Adaptive Radar Signal Processing , 1st Edition by Simon Haykin

By Simon Haykin

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

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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.

These subsets are used to form estimates of a given parameter, which are then combined to give estimates of bias and variance for this parameter, valid under a wide range of parent distributions. Thomson and Chave [40] discuss the extension of this concept to spectra, coherences, and transfer functions. 2 Angle-of-Arrival Estimation in the Presence of Multipath The Composite Spectrum The use of adaptive weighting as developed above provides superior protection against leakage and bias. Thomson also offers a further refinement to achieve higher resolution by considering each specific frequency point f0 as a free parameter in (f − W ≤ f0 ≤ f + W).

7 F-Test for the Line Components δ1 = ∫ f1 +W f1 −W δ2 = ∫ γ =∫ f1 +W f1 −W 47 K −1 ∑ d1df = . . = d2 df = . . = λk k =0 K −1 N −1 N −1 ∑ ∑ ∑ υ(nk ) υ(mk )e j 2π(n− m)( f − f ) 2 1 sin 2 π ( n − m ) W π (n − m ) k =0 n=0 m=0 N −1 K −1 f1 +W 2 λ k υ(nk ) e j 2 πn( f2 − f1 ) cdf = . . = f1 −W n=0 k =0 N −1 K −1 f1 +W 2 c1df = . . = λ k υ(nk ) x ( n ) e − j 2 πnf1 f1 −W n=0 k =0 K −1 N −1 N −1 f1 +W c2 df = . . 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).

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