Self-tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi-channel autoregressive moving average signals

2014 ◽  
Vol 29 (6) ◽  
pp. 725-740 ◽  
Author(s):  
Guili Tao ◽  
Zili Deng
Keyword(s):  
Kalman Filter ◽  
Convergence Analysis ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
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.

A Bayesian Approach to Kalman Filter for Elliptically Contoured Distribution and its Application in Time Series Models

1994 ◽  
Vol 44 (1-2) ◽  
pp. 11-28 ◽  
Author(s):  
A. K. Basu ◽  
J. K. Das
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Moving Average ◽  
State Equation ◽  
Autoregressive Moving Average ◽  
Missing Observations ◽  
Estimation Of Parameters ◽  
Bayesian Formulation ◽  
Set Up ◽  
Elliptically Contoured

This paper develops a Bayesian formulation of Kalman filter under the errors having elliptically contoured distributions in both observation equation and system (or state) equation, using some recent results in multivariate analysis. Estimation of parameters in case of missing observations and prediction of missing observations as well are dealt with under the above set up of autoregressive-moving average process in time series. Two illustrative examples are presented with the help of AR(1) model and ARMA (1, 1) model.

Application of Kalman Filtering for Natural Gray Image Denoising

2011 ◽  
Vol 187 ◽  
pp. 92-96 ◽  
Author(s):  
Zhi Kai Huang ◽  
De Hui Liu ◽  
Xing Wang Zhang ◽  
Ling Ying Hou
Keyword(s):  
Kalman Filter ◽  
Image Denoising ◽  
Kalman Filtering ◽  
Moving Average ◽  
Arma Model ◽  
Adaptive Method ◽  
Natural Image ◽  
Autoregressive Moving Average ◽  
Processing Step ◽  
Reduction Methods

Image denoising is one of the classical problems in digital image processing, and has been studied for nearly half a century due to its important role as a pre-processing step in various image applications. In this work, a denoising algorithm based on Kalman filtering was used to improve natural image quality. We have studied noise reduction methods using a hybrid Kalman filter with an autoregressive moving average (ARMA) model that the coefficients of the AR models for the Kalman filter are calculated by solving for the minimum square error solutions of over-determined linear systems. Experimental results show that as an adaptive method, the algorithm reduces the noise while retaining the image details much better than conventional algorithms.

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 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.

Sign in / Sign up

Export Citation Format

Share Document