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Non-Linear Signal Processing

Non-Linear Signal Processing

Francis Castanié

ISBN: 978-1-848-21456-9

Jul 2016, Wiley-ISTE

160 pages

Select type: Hardcover


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The continuously increasing computing power of Digital Signal Processing makes it now possible to efficiently implement Non-linear Algorithms for Signal Processing (NLSP). This book proposes a comprehensive review of Non-Linear Signal Processing Methods and the associated Parameter Estimation principles.  The various existing approaches are considered: Classical descriptions (Hammerstein models, Volterra Equations …), and more modern ones like Neural Network based ones, Wavelet Transform based decompositions, etc.  The estimation of parameters is also considered: Classical Kalman Filter, Particle Filtering, and Self Learning Networks.


1. Basic classification of Non Linear (NL) representations of signals: with or without memory

1.1 Memoryless systems effects on signals: Probability Density transformations. Random Processes Moment transformations: Price theorem and its generalizations.

1.2 Time Dependent NL signal models: integral and differential equations (Fredholm, Volterra, etc.).

2. Modeling Non-Linear systems

2.1 Hammerstein separable Models

2.2 Cellular networks: Neural Networks, Support Vector Machines

2.3 State Space Equation based:  Extended Kalman Filter

3. Parameter estimation in NL systems

3.1 Known Input Methods: Kalman, Least Squares and Recursive Least Squares, Supervised (i.e. 'with learning phase'): Neural Networks 

3.2 Self-learning mode: Kohonen-like algorithms

4. Selected application examples derived from:

4.1 Basic Signal Processing: Polynomial NL systems, hard-limiters, clippers, etc.

4.2 Space Telecommunications: Satellite On-board Solid State Power Amplifier, Non-Linear Channel Equalizers.