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
减小非视距(Non Line Of Sight,NLOS)误差定位算法大多要求在移动台和基站之间至少存在一条视距(Line Of Sight,LOS)路径。提出一种新的NLOS环境中基于散射模型分类传播环境的TOA(Time Of Arrival)定位方法,将散射模型中NLOS传播的统计...减小非视距(Non Line Of Sight,NLOS)误差定位算法大多要求在移动台和基站之间至少存在一条视距(Line Of Sight,LOS)路径。提出一种新的NLOS环境中基于散射模型分类传播环境的TOA(Time Of Arrival)定位方法,将散射模型中NLOS传播的统计特性加入到定位算法中,使用散射模型研究了3种定位算法,方差匹配算法,期望最大算法和贝叶斯算法。并对算法进行仿真,仿真结果表明,本算法性能优于传统定位算法。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.
文摘减小非视距(Non Line Of Sight,NLOS)误差定位算法大多要求在移动台和基站之间至少存在一条视距(Line Of Sight,LOS)路径。提出一种新的NLOS环境中基于散射模型分类传播环境的TOA(Time Of Arrival)定位方法,将散射模型中NLOS传播的统计特性加入到定位算法中,使用散射模型研究了3种定位算法,方差匹配算法,期望最大算法和贝叶斯算法。并对算法进行仿真,仿真结果表明,本算法性能优于传统定位算法。
