Adaptive Radar Signal Processing by Simon Haykin

By Simon Haykin

This collaborative paintings offers the result of over 20 years of pioneering study through 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 examine on which this ebook is predicated.

Show description

Read or Download Adaptive Radar Signal Processing PDF

Best signal processing books

Data Hiding - Fundamentals and Applications

Multimedia applied sciences have gotten extra refined, allowing the net to house a swiftly transforming into viewers with an entire variety of companies and effective supply equipment. even if the net now places conversation, schooling, trade and socialization at our finger assistance, its quick development has raised a few weighty protection issues with appreciate to multimedia content material.

Computer Explorations in Signals and Systems Using MATLAB

Designed to advance higher figuring out of the rules of indications and platforms. makes use of MATLAB workouts to actively problem the reader to use mathematical recommendations to genuine global difficulties.

Advances in Nonlinear Signal and Image Processing (EURASIP Book Series on Signal Processing and Communications)

The curiosity in nonlinear tools in sign processing is gradually expanding, considering these days the advances in computational capacities give the opportunity to enforce refined nonlinear processing ideas which in flip permit outstanding advancements with recognize to straightforward and well-consolidated linear processing ways.

Extra resources for Adaptive Radar Signal Processing

Sample text

16, computed for a time–bandwidth product NW = 2 and 4, illustrate that this is indeed the case. 21 (where we know they should). 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.

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. 8) that there is no problem in picking out the line components, but the methods, as expected, try to fit the continuous part of the spectrum with sinusoids as well. 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.

A colored noise process, generated by passing two independently generated zero-mean real white noise processes through identical moving average filters to separately generate the real and imaginary components of the test data noise process. Each filter has the identical raised cosine response, seen in Fig. 5 The eigenvalue spectrum for NW = 4 and N = 64. As we can see, the first 8 eigenvalues are very close to 1, corresponding to the first K = 2NW = 8 windows that have a negligible effect on the bias of the spectrum estimator.

Download PDF sample

Rated 4.90 of 5 – based on 21 votes