Adaptive Radar Signal Processing by Simon Haykin

By Simon Haykin

This collaborative paintings provides the result of over two decades of pioneering learn through 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 learn on which this e-book relies.

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N − 1) and m ≠ n for m = n Both Thomson [36] and Slepian [31–34] give asymptotic expressions for the computation of the DPSSs and DPSWFs; it is probably the complexity of these expressions that would initially discourage people from using the prolate basis. 14) The matrix S (N,W) is tridiagonal in the sense that ⎧ 1 i ( N − 1) , ⎪2 ⎪ 2 ⎪ N − 1 − i cos 2 πW , S ( N , W )ij = ⎨ 2 ⎪1 ⎪ ( i + 1) ( N − 1 − i ) , ⎪2 ⎩0, ( ) j = i −1 j =i j = i +1 otherwise where i, j = 0, 1, . . , N − 1. Even though the eigenvalues θk are not equal to λk, they are ordered in the same way and the eigenvectors are the same.

47). We just have to set f0 = f1. 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.

3. 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.

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