[下载]A First Course in Statistics for Signal Analysis

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<p>pdf格式，非扫描版</p><p> </p><p><strong><font size="4"><em>A First Course in Statistics for Signal Analysis <br/></em></font>Authors:</strong> Wojbor A. Woyczynski<br/><strong>Paperback:</strong> 206 pages <br/><strong>Publisher:</strong> Birkhauser Boston; 1 edition (August 10, 2006) <br/><strong>Language:</strong> English <br/><strong>ISBN-10:</strong> 0817643982 <br/><strong>ISBN-13:</strong> 978-0817643980 </p><p> </p><p><font size="3"><strong>Customer Reviews:</strong></font></p><p>This text serves as an excellent introduction to statistics for signal analysis. Be aware that it emphasizes theory over numerical methods - and that it is dense. If one is not looking for lengthy explanations but instead wants to get to the point quickly this book may be for them.  </p><p>---by John Coroneus(Calfornia) ， July 31, 2008 </p><p> </p><font size="3"><strong>Contents</strong></font>
        <p></p>  <p></p><strong>Introduction .</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix <p></p><strong>Notation .</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi <p></p><strong>1 Description of Signals</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 <p></p>1.1 Types of random signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 <p></p>1.2 Time domain and frequency domain descriptions . . . . . . . . 8 <p></p>1.3 Characteristics of signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 <p></p>1.4 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 <p></p><p></p><p><strong>2 Spectral Representation of Deterministic Signals:</strong>  <strong>Fourier Series and Transforms</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 </p><p></p>2.1 Complex Fourier series for periodic signals . . . . . . . . . . . . . . 17 <p></p>2.2 Approximation of periodic signals by finite Fourier sums . 26 <p></p>2.3 Aperiodic signals and Fourier transforms . . . . . . . . . . . . . . . . 31 <p></p>2.4 Basic properties of the Fourier transform . . . . . . . . . . . . . . . . 35 <p></p>2.5 Fourier transforms of some nonintegrable signals; Dirac delta impulse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 <p></p>2.6 Discrete and fast Fourier transforms . . . . . . . . . . . . . . . . . . . . . 42 <p></p>2.7 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 <p></p><p></p><p><strong>3 Random Quantities and Random Vectors . . .</strong> . . . . . . . . . . . . . . . . . . . 47 </p><p></p>3.1 Discrete, continuous, and singular random quantities . . . . 48 <p></p>3.2 Expectations and moments of random quantities . . . . . . . . . 62 <p></p>3.3 Random vectors, conditional probabilities, statistical independence, and correlations . . . . . . . . . . . . . . . . . . . . . . . . . . 67 <p></p>3.4 The least-squares fit, regression line . . . . . . . . . . . . . . . . . . . . . 77 <p></p>3.5 The law of large numbers and the stability of  fluctuations law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 <p></p>3.6 Estimators of parameters and their accuracy; confidence intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 <p></p>3.7 Problems, exercises, and tables . . . . . . . . . . . . . . . . . . . . . . . . . . 86 <p></p><p></p><p><strong>viii Contents</strong>
        </p><p></p><strong>4 Stationary Signals.</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 <p></p>4.1 Stationarity, autocovariance, and autocorrelation . . . . . . . . . 93 <p></p>4.2 Estimating the mean and the autocorrelation function, ergodic signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105 <p></p>4.3 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109 <p></p><p></p><p><strong>5 Power Spectra of Stationary Signals . .</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . 113 </p><p></p>5.1 Mean power of a stationary signal . . . . . . . . . . . . . . . . . . . . . . . .113 <p></p>5.2 Power spectrum and autocorrelation function . . . . . . . . . . . .114 <p></p>5.3 Power spectra of interpolated digital signals . . . . . . . . . . . . .121 <p></p>5.4 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .124 <p></p><p></p><p><strong>6 Transmission of Stationary Signals through Linear Systems . 127</strong>
        </p><p></p>6.1 The time domain analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .128 <p></p>6.2 Frequency domain analysis and system bandwidth . . . . . . .136 <p></p>6.3 Digital signal, discrete-time sampling . . . . . . . . . . . . . . . . . . . .140 <p></p>6.4 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .144 <p></p><p></p><p><strong>7 Optimization of Signal-to-Noise Ratio in Linear Systems. . . . . .147</strong>
        </p><p></p>7.1 Fixed filter structure, known input signal . . . . . . . . . . . . . . . .147 <p></p>7.2 Filter structure matched to signal . . . . . . . . . . . . . . . . . . . . . . . .151 <p></p>7.3 The Wiener filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .154 <p></p>7.4 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .156 <p></p><p></p><p><strong>8 Gaussian Signals, Correlation Matrices, and Sample Path Properties. . . .</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .159 </p><p></p>8.1 Linear transformations of random vectors . . . . . . . . . . . . . . .160 <p></p>8.2 Gaussian random vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .162 <p></p>8.3 Gaussian stationary signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165 <p></p>8.4 Sample path properties of general and Gaussian stationary signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .167 <p></p>8.5 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .173 <p></p><p></p><p><strong>9 Discrete Signals and Their Computer Simulations .</strong> . . . . . . . . . . . .175 </p><p></p>9.1 Autocorrelation as a positive definite function . . . . . . . . . . .175 <p></p>9.2 Cumulative power spectrum of discrete-time stationary signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .176 <p></p>9.3 Stochastic integration with respect to signals with uncorrelated increments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .179 <p></p>9.4 Spectral representation of stationary signals . . . . . . . . . . . . .184 <p></p>9.5 Computer algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .188 <p></p>9.6 Problems and exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .196 <p></p><p><strong></strong></p><p><strong>Bibliographical Comments. .</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .197 </p><p></p><p><strong>Index . .</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201</p><p> </p> 307558.rar (2.7 MB, 需要: 10 个论坛币) 本附件包括：

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