Open Access
Issue
Int. J. Metrol. Qual. Eng.
Volume 3, Number 3, 2012
Page(s) 137 - 143
DOI https://doi.org/10.1051/ijmqe/2012021
Published online 13 May 2013
  1. R.E. Kalman, A new approach to linear filtering and prediction problems, Trans. ASME D, J. Basic Eng. 82, 35–45 (1960) [CrossRef]
  2. H.W. Sorenson, Least-squares estimation: from Gauss to Kalman, IEEE Spectrum 7, 63–68 (1970) [CrossRef]
  3. C. Mitsantisuk, S. Katsura, K. Ohishi, Kalman-filter-based sensor integration of variable power assist control based on human stiffness estimation, IEEE Trans. Ind. Electron. 56, 3897–3905 (2009) [CrossRef]
  4. N. Salvatore, A. Caponio, F. Neri, S. Stasi, G.L. Cascella, Optimization of delayed-state Kalman-filter-based algorithm via differential evolution for sensorless control of induction motors, IEEE Trans. Ind. Electron. 57, 385–394 (2010) [CrossRef]
  5. W.L. Chan, C.S. Lee, F.B. Hsiao, Real-time approaches to the estimation of local wind velocity for a fixed-wing unmanned air vehicle, Meas. Sci. Technol. 22, 105203 (2011) [CrossRef]
  6. G.E. D’Errico À la Kalman filtering for metrology tool with application to coordinate measuring machines, IEEE Trans. Ind. Electron. 59, 4377–4382 (2012) [CrossRef]
  7. G.E. D’Errico N. Murru, An algorithm for concurrent estimation of time-variant quantities, Meas. Sci. Technol. 23, 045008 (2012) [CrossRef]
  8. R.K. Mehra On the identification of variances and adaptive Kalman filtering, IEEE Trans. Autom. Control AC–15, 175–184 (1970) [CrossRef]
  9. K.A. Myers, B.D. Tapley, Adaptive sequential estimation with unknown noise statistics, IEEE Trans. Autom. Control 21, 520–523 (1976) [CrossRef]
  10. B.J. Odelson, M.R. Rajamani, J.B. Rawlings, A new autocovariance least-squares method for estimating noise covariances, Automatica 42, 303–308 (2006) [CrossRef] [MathSciNet]
  11. G.E. D’Errico Paradigms for uncertainty treatments: a comparative analysis with application to measurement, Measurement 42, 494–500 (2009) [CrossRef]
  12. Z.M. Durovic, B.D. Kovacevic, Robust estimation with unknown noise statistics, IEEE Trans. Autom. Control 44, 1292–1296 (1999) [CrossRef]
  13. G.E. D’Errico, N. Murru, Fuzzy treatment of candidate outliers in measurements, Advances in Fuzzy Systems 2012, Article ID 783843 (2012)
  14. R.B. Nelsen, An introduction to Copulas, 2nd edn. (Springer, New York, 2007)
  15. BIPM, IEC, IFCC, ISO, IUPAC, IUPAP, and OIML, Evaluation of measurement data-Guide to the expression of uncertainty in measurement (GUM 1995 with minor corrections), JCGM 100 (2008)
  16. BIPM, IEC, IFCC, ISO, IUPAC, IUPAP, and OIML, Evaluation of measurement data–Supplement 1 to the “Guide to the expression of uncertainty in measurement”–Propagation of distributions using a Monte Carlo method, JCGM 101 (2008)
  17. ASTM E178-08, Standard practice for dealing with outlying observations, American Society for Testing and Materials (2008)
  18. G.E. D’Errico, Testing for outliers based on Bayes rule, in Proc. XIX IMEKO World Congress on Fundamental and Applied Metrology, Lisbon, Portugal, 2009
  19. L.A. Zadeh, Fuzzy sets, Inf. Control 8, 338–353 (1965) [CrossRef] [MathSciNet]
  20. E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man-Machine Studies 7, 1–13 (1975) [CrossRef]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.