中文摘要：

多目标跟踪是无线传感器网络当前研究的热点问题。针对多目标跟踪存在耗能较大，跟踪丢失等问题，提出了一种自适应采样间隔的多目标跟踪算法。采用跟踪目标的定位元数据来对目标的运动模式进行建模。基于扩展的卡尔曼滤波器来预测跟踪目标状态，采用预测目标定位的概率密度函数构建跟踪簇。通过定义跟踪目标中心，基于马氏距离来量化主节点MN的选举过程。通过跟踪目标重要性和其与MN之间的距离来量化目标的影响强度，并以此构建自适应采样间隔的多目标跟踪算法。基于MATLAB进行了仿真实验，实验结果显示，本文设计的跟踪算法能准确预测目标的运动轨迹，能随着运动目标的状态实时采用自适应的采样间隔。通过数据分析得知，本文提出的算法能在实现WSN网络节能的基础上提高跟踪精度。

英文摘要：

Multi-target tracking is a hot topic of current research on wireless sensor networks （WSN）. Based on adaptive sampling interval, we propose a multi-target tracking algorithm in order to save energy consumption and prevent tracking lost for WSN. We contrast the targets moving model by using the position metadata, and predicte the targets moving status based on extended Kalman filter （EKF）. we adopt the probability density function （PDF） of the estimated targets to establish the tracking cluster. By defining the tracking center, we use Markov distance to quantify the election process of the main node （MN）. We comput targets impact strength through the targets importance and the distance to MN node, and then use it to build tracking algorithm. We do the simulation experiment based on MATLAB, and the experiment results show that the proposed algorithm can accurate predict the trajectory of the targets, and adjust the sampling interval while the targets were moving. By analyzing the experiments data, we know that the proposed algorithm can improve the tracking precision and save the energy consumption of WSN obviously.