This paper develops Rao-Blackwellized particle filter with asynchronous dependence between system noise and measurement noise. It is pointed out that this dependence affects both the particle filter update step for the nonlinear sub-system and the Kalman filter update step for the conditionally linear sub-system in Rao-Blackwellized particle filter. A de-correlation method is suggested to deal with such influence. The optimal importance density function for sampling the nonlinear sub-state is found out, and a suboptimal one for approximating the optimal importance density function is proposed. The proposed methods are applied to target tracking to testify their effectiveness and superiority.

本文开发了一种Rao-Blackwellized粒子滤波器，它在系统噪声和测量噪声之间具有异步依赖性。要指出的是，这种依赖性既影响非线性子系统的粒子滤波器更新步骤，又影响Rao-Blackwellized粒子滤波器中条件线性子系统的卡尔曼滤波器更新步骤。建议使用去相关方法来处理这种影响。找出了用于采样非线性子状态的最优重要性密度函数，并提出了一个近似最优重要性密度函数的次优模型。所提出的方法应用于目标跟踪，以证明其有效性和优越性。