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A team of launched and coordinated Unmanned aerial vehicles (UAVs), requires advanced technologies in sensing, communication, computing, and control to improve their intelligence and robustness towards autonomous operations. To enhance reliability, robustness, and mission capability of a team of UAVs, a system-oriented and holistic approach is desirable in which all components and subsystems are considered in terms of their roles and impact on the entire system. This volume aims to summarize the recent progress, identify challenges and opportunities, and develop new methodologies and systems on coordinated UAV control.A group of experts working in this area have contributed to this volume in several related aspects of autonomous control of networked UAVs. Their papers introduce new control methodologies, algorithms, and systems that address several important issues in developing intelligent, autonomous or semi-autonomous, networked systems for the next generation of UAVs. The papers share a common focus on improved coordination of the members of the networked system to accomplish a common mission, to achieve heightened capability in system reconfiguration to compensate for lost members or connections, and to enhance robustness against terrain complications and attacks.

​This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications.

This book concerns the identi?cation of systems in which only quantized output observations are available, due to sensor limitations, signal quan- zation, or coding for communications. Although there are many excellent treaties in system identi?cation and its related subject areas, a syst- atic study of identi?cation with quantized data is still in its early stage. This book presents new methodologies that utilize quantized information in system identi?cation and explores their potential in extending control capabilities for systems with limited sensor information or networked s- tems. The book is an outgrowth of our recent research on quantized iden-?cation; it o?ers several salient features. From the viewpoint of targeted plants, it treats both linear and nonlinear systems, and both time-invariant and time-varying systems. In terms of noise types, it includes independent and dependent noises, stochastic disturbances and deterministic bounded noises, and noises with unknown distribution functions. The key meth- ologies of the book combine empirical measures and information-theoretic approaches to cover convergence, convergence rate, estimator e?ciency, - put design, threshold selection, and complexity analysis. We hope that it can shed new insights and perspectives for system identi?cation.