Sensors arrays are used in diverse applications across a broad range of disciplines. Regardless of the application, however, the tools of sensor array signal processing remain the same. Furthermore, whether your interest is in acoustic, seismic, mechanical, or electromagnetic wavefields, they all have a common mathematical framework. Mastering this framework and those tools lays a strong foundation for more specialized study and research. Sensor Array Signal Processing helps build that foundation. It unravels the underlying principles of the subject without reference to any particular application. Instead, the author focuses on the common threads that exist in wavefield analysis. After introducing the basic equations governing different wavefields, the treatment includes topics from simple beamformation, spatial filtering, and high resolution DOA estimation to imaging and reflector mapping. It studies different types of sensor configurations, but focuses on the uniform linear and circular arrays-the most useful configurations for understanding array systems in practice. Unique in its approach, depth, and quantitative focus, Sensor Array Signal Processing offers the ideal starting point and an outstanding reference for those working or interested in medical imaging, astronomy, radar, communications, sonar, seismology-any field that studies propagating wavefields. Its clear exposition, numerical examples, exercises, and wide applicability impart a broad picture of array signal processing unmatched by any other text on the market.
Spectrum analysis can be considered as a topic in statistics as well as a topic in digital signal processing (DSP). This book takes a middle course by emphasizing the time series models and their impact on spectrum analysis. The text begins with elements of probability theory and goes on to introduce the theory of stationary stochastic processes. The depth of coverage is extensive. Many topics of concern to spectral characterization of Gaussian and non-Gaussian time series, scalar and vector time series are covered. A section is devoted to the emerging areas of non-stationary and cyclostationary time series. The book is organized more as a textbook than a reference book. Each chapter includes many examples to illustrate the concepts described. Several exercises are included at the end of each chapter. The level is appropriate for graduate and research students.
Discusses localization in 2D and 3D space Includes random and sparse sensor distribution Provides effective algorithms and illustrative numerical examples Concepts are described in detailed mathematical form Contains simple lab experiments
&Quot;DSP is a mathematics-oriented subject and this text provides a precise mathematics based approach to the subject along with a concise and clear narrative to help the students. A general background in college mathematics is assumed."--BOOK JACKET.
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