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.