In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based po...In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.展开更多
针对卫星遥感影像目标检测中,小目标检测精度低、漏检率高,以及实际应用场景中检测效率低等问题,文章提出一种基于改进YOLOv7(You Only Look Once)的卫星遥感影像多尺度目标检测方法。在检测网络中,聚焦提升小目标检测能力,添加类注意...针对卫星遥感影像目标检测中,小目标检测精度低、漏检率高,以及实际应用场景中检测效率低等问题,文章提出一种基于改进YOLOv7(You Only Look Once)的卫星遥感影像多尺度目标检测方法。在检测网络中,聚焦提升小目标检测能力,添加类注意力机制的卷积模块(ConvNeXt Block,CNeB),提升对小目标细粒度特征的提取及利用能力;同时,提出后处理机制,通过建立小目标与大目标的相互关系,实现使用单个模型对多种尺度目标进行检测。实验结果表明,在TGRS-HRRSD数据集4个小目标上,改进后的检测模型相较原始YOLOv7在平均精确率均值指标上提升了16.6个百分点。在检测特定大目标任务中,通过后处理机制,在保持精度的条件下,相较YOLT(You Only Look Twice)时间减少了70%。相较于主流的面向遥感影像的检测方法,该方法在检测多尺度目标上,检测精度更高、速度更快。展开更多
基金Project(52272339)supported by the National Natural Science Foundation of ChinaProject(2023YFB390730303)supported by the National Key Research and Development Program of China+2 种基金Project(L2023G004)supported by the Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.Project(QZKFKT2023-005)supported by the State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive,ChinaProject(2022JZZ05)supported by the Open Foundation of MOE Key Laboratory of Engineering Structures of Heavy Haul Railway(Central South University),China。
文摘In this paper,a novel train positioning method considering satellite raw observation data was proposed,which aims to promote train positioning performance from an innovative perspective of the train satellite-based positioning error sources.The method focused on overcoming the abnormal observations in satellite observation data caused by railway environment rather than the positioning results.Specifically,the relative positioning experimental platform was built and the zero-baseline method was firstly employed to evaluate the carrier phase data quality,and then,GNSS combined observation models were adopted to construct the detection values,which were applied to judge abnormal-data through the dual-frequency observations.Further,ambiguity fixing optimization was investigated based on observation data selection in partly-blocked environments.The results show that the proposed method can effectively detect and address abnormal observations and improve positioning stability.Cycle slips and gross errors can be detected and identified based on dual-frequency global navigation satellite system data.After adopting the data selection strategy,the ambiguity fixing percentage was improved by 29.2%,and the standard deviation in the East,North,and Up components was enhanced by 12.7%,7.4%,and 12.5%,respectively.The proposed method can provide references for train positioning performance optimization in railway environments from the perspective of positioning error sources.
文摘针对卫星遥感影像目标检测中,小目标检测精度低、漏检率高,以及实际应用场景中检测效率低等问题,文章提出一种基于改进YOLOv7(You Only Look Once)的卫星遥感影像多尺度目标检测方法。在检测网络中,聚焦提升小目标检测能力,添加类注意力机制的卷积模块(ConvNeXt Block,CNeB),提升对小目标细粒度特征的提取及利用能力;同时,提出后处理机制,通过建立小目标与大目标的相互关系,实现使用单个模型对多种尺度目标进行检测。实验结果表明,在TGRS-HRRSD数据集4个小目标上,改进后的检测模型相较原始YOLOv7在平均精确率均值指标上提升了16.6个百分点。在检测特定大目标任务中,通过后处理机制,在保持精度的条件下,相较YOLT(You Only Look Twice)时间减少了70%。相较于主流的面向遥感影像的检测方法,该方法在检测多尺度目标上,检测精度更高、速度更快。
