With the wide variety of sensor network applications being envisioned and implemented, it is clear that in certain situations the applications need more accurate measurements than uncalibrated, low-cost sensors provide. Arguably, calibration errors are one of the major obstacles to the practical use of sensor networks [3], because they allow a user to infer a difference between the readings of two spatially separated sensors when in fact that difference may be due in part to miscalibration. Consequently, automatic methods for jointly calibrating sensor networks in the field, without dependence on controlled stimuli or high-fidelity groundtruth data, is of significant interest. We call this problem blind calibration.
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
L. Balzano and R. Nowak. Blind calibration for signals with bandlim-ited subspaces. Technical report, Information Sciences Laboratory at the University of Wisconsin-Madison, February 2007.
L. Balzano, N. Ramanathan, E. Graham, M. Hansen, and M. B. Sri-vastava. An investigation of sensor integrity. Technical Report UCLA-NESL-200510-01, Networked and Embedded Systems Laboratory, 2005.
P. Buonadonna, D. Gay, J. Hellerstein, W. Hong, and S. Madden. Task: Sensor network in a box. Technical Report IRB-TR-04-021, Intel Re-search Berkeley, January 2005.
V. Bychkovskiy, S. Megerian, D. Estrin, and M. Potkonjak. A collabora-tive approach to in-place sensor calibration. Lecture Notes in Computer Science, 2634:301-316, 2003.
E. Candes and J. Romberg. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Computational Mathematics, 2006.
J. Feng, S. Megerian, and M. Potkonjak. Model-based calibration for sensor networks. Sensors, pages 737 - 742, October 2003.
M. Gurelli and C. Nikias. Evam: An eigenvector-based algorithm for multichannel blind deconvolution of input colored signals. IEEE Trans-actions on Signal Processing, 43:134-149, January 1995.
G. Harikumar and Y. Bresler. Perfect blind restoration of images blurred by multiple filters: Theory and efficient algorithms. IEEE Transactions on Image Processing, 8(2):202-219, February 1999.
B. Hoadley. A bayesian look at inverse linear regression. Journal of the American Statistical Association, 65(329):356-369, March 1970.
A. Ihler, J. Fisher, R. Moses, and A. Willsky. Nonparametric belief prop-agation for self-calibration in sensor networks. In Proceedings of the Third International Symposium on Information Processing in Sensor Networks, 2004.
N. Ramanathan, L. Balzano, M. Burt, D. Estrin, T. Harmon, C. Harvey, J. Jay, E. Kohler, S. Rothenberg, and M.Srivastava. Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks. Technical Report CENS TR 62, Center for Embedded Networked Sensing, 2006.
O. Shalvi and E. Weinstein. New criteria for blind deconvolution of nonmimimum phase systems (channels). IEEE Trans. on Information Theory, IT-36(2):312-321, March 1990.
C. Taylor, A. Rahimi, J. Bachrach, H. Shrobe, and A. Grue. Simultane- ous localization, calibration, and tracking in an ad hoc sensor network. In IPSN ’06: Proceedings of the Fifth International Conference on Infor-mation Processing in Sensor Networks, pages 27-33, 2006.
G. Tolle, J. Polastre, R. Szewczyk, D. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, and W. Hong. A macroscope in the redwoods. In Proceedings of Sensys, 2005.
K. Whitehouse and D. Culler. Calibration as parameter estimation in sensor networks. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pages 59-67, 2002.
W. M. Wonham. Linear Multivariable Control. Springer-Verlag, New York, 1979.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Balzano, L., Nowak, R. (2008). Blind Calibration of Networks of Sensors: Theory and Algorithms. In: Saligrama, V. (eds) Networked Sensing Information and Control. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-68845-9_1
Download citation
DOI: https://doi.org/10.1007/978-0-387-68845-9_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-68843-5
Online ISBN: 978-0-387-68845-9
eBook Packages: EngineeringEngineering (R0)