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中文摘要：
在追踪的目标，节点聚集他们目标的到达的方向的观察。网络然后使用一个扩大 Kalman 过滤器(EKF ) 从多重快照联合大小追踪目标。为了很快选择节点的最好的子集与最小的吝啬的平方本地化目标，放错误和低电源消费，这篇论文建议一个简单算法，它使用目标和网络的地点信息。本地化错误的更低的界限根据在目标和选择活跃节点之间的距离被利用。而且，活跃节点的方向可能性通过忍受散布关系的节点 / 目标被预言。
英文摘要：
In the target tracking, the nodes aggregate their observations of the directions of arrival of the target. The network then uses an extended Kalman filter （EKF） to combine the measurements from multiple snapshots to track the target. In order to rapidly select the best subset of nodes to localize the target with the minimum mean square position error and low power consumption, this paper proposes a simple algorithm, which uses the location information of the target and the network. The lower botmd of localization error is utilized according to the distances between the target and the selected active nodes. Furthermore, the direction likelihoods of the active nodes is predicted by way of the node/target bearing distributing relationships.
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