scholarly journals Self-Tuning Distributed Fusion Filter for Multi-Sensor Systems Subject to Unknown Model Parameters and Missing Measurement Rates

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 61519-61528 ◽  
Author(s):  
Guang-Quan Duan ◽  
Shu-Li Sun
Keyword(s):  
Model Parameters ◽  
Sensor Systems ◽  
Self Tuning ◽  
Distributed Fusion

scholarly journals Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4436 ◽  
Author(s):  
Wang ◽  
Sun
Keyword(s):  
Optimal Estimation ◽  
Original System ◽  
Identification Problem ◽  
Sensor Data ◽  
Model Parameters ◽  
Packet Dropouts ◽  
Estimation Algorithms ◽  
Self Tuning ◽  
Time Systems ◽  
Distributed Fusion

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

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.

Real-Time Self-Tuning Speed Controller: Performance Comparison between Engine Fuelled with Palm Methyl Esters and Petroleum Diesel

2015 ◽  
Vol 815 ◽  
pp. 408-412
Author(s):  
M.N. Azuwir ◽  
Mohd Sazli Saad ◽  
Mohd Zakimi Zakaria
Keyword(s):  
Real Time ◽  
Least Squares Method ◽  
Methyl Esters ◽  
Performance Comparison ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Automotive Engine ◽  
Speed Controller ◽  
Self Tuning

This paper investigates the performance of a real-time self-tuning speed controller designed to track and regulate at various engine speeds. The controller was tested with an automotive engine fuelled with petroleum diesel and and palm oil biodiesel (Palm Methyl Esters) within speed range of 1800 rpm to 2400 rpm. A self-tuning control algorithm based on pole assignment method together with on-line model parameters estimation strategy based on the recursive least squares method are adopted. The ability of the controller to track, regulate at various engine speed and also to reject disturbances applied for both type of fuel are compared and presented. The results confirmed that the controller performed very satisfactorily.

scholarly journals Distributed fusion filtering for multi-sensor systems with correlated random transition and measurement matrices

2018 ◽  
Vol 97 (1-2) ◽  
pp. 263-274 ◽  
Author(s):  
Raquel Caballero-Águila ◽  
Irene García-Garrido ◽  
Josefa Linares-Pérez
Keyword(s):  
Sensor Systems ◽  
Random Transition ◽  
Distributed Fusion

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.

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