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Sequential Bayesian Detection: A Model-Based Approach

  • James V. Candy
Chapter

Abstract

Sequential detection is a methodology developed essentially by Wald [1] in the late 1940s providing an alternative to the classical batch methods evolving from the basic Neyman–Pearson theory of the 1930s [2, 3]. From the detection theoretical viewpoint, the risk (or error) associated with a decision typically decreases as the number of measurements increases. Sequential detection enables a decision to be made more rapidly (in most cases) employing fewer measurements while maintaining the same level of risk. Thus, the aspiration is to reduce the decision time while maintaining the risk for a fixed sample size. Its significance was truly brought to the forefront with the evolution of the digital computer and the fundamental idea of acquiring and processing data in a sequential manner. The seminal work of Middleton [2, 4, 5, 6, 7] as well as the development of sequential processing techniques [8, 9, 10, 11, 12, 13] during the 1960s provided the necessary foundation for the sequential processor/detector that is applied in a routine manner today [7, 8, 9, 10, 11, 13].

Keywords

Kalman Filter Extend Kalman Filter Unscented Kalman Filter Sequential Probability Ratio Test Markov Assumption 
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, LLC 2012

Authors and Affiliations

  1. 1.Lawrence Livermore National LaboratoryLivermoreUSA

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