Abstract
In Chapter VI we treated the problem of signal detection with continuous-time observations. In this chapter we consider the problem of signal estimation in continuous time. We treat three basic problems: parameter estimation for signals of known form (up to a set of unknown parameters) observed in additive Gaussian noise; linear/Gaussian estimation in which either we assume that the signals and noise of interest are Gaussian processes or we restrict attention to linear estimators; and nonlinear filtering, in which we derive estimators for non-Gaussian random signals generated by nonlinear differential equations when observed in additive Gaussian noise. In 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1988 Springer Science+Business Media New York
About this chapter
Cite this chapter
Poor, H.V. (1988). Signal Estimation in Continuous Time. In: An Introduction to Signal Detection and Estimation. Springer Texts in Electrical Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3863-6_7
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3863-6_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-3865-0
Online ISBN: 978-1-4757-3863-6
eBook Packages: Springer Book Archive