WSNs下一种自适应多传感器协同目标跟踪策略*

对WSNs中机动目标跟踪问题提出一种自适应多传感器协同跟踪策略.该策略能根据目标的移动位置,动态地唤醒无线传感器网络中部分传感器节点形成分簇,并选择合适的簇首和采样间隔进行目标跟踪.簇内节点通过协作感知以及测量信息融合,提高了跟踪精度,同时自适应可变采样间隔节约了通信能量和计算资源,满足了跟踪系统的实时性要求.提出了传感器网络能量均衡分配的指标,提高了网络的可靠性.由于模型的非线性和目标运动的机动性,采用IMM滤波器进行目标状态估计.仿真结果表明,与NSSS和DGSS相比,跟踪精度明显提高;与DCSS相比,在保证一定跟踪精度的同时,节约了能量消耗.

Adaptive multi-sensor collaborative strategy for target tracking in WSNs

Proposed an adaptive multi-sensor collaborative strategy for maneuvering target tracking in WSNs .According to the position of the maneuvering target, some sensor nodes in WSNs were awaken to activate a sensor cluster for target tracking dynamically. At the same time, selected the head sensor of the cluster and decided the sample interval. Improved the tracking accuracy by dynamic collaboration and measurement information fusion of the sensor nodes. Besides, saved the communication energy and computation resource by adaptive changing sample interval, and satisfied the real-time property. This paper presented the balanced distribution of energy in WSNs to improve the reliability of the network. Employed the IMM filter to estimate the target states due to nonlinear models and the maneuvering movement of the target. The simulation shows that the tracking accuracy of the proposed algorithm is improved compared with NSSS and DGSS, and it has a superior performance in saving energy over DCSS, with the tracking accuracy guaranteed.