作　　者： ; ; ; ;

机构地区： 华南理工大学机械与汽车工程学院

出　　处： 《华南理工大学学报（自然科学版）》 2008年第10期102-107,共6页

摘　　要： 针对无线传感器网络（WSN）节点定位方法中采用粗测距技术时，节点间较大的测距误差导致定位准确度不足的问题，提出一种基于特征量重要度最小二乘支持向量回归（LS-SVR）的定位方法．该方法把未知节点到锚节点的距离作为特征量，依据特征量的重要度进行特征提取，通过对探测区域网格化采样得到训练样本集，使用LS-SVR学习得到定位模型；在定位阶段，将未知节点的特征向量输入定位模型，利用LS—SVR良好的泛化能力实现对未知节点的准确定位．对均匀分布和C形区域随机分布的100个节点的定位实验表明，文中提出的定位方法能有效地降低测距误差对定位准确度的影响，减小平均定位误差；与采用相同测距技术的DV—Hop方法相比，均匀分布情况下该方法的平均定位误差减小7．5％～14．0％，C形区域随机分布情况下显著减小36．5％～55．2％． In order to improve the accuracy of the general localization method for wireless sensor network （WSN） due to the big inter-node ranging error contributed by the coarse ranging technology, a new localization method based on the least-square support vector regression （LS-SVR） of feature importance is proposed. In this method, the range from the unknown node to the anchor node is taken as the feature variable, and the feature is extracted according to the importance of the feature variable. Training samples are obtained via the gridding in the detection region and are then studied via the LS-SVR to establish a localization model. In the localization phase, the feature vector of the unknown node is input into the localization model, and the accurate location of the unknown node is achieved by utilizing the good generalization capability of LS-SVR. A localization experiment is finally performed with 100 uniformly-distributed nodes and 100 randomly-distributed nodes in the C-shape region. The results show that the proposed method effectively eliminates the influence of ranging error on the localization accuracy and it reduces the average location error to 7.5% - 14. 0% less than that of the DV-Hop method in uniform distribution condition and 36.5% -55.2% less in random distribution condition in the C-shape region.

关 键 词： 特征提取 最小二乘支持向量回归机 无线传感器网络 定位

领　　域： [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术]