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Parameter Optimization for Step-Adaptive Approximate Least Squares

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Computer Aided Systems Theory – EUROCAST 2015 (EUROCAST 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9520))

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Abstract

We discuss possible approaches for the adaption of the step width of Step-Adaptive Approximate Least Squares. We present and compare two low complexity and practically feasible adaptation functions whose parameters have been optimized based on computer simulations. We show that by applying these approaches the performance deviation of Step-Adaptive Approximate Least Squares lies within the single percentage range compared to the optimal least squares solution.

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References

  1. Choi, K.H., Ra, W.-S., Park, S.-Y., Park, J.B.: Robust least squares approach to passive target localization using ultrasonic receiver array. IEEE Trans. Ind. Electron. 61(4), 1993–2002 (2014)

    Article  Google Scholar 

  2. Thomas, R.R., Maharaj, B.T., Zayen, B., Knopp, R.: Multiband time-of-arrival positioning technique using an ultra-high-frequency bandwidth availability model for cognitive radio. IET Radar Sonar Navig. 7(5), 544–552 (2013)

    Article  Google Scholar 

  3. Gautier, M., Janot, A., Vandanjon, P.-O.: A new closed-loop output error method for parameter identification of robot dynamics. IEEE Trans. Control Syst. Technol. 21(2), 428–444 (2013)

    Article  Google Scholar 

  4. Unterrieder, C., Zhang, C., Lunglmayr, M., Priewasser, R., Marsili, S., Huemer, M.: Battery state-of-charge estimation using approximate least squares. J. Power Sources 278, 274–286 (2015)

    Article  Google Scholar 

  5. Yuqian, L., Sima, D.M., Van Cauter, S., Himmelreich, U., Sava, A.C., Yiming, P., Yipeng, L., Van Huffel, S.: Unsupervised nosologic imaging for glioma diagnosis. IEEE Trans. Biomed. Eng. 60(6), 1760–1763 (2013)

    Article  Google Scholar 

  6. Rouhani, M., Domingo Sappa, A.: The richer representation the better registration. IEEE Trans. Image Process. 22(12), 5036–5049 (2013)

    Article  MathSciNet  Google Scholar 

  7. Kay, S.M.: Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall, Englewood Cliffs (2005)

    MATH  Google Scholar 

  8. Lunglmayr, M., Unterrieder, C., Huemer, M.: Approximate least squares. In: 2014 Proceedings of the IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), pp. 4678–4682 (2014)

    Google Scholar 

  9. Kaczmarz, S.: Przyblizone rozwiazywanie ukladw rwnan liniowych. Angenäherte Auflösung von Systemen linearer Gleichungen. In: Bulletin International de lAcadmie Polonaise des Sciences et des Lettres. Classe des Sciences Mathmatiques et Naturelles, Srie A, Sciences Mathmatiques, pp. 355–357 (1937)

    Google Scholar 

  10. Censor, Y., Eggermont, P.P.B., Gordon, D.: Strong underrelaxation in Kaczmarz’s method for inconsistent systems. Numer. Math. 41(1), 83–92 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  11. Lunglmayr, M., Unterrieder, C., Huemer, M.: Step-adaptive least squares. Submitted to European Signal Processing Conference (EUSIPCO) (2015)

    Google Scholar 

  12. Björck, A.: Numerical Methods for Least Squares Problems. SIAM Philadelphia, Philadelphia (1996)

    Book  MATH  Google Scholar 

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Authors and Affiliations

  1. Institute of Signal Processing, Johannes Kepler University Linz, Linz, Austria

    M. Lunglmayr & M. Huemer

Authors
  1. M. Lunglmayr
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  2. M. Huemer
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Corresponding author

Correspondence to M. Lunglmayr .

Editor information

Editors and Affiliations

  1. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Lunglmayr, M., Huemer, M. (2015). Parameter Optimization for Step-Adaptive Approximate Least Squares. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_65

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  • DOI: https://doi.org/10.1007/978-3-319-27340-2_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27339-6

  • Online ISBN: 978-3-319-27340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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