丢包和量化约束下的不确定系统分布式滚动时域估计

研究了数据丢包和量化约束下的随机不确定系统分布式状态估计问题.将丢包现象描述为随机Bernoulli序列,采用预测补偿机制对数据丢包进行补偿,将量化引入的误差转化为观测方程中的不确定参数,将系统的模型不确定性描述为系数矩阵受到随机扰动;利用固定时域内的所有观测值构造代价函数,将状态估计问题建模为带不确定参数的鲁棒最小二乘优化问题,并通过将矢量优化问题转化为单峰函数的标量优化问题,实现了鲁棒滚动时域局部估计器的快速求解;对局部估计器的稳定性进行研究,给出了估计误差范数平方期望收敛的充分条件.应用协方差交叉(CI)融合算法进行加权融合,得到了分布式融合估计器.最后通过仿真验证了所提算法的有效性.

Distributed moving horizon estimation for stochastic uncertain system with packet dropouts and quantized measurements

The distributed state estimation problem of the stochastic uncertain system with quantized measurements and packet dropouts is studied. A group of Bernoulli distributed random variables is employed to describe the phenomenon of packet dropouts, and the predictor of lost observation is used as the observation when a packet is lost. The error introduced by data quantization is described as a bounded uncertain parameter in the observation equation, and the uncertainty of the model is described by stochastic parameter perturbbation in the coefficient matrix. All measurements in the fixed time domain are used to construct a cost function, and the state estimation problem is modeled as a regularized least squares problem with uncertain parameters, by reducing a vector optimization problem to a scalar optimization problem of an unimodal function, a robust moving horizon local estimator is obtained. The stability of local estimator is studied, and a sufficient condition for the convergence of the square norm of estimation error is obtained. A distributed fusion estimator is presented based on the covariance intersection algorithm. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method