In this paper, the filtering problem for systems with fading measurements is considered. Taking advantage of the cascaded structure of the system, the original filtering problem is decomposed into two subproblems: 1) recovery of the original measurements from the faded ones, and 2) state estimation based on the recovered signals. A two-stage particle filtering (PF) algorithm is proposed to achieve simultaneous measurement recovery and state estimation. In our scheme, the second-stage resampling procedure can be implemented concurrently, resulting in a significant reduction of execution time. Two examples are provided to demonstrate the effectiveness of our algorithm. A benchmark problem for nonlinear filtering is tackled in the first example and, in the second example, the proposed algorithm is applied to the object tracking problem where the measured signal is distorted by the communication channel. Simulation results show that, compared with the brute force PF, the two-stage PF can strike better balance between tracking accuracy and execution time.

本文考虑了带有衰落测量的系统的滤波问题。利用系统的级联结构，原始的滤波问题被分解为两个子问题：1）从衰落的测量中恢复原始测量，以及2）基于恢复的信号的状态估计。提出了一种两阶段粒子滤波算法，以实现同时的测量恢复和状态估计。在我们的方案中，第二阶段重采样过程可以同时执行，从而大大减少了执行时间。提供了两个例子来证明我们算法的有效性。第一个示例解决了非线性滤波的基准问题，在第二个示例中，该算法应用于目标跟踪问题，其中被测信号由于通信信道而失真。仿真结果表明，与蛮力PF相比，两阶段PF可以在跟踪精度和执行时间之间取得更好的平衡。