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为提高机动目标跟踪性能,降低无线传感器网络的能量消耗,提出一种可扩展的动态平均一致卡尔曼滤波算法.根据预测的下一步目标位置,将无线传感器网络的节点动态组织成簇,多个传感节点协作执行目标的检测及分布式状态估计.给出三种可扩展动态一致卡尔曼滤波算法,即基于观测值、观测新息和估计值的一致性卡尔曼滤波,适应于不同情况的目标跟踪.簇中传感节点仅需接收邻居节点的信息,簇头节点负责下一步任务节点的选择并将当前状态估计值和对应的误差协方差发送给下一步的任务节点以减少整个网络的通信量.仿真结果表明,基于观测值、新息及估计值的分布一致卡尔曼滤波在跟踪精度方面与集中卡尔曼滤波性能相当,而其分布式结构决定了算法具有更强的鲁棒性和容错能力,能够提高系统的可靠性.
In order to improve the maneuvering target tracking performance and reduce the energy consumption of the wireless sensor network, a scalable dynamic average consistent Kalman filter algorithm is proposed.According to the predicted next target location, the nodes of the wireless sensor network are dynamically organized into clusters, Sensor nodes cooperative execution target detection and distributed state estimation.This paper gives three scalable dynamic consistent Kalman filter algorithm, that is based on observations, observation of new information and consistent estimates of the Kalman filter to adapt to different situations Target tracking cluster sensor nodes only need to receive the information of neighbor nodes, cluster head node is responsible for the next task node selection and the current state estimate and the corresponding error covariance sent to the next task node to reduce the entire network The simulation results show that the distribution-consistent Kalman filter based on observations, interest and estimation values is comparable to the centralized Kalman filter in terms of tracking accuracy and its distributed structure determines the robustness of the algorithm and Fault tolerance, to improve system reliability.