Multisensor data fusion for high quality data analysis and processing in measurement and instrumentation

Multisensor data fusion (MDF) is an emerging technology to fuse data from multiple sensors in order to make a more accurate estimation of the environment through measurement and detection. Applications of MDF cross a wide spectrum in military and civilian areas. With the rapid evolution of computers and the proliferation of micro-mechanical/electrical systems sensors, the utilization of MDF is being popularized in research and applications. This paper focuses on application of MDF for high quality data analysis and processing in measurement and instrumentation. A practical, general data fusion scheme was established on the basis of feature extraction and merge of data from multiple sensors. This scheme integrates artificial neural networks for high performance pattern recognition. A number of successful applications in areas of NDI (Non-Destructive Inspection) corrosion detection, food quality and safety characterization, and precision agriculture are described and discussed in order to motivate new applications in these or other areas. This paper gives an overall picture of using the MDF method to increase the accuracy of data analysis and processing in measurement and instrumentation in different areas of applications.

[1]  M. J. Palakal,et al.  Intelligent Computational Methods for Corrosion Damage Assessment , 2001 .

[2]  James Llinas,et al.  Survey of multisensor data fusion systems , 1991, Defense, Security, and Sensing.

[3]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[4]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[5]  N. Noguchi,et al.  Vehicle Automation System Based on Multi-Sensor Integration , 1998 .

[6]  James Llinas,et al.  A survey of multi-sensor data fusion systems , 1991 .

[7]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[8]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[9]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[10]  Frédéric Lebeau,et al.  Measurements of the horizontal sprayer boom movements by sensor data fusion , 2002 .

[11]  Ivan Kalaykov,et al.  The artificial sensor head : A new approach in assessment of human based quality , 1999 .

[12]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[13]  Qin Zhang,et al.  Wireless Data Fusion System for Agricultural Vehicle Positioning , 2005 .

[14]  X. E. Gross NDT Data Fusion , 1997 .

[15]  Yubin Lan,et al.  Artificial senses for characterization of food quality , 2004 .

[16]  Mubarak Shah,et al.  Multi-sensor fusion: a perspective , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[17]  L. S. Guo,et al.  A Low-Cost Integrated Positioning System of GPS and Inertial Sensors for Autonomous Agricultural Vehicles , 2003 .

[18]  Yubin Lan,et al.  Development of Artificial Head for Characterization of Food SAFETY , 2005 .

[19]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[20]  P. Wide,et al.  The perceiving sensory estimated in an artificial human estimation based sensor system , 1997, IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings.