Journal ArticleDOI
Optimality of the sequential probability ratio test for nonstationary observations
Yong Liu,Steven D. Blostein +1 more
- Vol. 38, Iss: 1, pp 177-182
TLDR
Bayesian analysis is used to show that Wald's sequential probability ratio test with varying thresholds is optimal for the nonstationary situation, where the observed samples are independent but not identically distributed.Abstract:
Bayesian analysis is used to show that Wald's sequential probability ratio test with varying thresholds is optimal for the nonstationary situation, where the observed samples are independent but not identically distributed. Some important properties useful for the design of the test thresholds are discussed. Wald's lower bound, generalized to the nonstationary situation, is also presented. The results have important applications in situations where the observed signal is time-varying. such as in radar signal processing, image processing, and spread spectrum communications. >read more
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References
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Journal ArticleDOI
Optimum Character of the Sequential Probability Ratio Test
Abraham Wald,Jacob Wolfowitz +1 more
TL;DR: In this article, it was shown that the sequential probability ratio test for deciding between two simple alternatives (H_0 and H_1) requires on the average fewest observations.
Journal ArticleDOI
Detecting small, moving objects in image sequences using sequential hypothesis testing
TL;DR: An algorithm is proposed for the solution of the class of multidimensional detection problems concerning the detection of small, barely discernible, moving objects of unknown position and velocity in a sequence of digital images, modeled as GWN.