# Adaptive Radar Signal Processing - download pdf or read online

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 two decades of pioneering examine by means of Professor Simon Haykin and his colleagues, facing using adaptive radar sign processing to account for the nonstationary nature of our environment. 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 ebook is predicated.

**Read Online or Download Adaptive Radar Signal Processing PDF**

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**Extra info for Adaptive Radar Signal Processing **

**Sample text**

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. Thomson and Chave [40] discuss the extension of this concept to spectra, coherences, and transfer functions. 2 Angle-of-Arrival Estimation in the Presence of Multipath The Composite Spectrum The use of adaptive weighting as developed above provides superior protection against leakage and bias.

Note the effect of the different values of NW and K. The 99% and 95% conﬁdence levels are drawn as well. 3 The Integral Regression Single-Line F-Test Thomson [36] also suggests an integral regression test instead of the point regression at f0 that was developed above. 12 The single-line point regression F-test for NW = 2 and 4, applied to the Marple dataset. The corresponding F values for 99% and 95% conﬁdence levels are drawn as well. 42) with respect to μ. The development is more complex now, but the underlying logic is the same as before.

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. Note the absence of spurious peaks. 19 The double-line integral regression F-test for NW = 2. 15. 62) i ˆ (fi). 63) for reshaping the spectrum around a line component at f0, where Sr ( f ) is the residual spectrum after extracting that particular line component.

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