Redrawing-resampling rejection controlled sequential importance sampling

Monte Carlo computation has been widely applied in the field of dynamic systems. This paper focuses on the general framework in the implementation of sequential importance sampling by combining redrawing, resampling and rejection control simultaneously. The proposed algorithm is named as Redrawing Resampling Rejection Controlled Sequential Importance Sampling (RR-RC-SIS). It can reduce sampling computation and meanwhile maintain the diversity of random samples. Theoretical basis is given to prove that RR-RC-SIS has advantages in comparison with Rejection Controlled Sequential Importance Sampling. It also has practical value as illustrated in numerical simulation on blind deconvolution problem in digital communications.

Automated summary

This paper introduces a new sequential importance sampling algorithm named RR-RC-SIS, which shows advantages in sampling computation speed and maintaining sample diversity. Numerical simulation on blind deconvolution problem in digital communications demonstrates the practical value of the algorithm.