Get Adaptive Radar Signal Processing PDF

By Simon Haykin

ISBN-10: 0470069112

ISBN-13: 9780470069110

ISBN-10: 0470069120

ISBN-13: 9780470069127

ISBN-10: 0471735825

ISBN-13: 9780471735823

This collaborative paintings offers the result of over 20 years of pioneering study through Professor Simon Haykin and his colleagues, facing using adaptive radar sign processing to account for the nonstationary nature of our surroundings. those effects have profound implications for defense-related sign processing and distant sensing. References are supplied in every one bankruptcy guiding the reader to the unique study on which this e-book relies.

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This, together with a variance efficiency coefficient that is also developed in reference 36, can provide a useful stopping rule when W and K are varied. In more complicated cases, jackknifed10 error estimates for the spectrum can be computed as well [40]. 10 The jackknife, in the simplest form, refers to the following procedure. Given a set of N observations, each observation is deleted in turn, forming N subsets of N − 1 observations. These subsets are used to form estimates of a given parameter, which are then combined to give estimates of bias and variance for this parameter, valid under a wide range of parent distributions.

These samples are assumed to follow a χ2 distribution, which is the case for an unknown mean and variance sample; it consists of a sum of squares, taken from a Gaussian (normal) population. A brief outline of this test applied to linear regression models, is given in the following subsection; for more details, see Draper and Smith [7]. 1 Brief Outline of the F-Test Let us assume that we have a model described by y = Ax + e that is linear with respect to the p × 1 parameter vector x, where the n × p coefficient matrix A and n × 1 vector y are known or can be estimated from a given dataset.

60) tests the existence of two lines only. If again, we let f1 vary over the entire frequency range, f2 will vary in (f1 − W, f1 + W). As we can see from Figs. 19 for NW = 2, the doublet is resolved and the spurious peak problem disappears. 20 shows the corresponding result for NW = 4. 6 Line Component Extraction The next step in the spectrum estimation procedure is to extract the line components in order to be left with only the continuous part of the spectrum under investigation. 18 The projection of max F( f,Δf ), onto the f axis.

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Adaptive Radar Signal Processing by Simon Haykin


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