Abstract：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.
单志龙, 刘兰辉, 张迎胜, 黄广雄. 一种使用灰度预测模型的强自适应性移动节点定位算法[J]. 电子与信息学报, 2014, 36(6): 1492-1497.
Shan Zhi-Long, Liu Lan-Hui, Zhang Ying-Sheng, Huang Guang-Xiong. A Strong Self-adaptivity Localization Algorithm Based on Gray Prediction Model for Mobile Nodes. , 2014, 36(6): 1492-1497.
2014, Vol. 36 
