测量数据丢失的随机不确定系统滚动时域估计

研究存在传感器测量数据丢失的随机不确定系统状态估计问题,用概率已知的Bernoulli随机序列描述丢包现象,并采用丢失测量数据的预测值进行丢包补偿,将不确定条件下的最优化问题表示为Min-Max问题,并通过引入拉格朗日算子,将Min-Max问题转化为受限条件下的Min-Min问题,进而实现最优状态估计的求解.对所提算法的稳定性进行研究,推导出估计误差范数平方期望的上界,并给出估计误差范数平方期望收敛的充分条件.最后通过仿真验证所提算法的有效性.

Moving horizon estimation for stochastic uncertain system with missing measurements

The state estimation problem of stochastic uncertain systems with missing measurements 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 optimization problem under uncertain conditions is described as a Min-Max problem, by using two groups of Lagrange multipliers, the Min-Max problem is transformed into a constrained Min-Min problem, and then the optimal estimator is obtained. The stability of the proposed algorithm is studied, the upper bound of the expectation of the square norm of estimation error is obtained, and a sufficient condition for the convergence of the square norm of estimation error is given. Finally, an example is given to demonstrate the effectiveness of the proposed method.