Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
Environmental conflicts are the source of many large-scale popular protests in China, with some protests substantially endangering social order. Such protests have often prompted severe counter measures by both national and local government, but have often then gone on to result in compromises whereby the demands of protesters have been largely met. This book considers the nature of environmental conflicts in China and the way in which national and local governments have handled the situations. It includes detailed case studies of particular conflicts, relates the governance of environmental conflicts in China to wider discussions on the nature of governance and examines under what conditions government in China makes compromises. The book concludes by assessing the lessons for the future.
This book is in the field of Engineering Thermophysics. It first introduces the authors’ academic thoughts of photo-thermal energy cascade conversion in the fuel combustion. Afterward, a series of thermal radiation theories and models have been developed based on the aim of radiative energy utilization, including spectral radiation available energy theory, gas radiation model under complex combustion conditions, and calculation model of radiation available energy transfer in combustion medium. Based on simulation and experimental results, the radiative energy characteristics of different fuel combustion are introduced. This book develops the radiation theory of the combustion process from a new perspective, integrating theories, models, and experimental results. This book can be used as a reference for scientists, engineers, and graduate students engaged in energy environment, combustion, and thermal radiation.
On-orbit operations optimization among multiple cooperative or noncooperative spacecraft, which is often challenged by tight constraints and shifting parameters, has grown to be a hot issue in recent years. The authors of this book summarize related optimization problems into four planning categories: spacecraft multi-mission planning, far-range orbital maneuver planning, proximity relative motion planning and multi-spacecraft coordinated planning. The authors then formulate models, introduce optimization methods, and investigate simulation cases that address problems in these four categories. This text will serve as a quick reference for engineers, graduate students, postgraduates in the fields of optimization research and on-orbit operation mission planning.
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
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