中文摘要：

研究了一类基于RSSI（Received signal strength indication）测距的分布式移动目标跟踪问题，提出了一种适用于事件触发无线传感器网络（Wireless sensor networks，WSNs）的分布式随机目标跟踪方法．首先考虑移动机器人模型的不确定性，引入了带有随机参数的过程噪声协方差，应用改进平方根容积卡尔曼滤波（Square root cubature Kalmanfilter，SRCKF）得到局部估计；然后采用无模型CI（Covariance intersection）融合估计方法以降低随机过程噪声协方差带来的不利影响．该方法充分利用有模型和无模型方法的优势，实现系统模型和量测不理想情况下的分布式目标跟踪．基于E—puck机器人的目标跟踪实验表明，事件触发的工作模式可有效地减少能量消耗，带随机参数的滤波方法更适合于随机目标的跟踪．

英文摘要：

This paper is concerned with distributed target tracking problem using RSSI method and presents a distributed tracking method for maneuvering targets with event-triggered wireless sensor networks （WSNs）. Firstly process noise covariance with random parameter is introduced under consideration of modeling uncertainties, and then a modified square root cubature Kalman filter （SRCKF） is employed to generate local estimates. Secondly, the non-model-based CI fusion estimation method is employed to reduce the adverse effects of random process noise covariance. The method combines advantages of both model-based and non-model-based estimation methods in the case of inaccurate model and unreliable measurements. Simulation and experiment of the E-puck robot tracking show that the event-triggered mechanism can greatly reduce energy consumption and that the filtering method with random parameters is more suitable for maneuvering target tracking.