By Simon Haykin

ISBN-10: 0470069112

ISBN-13: 9780470069110

ISBN-10: 0470069120

ISBN-13: 9780470069127

ISBN-10: 0471735825

ISBN-13: 9780471735823

This collaborative paintings provides the result of over 20 years of pioneering learn 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 examine on which this booklet is predicated.

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**Additional info for Adaptive Radar Signal Processing **

**Sample text**

A larger boundary results in a larger mismatch of the power levels between the known signal components. Note the detail of the composite spectrum despite the larger spectral window. 7 F-Test for the Line Components 35 features that are of interest. Thomson [39] recommends that W be between 1/N and 20/N, with a time–bandwidth product of 4 or 5 being a common starting point. 3 are not expected to be good estimates, since we know that line components exist and that the spectrum estimation techniques developed have implicitly assumed none.

41) where ν is equal to two degrees of freedom (real and imaginary parts of the complex line amplitude). The total number 2K of degrees of freedom come about from the K complex data points we have available to draw information from. If F is large at a certain frequency, then the hypothesis is rejected; that is, a line component does exist there. The location of the maximum of F provides an estimate of the line frequency that has a resolution within 5–10% of the Cramér–Rao bound. The test works well if the lines considered are isolated—that is, if there is only a single line in the interval ( f − W, f + W).

Note, however, the appearance of spurious peaks, particularly near the edges of the window boundary. These are explained by the fact that the sliding window (Fig. 4) is not an ideal bandpass ﬁlter; hence, energy from outside the window, particularly near the window boundaries, can affect the estimation inside the window. 13 The projection of max F( f ,Δf ), onto the f axis. The 99% conﬁdence level is also drawn and we see some spurious peaks above it. As the surface plot in Fig. 14 shows, the largest peaks are mostly due to leakage outside the window.

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