By L.D. Davisson, G. Longo
The 4 chapters of this quantity, written by way of widespread staff within the box of adaptive processing and linear prediction, handle quite a few difficulties, starting from adaptive resource coding to autoregressive spectral estimation. the 1st bankruptcy, by way of T.C. Butash and L.D. Davisson, formulates the functionality of an adaptive linear predictor in a sequence of theorems, with and with out the Gaussian assumption, below the speculation that its coefficients are derived from both the (single) remark series to be envisioned (dependent case) or a moment, statistically self sufficient realisation (independent case). The contribution through H.V. terrible experiences 3 lately built common methodologies for designing sign predictors lower than nonclassical working stipulations, particularly the strong predictor, the high-speed Levinson modeling, and the approximate conditional suggest nonlinear predictor. W. Wax offers the foremost ideas and methods for detecting, localizing and beamforming a number of narrowband assets by way of passive sensor arrays. particular coding algorithms and methods in keeping with using linear prediction now enable fine quality voice copy at remorably low bit premiums. The paper by means of A. Gersho studies the various major rules underlying the algorithms of significant curiosity today.
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Extra resources for Adaptive Signal Processing
27). 2. 12), it immediately follows that the 1st order adaptive linear predictor of this process exhibits a MSE given by Adaptive Linear Prediction 33 u2[1,N] = u2[1,oo] [1 + 3~ ] + o ( ~) . l suggested by the Minimum FPE conjecture. 32) of the adaptive linear predictor's MSE in counterexamples 1 and 2. Although the bias of the FPE estimate in these counterexamples is, for long learning periods, N, not alarming, it should be noted that the subject examples were chosen only for the relative computational simplicity which they afforded.
C. D. Davisson Therefore, in view of the unsubstantiated performance of the AIC criterion and its variants in the non-Gaussian case, as well as the limited practical utility of such criteria in adaptive applications, we elected to pursue an extension of Davisson's Method that promised to be free of such deficiencies. This extension, as proposed and demonstrated in Chapter 4, necessitates neither the Gaussian assumption nor speculation on the conditional pdf that governs the observed data. In closing this chapter we note that the AIC criterion and its variants have commanded considerable attention from the research community in recent years due, primarily, to their intellectually appealing theoretical foundations.
Q5), we note that since the fourth central moment of a Gaussian random variable is precisely 3 times its variance, this factor may be viewed as a metric of the distance between the Gaussian and non-Gaussian cases. Indeed, Cramer (cf. , pp. 184-185) defines this factor as the coefficient of excess and observes that it is often taken as a measure of the deviation of a random variate's probability distribution from the normal distribution. Unfortunately, the magnitude of the coefficient of excess cannot be bounded without prior knowledge of the distribution in question.