Asynchronous Data Fusion With Parallel Filtering Frame

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Na Li 1,* Junhui Liu 2

1. Department of Information Engineering Zhengzhou College of Animal Husbandry Engineering Zhengzhou, P.R.China 450011

2. Studies Affairs Office Zhengzhou College of Animal Husbandry Engineering Zhengzhou, P.R.China 450011

* Corresponding author.


Received: 7 Aug. 2010 / Revised: 5 Dec. 2010 / Accepted: 13 Feb. 2011 / Published: 8 Jun. 2011

Index Terms

Data fusion, asynchronous system, integer times sampling, parallel filtering, sequential filtering


This paper studies the design of data fusion algorithm for asynchronous system with integer times sampling. Firstly, the multisensor asynchronous samplings is mapped to the basic axis, accordingly a sampling sequence of single sensor can be taken. Secondly, aiming at the sensor with the densest sampling points, the modified parallel filtering is given. Afterwards, the sequential filtering fusion method is introduced to deal with the case that there are multiple mapped measurements at some sampling point. Finally, a novel parallel filtering fusion algorithm for asynchronous system with integer times sampling is proposed. Besides, a judgment scheme to distinguish measurement number at every sampling point in the fusion period is also designed. One simple computer numerical value simulation is demonstrated to validate the effectiveness of the judgment scheme and the proposed asynchronous fusion algorithm.

Cite This Paper

Na Li, Junhui Liu, "Asynchronous Data Fusion With Parallel Filtering Frame", International Journal of Information Technology and Computer Science(IJITCS), vol.3, no.3, pp.43-49, 2011. DOI:10.5815/ijitcs.2011.03.07


[1]Chenglin Wen, Donghua Zhou, Quan Pan, and Hongcai Zhang, “Distributed Information Fusion Algorithm for Single Sensor Synamic System On The Basis Of Multiscale Dynamic Models,”Acta Automatic Sinica of China, vol.27, no.2, pp.158~165, 2001.

[2]Jie Wang, Chongzhao Han, and Xiaorong Li, “Asynchronous Multisesnro Data Fusion,” Journal Control and Decision of China, vol.16, no.6, pp.877-881, 2001.

[3]Alouani A T, Rice T R, “On asynchronous data fusion,” Proc of the Annual Southeastern Symposium on System Theory. Athens, pp.143-146, 1994.

[4]Alouani A T, Rice T R, “Performance analysis of an asynchronous track fusion and architecture,” Proc of SPIE. Orlando, 1997, 194-205. 

[5]Hamid R. Hashemipour, Sumit Roy, Alan J. Laub, “ Decentralized structures for parallel kalman filtering,” IEEE Transactions on Automatic Control, vol.33, no.1, pp. 88-94, 1988.

[6]Chenglin Wen, “Multiscale Data Fusion for Multi Sensor Single Model Dynamic Systems,” Acta Electronica Sinica, vol.29, no.3, pp.341-345,2001.

[7]Liping Yan, Bing Wang, and Feng Lv, “A New Algorithm of Multiscale Fusion Estimation Based on Kalman Filtering,” Journal of Henan University of China (Natural Science), vol.32, no.2, pp.36-39, 2002.

[8]Baoshu Wang, FangsheLi, “The Research On Multiple Targets Tracking Based On The Data Fusion Technique,” Jounal of Xidian University of China, vol.25, no.3, pp.269-272, 1998.

[9]Chenglin Wen, Bing Lv, Quanbo Ge, “A Data Fusion Algorithm Based on Filtering Step by Step,” Acta Electronica Sinica of China, vol.32, no.8, pp. 1264-1267, 2004.

[10]Quanbo Ge, Guo’an Wang, Tianhao Tang, and Chenglin Wen, “The Research on Asynchronous Data Fusion Algorithm Based on Sampling of Rational Number Times,”, Acta Electronica Sinica of China, vol.34, no.3, pp. 543-548, 2006

[11]L.Y.Yan, B.S.Liu, and D.H.Zhou, “The modeling and estimation of asynchronous multirate multisensor dynamic systems,” Aerospace Science and Technology, no.10, pp.63-71, 2006.