摘要
针对经典3DDV-Hop算法定位精度不高的问题,提出一种基于改进麻雀搜索算法优化的改进的3DDV-Hop算法;算法首先通过多通信半径优化传感器节点之间跳数,并且在平均跳距计算过程中引入动态加权因子提高平均跳距计算精度,其次在麻雀搜索算法的基础上融合反向学习策略与萤火虫算法分别对麻雀搜索算法的种群与位置更新迭代进行优化,最后将未知节点坐标计算问题转化成改进后的麻雀搜索算法寻优问题,利用改进后的麻雀搜索算法替代最小二乘法计算未知节点坐标,进一步提高未知节点位置计算精度;经过MATLAB仿真验证,改进算法对比于经典3DDV-Hop算法和相关算法,定位精度得到有效提高。
Aimed at the problem that the positioning accuracy of the classical 3DDV-Hop algorithm is not high,an improved 3DDV-Hop algorithm based on the optimization of the improved sparrow search algorithm is proposed.Firstly,the algorithm optimizes the hop count between sensor nodes through multiple communication radii optimization,and a dynamic weighting factor is introduced to improve the calculation accuracy of average hop distance during the average hop distance calculation;Secondly,on the basis of the sparrow search algorithm,the reverse learning strategy and firefly algorithm are integrated to optimize the population and position update iteration of the sparrow search algorithm,respectively.Finally,the unknown node coordinate calculation is transformed into the improved sparrow search algorithm optimization,and the improved sparrow search algorithm is used to replace the least squares method to calculate the unknown node coordinates,further improve the unknown node position calculation accuracy.Through the simulation verification of MATLAB,the results show that compared with the classical 3DDV-Hop algorithm and other related algorithms,the improved algorithm effectively improves the positioning accuracy.
作者
高翔宇
韩慧子
孟亚男
刘美
GAO Xiangyu;HAN Huizi;MENG Yanan;LIU Mei(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin 132022,China;School of Automation,Guangdong Institute of Petrochemical Technology,Maoming 525000,China;School of Engineering,The Hong Kong Polytechnic University,Hongkong 999077,China)
出处
《计算机测量与控制》
2024年第5期246-252,共7页
Computer Measurement &Control
