多路径误差是全球卫星导航系统(global navigation satellite system,GNSS)精密数据处理中的主要误差源之一,非直射(non line of sight,NLOS)信号与多路径信号具有不同的信号特性,在厘米至毫米级别的GNSS定位中可能导致显著的误差。然...多路径误差是全球卫星导航系统(global navigation satellite system,GNSS)精密数据处理中的主要误差源之一,非直射(non line of sight,NLOS)信号与多路径信号具有不同的信号特性,在厘米至毫米级别的GNSS定位中可能导致显著的误差。然而目前针对载波相位观测值的多路径误差改正方法均未对两者进行有效区分。通过三维(3 dimensional,3D)点云数据检测静态测站处的NLOS载波信号,评估了半天球格网点模型(multi-point hemispherical grid model,MHGM)对多路径和NLOS信号的削弱效果,并在原有MHGM的基础上进一步提出剔除NLOS信号的多路径误差建模改进策略。实验中,模糊度固定时段内的双差残差统计显示,MHGM对多路径和NLOS误差分别有73.5%和81.2%的削弱,但MHGM改正后NLOS观测值的精度仍然显著低于多路径观测值。在将NLOS信号在多路径误差建模和应用阶段进行剔除之后,模糊度固定时段内载波相位双差观测值残差的均方根进一步降低,相比不剔除NLOS时提升了8.8%。动态定位测试结果表明,在多系统和可用卫星数量充足的情况下,MHGM对定位结果3D精度有68.6%的提升,而采用剔除NLOS信号的MHGM时,3D定位精度的改善率可以达到76.0%。展开更多
Accurate non-line of sight(NLOS)identification technique in ultra-wideband(UWB)location-based services is critical for applications like drone communication and autonomous navigation.However,current methods using bina...Accurate non-line of sight(NLOS)identification technique in ultra-wideband(UWB)location-based services is critical for applications like drone communication and autonomous navigation.However,current methods using binary classification(LOS/NLOS)oversimplify real-world complexities,with limited generalisation and adaptability to varying indoor environments,thereby reducing the accuracy of positioning.This study proposes an extreme gradient boosting(XGBoost)model to identify multi-class NLOS conditions.We optimise the model using grid search and genetic algorithms.Initially,the grid search approach is used to identify the most favourable values for integer hyperparameters.In order to achieve an optimised model configuration,the genetic algorithm is employed to fine-tune the floating-point hyperparameters.The model evaluations utilise a wide-ranging dataset of real-world measurements obtained with a Qorvo DW1000 UWB device,covering various indoor scenarios.Experimental results show that our proposed XGBoost achieved the highest overall accuracy of 99.47%,precision of 99%,recall of 99%,and an F-score of 99%on an open-source dataset.Additionally,based on a local dataset,the model achieved the highest performance,with an accuracy of 96%,precision of 96%,recall of 97%,and an F-score of 97%.In contrast to current machine learning methods in the literature,the suggestion model enhances classification accuracy and effectively addresses the NLOS/LOS identification as a multiclass propagation channel.This approach provides a robust solution with generalisation and adaptability across various dataset types and environments for more reliable and accurate indoor positioning technologies.展开更多
在城市复杂环境中,GNSS信号易发生折射、衍射、遮挡等现象,产生严重的多路径效应和非视距(none line of sight,NLOS)误差;特别是NLOS信号可能会导致数十、上百米的伪距和载波相位误差,严重影响复杂环境下的GNSS定位精度和稳定性。本文...在城市复杂环境中,GNSS信号易发生折射、衍射、遮挡等现象,产生严重的多路径效应和非视距(none line of sight,NLOS)误差;特别是NLOS信号可能会导致数十、上百米的伪距和载波相位误差,严重影响复杂环境下的GNSS定位精度和稳定性。本文重点分析了城市动态场景下GNSS NLOS信号在高度角、信噪比、伪距一致性、相位一致性、双差伪距残差和双差相位残差等6个方面的特征,并使用随机森林(random forests,RF)就信号特征对NLOS自主识别的影响加以初步分析。结果表明:80%以上的NLOS信号的高度角低于60度、信噪比低于45 dB-Hz;NLOS信号的伪距一致性和相位一致性会出现明显的离群现象,双差伪距残差和双差相位残差存在明显的大幅度波动。实时动态场景下的NLOS自主识别结果表明,高度角和信噪比对NLOS识别准确度的影响最大,伪距一致性和相位一致性次之;而双伪距残差和双差相位残差则是离线进行NLOS自主识别时的两个重要特征,仅使用高度角、信噪比、双差伪距残差、双差相位残差四个特征就能使基于随机森林的NLOS识别准确度达到80%左右。展开更多
文摘Accurate non-line of sight(NLOS)identification technique in ultra-wideband(UWB)location-based services is critical for applications like drone communication and autonomous navigation.However,current methods using binary classification(LOS/NLOS)oversimplify real-world complexities,with limited generalisation and adaptability to varying indoor environments,thereby reducing the accuracy of positioning.This study proposes an extreme gradient boosting(XGBoost)model to identify multi-class NLOS conditions.We optimise the model using grid search and genetic algorithms.Initially,the grid search approach is used to identify the most favourable values for integer hyperparameters.In order to achieve an optimised model configuration,the genetic algorithm is employed to fine-tune the floating-point hyperparameters.The model evaluations utilise a wide-ranging dataset of real-world measurements obtained with a Qorvo DW1000 UWB device,covering various indoor scenarios.Experimental results show that our proposed XGBoost achieved the highest overall accuracy of 99.47%,precision of 99%,recall of 99%,and an F-score of 99%on an open-source dataset.Additionally,based on a local dataset,the model achieved the highest performance,with an accuracy of 96%,precision of 96%,recall of 97%,and an F-score of 97%.In contrast to current machine learning methods in the literature,the suggestion model enhances classification accuracy and effectively addresses the NLOS/LOS identification as a multiclass propagation channel.This approach provides a robust solution with generalisation and adaptability across various dataset types and environments for more reliable and accurate indoor positioning technologies.
文摘在城市复杂环境中,GNSS信号易发生折射、衍射、遮挡等现象,产生严重的多路径效应和非视距(none line of sight,NLOS)误差;特别是NLOS信号可能会导致数十、上百米的伪距和载波相位误差,严重影响复杂环境下的GNSS定位精度和稳定性。本文重点分析了城市动态场景下GNSS NLOS信号在高度角、信噪比、伪距一致性、相位一致性、双差伪距残差和双差相位残差等6个方面的特征,并使用随机森林(random forests,RF)就信号特征对NLOS自主识别的影响加以初步分析。结果表明:80%以上的NLOS信号的高度角低于60度、信噪比低于45 dB-Hz;NLOS信号的伪距一致性和相位一致性会出现明显的离群现象,双差伪距残差和双差相位残差存在明显的大幅度波动。实时动态场景下的NLOS自主识别结果表明,高度角和信噪比对NLOS识别准确度的影响最大,伪距一致性和相位一致性次之;而双伪距残差和双差相位残差则是离线进行NLOS自主识别时的两个重要特征,仅使用高度角、信噪比、双差伪距残差、双差相位残差四个特征就能使基于随机森林的NLOS识别准确度达到80%左右。
