基于三边测量的分簇目标跟踪算法

针对目标跟踪应用中目标移动的随机性和偶然性,以及跟踪节点的能量有限、通信半径小等问题,为了提高跟踪精度,并尽可能地减少节点能量消耗、延长网络寿命,提出了一种基于三边测量的分簇目标跟踪算法。所提方案采取三边测量技术进行移动目标的定位以提高定位精度,而且为了达到能效均衡,在建立唤醒簇阶段根据节点与目标之间距离、节点残余能量两个参数进行簇头及簇成员选举。仿真结果表明,所提方案与基于预测的能量节省(PES)方案、基于混合簇的目标跟踪(HCTT)协议相比,网络寿命更长,预测轨迹更精确,跟踪精度更高。

Trilateration based clustering target tracking algorithm

In target tracking applications, the moving targets have randomness and contingency, and the tracking nodes have limited energy and small communication radius. In order to improve the tracking accuracy with minimizing the energy consumption and extending the lifetime of network, a trilateration based clustering target tracking algorithm was proposed. It adopted trilateration technique for the target localization to improve the tracking accuracy. For the sake of achieving energy balance, on the basis of two parameters including the distance between the node and the target and the residual energy level of the node, the cluster head and members were elected in the stage of wake clustering establishment. The simulation results show that, compared with Prediction-based Energy Saving (PES) scheme and Hybrid Cluster-based Target Tracking (HCTT) protocol, the proposed algorithm has a better performance in lifetime of the network, predicting path and tracking accuracy.