摘要
为提高室内可见光定位精度,提出一种基于天牛须搜索算法优化最小二乘支持向量机(BAS-LSSVM)的可见光指纹定位方法。先利用LED的光强信号强度特征构建指纹库,然后用天牛须搜索算法对最小二乘支持向量机的超参数进行优化以实现提高精度和减少时间开销的目的,最后建立位置坐标和信号强度特征的映射关系实现定位。实验结果表明,BAS-LSSVM定位方法可以实现良好的定位效果,97.0%的测试点定位误差不大于0.10 m,所有测试点平均定位误差为0.031 m。
To improve the indoor visible light positioning accuracy,we proposed a visible light fingerprint positioning method using the least squares support vector machine(LSSVM)optimized by the beetle antenna search(BAS)algorithm.First,the signal strength characteristics of the LED light intensity were used to build the fingerprint database.Next,the BAS algorithm was applied to optimize the hyperparameters of the least square support vector machine to improve accuracy and reduce time cost.Finally,we obtained the mapping relationship between the position coordinates and signal strength characteristics to achieve positioning.The experimental results show that the BASLSSVM positioning method can achieve a good positioning effect,with the positioning error of 97.0%,test points being less than 0.10 m,and the average positioning error of all test points being 0.031 m.
作者
邓勇
刘赣
Deng Yong;Liu Gan(School of Mechatronic Engineering,Southwest Petroleum University,Chengdu 610500,Sichuan,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第11期217-224,共8页
Laser & Optoelectronics Progress
基金
四川省科技支撑计划项目(2017FZ0033)。
关键词
光通信
可见光定位
天牛须搜索算法
最小二乘支持向量机
optical communications
visible light positioning
beetle antennae search algorithm
least squares support vector machine
