Simon Haykin's Adaptive Radar Signal Processing PDF

By Simon Haykin

ISBN-10: 0471735825

ISBN-13: 9780471735823

This collaborative paintings provides the result of over 20 years of pioneering study 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 learn on which this publication is predicated.

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Extra resources for Adaptive Radar Signal Processing

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10 The jackknife, in the simplest form, refers to the following procedure. Given a set of N observations, each observation is deleted in turn, forming N subsets of N − 1 observations. 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.

6) The spectral representation concept is quite basic; in essence, it says that any stationary time series can be interpreted as the limit of a finite sum of sinusoids Aicos(2πfit + Φi) over all frequencies f = fi. The amplitudes Ai = A(fi) and phases Φi = Φ(fi) are uncorrelated random variables with S( f ) ≈ E{A2 (fi)} for fi ≈ f ≠ 0 and can be related to dZ( f ) in a simple way (see reference 19). 7) as the proper definition of the power spectrum. When a number of line components is present, the above relations are easily generalized to include them.

The fact that the surface plot displays two peaks is due to our mode of representation of using f and Δf as the independent variables. 21. The first pair is consistent with our model and has a larger F value, so it is the one we pick. The advantage with this particular representation is that we can project the maximum of F(f, Δ f) onto the f-axis and resolve the doublet from a simpler, onedimensional function. Note, however, the appearance of spurious peaks, particularly near the edges of the window boundary.

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Adaptive Radar Signal Processing by Simon Haykin


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