A note on estimating false alarm rates via importance sampling

When the statistics of the noise are non-Gaussian, analytic expressions for the probability of false alarms in detection systems are rarely available. Monte Carlo estimation techniques are therefore typically necessary. The author presents an importance sampling biasing distribution which renders exponential savings over standard Monte Carlo simulations. Two important features of this biasing strategy are that no importance sampling parameters need to be determined and no additional computations are required for implementation