Advanced digital signal processing and noise reduction

Saeed V. Vaseghi9780471626923, 0471626929

Advanced Digital Signal Processing and Noise Reduction, Third Edition, provides a fully updated and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Noise is the eternal bane of communications engineers, who are always striving to find new ways to improve the signal-to-noise ratio in communications systems and this resource will help them with this task. This is an invaluable text for senior undergraduates, postgraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also appeal to engineers in telecommunications and audio and signal processing industries.

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Table of contents :
Contens……Page 5
Preface……Page 15
Frequently Used Symbols and Abbreviations……Page 18
1. Introducction……Page 21
1.1 Signals and Information……Page 22
1.2.1 Non-parametric Signal Processing……Page 23
1.2.3 Bayesian Statistical Signal Processing……Page 24
1.3.1 Adaptive Noise Cancellation and Noise Reduction……Page 25
1.3.2 Blind Channel Equalisation……Page 28
1.3.3 Signal Classification and Pattern Recognition……Page 29
1.3.4 Linear Prediction Modelling of Speech……Page 31
1.3.5 Digital Coding of Audio Signals……Page 32
1.3.6 Detection of Signals in Noise……Page 34
1.3.7 Directional Reception of Waves: Beam-forming……Page 36
1.3.8 Dolby Noise Reduction……Page 38
1.3.9 Radar Signal Processing: Doppler Frequency Shift……Page 39
1.4 Sampling and Analog-to-Digital Conversion……Page 41
1.4.1 Time-Domain Sampling and Reconstruction of Analog Signals……Page 42
1.4.2 Quantisation……Page 45
Bibliography……Page 47
2. Noise and Distortion……Page 49
2.1 Introduction……Page 50
2.2 White Noise……Page 51
2.3 Coloured Noise……Page 53
2.4 Impulsive Noise……Page 54
2.5 Transient Noise Pulses……Page 55
2.6 Thermal Noise……Page 56
2.8 Electromagnetic Noise……Page 58
2.9 Channel Distortions……Page 59
2.10 Modelling Noise……Page 60
2.10.2 Hidden Markov Model for Noise……Page 62
Bibliography……Page 63
3. Probability Models……Page 64
3.1 Random Signals and Stochastic Processes……Page 65
3.1.2 The Space or Ensemble of a Random Process……Page 67
3.2 Probabilistic Models……Page 68
3.2.1 Probability Mass Function (pmf)……Page 69
3.2.2 Probability Density Function (pdf)……Page 70
3.3 Stationary and Non-Stationary Random Processes……Page 73
3.3.1 Strict-Sense Stationary Processes……Page 75
3.3.3 Non-Stationary Processes……Page 76
3.4 Expected Values of a Random Process……Page 77
3.4.2 Autocorrelation……Page 78
3.4.3 Autocovariance……Page 79
3.4.4 Power Spectral Density……Page 80
3.4.6 Cross-Correlation ando Cross-Covariance……Page 82
3.4.8 Ergodic Processes and Time-Averaged Statistics……Page 84
3.4.9 Mean-Ergodic Processes……Page 85
3.4.10 Correlation-Ergodic Processes……Page 86
3.5.1 Gaussian (Normal) Process……Page 88
3.5.2 Multivariate Gaussian Process……Page 89
3.5.3 Mixture Gaussian Process……Page 91
3.5.4 A Binary-State Gaussian Process……Page 92
3.5.5 Poisson Process……Page 93
3.5.6 Shot Noise……Page 95
3.5.8 Markov Processes……Page 97
3.5.9 Markov Chain Processes……Page 99
3.6.1 Monotonic Transformation of Random Processes……Page 101
3.6.2 Many-to-One Mapping of Random Signals……Page 104
3.7 Summary……Page 106
Bibliography……Page 107
4. Bayesian Estimation……Page 109