Titlebook: An Introduction to Signal Detection and Estimation; H. Vincent Poor Textbook 19881st edition Springer Science+Business Media New York 1988

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Elements of Parameter Estimation,se of composite hypothesis testing in which we wish to make a binary decision about the parameter, we wish here to determine as accurately as possible the actual value of the parameter from the observation.

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Signal Detection in Continuous Time,e primary reason for restricting our observation sets in the way that we have done. In particular, as we have seen, all the problems considered thus far have been treated using the ordinary probability calculus of probability density functions and probability mass functions.?

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Signal Estimation in Continuous Time,n all cases, we consider primarily the case of white Gaussian noise, although as we have seen in Chapter VI, other Gaussian noise models can be transformed to this model, so that these results are more general.

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Textbook 19881st edition through V, which for the most part can be read with a background of only the basics of applied probability, including random vectors and conditional expectations. Sufficient background for the latter option is given for exam pIe in the book by Thomas (1986), also in this series.

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Elements of Hypothesis Testing,h we wish to decide among . possible statistical situations describing the observations. For example, in an .-ary communications receiver we observe an electrical waveform that consists of one of . possible signals corrupted by random channel or receiver noise, and we wish to decide which of the . p

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