摘要
可见光室内定位系统由于墙壁反射和外界噪声存在而产生误差.对现有的基于指纹识别的可见光室内定位算法进行仿真比较,提出了将确定型算法与概率分布算法融合的室内定位改进算法.首先用KNN算法选取几个与接收机位置相近的网格点,对接收信号进行高斯滤波,而后用贝叶斯算法计算其后验概率,后验概率最大的点即为估计位置.这一改进算法不仅降低了贝叶斯算法的复杂度,也大大提高了KNN算法的定位精度,平均误差为0.17 m.
Errors are caused by wall reflection and noise in the visible light indoor positioning system. Some indoor positioning algorithms based on fingerprint are compared through simulation. An improved indoor positioning algorithm that combines K -Nearest Neighbor and Bayesian theory algorithm is proposed. Firstly,several grid points which are close to the receiver ’ s position are selected by KNN algorithm,after the RSSI sampling value through Gauss filter is processed,then the posterior probability is calculated by Bayesian algorithm. The point with the largest posterior probability is the estimated position. The improved algorithm simplifies the Bayesian algorithm and improves the positioning accuracy of KNN algorithm,with an average error of 0.17 m.
作者
郭琛
邵建华
柯炜
张春艳
安爽
Guo Chen;Shao Jianhua;Ke Wei;Zhang Chunyan;An Shuang(School of Physics and Technology,Nanjing Normal University,Nanjing 210023,China;Key Laboratory for Opto-Electronic Technology of Jiangsu Province,Nanjing Normal University,Nanjing 210023,China)
出处
《南京师范大学学报（工程技术版）》
CAS
2019年第1期58-64,共7页
Journal of Nanjing Normal University(Engineering and Technology Edition)
关键词
室内定位
指纹
KNN
贝叶斯
indoor positioning
fingerprint
KNN
Bayes
