Linear minimum‐mean‐square error estimation of Markovian jump linear systems with randomly delayed measurements

2014 ◽  
Vol 8 (6) ◽  
pp. 658-667 ◽  
Author(s):  
Yanbo Yang ◽  
Yan Liang ◽  
Feng Yang ◽  
Yuemei Qin ◽  
Quan Pan
Keyword(s):  
Linear Systems ◽  
Mean Square Error ◽  
Error Estimation ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Markovian Jump ◽  
Jump Linear Systems ◽  
Markovian Jump Linear Systems ◽  
Mean Square Error Estimation ◽  
Delayed Measurements

scholarly journals FAST ARRAY ALGORITHMS FOR FILTERING OF MARKOVIAN JUMP LINEAR SYSTEMS WITH STRUCTURED TIME-VARIANT PARAMETERS

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Gildson Queiroz De Jesus ◽  
Guilherme Peixoto Andrade
Keyword(s):  
Linear Systems ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Error Estimator ◽  
Mean Square ◽  
Markovian Jump ◽  
Jump Linear Systems ◽  
Numerical Example ◽  
Markovian Jump Linear Systems ◽  
Type Algorithm

In this paper were developed fast array algorithms for the linear minimum mean square error estimator for a class of Markovian jump linear systems with structured time-variant parameters. The fast array algorithms for systems with structured time-variant parameters arises as an alternative to calculate this type algorithm for some variation in the time of the parameters. Numerical example to show the advantage of using fast array algorithm to filter this class of systems are provided.

scholarly journals OPTIMAL FILTERING IN DISCRETE-TIME SYSTEMS WITH TIME DELAYS AND MARKOVIAN JUMP PARAMETERS

2009 ◽  
Vol 51 (2) ◽  
pp. 218-233 ◽  
Author(s):  
CHUNYAN HAN ◽  
HUANSHUI ZHANG
Keyword(s):  
Discrete Time ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Markovian Jump ◽  
Free System ◽  
Markovian Jump Parameters ◽  
Markovian Jump Linear Systems ◽  
Measurement Delay ◽  
Innovation Analysis ◽  
Delayed Measurements

AbstractThis paper investigates the linear minimum mean-square error estimation for discrete-time Markovian jump linear systems with delayed measurements. The key technique applied for treating the measurement delay is reorganization innovation analysis, by which the state estimation with delayed measurements is transformed into a standard linear mean-square filter of an associated delay-free system. The optimal filter is derived based on the innovation analysis method together with geometric arguments in an appropriate Hilbert space. The solution is given in terms of two Riccati difference equations. Finally, a simulation example is presented to illustrate the efficiency of the proposed method.

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