Ren Hongru, Lu Renquan, Xiong Junlin, Wu Yuanqing, Shi Peng
IEEE Trans Cybern. 2020 Sep;50(9):4169-4181. doi: 10.1109/TCYB.2019.2924485. Epub 2019 Jul 15.
This paper concentrates on the linear least mean square (LLMS) filtered and smoothed estimators for networked linear stochastic systems. Multiple packet losses, Markovian communication constraints, and superposed process noise are considered simultaneously. In order to reduce the channel load during communication, at every step, just one transmission node is permitted to send data packets. Hence, a Markovian communication protocol is utilized to arrange the packets of these transmission nodes. Moreover, multiple data packet dropouts occur during transmission due to an imperfect communication channel. Therefore, the global observation information cannot be obtained by the state estimator. The real state of Markov chain is assumed to be unknown to the estimator except the transition probability matrix. By means of the innovation analysis approach and orthogonal projection principle, we design Kalman-like estimators in a recursive form. Finally, through simulation experiments, we verify the effectiveness and superiority of the designed algorithm.
本文主要研究网络化线性随机系统的线性最小均方（LLMS）滤波和平滑估计器。同时考虑了多重数据包丢失、马尔可夫通信约束和叠加过程噪声。为了在通信过程中减少信道负载，每一步仅允许一个传输节点发送数据包。因此，采用马尔可夫通信协议来安排这些传输节点的数据包。此外，由于通信信道不完善，传输过程中会出现多个数据包丢失的情况。因此，状态估计器无法获得全局观测信息。除转移概率矩阵外，估计器假定马尔可夫链的真实状态未知。借助创新分析方法和正交投影原理，我们以递归形式设计了类似卡尔曼的估计器。最后，通过仿真实验验证了所设计算法的有效性和优越性。
