Self-tuning weighted measurement fusion Wiener filter for autoregressive moving average signals with coloured noise and its convergence analysis

2012 ◽  
Vol 6 (12) ◽  
pp. 1899-1908 ◽  
Author(s):  
J. Liu ◽  
Z. Deng
Keyword(s):  
Convergence Analysis ◽  
Moving Average ◽  
Wiener Filter ◽  
Autoregressive Moving Average ◽  
Coloured Noise ◽  
Self Tuning

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 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 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.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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