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Download Detection, Estimation, and Modulation Theory - Part lll - by Van Trees, Harry L. PDF

By Van Trees, Harry L.

Initially released in 1968, this e-book is without doubt one of the nice time-tested classics within the box of sign processing. hugely readable and virtually equipped, it truly is as important this day for execs, researchers, and scholars in optimal sign processing because it was once over thirty years in the past.

  • half III specializes in radar-sonar sign processing and Gaussian indications in noise. the next issues are lined intimately:
  • Detection of Gaussian signs in white Gaussian noise
  • General binary detection: Gaussian strategies
  • Estimation of the parameters of a random approach
  • The radar-sonar challenge
  • Parameter estimation: slowly fluctuating aspect ambitions
  • Doppler-spread pursuits and channels
  • Range-spread goals and channels
  • Doubly-spread objectives and channels

the consequences are nonetheless mostly acceptable to present platforms

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Extra info for Detection, Estimation, and Modulation Theory - Part lll - Radar-Sonar Processing and Gaussian Signals in Noise

Example text

No. the average energy divided by the noise spectral height and the kT product, The 3-db-bandwidth of the spectrum is k radians per second, so that k T is a timebandwidth product. To use the approximate error expressions in ( I 54) and ( I 5 5 ) . we find p(s) and , i i (s) from (208). 51 versus k T for various values of 2&NW We shall not carry out this calculation at this point. In Example 1 of Chapter 4, we study this problem again from a different viewpoint. At that time we plot a detailed set of performance curves (see Figs.

This close connection is important because it means that all of our detailed studies of optimum linear filters are useful for the Gaussian detection problem. At this point, we have developed a set of important results but have not yet applied them to specific physical problems. We continue this development in Chapter 4, where we consider three important classes of physical problems and obtain specific results for a number of interesting examples. 1 before reading Chapter 3 in detail. 1. Consider the model described by (l)-(6).

1 . Consider the waveform 4 7 ) = nc(7) + ~ ( 4 , T, 5 7 S 1, where n,(~) can be generated as the output of a dynamic system, x ( r ) = F(r)x(r) nJr) + C(r)u(r). = c(r)x(r), 5 r, u 5 7''. OpfiMUM Receivers 49 driven by a statistically independent white noise u(f). Denote the MMSE realizable estimate of nc(s) as A&). Prove that the process r*(r) is white. a r ( r ) - k,(t) = r(r) - C(f)i(f) 2. Use the result of part 1 to derive the receiver in Fig. 1 1 by inspection. 4. 4 and the Appendix to Part I1 (sect.

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