Sensor Data Fusion

    Lecture for the 2nd Summer School 2008 on "ADVANCED TECHNOLOGIES FOR NEURO-MOTOR ASSESSMENT AND REHABILITATION". Promoted by: Dept. of Electronics Computer Science & Systems (DEIS) University of Bologna - Italy.
 

 

Course Programme

Lecture Abstract: The talk concerns a problem, which is basic to perception: the integration of perceptual information into a coherent description of the world. In this talk the perception is presented as a process of dynamically maintaining a model of the local external environment. Fusion of perceptual information is at the heart of this process. After a brief introduction, the background of the problem of fusion in machine vision is reviewed. Then data fusion is presented as part of the process of dynamic world modelling, and a set of principles is postulated for the ~fusion~ of independent observations. These principles lead to techniques, which permit perceptual fusion with qualitatively different forms of data, treating each source of information as constraints. For numerical information, these principles lead to specific well-known tools such as various forms of Kalman filter. For symbolic information, these principles suggest representing objects and their relations as a conjunction of properties encoded as schema. Dynamic world modelling is a cyclic process composed of the phases: predict, match and update. These phases provide a framework with which perceptual systems can be organised and designed. It is shown that in the case of numerical measurements, this framework leads to the use of a Kalman filter for the prediction and update phases, while other tools can be used for matching. In the case of symbolic information, elements of the framework can be constructed with schema and production rules. The framework for perceptual information is illustrated with the architectures of several systems.
 
 
  Lecture Outline
• 1. Introduction
• 1.1 Perception and Sensing
• 1.2 Background and State of the Art in Sensor Fusion
• 2. Sensor Fusion and Dynamic World Modelling
• 2.1 A General Framework for Dynamic World Modelling
• 2.2 Principles for Integrating Perceptual Information
• 3. Techniques for Fusion of Numerical Properties
• 3.1 State Representation
• 3.2 Prediction: Discrete State Transition Equations
• 3.3 Matching Observation to Prediction
• 3.4 The Kalman Filter Update Equations
• 4. Example Systems and Practical Applications
• 4.1 Dynamic World Modelling
• 4.2 An Integrated Supervision System
• 5. Conclusions

 
 
  Bibliographic References
• K. Brammer, G. Siffling, Kalman Bucy Filters, Artech House Inc., Norwood MA, USA, 1989.
• J. Jazwinski, Stochastic Processes and Filtering Theory. Academic Press, New York, 1970.
• R. Kalman, A new approach to Linear Filtering and Prediction Problems, Transactions of the ASME, Series D. J. Basic Eng., Vol 82, 1960.
• R. Kalman, R. Bucy, New Results in Linear Filtering and Prediction Theory, Transaction of the ASME, Series D. J. Basic Eng., Vol 83, 1961.
• A. Papoulis, Probability, random variables, and stochastic processes. New York: McGraw-Hill, 1991.
• D. L. Hall, Mathematical techniques in multisensor data fusion. Boston: Artech House, 1992.
• Brian D. O. Anderson, John B. Moore, Optimal Filtering. Information and System Science Series. Thomas Kailath Editor, 2005.

 
 
 

Download

• "Lecture Slides" (in English): (PDF file, KB). (PDF file, KB, 2 slides per page). (PDF file, KB, 4 slides per page).
• Movies used for the Presentation: (WMF and MPG files in zipped format, 24MB).
• Matlab and Simulink files and software used for the presentation: (Matlab and Simulink files in zipped format, 185KB).
• James L Crowley and Yves Demazeau, "Principles and Techniques for Sensor Data Fusion". LIFIA (IMAG) 46 avenue Felix Viallet. F-38031 Grenoble Cédex, FRANCE. (PDF file, 90KB).
• RON ROTH, "Trends in Sensor and Data Fusion". 'Photogrammetric Week 05', Dieter Fritsch, Ed. Wichmann Verlag, Heidelberg 2005. (PDF file, 3.74MB).
• G. GIRIJA, J. R. RAOL, R. APPAVU RAJ and SUDESH KASHYAP, "Tracking filter and multi-sensor data fusion". Sadhana, Vol. 25, Part 2, April 2000, pp. 159-167. Printed in India, 2000. (PDF file, 117KB).
• SHRABANI BHATTACHARYA and R APPAVU RAJ, "Performance evaluation of multi-sensor data fusion technique for test range application", Sadhana Vol. 29, Part 2, April 2004, pp. 237-247. Printed in India, 2004. (PDF file, 257KB).
• Introduction paper: "Kalman Filtering: Whence, What and Whither?" by B. D. O. Anderson and J. B. Moore. (PDF file).
• The book by BRIAN D. O. ANDERSON and JOHN B. MOORE, "Optimal Filtering". INFORMATION AND SYSTEM SCIENCES SERIES. Thomas Kailath Editor. (PDF file).
• R. E. KALMAN, Research Institute for Advanced Study, Baltimore, Md. "A New Approach to Linear Filtering and Prediction Problems" (1960). Transactions of the ASME Journal of Basic Engineering, 82 (Series D): 35-45. (PDF file).
• "An Introduction to the Kalman Filter", by Greg Welch and Gary Bishop. Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 27599-3175. April 5, 2004 (PDF file).
• Pattern Recognition and Machine Vision: Kalman Filter. By Dr. Simon J.D. Prince Computer Science University College London. Gower Street, London, WC1E 6BT: (PDF file).
• "Least-Squares Estimation: From Gauss to Kalman". By H.W. Sorenson. University of California. San Diego. IEEE Spectrum, vol. 7, pp. 63-68. July, 1970. (PDF file).
• Kalman filter Main Home Page: . Some tutorials, references, and research on the Kalman filter. The site is maintained by Greg Welch and Gary Bishop, faculty members of the Department of Computer Science at the University of North Carolina at Chapel Hill.

 
 
 
 

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