无线传感器网络中的线性移动目标运动参数捕获方法

移动目标跟踪即移动目标的运动路径与参数获取在无线传感器网络应用中具有重要的研究价值.采用移动目标节点与信标节点间的TOA测量方法,提出了无线传感器网络中移动目标运动参数的捕获方法.通过建立移动目标运动参数的估计模型,本文首先推导了线性移动目标初始位置及移动速度估计的非约束线性最小二乘(ULLS)和约束线性最小二乘(CLLS)方法.将估计模型松弛为凸优化的半正定规划(SDP)问题,又设计了运动参数捕获的SDP算法.仿真分析结果表明,在3种所设计算法中ULLS算法的估计误差最大,SDP算法其次,CLLS算法的估计误差最小.随着采样周期的增加,初始位置的估计误差亦稍有增大,但速度估计误差却在减少.更多的采样点数量有利于增加测量信息量,可以有效减少位置及速度估计误差.

Capturing Method of Motion Parameter for Linear Moving Target in Wireless Sensor Networks

Moving target tracking,namely the motion path and parameter obtaining of moving target has important re-search value in the application of wireless sensor networks(WSNs). Based on the TOA measurement method be-tween mobile target node and beacon node,a method to capture the parameters of moving targets is proposed in WSNs. Using the built estimation model of motion parameter for mobile target,the unconstrained linear least squared (ULLS)and constrained linear least squares(CLLS)methods are deduced to estimate the initial position and moving speed of the linear moving target. By relaxing the estimation model into convex semidefinite programming(SDP) problem,the SDP algorithm is designed to capture the motion parameters. The simulation results show that in the three designed algorithms,the estimation error of the ULLS algorithm is the largest. The SDP algorithm ranks sec-ondly,and the estimation error of the CLLS algorithm is the smallest. With the increasing of sampling period,the es-timation error of initial position also increases slightly,but the speed estimation error is reduced. More number of samplings means the increasing of the measurement information which can effectively reduce the estimation error of position and velocity.