基于多胞体双重滤波的系统状态估计方法

针对未知但有界噪声离散时间状态空间系统,提出一种基于多胞体双重滤波的系统状态估计方法.首先,采用有界误差方法对测量噪声和状态预测过程进行分析,利用正多胞体预测状态集包裹后离散成初始约束条件;然后,根据更新最小边,全对称多胞体经过正多胞体紧致包裹后离散成约束条件,与测量方程约束条件组成3重约束;最后,通过求解线性规划问题得到全部状态的上下界,并获得包裹状态可行集的最紧致正多胞体.仿真示例验证了该方法估计离散状态空间系统状态的有效性和准确性.

Orthotopic double filtering based state estimation algorithm

For the unknown but bounded noise discrete-time state space system, a state estimation method based on orthotope filtering is proposed. The bounded error method is first used to analyze the measurement noise and state prediction process, and the prediction state set uses orthotope after being wrapped, which is dispersed into constraints. Then due to the updated minimum edge, the zonotope is tightly wrapped by the orthotope before being dispersed into constraints, which forms a triple constraint with the measurement equation constraints. The upper and lower bounds of all states are obtained by solving linear programming problems. The bounds of this method are the most compact orthotope of the feasible set of wrapped states. Simulation examples illustrate the effectiveness and accuracy of the proposed method for estimating the state of discrete state space systems.