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Adaptive Radar Signal Processing by Simon Haykin PDF

By Simon Haykin

This collaborative paintings offers the result of over two decades of pioneering learn by way of 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 each one bankruptcy guiding the reader to the unique learn on which this ebook relies.

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The ergodicity property, which holds for a zero-mean Gaussian process with no line components, is also frequently invoked, so that ensemble averages can be replaced by time averages. However, interest in classical spectrum estimation was renewed [25] only after the publication of Thomson’s classic 1982 paper [36], where the power of MTM is demonstrated. Basically, Thomson has proved that a more fruitful approach to a spectrum estimator is through the spectral representation of X(t) itself (Cramér representation).

7 F-Test for the Line Components s2 = 37 S1 2 (n − p) is also an estimate for the squared variance for model 1. For the second model, we have respectively −1 xˆ q = ( AqH Aq ) AqH y and S2 = y − Aq xˆ q 2 where S2 has 2(n − q) degrees of freedom. To test the hypothesis that the extra terms are unnecessary, we consider the ratio 1 ( S2 − S1 ) ( 2 p − q) 1 S1 2 (n − p) and refer it to the F[2(p − q), 2(n − p)] distribution in the usual manner. This provides us with enough information to set up partial F-tests in the following subsections, for computing multiple spectral lines.

Line components that disappear from one case to the other are almost certainly sampling fluctuations. In Fig. 12, we see the results of applying this F-test to the Marple dataset. 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.

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