| 稀疏训练指纹库融合MMPSO-ELM室内可见光定位 |
| |
| 引用本文： | 张慧颖,梁誉,卢宇希,王凯,于海越.稀疏训练指纹库融合MMPSO-ELM室内可见光定位[J].激光技术,2022,46(6):788-795. |
| |
| 作者姓名： | 张慧颖 梁誉 卢宇希 王凯 于海越 |
| |
| 作者单位： | 吉林化工学院 信息与控制工程学院, 吉林 132022 |
| |
| 摘 要： | 为了解决采用极限学习机(ELM)神经网络室内可见光定位方法存在误差较大、网络模型训练时间较长、结果稳定性较差等缺点, 采用稀疏训练指纹库, 融合多目标动量粒子群算法(MMPSO), 结合ELM室内可见光定位方法, 形成MMPSO-ELM方案, 引入动量因子, 避免迭代过程中过度振荡, 加快系统收敛速度。在不同的定位空间内随机选取训练数据集方式, 在测试点数量不同的情况下, 将本方案与后向传播(BP)、ELM以及PSO-ELM 3种定位算法进行了比较。结果表明, MMPSO-ELM方案在20组训练数据条件下, 对80组待定位点进行预测定位, 定位误差最大为0.0225m, 最小误差为0.00093m, 平均定位误差低至0.00143m, 且定位性能受定位空间大小影响较小; MMPSO-ELM可见光定位方案具有定位精度高、速度快、泛化性强等优点。该研究为在室内场所实现快速准确定位提供了理论支撑。
|
| 关 键 词： | 光通信 极限学习机 粒子群算法 稀疏训练指纹库 动量因子 可见光定位 |
| 收稿时间： | 2021-09-13 |
Indoor visible light positioning using MMPSO-ELM neural network based on sparse training fingerprint database |
| |
| Abstract: | In order to solve the shortcomings of using the extreme learning machine (ELM) neural network to position indoor visible light, such as large error, long network model training time and poor stability of results, multi-objective momentum particle swarm optimization (MMPSO)-ELM scheme was formed by using sparse training fingerprint database, MMPSO and ELM indoor visible light positioning method. Momentum factor was introduced to avoid excessive oscillation during iteration and speed up the system convergence. Training data was set randomly in different positioning spaces. When the number of test points is different, the scheme of MMPSO-ELM was compared with back propagation, ELM and PSO-ELM. The simulation results show that, under the condition of 20 groups of training data and 80 points to be located, the maximum positioning error is 0.0225m, the minimum error is 0.00093mm and the average positioning error is as low as 0.00143m. The positioning performance is less affected by the size of the positioning space. MMPSO-ELM visible light positioning scheme has the advantages of high positioning accuracy, fast speed and strong generalization. This research provides theoretical support for fast and accurate positioning in indoor places. |
| |
| Keywords: | |
|
| 点击此处可从《激光技术》浏览原始摘要信息 |
|
点击此处可从《激光技术》下载全文 |
|
