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.

蒙特卡罗计算在动态系统领域得到了广泛的应用。本文重点介绍了通过同时结合重绘、重采样和拒绝控制来实现顺序重要性采样的一般框架。所提出的算法被命名为重绘重采样拒绝控制顺序重要性采样（RR-RC-SIS）。它可以减少抽样计算，同时保持随机样本的多样性。给出了理论依据证明RR-RC-SIS与拒绝控制顺序重要性采样相比具有优势。它还具有实用价值，如数字通信中盲解卷积问题的数值模拟所示。