Download PDF by Simon Haykin: Adaptive Radar Signal Processing

By Simon Haykin

ISBN-10: 0471735825

ISBN-13: 9780471735823

This collaborative paintings provides the result of over two decades of pioneering learn by means 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 each one bankruptcy guiding the reader to the unique study on which this e-book is predicated.

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Aqxq where q < p. The A’s in both models are the same when the subscripts are the same. We simply have fewer parameters to fit in the second one compared to the first. 1 The Basic ANOVA Table Variation Source Regression Residuals 13 Degrees of Freedom Sum of Squares (SS) Mean SS ν1 ν2 SS1 = ||Axˆ|| SS2 = ||y − Axˆ||2 MSreg = SS1/ν1 s2 = SS2 /ν2 2 In practice, of course, we require a computed F ratio that is much larger than the tabulated one. 7 F-Test for the Line Components s2 = 37 S1 2 (n − p) is also an estimate for the squared variance for model 1.

Apxp 2. E{Y} = A1x1 + A2 x2 + . . + Aqxq where q < p. The A’s in both models are the same when the subscripts are the same. We simply have fewer parameters to fit in the second one compared to the first. 1 The Basic ANOVA Table Variation Source Regression Residuals 13 Degrees of Freedom Sum of Squares (SS) Mean SS ν1 ν2 SS1 = ||Axˆ|| SS2 = ||y − Axˆ||2 MSreg = SS1/ν1 s2 = SS2 /ν2 2 In practice, of course, we require a computed F ratio that is much larger than the tabulated one. 7 F-Test for the Line Components s2 = 37 S1 2 (n − p) is also an estimate for the squared variance for model 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. Tridiagonal systems are easier than Toeplitz to solve, and this offers a practical way of numerically computing the eigenvectors. In actuality, only a small number of eigenvalues and eigenvectors is needed. 16) ∫−W Dn( f − ν)Vk ( ν)d ν = λ kVk ( f ) where, for notational simplicity, the dependence on N and W has been suppressed. The connection with Slepian’s original exposition [39] is established by writing Vk ( f ) = (1 ε k ) e − jπf ( N −1)U k ( − f ) 7 Thomson [38] uses the routines BISECT and TINVIT to evaluate the Slepian sequences, and λk (N, W ) = W 12 ∫−W Vk ( f ) 2 df ∫−1 2 Vk ( f ) 2 df for the eigenvalues.

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