基于Schmidt正交单位化的稀疏化定位算法

Sparse localization on the basis of Schmidt orthonormalization in wireless sensor networks

为了提高在一个移动信标节点下的无线传感器网络节点定位的精度,提出了一种稀疏化的无线传感器网络节点定位算法。该算法通过网格化感知区域把节点定位问题转化为稀疏信号重构问题,并提出了Schmidt正交单位化的预处理方法,对观测矩阵进行预处理,使其有效地满足了约束等距性条件。并针对稀疏定位模型中得到的稀疏信号是近似稀疏信号的问题,采用质心算法来优化算法的定位精度。实验结果表明,相比于MAP类算法,稀疏化的无线传感器网络节点定位算法的定位精度更优,同时所需要的信标节点的广播次数也更少。

To improve the localization accuracy of a node in the wireless sensor network with a mobile beacon node, a sparse localization algorithm using Schmidt orthonormalization （ SLSO） was proposed. With the SLSO, the node localization problem was converted to a reconstruction problem of the sparse signal by gridding the sensing area, and a new observation matrix which is able to effectively satisfy the restricted isometry property （ RIP ） was obtained by Schmidt orthonormalization. To solve the problem of the sparse signal being approximately sparse in the model, a centroid algorithm was adopted to improve the localization accuracy. The experiment results show that, compared with MAP algorithms, SLSO has better localization accuracy, and requires less broadcasting times.