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Network numerical analysis for the smoother and the lagged joint-process estimator

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Abstract

Motivated by the fact that the a priori least-squares-order-recursive lattice (LSORL) smoother is more robust than the LSORL joint-process estimator with lagged desired signals in the finite precision, we model numerical properties of the two algorithms by virtue of previous efforts. Then, we give the reason why the smoother is substantially more robust than the lagged joint-process estimator by providing the explicit analysis for the performance difference of the two algorithms.

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References

  1. Einicke GA (2006) Optimal and robust noncausal filter formulations. IEEE Trans Signal Process 54(3):1069–1077

    Article  Google Scholar 

  2. Zhao Q, Tong L (1999) Adaptive blind channel estimation by least squares smoothing. IEEE Trans Signal Process 47(11):3000–3012

    Article  MATH  Google Scholar 

  3. Nawrocka A, Kot A (2011) Methods for EEG signal analysis. In: Proc. 12th inter journ Carpathian control conf, Velke Karlovice, Czech Rep, pp 266–269

    Google Scholar 

  4. Xun C, Wang ZJ (2011) Design and implementation of a wearable, wireless EEG recording system. In: 5th inter conf on bioinformatics and biomedical engineering, Wuhan, China, pp 1–4

    Google Scholar 

  5. Tarvainen MP, Hiltunen JK, Ranta-aho PO, Karjalainen PA (2004) Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS). IEEE Trans Biomed Eng 51(3):516–524

    Article  Google Scholar 

  6. Lee DT, Morf M, Friedlander B (1981) Recursive least squares LSORL estimation algorithms. IEEE Trans Circuits Syst CAS-28(6):467–481

    Article  MathSciNet  Google Scholar 

  7. Morf M, Vieira A, Lee DT (1977) Ladder forms for identification and speech processing. In: Proc IEEE conf decision contr, New Orleans, LA, pp 1074–1078

    Google Scholar 

  8. Reddy VU, Egardt B, Kailath T (1981) Optimized lattice-form adaptive line enhancer for a sinusoidal signal in broad-band noise. IEEE Trans Acoust Speech Signal Process 29:702–710

    Article  Google Scholar 

  9. Satorius EH, Pack JD (1981) Application of least squares lattice algorithms to adaptive equalization. IEEE Trans Commun 136–142

  10. Clement MJ, Judd GM, Morse BS, Flanagan JK (1999) Performance surface prediction for WAN-based clusters. J Supercomput 13(3):267–281

    Article  Google Scholar 

  11. Mathews VJ, Xie Z (1990) Fixed-point error analysis of stochastic gradient adaptive lattice filters. IEEE Trans Acoust Speech Signal Proces 38

  12. North R, Zeidler J, Ku W, Albert T (1993) A floating-point arithmetic error analysis of direct and indirect coefficient updating techniques for adaptive lattice filters. IEEE Trans Signal Process 41:1809–1823

    Article  MATH  Google Scholar 

  13. Ling F (1989) Efficient least-squares lattice algorithms based on givens rotation with systolic array implementations. In: Proc. ICASSP, Glasgow, Scotland, pp 1290–1293

    Google Scholar 

  14. Yang B (1994) A note on the error propagation analysis of recursive least squares algorithms. IEEE Trans Signal Process 42:3523–3525

    Article  Google Scholar 

  15. Levin MD, Cowan CFN The performance of eight recursive least squares adaptive filtering algorithms in a limited precision environment. In: Proc. European signal process conf, Edinburgh, Scotland pp 1261–1264

  16. Bunch JR, LeBorne RC (1995) Error accumulation effects for the a posteriori RLSLS prediction filter. IEEE Trans Signal Process 43(1):150–159

    Article  Google Scholar 

  17. Yuan JT, Stuller JA (1995) Least squares order recursive lattice smoothers. IEEE Trans Signal Process 43:1058–1067

    Article  Google Scholar 

  18. Kim DK, Park PG (2002) The normalized least squares order-recursive lattice smoothers. Signal Process 82:895–905

    Article  MATH  Google Scholar 

  19. Haykin S (1996) Adaptive filter theory, 3rd edn. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  20. Godana B, Ekman T (2011) Linear prediction of time-varying MIMO systems using Givens rotations. In: Proc. SPAWC, San Francisco, CA, pp 317–375

    Google Scholar 

  21. Proakis JG (1995) Digital communications, 3rd edn. McGraw-Hill, New York

    Google Scholar 

  22. Chen F, Kwong S, Kok CW (2009) Blind MMSE equalization of FIR/IIR channels using oversampling and multichannel linear prediction. ETRI J 31(2):162–172

    Article  Google Scholar 

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Acknowledgement

This work was supported by MKE/DDI [A2010D-D0004, Development of Green P2mP Wireless Backhaul System]

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Authors and Affiliations

  1. Division of Computer Engineering, Mokwon University, Daejeon, Korea

    Yang Sun Lee

  2. Electronics and Telecommunication Research Institute (ETRI), Daejeon, Korea

    Dong Kyoo Kim

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Leonard Barolli

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  1. Yang Sun Lee
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  2. Dong Kyoo Kim
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  3. Leonard Barolli
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Correspondence to Leonard Barolli.

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Lee, Y.S., Kim, D.K. & Barolli, L. Network numerical analysis for the smoother and the lagged joint-process estimator. J Supercomput 65, 1192–1204 (2013). https://doi.org/10.1007/s11227-012-0753-2

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  • DOI: https://doi.org/10.1007/s11227-012-0753-2

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