Blind Calibration of Networks of Sensors: Theory and Algorithms

  • Laura Balzano
  • Robert Nowak

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.


Sensor Network Discrete Fourier Transform Sensor Reading True Signal Signal Subspace 
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.


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  1. [1]
    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.Google Scholar
  2. [2]
    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.Google Scholar
  3. [3]
    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.Google Scholar
  4. [4]
    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.CrossRefGoogle Scholar
  5. [5]
    E. Candes and J. Romberg. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Computational Mathematics, 2006.Google Scholar
  6. [6]
    J. Feng, S. Megerian, and M. Potkonjak. Model-based calibration for sensor networks. Sensors, pages 737 - 742, October 2003.Google Scholar
  7. [7]
    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.CrossRefGoogle Scholar
  8. [8]
    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.CrossRefGoogle Scholar
  9. [9]
    B. Hoadley. A bayesian look at inverse linear regression. Journal of the American Statistical Association, 65(329):356-369, March 1970.MATHCrossRefGoogle Scholar
  10. [10]
    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.Google Scholar
  11. [11]
    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.Google Scholar
  12. [12]
    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.CrossRefMathSciNetGoogle Scholar
  13. [13]
    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.Google Scholar
  14. [14]
    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.Google Scholar
  15. [15]
    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.Google Scholar
  16. [16]
    W. M. Wonham. Linear Multivariable Control. Springer-Verlag, New York, 1979.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Laura Balzano
    • 1
  • Robert Nowak
    • 2
  1. 1.University of CaliforniaLos AngelesUSA
  2. 2.University of WisconsinMadisonUSA

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