Importance sampling for estimation of false alarm probability

Abstract The purpose of this paper is to describe a technique of importance sampling for Monte Carlo simulation of Radar signal detectors under the ‘No Signal’ condition. The input to the detector is ‘restricted’ to a range that is more likely to cause a ‘false alarm’, thereby increasing the number of false alarms in a given sample. A model based on the theory of the Gauss-Markov process is developed so as to allow consideration of the case when the successive noise (clutter) samples are correlated. The technique enables estimation of very low false alarm probabilities with a relatively moderate sample size.

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