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 onImproved Particle Swarm Optimization

摘要:

为了提高现行室内可见光定位系统定位精度，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，进一步提升定位精度；最后，通过仿真与实验测试对提出的定位算法进行了验证。结果表明，仿真测试时，在 两种定位模型中，0、0.5、1、1.5m四个高度平面的空间定位平均误差分别为0.65cm、0.54cm；实验测试时，在搭建的室内空间中的平均定位误差分别为2.67cm、1.81cm。

Abstract:

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 determines the positioning accuracy is transformed into the optimization problem of the minimum value of the objective function. Secondly, the dynamic assignment of inertia weight is 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 is reduced nonlinearly by sine function, and the value of group cognitive factors is increased linearly by cosine function, which further improves the positioning accuracy. Finally, the proposed localization algorithm is verified by simulation and experimental test. The results show that in the simulation test, in the two positioning models, the average spatial positioning errors of the four height planes of 0, 0.5, 1 and 1.5m are 0.65cm and 0.54cm respectively. In the experimental test, the average positioning errors in the built and indoor space are 2.67cm and 1.81cm respectively.