Optimum envelope-constrained filter design is concerned with time-domain synthesis of a filter such that its response to a specific input signal stays within prescribed upper and lower bounds, while minimizing the impact of input noise on the filter output or the impact of the shaped signal on other systems depending on the application. In many practical applications, such as in TV channel equalization, digital transmission, and pulse compression applied to radar, sonar and detection, the soft least square approach, which attempts to match the output waveform with a specific desired pulse, is not the most suitable one. Instead, it becomes necessary to ensure that the response stays within the hard envelope constraints defined by a set of continuous inequality constraints. The main advantage of using the hard envelope-constrained filter formulation is that it admits a whole set of allowable outputs. From this set one can then choose the one which results in the minimization of a cost function appropriate to the application at hand. The signal shaping problems so formulated are semi-infinite optimization problems. This monograph presents in a unified manner results that have been generated over the past several years and are scattered in the research literature. The material covered in the monograph includes problem formulation, numerical optimization algorithms, filter robustness issues and practical examples of the application of envelope constrained filter design. Audience: Postgraduate students, researchers in optimization and telecommunications engineering, and applied mathematicians.
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First Sentence:
In signal processing, the design of many filters can often be cast as a constrained optimization problem where the constraints are defined by the specifications of the filter. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
envelope constrained filter design, equalized cable, unconstrained dual problem, pulse template, dual positive cone, augmented cost function, conic combination, output mask, envelope constraints, mainlobe peak, finite dimensional filters, constraint robustness, directional differential, equalizing filter, active set strategy, filtering problem, pulse shaping filter, robustness margin, output envelope, hybrid filters, linear interpolator, noise gain, ascent direction, dual functional, output noise power Key Phrases - Capitalized Phrases (CAPs): (learn more)
Dimensionality Theorem, Appendix Lemma, Using Theorem New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
In signal processing, the design of many filters can often be cast as a constrained optimization problem where the constraints are defined by the specifications of the filter. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
envelope constrained filter design, equalized cable, unconstrained dual problem, pulse template, dual positive cone, augmented cost function, conic combination, output mask, envelope constraints, mainlobe peak, finite dimensional filters, constraint robustness, directional differential, equalizing filter, active set strategy, filtering problem, pulse shaping filter, robustness margin, output envelope, hybrid filters, linear interpolator, noise gain, ascent direction, dual functional, output noise power Key Phrases - Capitalized Phrases (CAPs): (learn more)
Dimensionality Theorem, Appendix Lemma, Using Theorem New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 62
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- Optimization Methods and Applications (Applied Optimization, Volume 52) by Xiao-qi Yang in Back Matter (1), and Back Matter (2)
- Theory and Application of Digital Signal Processing by Lawrence R. Rabiner in Back Matter
- Cyclostationarity in Communications and Signal Processing by William A. Gardner in Back Matter
- Introduction to Optimization Theory (Prentice-Hall International Series in Industrial and Systems Engineering) by Byron S. Gottfried in Back Matter
- Digital Signal Processing (4th Edition) by John G. Proakis in Back Matter
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- Models for Parallel and Distributed Computation: Theory, Algorithmic Techniques and Applications (Applied Optimization) by R. Correa in Back Matter
- Financial Engineering, E-commerce and Supply Chain (Applied Optimization) by Panos M. Pardalos in Back Matter
- Operations Research in Space and Air (Applied Optimization) by Tito A. Ciriani in Back Matter
- Mixed-Mode Modelling: Mixing Methodologies for Organisational Intervention (Applied Optimization) by M.G. Nicholls in Back Matter
- Computational Methods in Decision-Making, Economics and Finance (Applied Optimization) by Erricos John Kontoghiorghes in Back Matter
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