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A new approach to importance sampling for the simulation of false alarmsIn this paper a modified importance sampling technique for improving the convergence of Importance Sampling is given. By using this approach to estimate low false alarm rates in radar simulations, the number of Monte Carlo runs can be reduced significantly. For one-dimensional exponential, Weibull, and Rayleigh distributions, a uniformly minimum variance unbiased estimator is obtained. For Gaussian distribution the estimator in this approach is uniformly better than that of previously known Importance Sampling approach. For a cell averaging system, by combining this technique and group sampling, the reduction of Monte Carlo runs for a reference cell of 20 and false alarm rate of lE-6 is on the order of 170 as compared to the previously known Importance Sampling approach.
Document ID
19880046154
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Lu, D.
(California Univ. Los Angeles, CA, United States)
Yao, K.
(California, University Los Angeles, United States)
Date Acquired
August 13, 2013
Publication Date
January 1, 1987
Subject Category
Quality Assurance And Reliability
Meeting Information
Meeting: Radar - 87
Location: London
Start Date: October 19, 1987
End Date: October 21, 1987
Sponsors: Convention of National Societies of Electrical Engineers of Western Europe, Institute of Mathematics and Its Applicationss, Royal Aeronautical Society.
Accession Number
88A33381
Funding Number(s)
CONTRACT_GRANT: NAS2-304
Distribution Limits
Public
Copyright
Other

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