Download Adaptive Radar Signal Processing by Simon Haykin PDF

By Simon Haykin

This collaborative paintings offers the result of over two decades of pioneering learn by way of 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 study on which this ebook is predicated.

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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. To summarize, both the classical and eigendecomposition methods mentioned above, fail in estimating fully and correctly both the line and continuous parts of the given spectrum. 8 MFBLP (a) and MUSIC (b) spectra. 11) by expanding its factors in ( f − W, f + W) using the Slepian basis. 19) k =0 where the asterisk denotes complex conjugation.

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. For the second model, we have respectively −1 xˆ q = ( AqH Aq ) AqH y and S2 = y − Aq xˆ q 2 where S2 has 2(n − q) degrees of freedom.

3) (modified 2 Generalization of the treatment to more dimensions is done by simply allowing time t to become a d-dimensional vector t, where d is the dimension of the process. See references 5 and 6 for a concise explanation. In the following, we consider one-dimensional processes only, since they adequately describe our experimental data. 3 Spectrum Estimation Background 15 periodogram). The Blackman and Tukey spectrum estimate, for a data sample of size N, is given by the formula N −1 Sˆ ( f ) = ∑ rˆ ( Δm ) d ( m ) e− j 2 πf Δm m =1− N where the autocorrelation sequence is estimated as rˆ ( Δm ) = 1 N −m ∑ x [Δ (n + m )] x* (Δn) N − m n =1 where 0 ≤ m ≤ N − 1, rˆ (Δm) = r*(−Δm) for m < 0, and Δ is the sampling period.

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