基于半定规划的无线传感器网络定位算法性能分析

基于测距的定位方法是无线传感器网络节点定位中一种常见的方法。测距定位方法往往将定位过程转化为数学优化问题。介绍了实现测距定位下的LS-SDR与SAL-SDR两种半定规划定位算法,并同时介绍了LS-LM、SLS-LM、SLS-US与SLS-S等4种定位算法。仿真实验分析和比较了6种不同定位算法的平均RMS定位误差,结果表明相对于其他4种定位算法,LS-SDR和LS-LM算法的定位误差较小。在信标节点数量较少的情况下,LS-LM算法的定位结果有可能发生奇异,造成定位结果的严重失真。而LS-SDR算法对于奇异的发生具有较好的抵抗能力,此时LS-SDR算法的平均RMS定位误差将优于LS-LM算法。

Performance Analysis of Localization Algorithm for Wireless Sensor Networks Based on Semidefinite Programming

Range-based localization is a common method of node localization for wireless sensor networks. The localization algorithms are often changed to mathematical optimization problems in current localization approaches. This paper introduces two different localization algorithms of LS-SDR and SAL-SDR to realize the ranged-based localization with semidefinite optimization localization algorithm and also represents other four localization algorithms of LS-LM, SLS-LM, SLS-US and SLS-S. Simulation experiments analyse and compare the mean RMS error under six different localization algorithm s. The experiment results also show the located errors of LS-LM and LS-SDR algorithm are small compared with other four localization algorithms. When the number of anchors is small, LS-LM algorithm produces a small amount of singular localization results. But the algorithm of LS-LM has the capability of resisting the singularity, which leads the mean RMS error of LS-SDR better than that of LS-LM.