Implementation of Multisensor Data Fusion Algorithm

Myat Su Nwe; Hla Myo Tun

doi:doi:10.11648/j.ijssn.20170504.11

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Implementation of Multisensor Data Fusion Algorithm

Myat Su Nwe, Hla Myo Tun

Published in International Journal of Sensors and Sensor Networks (Volume 5, Issue 4)

Received: 20 October 2017 Accepted: 1 November 2017 Published: 15 December 2017

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Abstract

Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization. This work has three parts: methods, architectures and applications. Most current data fusion methods employ probabilistic descriptions of observations and processes and use Bayes’ rule to combine this information. Data fusion systems are often complex combinations of sensor devices, processing and fusion algorithms. This work provides an overview of key principles in data fusion architectures from both a hardware and algorithmic viewpoint. The applications of data fusion are pervasive in UAV and underlay the core problem of sensing, estimation and perception. The highlighted is many applications that bring out these features. The first describes a navigation or self-tracking application for an autonomous vehicle. The second describes an application in mapping and environment modeling. The essential algorithmic tools of data fusion are reasonably well established. However, the development and use of these tools in realistic robotics applications is still developing.

Published in	International Journal of Sensors and Sensor Networks (Volume 5, Issue 4)
DOI	10.11648/j.ijssn.20170504.11
Page(s)	48-53
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Multisensor Data Fusion, Sensor Management, MATLAB, GUI, UAV

References

[1]	RAO B. S., and DURRANT-WHYTE H. F., (September 1991). “Fully decentralized algorithm for multisensor Kalman filtering,” IEE PROCEEDINGS-D, Vol. 138, No. 5, pp. 413-420. http://portal.acm.org/citation.cfm?id=942356&dl=ACM&coll=portal.
[2]	PAO L. Y., and BALTZ N. T. (June 1999). “Control of Sensor Information in Distributed Multisensor Systems,” Proc. American Control Conference, San Diego, CA, pp. 2397-2401.
[3]	DURRANT-WHYTE H. F., and LEONARD J. J., (13-18 May 1990). “Toward a fully decentralized architecture for multi-sensor data fusion,” Robotics and Automation, 1990. Proceedings., IEEE International Conference, Vol. 2, pp. 1331 – 1336.
[4]	DURRANT-WHYTE H. F., RAO B. Y. S., and HU H., (4 Feb 1991). “Toward a fully decentralized architecture for multi-sensor data fusion,” Principles and Applications of Data Fusion, IEE Colloquium, pp. 2/1 - 2/4.
[5]	Jiang Dong, Dafang Zhuang, Yaohuan Huang and Jingying Fu, "Advances in Multi-Sensor Data Fusion: Algorithms and Applications", Sensors 2009, 9, 7771-7784; doi: 10.3390/s91007771.
[6]	Mohammad Amin Ahmad Akhoundi, Ehsan Valavi, "Multi-Sensor Fuzzy Data Fusion Using Sensors with Different Characteristics", The CSI Journal on Computer Science and Engineering (JCSE), submitted.
[7]	Hugh Durrant-Whyte, "Multi Sensor Data Fusion", Australian Centre for Field Robotics The University of Sydney NSW 2006, Australia, January 22, 2001.
[8]	S. A. Quadri, Othman Sidek, "Optimization and Comparison of Two Data Fusion Algorithms for an Inertial Measurement Unit", International Journal of Computer Science Engineering (IJCSE), Vol. 2 No. 04 July 2013.
[9]	Y. A. Vershinin,"A Data Fusion Algorithm for Multisensor Systems", ISIF, 2002.
[10]	Jitendra R. Raol, "Multi-Sensor Data Fusion with MATLAB", CRC Press Taylor & Francis Group, 2010.
[11]	Joseph S. J. Peri, "Approaches to Multisensor Data Fusion", JOHNS HOPKINS APL TECHNICAL DIGEST, VOLUME 22, NUMBER 4 (2001).
[12]	Sandra Rodríguez-Valenzuela, Juan A. Holgado-Terriza, José M. Gutiérrez-Guerrero and Jesús L. Muros-Cobos, "Distributed Service-Based Approach for Sensor Data Fusion in IoT Environments", Sensors 2014, 14, 19200-19228; doi: 10.3390/s141019200.
[13]	KS Nagla, Moin Uddin, and Dilbag Singh, "Multisensor Data Fusion and Integration for Mobile Robots: A Review", International Journal of Robotics and Automation (IJRA) Vol. 3, No. 2, June 2014, pp. 131~138.
[14]	Sajjad Safari, Faridoon Shabani, Dan Simon, "Multirate multisensor data fusion for linear systems using Kalman filters and a neural network", Aerospace Science and Technology 39 (2014) 465–471.

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APA Style

Myat Su Nwe, Hla Myo Tun. (2017). Implementation of Multisensor Data Fusion Algorithm. International Journal of Sensors and Sensor Networks, 5(4), 48-53. https://doi.org/10.11648/j.ijssn.20170504.11

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ACS Style

Myat Su Nwe; Hla Myo Tun. Implementation of Multisensor Data Fusion Algorithm. Int. J. Sens. Sens. Netw. 2017, 5(4), 48-53. doi: 10.11648/j.ijssn.20170504.11

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AMA Style

Myat Su Nwe, Hla Myo Tun. Implementation of Multisensor Data Fusion Algorithm. Int J Sens Sens Netw. 2017;5(4):48-53. doi: 10.11648/j.ijssn.20170504.11

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@article{10.11648/j.ijssn.20170504.11,
  author = {Myat Su Nwe and Hla Myo Tun},
  title = {Implementation of Multisensor Data Fusion Algorithm},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {5},
  number = {4},
  pages = {48-53},
  doi = {10.11648/j.ijssn.20170504.11},
  url = {https://doi.org/10.11648/j.ijssn.20170504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20170504.11},
  abstract = {Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization. This work has three parts: methods, architectures and applications. Most current data fusion methods employ probabilistic descriptions of observations and processes and use Bayes’ rule to combine this information. Data fusion systems are often complex combinations of sensor devices, processing and fusion algorithms. This work provides an overview of key principles in data fusion architectures from both a hardware and algorithmic viewpoint. The applications of data fusion are pervasive in UAV and underlay the core problem of sensing, estimation and perception. The highlighted is many applications that bring out these features. The first describes a navigation or self-tracking application for an autonomous vehicle. The second describes an application in mapping and environment modeling. The essential algorithmic tools of data fusion are reasonably well established. However, the development and use of these tools in realistic robotics applications is still developing.},
 year = {2017}
}

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TY - JOUR
T1 - Implementation of Multisensor Data Fusion Algorithm
AU - Myat Su Nwe
AU - Hla Myo Tun
Y1 - 2017/12/15
PY - 2017
N1 - https://doi.org/10.11648/j.ijssn.20170504.11
DO - 10.11648/j.ijssn.20170504.11
T2 - International Journal of Sensors and Sensor Networks
JF - International Journal of Sensors and Sensor Networks
JO - International Journal of Sensors and Sensor Networks
SP - 48
EP - 53
PB - Science Publishing Group
SN - 2329-1788
UR - https://doi.org/10.11648/j.ijssn.20170504.11
AB - Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization. This work has three parts: methods, architectures and applications. Most current data fusion methods employ probabilistic descriptions of observations and processes and use Bayes’ rule to combine this information. Data fusion systems are often complex combinations of sensor devices, processing and fusion algorithms. This work provides an overview of key principles in data fusion architectures from both a hardware and algorithmic viewpoint. The applications of data fusion are pervasive in UAV and underlay the core problem of sensing, estimation and perception. The highlighted is many applications that bring out these features. The first describes a navigation or self-tracking application for an autonomous vehicle. The second describes an application in mapping and environment modeling. The essential algorithmic tools of data fusion are reasonably well established. However, the development and use of these tools in realistic robotics applications is still developing.
VL - 5
IS - 4
ER -

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Author Information

Myat Su Nwe

Department of Mechanical Engineering, Yangon Technological University, Yangon, Republic of the Union of Myanmar
Hla Myo Tun

Department of Electronic Engineering, Yangon Technological University, Yangon, Republic of the Union of Myanmar

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APA Style

Myat Su Nwe, Hla Myo Tun. (2017). Implementation of Multisensor Data Fusion Algorithm. International Journal of Sensors and Sensor Networks, 5(4), 48-53. https://doi.org/10.11648/j.ijssn.20170504.11

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ACS Style

Myat Su Nwe; Hla Myo Tun. Implementation of Multisensor Data Fusion Algorithm. Int. J. Sens. Sens. Netw. 2017, 5(4), 48-53. doi: 10.11648/j.ijssn.20170504.11

Copy | Download

AMA Style

Myat Su Nwe, Hla Myo Tun. Implementation of Multisensor Data Fusion Algorithm. Int J Sens Sens Netw. 2017;5(4):48-53. doi: 10.11648/j.ijssn.20170504.11

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@article{10.11648/j.ijssn.20170504.11,
  author = {Myat Su Nwe and Hla Myo Tun},
  title = {Implementation of Multisensor Data Fusion Algorithm},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {5},
  number = {4},
  pages = {48-53},
  doi = {10.11648/j.ijssn.20170504.11},
  url = {https://doi.org/10.11648/j.ijssn.20170504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20170504.11},
  abstract = {Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization. This work has three parts: methods, architectures and applications. Most current data fusion methods employ probabilistic descriptions of observations and processes and use Bayes’ rule to combine this information. Data fusion systems are often complex combinations of sensor devices, processing and fusion algorithms. This work provides an overview of key principles in data fusion architectures from both a hardware and algorithmic viewpoint. The applications of data fusion are pervasive in UAV and underlay the core problem of sensing, estimation and perception. The highlighted is many applications that bring out these features. The first describes a navigation or self-tracking application for an autonomous vehicle. The second describes an application in mapping and environment modeling. The essential algorithmic tools of data fusion are reasonably well established. However, the development and use of these tools in realistic robotics applications is still developing.},
 year = {2017}
}

Copy | Download