Elements of Signal Detection and Estimation: Carl W. Helstrom

Description for Elements of Signal Detection and Estimation Carl W Helstrom

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Elements of Signal Detection and Estimation: Carl W. Helstrom
Prentice Hall | ISBN: 013808940X | August 1994 | PDF (OCR) | 586 pages | 30.2 Mb
Written by a highly-regarded authority and researcher, this volume provides an introduction to signal-detection theory (the detection of a signal and the estimation of its parameters)--a subject fundamental to the design of detectors of weak signals in the presence of random noise, and, in particular, to the design of optimal and near-optimal receivers of communication, radar, sonar, and optical signals. Applies signal-detection theory to a succession of problems of increasing complexity--beginning with the detection of known signals in Gaussian noise, then taking into account the designer's unavoidable ignorance of signal parameters such as phase, arrival time, and Doppler shift.
Written by a highly-regarded authority and researcher, this volume provides an introduction to signal-detection theory (the detection of a signal and the estimation of its parameters)--a subject fundamental to the design of detectors of weak signals in the presence of random noise, and, in particular, to the design of optimal and near-optimal receivers of communication, radar, sonar, and optical signals. Applies signal-detection theory to a succession of problems of increasing complexity--beginning with the detection of known signals in Gaussian noise, then taking into account the designer's unavoidable ignorance of signal parameters such as phase, arrival time, and Doppler shift. Examines inter alia beamforming by transducer arrays, the structure of constant-false-alarm-rate (CFAR), nonparametric, and sequential receivers, the resolution of close signals, the detection of random signals at radio frequenies, and the detection of optical signals. Introduces topics such as matched filtering, signal space, time-series estimation, the method of maximum likelihood, and Kalman filtering in context.

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