Title :
A Method of Data Fusion Based on the Robust Minimum Variance Filtering
Author :
Chang, Hong ; Feng, Zuren
Author_Institution :
Xi´´an Jiaotong Univ, Xi´´an
fDate :
May 30 2007-June 1 2007
Abstract :
In the data fusion, Kalman filter is widely used to process the synchronous and asynchronous sensor data. However, the filter will not present a good performance when the model´s parameters and the noise´s characteristic are not assured. This paper gives a method of data fusion based on the robust filtering. The method can guarantee the stable filtering as long as the system´s parameters are in a certain range. This method can process both the synchronous data and the asynchronous data. The result of this experiment verified that this method can deal with the data fusion in the situation that the system parameters are not exact.
Keywords :
Kalman filters; sensor fusion; Kalman filter; asynchronous sensor data; data fusion; robust minimum variance filtering; Automatic control; Data engineering; Filtering; Laboratories; Manufacturing systems; Noise robustness; Sampling methods; Sensor fusion; State estimation; Systems engineering and theory; asynchronous; robust filtering; sensor fusion; synchronous;
Conference_Titel :
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4244-0817-7
Electronic_ISBN :
978-1-4244-0818-4
DOI :
10.1109/ICCA.2007.4376663
