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Download e-book for kindle: Adaptive Radar Signal Processing by Simon Haykin

By Simon Haykin

ISBN-10: 0470069112

ISBN-13: 9780470069110

ISBN-10: 0470069120

ISBN-13: 9780470069127

ISBN-10: 0471735825

ISBN-13: 9780471735823

This collaborative paintings offers the result of over 20 years of pioneering examine 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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2 MUSIC and MFBLP MUlitple SIgnal Classification (MUSIC) and Modified Forward Backward Linear Prediction (MFBLP) are two of the modern algorithms for estimating line components in a data sequence. They both use the concept of a signal and noise subspace. Projection operators are constructed so as to map the data onto one or the other subspace. , background noise correlation matrix is known, the SNR is above a certain threshold, and the data are properly calibrated). Choosing the number of signals arbitrarily to be 10, we see (Fig.

A detailed expression for each component is given in the previous section. 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.

Note, however, the appearance of spurious peaks, particularly near the edges of the window boundary. These are explained by the fact that the sliding window (Fig. 4) is not an ideal bandpass filter; hence, energy from outside the window, particularly near the window boundaries, can affect the estimation inside the window. 13 The projection of max F( f ,Δf ), onto the f axis. The 99% confidence level is also drawn and we see some spurious peaks above it. As the surface plot in Fig. 14 shows, the largest peaks are mostly due to leakage outside the window.

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Adaptive Radar Signal Processing by Simon Haykin


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