Self-Tuning Weighted Measurement Fusion Kalman Signal Filter

2013 ◽  
Vol 274 ◽  
pp. 579-582
Author(s):  
Wen Qiang Liu ◽  
Gui Li Tao ◽  
Ze Yuan Gu ◽  
Song Li
Keyword(s):  
Signal Processing ◽  
Single Channel ◽  
Moving Average ◽  
Measurement Noise ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Dead Band ◽  
Identification Method ◽  
Self Tuning ◽  
Signal Filter

For the single channel autoregressive moving average (ARMA) signals with multisensor and a colored measurement noise, when the model parameters and noise variances are partially unknown, based on identification method and Gevers-Wouters algorithm with a dead band, a self-tuning weighted measurement fusion Kalman signal filter is presented. A simulation example applied to signal processing shows its effectiveness.

Self-Tuning Fusion Kalman Filter for ARMA Signals

2012 ◽  
Vol 229-231 ◽  
pp. 1768-1771
Author(s):  
Wen Qiang Liu ◽  
Na Han ◽  
Man Yan ◽  
Gui Li Tao
Keyword(s):  
Kalman Filter ◽  
Instrumental Variable ◽  
Single Channel ◽  
Moving Average ◽  
Correlation Method ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Self Tuning ◽  
Optimal Fusion

For the single-channel autoregressive moving average (ARMA) signals with multisensor, and with unknown model parameters and noise variances, the local estimators of unknown model parameters and noise variances are obtained by the recursive instrumental variable (RIV) algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. Substituting them into the optimal fusion Kalman filter, a self-tuning fusion Kalman filter for single-channel ARMA signals is presented. A simulation example shows its effectiveness.

Self-Tuning Weighted Fusion Kalman Filter for ARMA Signals

2014 ◽  
Vol 538 ◽  
pp. 439-442
Author(s):  
Wen Qiang Liu ◽  
Gui Li Tao ◽  
Na Han
Keyword(s):  
Kalman Filter ◽  
Single Channel ◽  
Moving Average ◽  
Correlation Method ◽  
Filter Method ◽  
Model Parameters ◽  
Weighted Fusion ◽  
Self Tuning ◽  
Kalman Filter Method ◽  
Noise Statistics

For the multisensor single channel autoregressive moving average (ARMA) signal with a white measurement noise and autoregressive (AR) colored measurement noises as common disturbance noises, when model parameters and noise statistics are partially unknown, a self-tuning weighted fusion Kalman filter is presented based on classical Kalman filter method. The local estimates are obtained by applying the recursive instrumental variable (RIV) and correlation method. Then the optimal weighted fusion Kalman filter is obtained by substituting all the fusion estimates into the corresponding optimal Kalman filter. A simulation example shows its effectiveness.

The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals

2011 ◽  
Vol 48-49 ◽  
pp. 1305-1309
Author(s):  
Gui Li Tao ◽  
Zi Li Deng
Keyword(s):  
Kalman Filter ◽  
System Analysis ◽  
Moving Average ◽  
Dynamic Error ◽  
Asymptotic Optimality ◽  
Correlation Method ◽  
The Self ◽  
Model Parameters ◽  
Self Tuning ◽  
Signal Filter

For the multisensor Autoregressive Moving Average (ARMA) signals with unknown model parameters and noise variances, using the Recursive Instrumental Variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the fused estimators of unknown model parameters and noise variances can be obtained. Then substituting them into optimal fusion signal filter weighted by scalars, a self-tuning distributed fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter, so that it has asymptotic optimality. A simulation example shows its effectiveness.

Self-Tuning Information Fusion Wiener Smoother for ARMA Signals

2011 ◽  
Vol 48-49 ◽  
pp. 1018-1023
Author(s):  
Jin Fang Liu ◽  
Zi Li Deng
Keyword(s):  
Information Fusion ◽  
System Analysis ◽  
Moving Average ◽  
Dynamic Error ◽  
Asymptotic Optimality ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Self Tuning ◽  
Optimal Fusion

For the multisensor autoregressive moving average (ARMA) signals, based on the modern time series analysis method, a self-tuning information fusion Wiener smoother is presented when both model parameters and noise variances are unknown. The principle is that substituting the estimators of unknown parameters and noise variances into the corresponding optimal fusion Wiener smoother will yield a self-tuning fuser. Further, applying the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Wiener smoother converges to the optimal fused Wiener smoother in a realization, i.e. it has asymptotic optimality. A simulation example shows its effectiveness.

scholarly journals A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Za'er Abo-Hammour ◽  
Othman Alsmadi ◽  
Shaher Momani ◽  
Omar Abu Arqub
Keyword(s):  
Genetic Algorithm ◽  
Dynamical Systems ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Input Output ◽  
Data Sequence ◽  
Output Data ◽  
Linear Dynamical Systems ◽  
Genetic Algorithm Approach

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.

Recursive Identification of the Dynamic Behavior in a Distillation Column by Means of Autoregressive Models

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Lakhdar Aggoune ◽  
Yahya Chetouani ◽  
Hammoud Radjeai
Keyword(s):  
Performance Measures ◽  
Distillation Column ◽  
Moving Average ◽  
Information Criterion ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Recursive Algorithms ◽  
Optimal Model ◽  
Armax Model

In this study, an Autoregressive with eXogenous input (ARX) model and an Autoregressive Moving Average with eXogenous input (ARMAX) model are developed to predict the overhead temperature of a distillation column. The model parameters are estimated using the recursive algorithms. In order to select an optimal model for the process, different performance measures, such as Aikeke's Information Criterion (AIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE), are calculated.

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