Adaptive digital processing of Multidimensional signals with by Victor F. Kravchenko

By Victor F. Kravchenko

During this monograph, the unconventional promising tendencies in adaptive electronic processing of multidimensional 1D–3D indications with diversified purposes to radio physics, radio engineering, and drugs are thought of

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624z −2 ) Now, let us synthesize a filter using the AFs. We set h(t) = fup 3 (t) and use a four-point approximation of derivatives. 601z −3 with b1 = b2 = b3 = 0. 2 demonstrates the frequency responses obtained for H1 (z) and H2 (z). As compared to known frequency-conversion techniques, the AF method proposed for the synthesis of digital filters provides a simple computational algorithm, which is easy to implement. Fig. 2. Frequency responses of the digital Chebyshev LPF synthesized using (solid line) the bilinear transformation (H1 ) and (dashed line) AFs (H2 ).

5 bins, the sidelobe level is −43 dB, which exceeds the powerful signal’s sidelobe by 3 dB at the same frequency. Here, mutual signal suppression due to the phase opposition is observed along with a leakage of spectral components at positive and negative frequencies. Signals with the level lower than that of the powerful signal by 50 dB cannot be detected. 9. Signal Filtration Using the New Windows 51 Consider the results of using Kravchenko windows 42 and 44 (Fig. 10). For the first of them (Fig.

In the second stage, values of the performance functional J(w) for specific windows are evaluated. 5. 6. 7. 7 are normalized by w(0). Some known windows are also shown for comparison: The Gauss function: Gα (t) = exp(−(αt)2 /2). The Bernstein–Rogozinskii function: B(t) = cos(πt/2). The Dolph–Chebyshev function: Dα (n) = F−1 [Wα (n)], 46 Ch. 2. Spectral Properties of Atomic Functions and Novel Windows where Wα (n) = (−1) n cos N arccos βα cos π n 1 − N 2 ch N ch−1 (βα ) , 1 and βα = ch ch−1 (10α ) .

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