数据丢包和量化约束下的分布式滚动时域估计

针对网络约束问题，对带丢包和量化的网络化系统分布式状态估计进行了研究。用一组满足Bernouli分布的随机变量来描述丢包现象，采用预测补偿机制进行丢包补偿。将数据量化引入的误差描述为观测方程中的参数不确定性，通过求解固定时域内的min-max问题，得到局部估计器。对局部估计器的稳定性进行研究，给出了误差范数平方的期望收敛的充分条件。推导了局部估计器误差协方差上界的递推公式，在此基础上，应用协方差交叉（CI）融合算法得到了分布式融合估计器。仿真结果表明，所提算法能够有效减小数据丢包和量化对状态估计的影响。 

Distributed moving horizon estimation under constraints of quantized measurements and packet dropouts

Aimed at the problem of network constraint, distributed state estimation for networked systems with packet dropouts and quantized measurements is studied. A group of Bernoulli distributed random variables is employed to describe the phenomenon of packet dropouts, and a prediction compensation mechanism is applied to compensate the packet dropouts. Quantized errors introduced by data quantification are described as parameter uncertainty in the observation equation, and the local estimator is obtained by solving a min-max problem in fixed time domain. The stability of the local estimator is studied, and a sufficient condition for the convergence of the expectation of the square norm of estimation error is obtained. For each local estimator, the recursive formula of the upper bound of the error covariance is derived, based on which a distributed fusion estimator is presented by using the Covariance Intersection (CI) fusion algorithm. The simulation results show that the proposed algorithm can effectively reduce the influence of packet dropouts and quantization on state estimation.