摘要
针对基于接收信号强度(RSS)的定位技术易受环境干扰而导致定位精度不高和稳定性较差的问题,提出了一种基于多源信息融合的径向基函数(RBF)神经网络室内可见光定位算法。通过将图像的颜色矩特征与RSS矩特征融合,构建指纹库,并采用RBF神经网络进行预测,实现了图像与RSS之间的优势互补,最后对定位算法进行了验证。实验结果表明,经过优化的多源信息融合定位算法较单一RSS定位算法的定位精度提高了9.4%。
Aiming at the problem of 1low positioning accuracy and poor stability caused by the environmental interference of the positioning technology based on received signal strength(RSS),an radial basis function(RBF)neural network indoor visible light positioning algorithm based on multi-source information fusion is proposed.By fusing the color moment feature of the im-age with the RSS moment feature,a fingerprint database is constructed,and the RBF neural network is used for prediction to achieve complementary advantages between the image and RSS.Finally,the positioning algorithm is verified.The experimental results show that the optimized multi-source information fusion positioning algorithm improves the positioning accuracy by 9.4%compared with the single RSS positioning algorithm.
作者
王琪
孟祥艳
赵黎
WANG Qi;MENG Xiangyan;ZHAO Li(School of Electronic Information Engineering,Xi'an Technological University,Xi'an 710021,China)
出处
《光通信技术》
北大核心
2024年第2期30-35,共6页
Optical Communication Technology
基金
陕西省科技厅一般项目-工业领域(No.2020GY-054)资助。
关键词
可见光
室内定位
多源信息融合
颜色矩
神经网络
径向基函数
特征提取
visible light
indoor position
multi-source information fusion
color momentn
neural networks
radial basis function
feature extraction
