Real-time and high-precision Fifth-generation mobile communication technology(5G)positioning is essential for establishing a wide-area and high-accuracy spatiotemporal reference framework in urban environments.However...Real-time and high-precision Fifth-generation mobile communication technology(5G)positioning is essential for establishing a wide-area and high-accuracy spatiotemporal reference framework in urban environments.However,a main challenge is the Non-Line-Of-Sight(NLOS)error significantly impact positioning accuracy,limiting the full deployment and application of 5G technology.In this study,a novel NLOS error mitigation method using Virtual Base-Station(VBS)-assisted algorithm is developed to enhance both kinematic and static positioning performance of 5G systems in complex urban environments.The proposed method consists of three modules:(1)a Time-Of-Arrival(TOA)positioning model,(2)a VBS generation method,and(3)a stable-state discrimination method.The TOA positioning model utilizes raw TOA measurements and a conventional four-station localization algorithm to estimate the location of user equipment.The VBS generation method optimizes Base-Station(BS)performance with particle filter combined with a random-distribution algorithm.The stable-state discrimination method employs the Augmented Dickey-Fuller(ADF)test to assess the stationarity of the feedback iteration process in VBS optimization.Several experiments are conducted in diverse scenario areas to evaluate the effectiveness,accuracy,and robustness of the proposed method.The results demonstrate that the proposed method significantly outperforms the traditional localization method,a 21.09%improvement in Three-Dimensional(3D)positioning accuracy.Compared to the state-of-the-art method,the proposed algorithm not only achieves slightly higher accuracy but,more importantly,reduces significantly the computation time.展开更多
This article puts forward a scalar weighting information fusion （IF） smoother with modified biased Kalman filter （BKF） and maximum likelihood estimation （MLE） to mitigate the ranging errors in ultra wide band （...This article puts forward a scalar weighting information fusion （IF） smoother with modified biased Kalman filter （BKF） and maximum likelihood estimation （MLE） to mitigate the ranging errors in ultra wide band （UWB） systems. The information fusion algorithm uses both the time of arrival （TOA） and received signal strength （RSS） measurement data to improve the ranging accuracy. At first, the ranging protocol of IEEE 802.15.4a acts as a multi-sensor system with multi-scale sampling. Then the scalar-based IF smoother accurately estimates the range measurement in the line of sight （LOS） and non-line of sight （NLOS） condition of UWB sensor network, during which the effectiveness of the IF in mitigating errors is especially focused during the LOS/NLOS transitions. Simulation results show that the proposed hybrid TOA-RSS fusion approach indicates a performance improvement compared with the usual TOA-only and other IF method, and the estimated ranging metrics can be used for achieving higher accuracy in location estimation and target tracking.展开更多
It is well known that non-line-of-sight （NLOS） error has been the major factor impeding the enhancement of accuracy for time of arrival （TOA） estimation and wireless positioning. This article proposes a novel meth...It is well known that non-line-of-sight （NLOS） error has been the major factor impeding the enhancement of accuracy for time of arrival （TOA） estimation and wireless positioning. This article proposes a novel method of TOA estimation effectively reducing the NLOS error by 60%, comparing with the traditional timing and synchronization method. By constructing the orthogonal training sequences, this method converts the traditional TOA estimation to the detection of the first arrival path （FAP） in the NLOS multipath environment, and then estimates the TOA by the round-trip transmission （RTT） technology. Both theoretical analysis and numerical simulations prove that the method proposed in this article achieves better performance than the traditional methods.展开更多
基金supported by China Mobile Group Device Co.,Ltd Fund(CMDC-202401967,CMDC-202402083).
文摘Real-time and high-precision Fifth-generation mobile communication technology(5G)positioning is essential for establishing a wide-area and high-accuracy spatiotemporal reference framework in urban environments.However,a main challenge is the Non-Line-Of-Sight(NLOS)error significantly impact positioning accuracy,limiting the full deployment and application of 5G technology.In this study,a novel NLOS error mitigation method using Virtual Base-Station(VBS)-assisted algorithm is developed to enhance both kinematic and static positioning performance of 5G systems in complex urban environments.The proposed method consists of three modules:(1)a Time-Of-Arrival(TOA)positioning model,(2)a VBS generation method,and(3)a stable-state discrimination method.The TOA positioning model utilizes raw TOA measurements and a conventional four-station localization algorithm to estimate the location of user equipment.The VBS generation method optimizes Base-Station(BS)performance with particle filter combined with a random-distribution algorithm.The stable-state discrimination method employs the Augmented Dickey-Fuller(ADF)test to assess the stationarity of the feedback iteration process in VBS optimization.Several experiments are conducted in diverse scenario areas to evaluate the effectiveness,accuracy,and robustness of the proposed method.The results demonstrate that the proposed method significantly outperforms the traditional localization method,a 21.09%improvement in Three-Dimensional(3D)positioning accuracy.Compared to the state-of-the-art method,the proposed algorithm not only achieves slightly higher accuracy but,more importantly,reduces significantly the computation time.
基金supported by the National Natural Science Foundation for Distinguished Young Scholars of China (60825304)the National Basic Research Development Program of China(2009cb320600)
文摘This article puts forward a scalar weighting information fusion （IF） smoother with modified biased Kalman filter （BKF） and maximum likelihood estimation （MLE） to mitigate the ranging errors in ultra wide band （UWB） systems. The information fusion algorithm uses both the time of arrival （TOA） and received signal strength （RSS） measurement data to improve the ranging accuracy. At first, the ranging protocol of IEEE 802.15.4a acts as a multi-sensor system with multi-scale sampling. Then the scalar-based IF smoother accurately estimates the range measurement in the line of sight （LOS） and non-line of sight （NLOS） condition of UWB sensor network, during which the effectiveness of the IF in mitigating errors is especially focused during the LOS/NLOS transitions. Simulation results show that the proposed hybrid TOA-RSS fusion approach indicates a performance improvement compared with the usual TOA-only and other IF method, and the estimated ranging metrics can be used for achieving higher accuracy in location estimation and target tracking.
基金the National Natural Science Foundation of China（60496312）Program for New Century Excellent Talents in University（NCET-05-0116）+1 种基金the Hi-Tech Research and Development Program of China（2006AA01Z260）the Fund for Foreign Scholars in University Research and Teaching Programs（B07005）.
文摘It is well known that non-line-of-sight （NLOS） error has been the major factor impeding the enhancement of accuracy for time of arrival （TOA） estimation and wireless positioning. This article proposes a novel method of TOA estimation effectively reducing the NLOS error by 60%, comparing with the traditional timing and synchronization method. By constructing the orthogonal training sequences, this method converts the traditional TOA estimation to the detection of the first arrival path （FAP） in the NLOS multipath environment, and then estimates the TOA by the round-trip transmission （RTT） technology. Both theoretical analysis and numerical simulations prove that the method proposed in this article achieves better performance than the traditional methods.
