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. >

[1]  Kung Yao,et al.  Improved importance sampling technique for efficient simulation of digital communication systems , 1988, IEEE J. Sel. Areas Commun..

[2]  B. Aazhang,et al.  A New Class of Optimum Importance Sampling Strategies Derived from Statistical Distance Measures , 1993, Proceedings. IEEE International Symposium on Information Theory.

[3]  Michel C. Jeruchim,et al.  On optimum and suboptimum biasing procedures for importance sampling in communication simulation , 1990, IEEE Trans. Commun..

[4]  Behnaam Aazhang,et al.  On the theory of importance sampling applied to the analysis of detection systems , 1989, IEEE Trans. Commun..

[5]  Behnaam Aazhang,et al.  Constrained solutions in importance sampling via robust statistics , 1991, IEEE Trans. Inf. Theory.

[6]  R.L. Mitchell.  Importance Sampling Applied to Simulation of False Alarm Statistics , 1981, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Michel C. Jeruchim,et al.  Developments in the Theory and Application of Importance Sampling , 1987, IEEE Trans. Commun..

[8]  D. Siegmund.  Importance Sampling in the Monte Carlo Study of Sequential Tests , 1976 .

[9]  Norman C. Beaulieu.  An investigation of Gaussian tail and Rayleigh tail density functions for importance sampling digital communication system simulation , 1990, IEEE Trans. Commun..

[10]  G. W. Lank.  Theoretical aspects of importance sampling applied to false alarms , 1983, IEEE Trans. Inf. Theory.

[11]  Jean Walrand,et al.  A quick simulation method for excessive backlogs in networks of queues , 1989 .

[12]  P. Balaban,et al.  A Modified Monte-Carlo Simulation Technique for the Evaluation of Error Rate in Digital Communication Systems , 1980, IEEE Trans. Commun..

[13]  N.C. Beaulieu.  A composite importance sampling technique for digital communication system simulation , 1990, IEEE Trans. Commun..

[14]  J.S. Sadowsky,et al.  On large deviations theory and asymptotically efficient Monte Carlo estimation , 1990, IEEE Trans. Inf. Theory.