带有等式状态约束的多传感器数据融合算法

在状态估计理论的实际应用中,状态向量常常包含可以预先获知的约束信息,有效地利用这些先验信息可以进一步明确状态元素之间的关系,理论上可以提高对系统的状态估计精度.针对约束滤波的已有研究成果,将其引入到多传感器系统,提出了约束系统多传感器数据融合算法.通过建立线性等式约束方程,将传统卡尔曼滤波结果投影到约束子空间,然后对局部传感器的约束滤波结果采用分布式最优加权融合,并且通过协方差匹配技术检测观测数据异常的传感器,使之不参与到数据融合中.仿真结果表明,约束系统分布式加权融合算法的有效性和可行性,并且比集中式融合算法具有更好的稳定性.

Multi-Sensor Data Fusion Algorithm with State Equality Constraints

In applications of the state estimation theory, the state vector usually implies some constraints that can be known in advance. Making full use of these constraints will enable researchers to have a better understanding of the relationship between state elements, and theoretically enhance the accuracy of state estimation.Considering the recent achievements in constrained filtering, a brand new data fusion algorithm was provided for systems with constraints. Using linear equalities as constrained functions, the method was implemented by projecting the Kalman filtering results onto the constrained subspace, and using distributed, optimal weighting fusion to process local filtering consequences. With the assistance of covariance matching technique, sensors with abnormal measurements were eliminated during data fusion. Simulation proves the feasibility and efficiency of the algorithm, which shows better stability than the centralized fusion algorithm.