该文设计了一种光载一机多天线远程全球导航卫星系统(GNSS)差分监测系统。该系统利用微波光子链路远程采集多个远端天线接收的GNSS信号,并传输回本地端;然后在本地端借助高速光开关,以时分模式依次建立各远端信号与参考基准信号的载波...该文设计了一种光载一机多天线远程全球导航卫星系统(GNSS)差分监测系统。该系统利用微波光子链路远程采集多个远端天线接收的GNSS信号,并传输回本地端;然后在本地端借助高速光开关,以时分模式依次建立各远端信号与参考基准信号的载波相位双差模型方程,处理后实时获得高定位精度。实验中布设了10 km微波光子链路,3个远程监测点在E, N, U方向定位精度都达到毫米量级、实时响应时间低于10 ms。与传统一机单天线方案相比,该光载一机多天线GNSS差分监测系统在不降低定位精度的前提下,显著提升了监测/覆盖范围、实时监测/响应时间,以及大规模监测的性价比。因此,该系统在大型土建工程、自然环境大规模监测具有重要应用价值。展开更多
Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitor...Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.展开更多
文摘该文设计了一种光载一机多天线远程全球导航卫星系统(GNSS)差分监测系统。该系统利用微波光子链路远程采集多个远端天线接收的GNSS信号,并传输回本地端;然后在本地端借助高速光开关,以时分模式依次建立各远端信号与参考基准信号的载波相位双差模型方程,处理后实时获得高定位精度。实验中布设了10 km微波光子链路,3个远程监测点在E, N, U方向定位精度都达到毫米量级、实时响应时间低于10 ms。与传统一机单天线方案相比,该光载一机多天线GNSS差分监测系统在不降低定位精度的前提下,显著提升了监测/覆盖范围、实时监测/响应时间,以及大规模监测的性价比。因此,该系统在大型土建工程、自然环境大规模监测具有重要应用价值。
基金Projects(2007AA01Z126, 2007AA01Z474) supported by the National High-Tech Research and Development Program of ChinaProject(NCET-06-0928) supported by the Program for New Century Excellent Talents in University
文摘Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.
