LAD准则下的无线传感器网络节点定位方法

多维标度算法广泛应用于无线传感器网络的节点定位。经典的MDS算法通过构造距离平方矩阵(非相似性矩阵)和进行双质心变换，在相似性空间中根据最小二乘准则进行求解。若测量噪声为高斯白噪声，经过变换后，相似性矩阵中元素的误差不再服从高斯分布，基于LS的估计不再是最优的。针对这一问题，用最小绝对值偏差准则改进MDS算法代价函数，对无线传感器网络节点定位进行研究。仿真结果表明，该方法具有良好的稳健性，比经典MDS算法具有更好的定位性能。

LAD Solution for Node Localization in Wireless Sensor Networks

Multidimensional scaling (MDS) algorithms are widely used in node localization for wireless sensor networks by constructing a pair-wise squared distance matrix and performing a double-centered transformation. Classical MDS algorithms reconfigure relative coordinates of nodes in a similarity space and give the solution based on least squares (LS) criterion. However, the transformation of classical MDS algorithms result in non-Gaussian distribution of the noise in the similarity matrix when white Gaussian noise exists in distance measurements. Thus the LS based estimator can not optimize the node location. To overcome this problem, a least absolute deviation (LAD) based cost function of classical MDS algorithm is presented. Simulation results show that the LAD based method yields better performance.