Download Adaptive Signal Processing: Theory and Applications by Thomas S. Alexander PDF

By Thomas S. Alexander

The construction of the textual content particularly begun in 1976 with the writer being concerned with a gaggle of researchers at Stanford collage and the Naval Ocean platforms middle, San Diego. at the moment, adaptive recommendations have been extra laboratory (and psychological) curiosities than the accredited and pervasive different types of sign processing that they have got turn into. Over the lasl 10 years, adaptive filters became ordinary elements in telephony, info communications, and sign detection and monitoring platforms. Their use and buyer popularity will surely merely bring up sooner or later. The mathematical rules underlying adaptive sign processing have been in the beginning interesting and have been my first adventure in seeing utilized arithmetic paintings for a paycheck. considering the fact that that point, the applying of much more complex mathematical innovations have saved the world of adaptive sign processing as intriguing as these preliminary days. The textual content seeks to be a bridge among the open literature within the expert journals, that is frequently fairly centred, concise, and complicated, and the graduate lecture room and study surroundings the place underlying ideas are frequently extra important.

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17) Note that when w" is fixed the remaining variables, (l1i, p", R"N) are related to the second-order statistics of the data signals. Therefore, selecting a specilic 30 2 The Mean Square Error {MSE1 Performance Critera filter vectorwN is equivalent to choosing a point in the wN"plane, and the height of the MSE surface above the plane at that point is then determined only by the signal correlation properties. The height arthe surface above the wN·plane at that point may become higher or lower as the signal statistics change, which translates into an expansion or contraction of the elliptical error loci.

8. R. W. Schafer, Digital Processing of Speech Signals, Prentice-Hall, Englewood Cliffs, N], 1978. 9. V. W. Schafer, Digital Signal Processing, Prentice-H all, Englewood Cliffs, NJ, 1975. 10. J. Makhoul, "Linear Prediction: A Tutorial," Proceedings of the IEEE, vol. 63, pp. 561 - 580, April 1975. 11. M. L. Marple, "Spectrum Analysis- A Modern Perspective," Proceeding of the IEEE, vol. 69, pp. 1380- 1419, November 1981. 12. 1. Makhoul, "Stable and Efficient Lattice Methods for Linear Prediction,~ IEEE Trans.

5) - iJ OWN e(n) = - iJ ow". (n)] = ~ XN(n). 1) and simplifying gives E{,(n)xN(n) } ~ O. :o:::;i ::;;; N ~ 1. 4) This very important sel of conditions is known as the orthogonality conditions and results directly from minimizing the MSE. 4); that is, two random sequences are defined to be orthogonal if [13] E{p(n)q(n)} ~ O. 5) The orthogonality condition states that the expectation of the product of the prediction error e(n) with the data used in the prediction is zero. The orthogonality condition must be maintained by the minimum MSE predictor, regardless of the manner in which the predictor coefficients are computed.

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