By Simon Haykin

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 each one bankruptcy guiding the reader to the unique examine on which this ebook is predicated.

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16, computed for a time–bandwidth product NW = 2 and 4, illustrate that this is indeed the case. 21 (where we know they should). The fact that the surface plot displays two peaks is due to our mode of representation of using f and Δf as the independent variables. 21. The ﬁrst pair is consistent with our model and has a larger F value, so it is the one we pick. The advantage with this particular representation is that we can project the maximum of F(f, Δ f) onto the f-axis and resolve the doublet from a simpler, onedimensional function.

Background noise correlation matrix is known, the SNR is above a certain threshold, and the data are properly calibrated). Choosing the number of signals arbitrarily to be 10, we see (Fig. 8) that there is no problem in picking out the line components, but the methods, as expected, try to ﬁt the continuous part of the spectrum with sinusoids as well. Without any a priori knowledge, it is easy to mistake a noise peak for a signal peak. We also observe a gradual decay of the eigenvalue spectrum, a clear indication of the existence of colored noise.

A colored noise process, generated by passing two independently generated zero-mean real white noise processes through identical moving average ﬁlters to separately generate the real and imaginary components of the test data noise process. Each ﬁlter has the identical raised cosine response, seen in Fig. 5 The eigenvalue spectrum for NW = 4 and N = 64. As we can see, the ﬁrst 8 eigenvalues are very close to 1, corresponding to the ﬁrst K = 2NW = 8 windows that have a negligible effect on the bias of the spectrum estimator.