Signal Detection in Continuous Time

  • H. Vincent Poor
Part of the Springer Texts in Electrical Engineering book series (STELE)


In the preceding chapters we have presented the basic principles of signal detection and estimation, assuming throughout that our observation set is either a set of vectors or is a discrete set. Throughout this analysis a key role was played by a family of densities {pθ; θ ∞ Λ} on the observation space, either through the likelihood ratio in hypothesis testing, through the computation of an a posteriori parameter distribution in Bayesian estimation, or through the study of MVUEs and MLEs in nonBayesian parameter estimation. This necessity of specifying a family of densities on the observation space is the 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.†


Likelihood Ratio Wiener Process Autocovariance Function Gaussian Random Process Gaussian Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1988

Authors and Affiliations

  • H. Vincent Poor
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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