Abstract
Several modified RLS algorithms are studied in order to improve the rate of convergence, increase the tracking performance and reduce the computational cost of the regular RLS algorithm. . In this paper a new quantized input RLS, QI-RLS algorithm is introduced. The proposed algorithm is a modification of an existing method, namely, CRLS, and uses a new quantization function for clipping the input signal. We showed mathematically the convergence of the QI-RLS filter weights to the optimum Wiener filter weights. Also, we proved that the proposed algorithm has better tracking than the conventional RLS algorithm. We discuss the conditions which one have to consider so that he can get better performance of QI-RLS against the CRLS and standard RLS algorithms. The results of simulations confirm the presented analysis.
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References
Deivasigamani, L.: A fast clipped-data LMS algorithm. IEEE Trans. Acoustics, Speech, and Signal Processing 30(4), 648–649 (1982)
Eweda, E.: Analysis and design of a signed regressor LMS algorithm for stationary and nonstationary adaptive filtering with correlated Gaussian data. IEEE Trans. Circuits Syst. 37(11), 1367–1374 (1990)
Eweda, E.: Comparison of RLS, LMS, and sign algorithms for tracking randomly time-varying channels. IEEE Trans. Signal Processing 42(11), 2937–2944 (1994)
Feur, A., Weinstein, E.: Convergence analysis of LMS filters with uncorrelated Gaussian data. IEEE Trans. Acoust, Speech, Signal Processing 33, 222–230 (1985)
Haykin, S.: Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs (1996)
Haykin, S., Sayed, A.H., Zeidler, J., Yee, P., Wei, P.: Tracking of linear time-variant systems. In: Proc. IEEE Military Communications Conference (MILCOM ’95), San Diego, Calif, USA, vol. 2, pp. 602–606 (1995)
Kwong, C.: Dual sign algorithm for adaptive filtering. IEEE Trans. Commun. 34(12), 1272–1275 (1986)
Mathews, V., Cho, S.H.: Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm. IEEE Trans. Acoustics, Speech, and Signal Processing 35(4), 450–454 (1987)
Sadoghi Yazdi, H., Fathy, M., Lotfizad, M.: Vehicle tracking at traffic scene with modified RLS. In: Campilho, A., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 623–632. Springer, Heidelberg (2004)
Sadoghi Yazdi, H., Lotfizad, M., Kabir, E., Fathy, M.: Application of trajectory learning in tracking vehicles in the tra.c scene. In: Proc. 9th Annual Computer Society of Iran Computer Conference(CSICC ’04), vol. 1, Tehran, Iran, pp. 180–187 (2004)
Sadoghi Yazdi, H., Lotfizad, M., Kabir, E., Fathy, M.: Clipped input RLS Applied to Vehicle Tracking. EURASIP Journal on Applied Signal Processing 8, 1229–1234 (2005)
Vaseghi, S.: Advanced Signal Processing and Digital Noise Reduction. JohnWiley & Sons, New York (1996)
Widrow, B., Stearns, S.D.: Adaptive Signal Processing, Chs. 2-6. Prentice-Hall, Englewood Cliffs (1985)
Sethares, W.A., Mareels, I.M.Y., Anderson, B.D.O., Johnson Jr., C.R., Bitmead, R.R.: Excitation conditions for signed regressor least mean squares adaptation. IEEE Trans. Circuits Syst. 35(6), 613–624 (1988)
Eleftheriou, E., Falconer, D.: Tracking properties and steady-state performance of RLS adaptive filter algorithms. IEEE Trans. Acoustics, Speech, and Signal Processing 34(5), 1097–1110 (1986)
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Amiri, A., Fathy, M., Amintoosi, M., Sadoghi, H. (2007). A New Quantized Input RLS, QI-RLS, Algorithm. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74484-9_43
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DOI: https://doi.org/10.1007/978-3-540-74484-9_43
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