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 every one bankruptcy guiding the reader to the unique study on which this e-book relies.
Read or Download Adaptive Radar Signal Processing PDF
Best signal processing books
During this quantity, the authors expand the calculus of finite transformations to Dirac's equation. They receive strategies for debris with unfavourable mass which are thoroughly similar to the suggestions with optimistic mass. additionally, they receive strategies for nuclear distances of the order of 10-13m and no more instead of for the standard atomic distances.
The realm of data fusion has grown significantly over the last few years, resulting in a speedy and ambitious evolution. In such fast-moving occasions, you will need to take inventory of the alterations that experience happened. As such, this books deals an outline of the final rules and specificities of knowledge fusion in sign and snapshot processing, in addition to masking the most numerical tools (probabilistic methods, fuzzy units and chance conception and trust functions).
. .. might be integrated in each communications technician's "essential" library. -Mobile Radio TechnologyContent: Preface, web page ixChapter One - creation to Radio Frequency Electronics and size thought, Pages 1-16Chapter - Small elements utilized in Radio Frequency attempt and size, Pages 17-48Chapter 3 - Smith Charting the Radio Frequency Circuit, Pages 49-78Chapter 4 - sign resources and sign turbines, Pages 79-101Chapter 5 - Spectrum and community Analyzers, Pages 102-120Chapter Six - Radio Frequency energy Measurements, Pages 121-150Chapter Seven - Measuring Frequency and interval, Pages 151-166Chapter 8 - Radio Receivers and Their Measurements, Pages 167-216Chapter 9 - Radio Transmitter Measurements, Pages 217-263Chapter Ten - Amplifier Measurements, Pages 264-286Chapter 11 - Antenna achieve and trend Measurements, Pages 287-292Chapter Twelve - Antenna and Transmission Line Measurements, Pages 293-321Chapter 13 - Measuring Inductors and Capacitors at RF Frequencies, Pages 322-334Chapter Fourteen - Time-Domain Reflectometry, Pages 335-339Bibliography, Pages 341-342Index, Pages 343-348
- System Engineering For IMS Networks
- Continuous-time signals
- Linear and Nonlinear Video and TV Applications: Using IPv6 and IPv6 Multicast
- Signal Processing for Digital Communications
Additional info for Adaptive Radar Signal Processing
This, together with a variance efﬁciency coefﬁcient that is also developed in reference 36, can provide a useful stopping rule when W and K are varied. In more complicated cases, jackknifed10 error estimates for the spectrum can be computed as well . 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.
These samples are assumed to follow a χ2 distribution, which is the case for an unknown mean and variance sample; it consists of a sum of squares, taken from a Gaussian (normal) population. A brief outline of this test applied to linear regression models, is given in the following subsection; for more details, see Draper and Smith . 1 Brief Outline of the F-Test Let us assume that we have a model described by y = Ax + e that is linear with respect to the p × 1 parameter vector x, where the n × p coefﬁcient matrix A and n × 1 vector y are known or can be estimated from a given dataset.
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). As we can see from Figs. 19 for NW = 2, the doublet is resolved and the spurious peak problem disappears. 20 shows the corresponding result for NW = 4. 6 Line Component Extraction The next step in the spectrum estimation procedure is to extract the line components in order to be left with only the continuous part of the spectrum under investigation. 18 The projection of max F( f,Δf ), onto the f axis.
Adaptive Radar Signal Processing by Simon Haykin