On the theory of importance sampling applied to the analysis of detection systems

Adaptive simulated annealing for optimization in signal processing applications

“…For the cases of high SNR, the conventional Monte Carlo method will require a large number of training samples. However, this problem can easily be overcome by adopting importance sampling techniques [17,21,26] in estimating the SER.…”

Applications of POD studies and robust design to electromagnetic NDE

“…The cost of importance sampling is that it is not easy to construct nonuniform random variables that have densities similar to p(y I x, ) and p(y l*o) respectively [116]. This problem has been solved in many applications in signal detection that estimate small probability of false alarm [117][118][119][120][121][122]. However, they are efficient only when the PFA is very small (generally smaller than l.e-4), which is not the case in current NDE techniques.…”

Efficient Simulation Of Random Signal Detectors

“…Orsak and Aazhang [4] have shown that minimizing the variance of P is equivalent to minimizing the quantity…”

“…Orsak and Aa.zhang [4] have shown that an estimate of the importance sampling gain is given by P(1-P.)…”

“…Many researchers have utilized importance sampling to reduce the number of trials in Monte Carlo simulations; some of the more recent results are contained in [4,5,8] which also reference many of the earlier works. However, the main hindrance to using many of these results has been the analytical complexity in determining a "good" importance sampling approach.…”