容积卡尔曼一致滤波

针对卡尔曼一致滤波的应用受限于被估计系统需 满足线性条件的问题,通过容积卡尔曼滤波(CKF)和一致性策 略的动态结合,提出一种容积卡尔曼一致滤波(CKCF)算法。算法采用分布式融合机制, 传感器节点采集可通信相邻 节点的信息,并作为自身节点的量测信息应用于CKF,获取局部状态估计 值。在此基础上,利用一 致性策略实现对整个量测系统中传感器节点局部估计值的优化,进而通过增强传感器节点估 计值一致性实现目标 状态估计精度的提升。相对于标准卡尔曼一致滤波,本文算法将一致性策略推广到非线性系 统估计领域。理论分析 与仿真实验验证了算法的可行性与有效性。

Cubature Kalman consensus filter

Aiming at the limit of linear conditions for estimated system in the a pplication of Kalman consensus filter, combining with the cubature Kalman filter (CKF) and consensus strategy,a novel c ubature Kalman consensus filtering (CKCK) algorithm is proposed.In the realization of algorithm,the distributed fusion framework i s adopted.Firstly,measurement data from the capable-communication adjacent nodes are sampled,which are applied for cuba ture Kalman filter to achieve the distributed estimation of system state.Secondly,according to consensus strateg y,these local state estimations in the whole sensor network are optimized.And then the estimation precision of system state is improved by enhancing the consensus of each sensor node.Compared with standard Kalman consensus filter,the algorithm makes consensus strategy extend to nonlinear system estimation.The theoretical analyses and experimental results v erify the feasibility and efficiency of the proposed algorithm.