Schonhoff & Giordano, Detection and Estimation Theory | PearsonWe are always looking for ways to improve customer experience on Elsevier. We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit. If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website. Thanks in advance for your time. Skip to content. Search for books, journals or webpages
Introduction to Detection Theory (Hypothesis Testing)
Detection and Estimation Methods for Biomedical Signals
Theory of Point Estimation by E. London, England: Springer-Verlag. This can be seen as a very simple case of maximum spacing estimation. There will be 10 homework assignments in ECE, each worth 20 points.
Homework We value your input. Page Count: Detection and Estimation Theory.
If You're a Student
Unable to display preview. On-line Supplement. For other uses, see Estimation disambiguation? Due by pm on Apr Views Read Edit View history.
Underwater Acoustics and Signal Processing pp Cite as. This paper has two aspects: one is tutorial in nature and its objective is to present in a concise way the fundamental ideas of detection and estimation theory which are necessary to easily undestand the matter presented in the following part; the second is more a presentation of new material in the field of adaptive detection, and particularly of signal detection in noise with fluctuating power. In 1 was discussed the concept of optimality for an adaptive detection system and particularly its application to the detection of a deterministic signal in spherically invariant noise. In 2 the concept of Noise Alone Reference NAE already used in spatial signal processing was introduced in order to present a geometrical interpretation of the classical matched filter using a phase of estimation. Moreover some adaptive detectors were suggested without effective calculation or simulations concerning their performances. In 3 some adaptive algorithms were presented in order to introduce the concept of recursivity. Unable to display preview.
Topics: Introduction Basic concepts of statistical decision theory: Main ingredients; concepts of optimality Bayesian and minimax approaches Binary hypothesis testing: Bayesian decision rules; minimax decision rules; Neyman-Pearson decision rules the radar problem ; composite hypothesis testing Signal detection in rstimation time: models and detector structures; performance evaluation; Chernoff bounds and large deviations; sequential detection, quickest change detecti. We're sorry. Homework 8. Lecture 6: Detection of discrete-time signals with random parameters.
Main category: Estimation theory. Nikulin, "Unbiased estimators and their applications. Imprint: Academic Press. Download preview PDF.