海量激光点云数据的关键特征准确定位输出方法
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摘要
为解决海量激光点云数据定位受干扰影响较大,准确性较差等问题,提出一种海量激光点云数据的关键特征准确定位输出方法。采用FP-tree微蜂窝树结构模型进行海量激光点云数据的存储结构分布式部署,提取海量激光点云数据的关联尺度特征值,对提取的特征量进行模糊指向性聚类处理,采用循环迭代检测方法进行海量激光点云数据的定位检测,实现海量激光点云数据的关键特征准确定位输出。仿真结果表明,采用该方法进行海量激光点云数据的关键特征提取的准确性较高,精度较好。
In order to solve the problem that the location of massive laser point cloud data is greatly affected by interference and the accuracy is poor,a method of accurate location and output of the key features of massive laser point cloud data is proposed. The FP-tree microcellular tree model is used to distribute the storage structure of the massive laser point cloud data,and the associated scale eigenvalues of the massive laser point cloud data are extracted,and the extracted features are processed by fuzzy directivity clustering. The method of cyclic iterative detection is used to detect the location of massive laser point cloud data,and the key features of mass laser point cloud data can be accurately located. The simulation results show that this method is used to carry out massive laser points. The key feature extraction of cloud data is accurate and accurate.
In order to solve the problem that the location of massive laser point cloud data is greatly affected by interference and the accuracy is poor,a method of accurate location and output of the key features of massive laser point cloud data is proposed. The FP-tree microcellular tree model is used to distribute the storage structure of the massive laser point cloud data,and the associated scale eigenvalues of the massive laser point cloud data are extracted,and the extracted features are processed by fuzzy directivity clustering. The method of cyclic iterative detection is used to detect the location of massive laser point cloud data,and the key features of mass laser point cloud data can be accurately located. The simulation results show that this method is used to carry out massive laser points. The key feature extraction of cloud data is accurate and accurate.
引文
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[3]JEON W S,HAN J A,DONG G J.A novel MAC scheme for multichannel cognitive radio Ad Hoc networks[J].IEEETransactions on Mobile Computing,2012,11(6):922-934.
[4]杨欢欢,李天瑞,陈馨菂.基于螺旋图的时间序列数据可视化[J].计算机应用,2017,37(9):2443-2448.
[5]邹鑫慈,郭哲,隋心,等.二维激光雷达室内定位数据预处理方法研究[J].矿山测量,2017,45(3):77-81.
[6]彭向阳,宋爽,钱金菊,等.无人机激光扫描作业杆塔位置提取算法[J].电网技术,2017,41(11):3670-3677.
[7]HOYDIS J,BRINK S T,DEBBAH M.Massive MIMO in the UL/DL of cellular networks:how many antennas do we need[J].IEEE Journal on Selected Areas in Communications,2013,31(2):160-171.
[8]胡莹,冀保峰,黄永明,等.大规模MIMO OFDMA下行系统能效资源分配算法[J].通信学报,2015,36(7):40-47.
[9]陈艳良,战荫伟.贪婪重构算法St OMP及其改进[J].计算机应用与软件,2016,33(4):258-261.
[10]郎利影,王勇,白文庆,等.基于压缩感知Co Sa MP算法的精确重构[J].计算机应用研究,2015,32(8):2554-2557.
[11]DONOHO D L,TSAIG Y,DRORI I,et al.Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2012,58(2):1094-1121.
[12]刘芳,冯仲科,杨立岩,等.基于三维激光点云数据的树冠体积估算研究[J].农业机械学报,2016,47(3):328-334.
[13]杨斌杰,鲁铁定.点云数据特征点提取方法的比较[J].江西科学,2015,33(1):10-14.
[14]LEONARDO E J,YACOUB M D.Exact formulations for the throughput of IEEE 802.11 DCF in Hoyt,Rice,and Nakagami-m fading channels[J].IEEE Transactions on Wireless Communications,2013,12(5):2261-2271.
[15]MARIMON M C,TANGONAN G,LIBATIQUE N J,et al.Development and evaluation of wave sensor nodes for ocean wave monitoring[J].IEEE Systems Journal,2015,9(1):292-302.
[16]AHSEN M,HASSAN S A.A Poisson point process model for coverage analysis of multi-hop cooperative networks[C]//Proceedings of the 2015 International Wireless Communications and Mobile Computing Conference.Piscataway,NJ:IEEE,2015:442-447.
[17]尤肖虎,潘志文,高西奇,等.5G移动通信发展趋势与若干关键技术[J].中国科学(信息科学),2014,44(5):551-563.
[18]LAABIDI M,ZAYANI R,BOUALLEGUE R.A novel multi-block selective mapping scheme for PAPR reduction in FBMC/OQAM systems[C]//Proceedings of the 2015World Congress on Information Technology and Computer Applications Congress.Piscataway,NJ:IEEE,2015:1-5.