基于AKPCA算法的井下WLAN位置指纹定位  

Location fingerprint positioning of underground WLAN based on AKPCA algorithm

作　　者：宋明智 钱建生 胡青松 SONG Mingzhi;QIAN Jiansheng;HU Qingsong(College of Information Engineering,Xuzhou University of Technology,Xuzhou 221018,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China)

机构地区：[1]徐州工程学院信息工程学院,江苏徐州221018 [2]中国矿业大学信息与控制工程学院,江苏徐州221116

出　　处：《煤炭科学技术》2022年第3期248-256,共9页Coal Science and Technology

基　　金：国家自然科学基金资助项目(51874299)。

摘　　要：为改进井下WLAN人员定位系统中位置指纹数据库的特征提取及在线定位匹配性能,提出了自适应核主成分分析(Adaptive Kernel Principal Component Analysis,AKPCA)算法。AKPCA算法将最优AP选择算法与核主成分分析(Kernel Principal Component Analysis,KPCA)算法相结合,使本征维数的计算具有一定的子区域自适应性,有效改善了KPCA算法中使用最大似然估计法求解的本征维数对于区域划分后的位置指纹数据库过于单一的问题。最优AP选择因子能够根据区域中AP信号的覆盖状态在位置指纹数据库构建及区域划分后完成最优本征维数的确定。井下人员定位试验结果中,AKPCA算法在各子区域本征维数的计算精度上要优于KPCA算法,且在定位误差为4 m时的置信概率达到了近100%,高于KPCA算法的91.4%。而在定位过程的内存占用对比方面,AKPCA算法的平均内存使用为0.832 GB,要优于KPCA算法的1.278 GB和其他位置指纹匹配算法。综上,AKPCA算法不仅在定位精度上要优于其他特征提取算法,同时也能够有效减少定位系统在线定位过程中的资源消耗。在未来的研究中,将致力于进一步改善区域划分后的井下定位精度。In order to improve the performance of feature extraction and online location matching in location fingerprints database of underground WLAN personnel positioning system,an adaptive kernel principal component analysis(AKPCA) algorithm is proposed.The AKPCA algorithm combines the optimal AP selection algorithm with the kernel principal component analysis(KPCA) algorithm,which makes the calculation of eigenvalue have certain sub-region adaptability,and effectively solve the problem that the eigenvalue dimension solved by the maximum likelihood estimation method in the KPCA algorithm is too simple for the partitioned location fingerprints database.According to the coverage status of AP signals in the region,the optimal AP selection factor can be used to calculate the optimal intrinsic dimension after the construction of the location fingerprints database.In the results,AKPCA algorithm is better than KPCA algorithm in the calculation accuracy of eigenvalue of each sub-region,and the confidence probability reaches nearly 100% when the positioning error is 4 m,which is higher than 91.4% of KPCA algorithm.In the comparison of memory occupation in the positioning process,the average memory usage of AKPCA algorithm is 0.832 GB,which is better than 1.278 GB of KPCA algorithm and other fingerprint matching algorithms.AKPCA algorithm is superior to other feature extraction algorithms in positioning accuracy.It can effectively reduce the resource consumption in the online positioning process of the positioning system.In the future research,we will strive to further improve the positioning accuracy of divided underground tunnel.

关 键 词：井下人员定位 位置指纹 特征提取 最优AP选择 

分 类 号：TD65.5[矿业工程—矿山机电] TD676