混合滤波与改进贝叶斯相融合的室内可见光指纹定位方法  

Indoor visible light fingerprint location method based on GF-KF and Improved-Bayes

作　　者：顾亚雄[1] 钟文 GU Yaxiong;ZHONG Wen(College of Mechatronic Engineering,Southwest Petroleum University,Chengdu 610500,China)

机构地区：[1]西南石油大学机电工程学院,四川成都610500

出　　处：《测绘通报》2023年第6期104-109,128,共7页Bulletin of Surveying and Mapping

基　　金：四川省教育厅科技计划(19YYJC0802);四川省科技支撑计划(2017FZ0033)。

摘　　要：针对室内环境光、噪声等因素会对移动终端接收到的可见光信号强度产生干扰从而导致定位精度不高的问题,本文提出了一种将高斯拟合+卡尔曼滤波(GF-KF)与改进贝叶斯(Improved-Bayes)融合的室内可见光指纹定位方法。首先通过GF-KF算法修正采集到的接收信号强度(RSS)作为指纹库数据,再通过对加权K近邻法的权值系数改造后与贝叶斯算法融合的方法将待测点与指纹点RSS数据进行匹配,计算分析出位置。试验结果表明,在该算法模型下,平均定位误差为1.42 cm,92.83%的测试点定位误差不大于2 cm,相较于卷积神经网络算法、加权K近邻算法和支持向量机法精度更高,稳健性更强。In view of the problem that indoor ambient light,noise and other factors will interfere with the intensity of the visible light signal strength received by the mobile terminal and cause the positioning accuracy to be low,this paper proposes a visible light fingerprint positioning method that integrates Gaussian fitting and Kalman filtering(GF-KF)with Improved-Bayes.Firstly,the RSS date collected by GF-KF algorithm is corrected as fingerprint database data,and then the weight coefficient of the k-neighbor method is transformed and fused with Bayesian algorithm,which matches the RSS data of the point to be measured and the fingerprint point Finally,the position is calculated.Experimental results show that under the algorithm model,the average positioning error is 1.42 cm,and 92.83%of the test point positioning error is not more than 2 cm,which is more accurate and robust than the convolutional neural network algorithm,the weighted K nearest neighbor algorithm and the support vector machine method.

关 键 词：光通信 可见光指纹定位 高斯拟合 卡尔曼滤波法 加权K近邻法 贝叶斯算法 

分 类 号：P228[天文地球—大地测量学与测量工程]