Adaptive Radar Signal Processing by Simon Haykin PDF

By Simon Haykin

ISBN-10: 0471735825

ISBN-13: 9780471735823

This collaborative paintings provides the result of over two decades of pioneering examine by way 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 learn on which this publication is predicated.

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Nk −) 1 ] T and T ( N , W )mn = {sin2W[2πW (m − n)] [π (m − n)], m, n = 0, 1, . . , ( 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, .

These operators do not commute, that is, PTPF ≠ PFPT. If they did, then their product would also be a projection and it would then be possible to isolate a signal component in both time and frequency. However, under certain conditions, PTPF ≈ PFPT and the product operator is close to a projection having rank NW, denoting the time–bandwidth product. It turns out that Thomson’s MTM is equivalent to a projection of the data onto a subspace where the signal power in a narrow spectral band is maximized; that is, the conditions required for the abovementioned operators to approximately commute are found.

The second one, by filling the tap in the inner band, has as expected value the average power of the process, σ2 (the process variance). 27) 9 Though unobservable, the exact coefficients of the expansion are important in the sense that they are the expansion coefficients which would be obtained if the entire process were passed through an ideal bandpass filter from ( f − W) to ( f + W) before truncation to the finite-sample size. 30) which is actually the upper bound for E{Bk ( f )}. 28), we need to know the true spectrum S( f ).

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Adaptive Radar Signal Processing by Simon Haykin

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