System Identification with Quantized Observations

This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed. System Identification with Quantized Observations is an excellent resource for graduate students, systems theorists, control engineers, applied mathematicians, as well as practitioners who use identification algorithms in their work. Selected material from the book may be used in graduate-level courses on system identification. TOC:Preface.- Conventions.- Glossary of Symbols.- Part I. Overview.- Introduction.- System Settings.- Part II. Stochastic Methods for Linear Systems.- Empirical-Measure-Based Identification.- Estimation Error Bounds: Including Unmodeled Dynamics.- Rational Systems.- Quantized Identification and Asymptotic Efficiency.- Input Design for Identification in Connected Systems.- Identification of Sensor Thresholds and Noise Distribution Functions.- Part III. Deterministic Methods for Linear Systems.- Worst-Case Identification.- Worst-Case Identification Using Quantized Observations.- Part IV. Identification of Nonlinear and Switching Systems.- Identification of Wiener Systems.- Identification of Hammerstein Systems.- Systems with Markovian Parameters.- Part V. Complexity Analysis.- Complexities, Threshold Selection, Adaptation.- Impact of Communication Channels.- Appendix. Background Materials.- References.- Index.