【摘 要】
：
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
【机 构】
：
河南大学图像处理与模式识别研究所,
论文部分内容阅读
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。
The application of Kalman filter is limited by the fact that the system to be evaluated must meet the linear conditions. By using the combination of volumetric Kalman filter (CKF) and consistency strategy, a volumetric Kalman filter (CKCF) algorithm is proposed. The algorithm adopts distributed fusion mechanism. Sensor nodes collect the information of neighboring nodes and apply them to CKF as the measurement information of their own nodes to obtain the local state estimates. On this basis, using the consistency strategy, the local estimation of sensor nodes in the whole measurement system is optimized, and then the accuracy of the target state estimation is improved by enhancing the consistency of the estimated values of the sensor nodes. Compared with the standard Kalman filter, the proposed algorithm generalizes the consistent strategy to the field of nonlinear system estimation. Theoretical analysis and simulation experiments verify the feasibility and effectiveness of the algorithm.
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