NORMAL PUGACHEV CONDITIONALLY-OPTIMAL FILTERS AND EXTRAPOLATORS FOR STATE LINEAR STOCHASTIC SYSTEMS

2016 ◽  
Keyword(s):  
Stochastic Systems ◽  
Optimal Filters ◽  
Linear Stochastic Systems

Filtering for Linear Stochastic Systems With Small Measurement Noise

1995 ◽  
Vol 117 (3) ◽  
pp. 425-429 ◽  
Author(s):  
Z. Aganovic ◽  
Z. Gajic ◽  
X. Shen
Keyword(s):  
Kalman Filter ◽  
Riccati Equation ◽  
Stochastic Systems ◽  
Measurement Noise ◽  
Algebraic Riccati Equation ◽  
Reduced Order ◽  
Complete Decomposition ◽  
Algebraic Riccati Equations ◽  
Optimal Filters ◽  
Linear Stochastic Systems

In this paper we present a method which produces complete decomposition of the optimal global Kalman filter for linear stochastic systems with small measurement noise into exact pure-slow and pure-fast reduced-order optimal filters both driven by the system measurements. The method is based on the exact decomposition of the global small measurement noise algebraic Riccati equation into exact pure-slow and pure-fast algebraic Riccati equations. An example is included in order to demonstrate the proposed method.

The stochastic observability for noisy non-linear stochastic systems†

1975 ◽  
Vol 22 (4) ◽  
pp. 461-480 ◽  
Author(s):  
YOSHIFUMI SUNAHARA ◽  
SHIN'ICHl AIHARA ◽  
MASAYUKI SHIRAIWA
Keyword(s):  
Stochastic Systems ◽  
Linear Stochastic Systems ◽  
Non Linear

scholarly journals Distributed Fusion Receding Horizon Filtering in Linear Stochastic Systems

2009 ◽  
Vol 2009 (1) ◽  
Author(s):  
IlYoung Song ◽  
DuYong Kim ◽  
YongHoon Kim ◽  
SukJae Lee ◽  
Vladimir Shin
Keyword(s):  
Stochastic Systems ◽  
Receding Horizon ◽  
Linear Stochastic Systems ◽  
Distributed Fusion
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