Introduction
In Chapt. 1 we introduced a formal framework in which we represented a multi-sensor data fusion system as a distributed system of autonomous modules. In this chapter we shall consider the architecture of a multi-sensor fusion system and, in particular, the architecture of the “data fusion block” (Sect. 1.4).
The modules in the data fusion block are commonly called fusion nodes. A communication system allows the transfer of information from one node to another node via an exchange of messages. An algorithmic description of the fusion block is provided by the software which is embedded in the nodes and which determines the behaviour of the block and coordinates its activities.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bar-Shalom, Y., Campo, L.: The effect of the common process noise on the two sensor fused track covariance. IEEE Trans. Aero Elec. Syst. 22, 803–805 (1986)
Bauer, G.: Transparent fault tolerance in a time-triggered architecture. PhD thesis. Institut fur Technische Informatik, Technischen Universitat Wien (2001)
Boutell, M., Luo, J.: Beyond pixels: exploiting camera metadata for photo classification. In: Proc. Comp. Vis. Patt. Recogn. CVPR 2004 (2004)
Brown, R.G., Hwang, P.Y.C.: Introduction to random signal analysis and Kalman filtering. John Wiley and Sons (1997)
Chen, L., Arambel, P.O., Mehra, R.K.: Estimation under unknown correlation: covariance intersection revisited. IEEE Trans. Automatic Control 47, 1879–1882 (2002)
Delvai, Eisenmann, U., Elmenreich, W.: A generic architecture for integrated smart transducers. In: Proc. 13th Int. Conf. Field Programmable Logic and Applications, Lisbon, Portugal (2003)
Elmenreich, W.: An introduction to sensor fusion. Institut fur Technische Informatik, Technischen Universitat Wien, Research Report 47/2001 (2001)
Elmenreich, W.: Sensor fusion in time-triggered systems PhD thesis, Institut fur Technische Informatik, Technischen Universitat Wien (2002)
Elmenreich, W., Pitzek, S.: The time-triggered sensor fusion model. In: Proceedings of the 5th IEEE Int Conf. Intell. Engng. Sys., Helsinki, Finland, pp. 297–300 (2001)
Franken, D., Hupper, A.: Improved fast covariance intersection for distributed data fusion. In: Proc. Inform. Fusion (2005)
Houzelle, S., Giraudon, G.: Contribution to multisensor fusion formalization. Robotics Autonomous Sys. 13, 69–75 (1994)
Julier, S., Uhlmann, J.: General decentralized data fusion with covariance intersection (CI). In: Hall, D., Llians, J. (eds.) Handbook of Multidimensional Data Fusion, ch. 12. CRC Press (2001)
Kopetz, H., Bauer, G.: The time-triggered architecture. Proc. IEEE 91, 112–126 (2003)
McLaughlin, S., Krishnamurthy, V., Challa, S.: Managing data incest in a distributed sensor network. In: Proc. IEEE Int. Conf. Accoust, Speech Signal Proc., vol. 5, pp. 269–272 (2003)
Movellan, J.R., Mineiro, P.: Robust sensor fusion: analysis and applications to audio-visual speech recognition. Mach. Learn. 32, 85–100 (1998)
Nandakumar, K.: Integration of multiple cues in biometric systems. MSc thesis, Michagan State University (2005)
Paulitsch, M.: Fault-tolerant clock synchronization for embedded distributed multi-cluster systems. PhD thesis, Institut fur Technische Informatik, Technischen Universitat Wien (2002)
Persson, N., Gustafsson, F., Drevo, M.: Indirect tire pressure monitoring using sensor fusion. In: Proc. SAE 2002, Detroit, Report 2002-01-1250, Department of Electrical Engineering, Linkoping University, Sweden (2002)
Poledna, S.: Replica determinism and flexible scheduling in hard real-time dependable systems. In: Research Report Nr. 21/97 November 1997, Institut fur Technische Informatik, Technishe Universitat Wien, Austria (1997)
Schlogl, A., Fortin, J., Habenbacher, W., Akay, M.: Adaptive mean and trend removal of heart rate variability using Kalman filtering. In: Proc. 23rd Int. Conf. IEEE Engng. Med. Bio. Soc. (2001)
Trodhandl, C.: Architectural requirements for TP/A nodes. MSc. thesis, Institut Technische Informatik, Technischen Universitat Wien (2002)
Uhlmann, J.K.: Covariance consistency methods for fault-tolerant distributed data fusion. Inform. Fusion 4, 201–215 (2003)
Wald, L.: Data Fusion: Definitions and Architectures. Les Presses de l’Ecole des Mines, Paris (2002)
Welch, G., Bishop, G.: (2001) An introduction to the Kalman filter. Notes to accompany Siggraph Course 8, (2001), http://www.cs.unc.edu/welch+
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Mitchell, H.B. (2012). Architecture. In: Data Fusion: Concepts and Ideas. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27222-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-27222-6_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27221-9
Online ISBN: 978-3-642-27222-6
eBook Packages: EngineeringEngineering (R0)