Self-localization Algorithm with Constraints Based on Weighted Bilinear Approach

doi:10.3969/j.issn.1000-3428.2016.04.013

Abstract

Abstract: In view of the application scenarios that the non anchor nodes and sensor nodes can not communicate with each other,this paper develops a self-localization algorithm for Wireless Sensor Network(WSN).Signal source is introduced,so that the sensor node can receive the signal emitted by the signal source.According to the Time of Flight(ToF) of the signal from the signal source to the sensor node to obtain the observation distance between the two,the linearization of the observation equation is expressed as the form of linear matrix multiplication.The rough estimates of sensor and source locations are found using Singular Value Decomposition(SVD) after linearizing measurement equations and the Weighted Least Squares(WLS) estimation is applied to refine the estimates.The formulation of the WLS estimator takes the effect of TOF noises on SVD results into account.Simulations show that the new algorithm improves localization accuracy over the original method that uses Least Squares(LS) only.

Key words: Wireless Sensor Network(WSN), Time of Flight(ToF), bilinear approach, position constraint, global coordinate system;Weighted Least Squares(WLS) estimation

摘要： 针对无线传感器网络(WSN)中无锚节点与传感器节点无法互相通信的应用场景,提出一种节点自定位算法。引入信号源,使传感器节点可接收到信号源发射的信号,根据信号从信号源到传感器节点的传播时间获得两者间的观测距离,将观测方程线性化表示为矩阵线性相乘的形式,对其中的距离矩阵采用奇异值分解(SVD)实现传感器节点的初始定位。为了消除自定位问题固有的平移、伸缩、旋转,引入位置约束,并考虑观测噪声的统计特性及其对SVD引起的扰动,再次对代价函数线性化后采用加权最小二乘估计实现对传感器节点初始定位的校正。仿真结果表明,该算法比仅使用最小二乘估计的自定位算法具有更高的定位精度。

关键词: 无线传感器网络, 传播时间, 双线性方法, 位置约束, 全局坐标系, 加权最小二乘估计

CLC Number:

TP301.6

GAO Xue,PENG Li. Self-localization Algorithm with Constraints Based on Weighted Bilinear Approach[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2016.04.013.

高雪,彭力. 基于加权双线性方法的带约束自定位算法[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2016.04.013.

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References

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