Modern Signal Processing
Description
The book systematically introduces theories of frequently-used modern signal processing methods and technologies, and focuses discussions on stochastic signal, parameter estimation, modern spectral estimation, adaptive filter, high-order signal analysis and non-linear transformation in time-domain signal analysis. With abundant exercises, the book is an essential reference for graduate students in electrical engineering and information science.
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Persons
Prof. Xianda Zhang
was a professor at Department of Automation of Tsinghua University. He was one of the first Changjiang Scholars of the Ministry of Education, P.R.C. He has published 120 SCI papers and more than ten books, including Matrix Analysis and Applications and A Matrix Algebra Approach to Artificial InteIIigence.
Content
Chapter 1: Stochastic signal - correlation function, covariance function, power spectral density, signal identification, signal transformation, linear system with random input signal
Chapter 2: Parameter estimation - estimators, Fisher information and Cramer-Rao inequality, Bayes estimation, maximum likelihood estimation, least-square estimation
Chapter 3: Modern spectral estimation - discrete stochastic process, non-parametric spectral analysis, stationary ARMA process and spectral density, ARMA spectral estimation, ARMA identification, maximum entropy spectrum estimation, Pisarenko harmonic decomposition, extended Prony method, MUSIC, ESPRIT
Chapter 4: Adaptive filter - Wiener filter for continuous time, Optimization, Kalman filter, LMS adaptive algorithm and filter, RLS adaptive algorithm, operator theory for adaptive filter, adaptive line enhancer, trap filter, generalized sidelobe canceller, blind adaptive multi-user detection
Chapter 5: High-order statistical analysis - matrix and cumulative domain, high-order spectral, non-Gussian signal and linear system, FIR system identification, ARMA model identification, harmonic retrieval in color noise, time delay estimation, double spectral and application in signal classification
Chapter 6: Linear transformation in time-domain signal analysis - local transformation, analytic signal, Fourier transformation, Gabor transformation, wavelet transformation and framework theory, multi-resolution analysis, quadrature filter, bi-quadrature filter, Gabor atoms and applications in radar signal detection
Chapter 7: Nonlinear transformation in time-domain signal analysis - time domain distribution, Wigner-Ville distribution, fuzzy function, Cohen qusi-time domain distribution, evaluation and optimization of time domain distribution, time domain distribution for FM signal
