一种使用灰度预测模型的强自适应性移动节点定位算法  

A Strong Self-adaptivity Localization Algorithm Based on Gray Prediction Model for Mobile Nodes

作　　者：单志龙[1] 刘兰辉[1] 张迎胜[1] 黄广雄[1] 

机构地区：[1]华南师范大学计算机学院,广州510631

出　　处：《电子与信息学报》2014年第6期1492-1497,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61102065);广州市科技计划项目(12C42091555);广州市珠江科技新星专项(2011J2200083);广东省教育部产学研结合项目(2011B090400520)资助课题

摘　　要：定位技术是无线传感器网络的关键技术,而关于移动节点的定位又是其中的技术难点。该文针对移动节点定位问题提出基于灰度预测模型的强自适应性移动节点定位算法(GPLA)。算法在基于蒙特卡罗定位思想的基础上,利用灰度预测模型进行运动预测,精确采样区域,用估计距离进行滤波,提高采样粒子的有效性,通过限制性的线性交叉操作来生成新粒子,从而加快样本生成,减少采样次数,提高算法效率。仿真实验中,该算法在通信半径、锚节点密度、样本大小等条件变化的情况下,表现出较好的性能与较强的自适应性。Localization of sensor nodes is an important issue in Wireless Sensor Networks （WSNs）, and positioning of the mobile nodes is one of the difficulties. To deal with this issue, a strong self-adaptive Localization Algorithm based on Gray Prediction model for mobile nodes （GPLA） is proposed. On the background of Monte Carlo Localization Algoritm, gray prediction model is used in GPLA, which can accurate sampling area is used to predict nodes motion situation. In filtering process, estimated distance is taken to improve the validity of the sample particles. Finally, restrictive linear crossover is used to generate new particles, which can accelerate the sampling process, reduce the times of sampling and heighten the efficiency of GPLA. Simulation results show that the algorithm has excellent performance and strong self-adaptivity in different communication radius, anchor node, sample size, and other conditions.

关 键 词：无线传感网络 定位 移动节点 灰度预测 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]