speaking up or asking for the rumor. The appetite for spreading or hearing the rumor is represented by a set of random variables whose distributions may depend on the individuals. Our goal is to understand - based on the distribution of the random variables - whether the probability of having an infinite set of individuals knowing the rumor is positive or not.

scholarly journals Robust H∞ control for a class of nonlinear discrete time-delay stochastic systems with missing measurements

Automatica ◽  
2009 ◽  
Vol 45 (3) ◽  
pp. 684-691 ◽  
Author(s):  
Zidong Wang ◽  
Daniel W.C. Ho ◽  
Yurong Liu ◽  
Xiaohui Liu
Keyword(s):  
Time Delay ◽  
Discrete Time ◽  
Stochastic Systems ◽  
Missing Measurements ◽  
Discrete Time Delay
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