基于动态集群和DKF的协作式目标跟踪

针对具有有限感知范围的无线传感器网络中的动态目标跟踪问题，提出了一种将卡尔曼一致滤波和动态集群自组织相结合的协作式动态目标跟踪算法。首先，算法采用一个由群头挑选阶段和集群重新配置阶段构成的动态集群协议来限制参与目标状态估计过程中节点间的信息交换，然后用一个分布式加权估计预测算法即卡尔曼一致滤波来估计目标状态并预测其下一个位置，这样有助于唤醒最合适的节点来进行目标跟踪并最恰当地组织网络通信，而其他节点保持在睡眠状态。仿真结果表明，提出的算法相比于集中式和其他2种常用的分布式动态目标跟踪算法，不仅能够降低网络的平均能耗，而且能够明显提高跟踪过程中的误差估计质量。

A cooperative target tracking based on dynamic clustering and Distributed Kalman Filtering

Aiming at the dynamic target tracking problem in Wireless Sensor Networks(WSN) with limited sensing range, a collaborative dynamic target tracking algorithm in which Kalman Consensus Filter(KCF) is combined with dynamic clustering self-organization is proposed. The proposed algorithm uses a dynamic clustering protocol which consists of a cluster leader selection phase and cluster reconfiguration phase to limit messages exchanges between nodes participating in the target state estimation process. Then, a distributedly weighted estimation-prediction algorithm, namely Calman consensus filtering can be adopted to estimate the target state and predict its next position. This helps waking-up the most appropriate nodes to track the target and well organizing the network communications, while other nodes remaining in sleep state. The simulation results show that the proposed algorithm can not only reduce the average energy consumption of the network, but also improve the error estimation quality in the tracking process significantly compared with a centralized algorithm and other two kinds of distributed dynamic target tracking algorithms in common use.