基于CKF 的分布式滤波算法及其在目标跟踪中的应用

针对已有基于Sigma点信息滤波的分布式滤波算法，其性能易受参数影响而导致应用范围受限的问题，以容积卡尔曼滤波(CKF)为基础，利用信息滤波和平均一致性理论提出一种分布式CKF算法。该算法在保持分布式滤波优良特性(即可扩展性和对节点故障强鲁棒性)的同时，兼具CKF的高滤波精度和强稳定性。仿真结果表明了所提出算法的有效性，与分布式Unscented卡尔曼滤波(UKF)算法相比，该算法显著提高了目标跟踪的精度和稳定性。

Distributed algorithm-based CKF and its applications to target tracking

For the problem that the performance of distributed filter based on Sigma point information filtering is affected by the parameters, which limits its scope of application, a distributed CKF based on cubature Kalman filter(CKF) is derived by using the information filter framework and the average-consensus theory. This algorithm not only keeps advantages of the distributed filtering, such as the scalability and the robustness to sensor failures, but also has the high accuracy and strong stability of CKF. The simulation result shows the effectiveness of the proposed algorithm. Compared with the distributed UKF algorithm, it improves the accuracy and stability of the target tracking issue.