带等式状态约束的集合卡尔曼滤波算法

Ensemble Kalman filter with state equality constraints

对含等式状态约束的非线性系统状态估计问题,本文考虑将集合卡尔曼滤波算法与估计投影方法结合,分别对每个状态粒子和加权平均后的状态估计向量使用估计投影方法,得到两种新的带约束的状态估计算法.实验表明,与粒子滤波和不带约束的集合卡尔曼滤波相比,新算法的估计精度有所提高.

For the problem of state estimation for nonlinear systems with state equality constraints, the method that combines ensemble Kalman filter with estimate projection approaches is presented. We de- rive two new state estimation algorithms with constraints, by using estimate projection method for each particle of state estimation and state estimation vector after calculating weighted average respectively. The simulation shows that the new algorithms perform better in terms of estimation accuracy, compa- ring to particle filter and unconstrained ensemble Kalman filter.