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.

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.

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.

Simulation and Research for Generalized Predictive Control

2013 ◽  
Vol 694-697 ◽  
pp. 2205-2210
Author(s):  
Xiao Li Yu
Keyword(s):  
Predictive Control ◽  
Least Square ◽  
The Self ◽  
Dynamic Responses ◽  
Generalized Predictive Control ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Input Signals ◽  
Self Tuning ◽  
Time Invariant

This paper presents analysis and experiments for Generalized Predictive Control (GPC) algorithm based on software simulation. First, we illustrate the time invariant GPC algorithm in detail. Then, we describe the principle for the control parameter selection of GPC based on empirical results. The Recursive Least Square (RLS) algorithm will be used to identify model parameters in the self-tuning GPC. The performance of GPC algorithm is validated by simulation results, which show that the algorithm has rapid and accurate dynamic responses for input signals, such as step signal and square wave. When the model parameters are unknown, with the assistance of RLS, the self-tuning GPC algorithm also presents good performance and robustness capability, even when white Gaussian noise exists.

Speed vs. comfort: Testing the efficacy of a single question for predicting transit users’ route choices

2019 ◽  
Author(s):  
Joseph John Pyne Simons ◽  
Ilya Farber
Keyword(s):  
Data Collection ◽  
Efficient Method ◽  
Choice Task ◽  
The Self ◽  
Self Report ◽  
Model Parameters ◽  
Single Item ◽  
Alternative Approach ◽  
Single Question ◽  
Insight Into

Not all transit users have the same preferences when making route decisions. Understanding the factors driving this heterogeneity enables better tailoring of policies, interventions, and messaging. However, existing methods for assessing these factors require extensive data collection. Here we present an alternative approach - an easily-administered single item measure of overall preference for speed versus comfort. Scores on the self-report item predict decisions in a choice task and account for a proportion of the differences in model parameters between people (n=298). This single item can easily be included on existing travel surveys, and provides an efficient method to both anticipate the choices of users and gain more general insight into their preferences.

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