基于改进粒子群算法的室内可见光定位研究  

Research on Indoor Visible Light Location Based on Improved Particle Swarm Optimization

作　　者：王加安 顾偲雯 张思琦 WANG Jiaan;GU Xiewen;ZHANG Siqi(School of Optoelectronic Engineering,Changzhou Institute of Technology,Changzhou 213032,CHN)

机构地区：[1]常州工学院光电工程学院,江苏常州213032

出　　处：《半导体光电》2023年第5期729-735,共7页Semiconductor Optoelectronics

基　　金：江苏省高等学校自然科学研究面上项目(20KJB510041,21KJD530002)。

摘　　要：为了提高现行室内可见光定位系统的定位精度,提出考虑噪声干扰的动态惯性权重及认知因素的改进型粒子群算法。首先,将决定定位精度的欧式距离转换为目标函数最小值优化问题;其次,利用惯性权重动态赋值,增强粒子群算法初期的全局搜索能力和后期的局部搜索能力;然后,利用正弦函数使得个体认知因素值非线性地减小,利用余弦函数使得群体认知因素值线性地增加,以进一步提升定位精度;最后,通过仿真与实验测试对所提定位算法进行验证。仿真测试结果表明,在5 m×5 m×3 m和5 m×4 m×3 m两种定位模型中,在0,0.5,1.0和1.5 m四个高度平面的空间定位平均误差分别为0.65和0.54 cm;实验结果显示,在搭建的1 m×1 m×0.8 m和1 m×0.8 m×0.8 m室内空间中的平均定位误差分别为2.67和1.81 cm。In order to improve the positioning accuracy of the current indoor visible light positioning system,an improved particle swarm optimization algorithm considering the dynamic inertia weight and cognitive factors of noise interference is proposed.Firstly,the Euclidean distance that determined the positioning accuracy was transformed into the optimization problem of the minimum value of the objective function.Secondly,the dynamic assignment of inertia weight was used to enhance the global search ability in the initial stage and the local search ability in the later stage of PSO.Then,the value of individual cognitive factors was reduced nonlinearly by sine function,and the value of group cognitive factors was increased linearly by cosine function,which further improved the positioning accuracy.Finally,the proposed localization algorithm was verified by simulation and experimental test.The results show that in the simulation test,in the 5 m×5 m×5 m and 5 m×4 m×3 m positioning models,the average spatial positioning errors of the four height planes of 0,0.5,1 and 1.5 m are 0.65 and 0.54 cm respectively.In the experimental test,the average positioning errors in the 1 m×1 m×0.8 m and 1 m×0.8 m×0.8 m indoor space are 2.67 and 1.81 cm respectively.

关 键 词：可见光通信 室内定位 动态惯性权重 改进型粒子群算法 

分 类 号：TN929.1[电子电信—通信与信息系统]