Fusion estimation for multi-sensor stochastic systems with unknown inputs and one-step random delays

2015 ◽  
Author(s):  
Chongyan Pang ◽  
Shuli Sun
Keyword(s):  
Stochastic Systems ◽  
Unknown Inputs ◽  
Random Delays ◽  
One Step

Steady-State Optimal State and Input Observer for Discrete Stochastic Systems

1989 ◽  
Vol 111 (2) ◽  
pp. 121-127 ◽  
Author(s):  
Y. Park ◽  
J. L. Stein
Keyword(s):  
Steady State ◽  
Numerical Simulations ◽  
Stochastic Systems ◽  
Linear Time ◽  
The State ◽  
Optimal State ◽  
Unknown Inputs ◽  
Linear Time Invariant ◽  
Error Covariance ◽  
Time Invariant

Model-based machine diagnostics techniques require the modeled states and machine inputs to be measured. Because measurement of all the states and inputs is not always possible or practical, a simultaneous state and input observer is required. Previous work has developed this type of acausal observer and shown it is susceptible to noise. This paper develops a steady-state optimal observer that minimizes the trace of the steady-state error covariance of the state and input estimates for discrete, linear, time-invariant, stochastic systems with unknown inputs. In addition, a method to distinguish the best measurement set among the available measurement sets is developed. Results from numerical simulations show that the optimal observer can greatly improve estimation results in some cases.

Fault Isolation Filter with Unknown Inputs in Stochastic Systems

2003 ◽  
Vol 36 (5) ◽  
pp. 501-506 ◽  
Author(s):  
H. Jamouli ◽  
J.Y. Keller ◽  
D. Sauter
Keyword(s):  
Stochastic Systems ◽  
Fault Isolation ◽  
Unknown Inputs

scholarly journals T-proper Hypercomplex Centralized Fusion Estimation for Randomly Multiple Sensor Delays Systems with Correlated Noises

2021 ◽  
Author(s):  
Rosa M. Fernández-Alcalá ◽  
Jesús Navarro-Moreno ◽  
Juan C. Ruiz-Molina
Keyword(s):  
Fixed Point ◽  
Stochastic Systems ◽  
Computational Cost ◽  
Linear Estimators ◽  
Widely Linear Processing ◽  
Multiple Sensor ◽  
One Step ◽  
Optimal Estimators ◽  
Widely Linear ◽  
Correlated Noises

The centralized fusion estimation problem for discrete-time vectorial tessarine signals in multiple sensor stochastic systems with random one-step delays and correlated noises is analyzed under different T-properness conditions. Based on Tk, k=1,2, linear processing, new centralized fusion filtering, prediction, and fixed-point smoothing algorithms are devised. These algorithms have the advantage of providing optimal estimators with a significant reduction in computational cost compared to that obtained through a real or widely linear processing approach. Simulation examples illustrate the effectiveness and applicability of the algorithms proposed, in which the superiority of the Tk linear estimators over their counterparts in the quaternion domain is apparent.

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