Impact of False Data Detection on Cloud Hosted Linear State Estimator Performance

2018 ◽  
Author(s):  
Vinaya Chakati ◽  
Madhurima Pore ◽  
Ayan Banerjee ◽  
Anamitra Pal ◽  
Sandeep K.S. Gupta
Keyword(s):  
Data Detection ◽  
State Estimator ◽  
Linear State ◽  
False Data Detection

Model-Based Discrete Linear State Estimator for Nonlinearizable Systems With State-Dependent Noise

1990 ◽  
Vol 112 (4) ◽  
pp. 774-781 ◽  
Author(s):  
R. J. Chang
Keyword(s):  
Discrete Time ◽  
Continuous Model ◽  
Equivalent Model ◽  
State Estimator ◽  
Time Model ◽  
State Dependent ◽  
Present Technique ◽  
Linear State ◽  
Noisy Measurement ◽  
Time Linear

A practical technique to derive a discrete-time linear state estimator for estimating the states of a nonlinearizable stochastic system involving both state-dependent and external noises through a linear noisy measurement system is presented. The present technique for synthesizing a discrete-time linear state estimator is first to construct an equivalent reference linear model for the nonlinearizable system such that the equivalent model will provide the same stationary covariance response as that of the nonlinear system. From the linear continuous model, a discrete-time state estimator can be directly derived from the corresponding discrete-time model. The synthesizing technique and filtering performance are illustrated and simulated by selecting linear, linearizable, and nonlinearizable systems with state-dependent noise.

scholarly journals Optimal guaranteed cost filtering for Markovian jump discrete-time systems

2004 ◽  
Vol 2004 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Magdi S. Mahmoud ◽  
Peng Shi
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Estimation Error ◽  
Linear Matrix ◽  
State Estimator ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Discrete Time Systems ◽  
Linear State ◽  
Time Systems

This paper develops a result on the design of robust steady-state estimator for a class of uncertain discrete-time systems with Markovian jump parameters. This result extends the steady-state Kalman filter to the case of norm-bounded time-varying uncertainties in the state and measurement equations as well as jumping parameters. We derive a linear state estimator such that the estimation-error covariance is guaranteed to lie within a certain bound for all admissible uncertainties. The solution is given in terms of a family of linear matrix inequalities (LMIs). A numerical example is included to illustrate the theory.

A Robust False Data Detection in Smart Grid Using Trees Decision Algorithm

2018 ◽  
Author(s):  
Abdelkarim El Khantach ◽  
Mohamed Hamlich ◽  
Nour Eddine Belbounaguia
Keyword(s):  
Smart Grid ◽  
Data Detection ◽  
Decision Algorithm ◽  
False Data Detection

Transition to a Two-Level Linear State Estimator—Part II: Algorithm

2011 ◽  
Vol 26 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Tao Yang ◽  
Hongbin Sun ◽  
Anjan Bose
Keyword(s):  
State Estimator ◽  
Linear State
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