To mitigate the Non-Line-of-Sight (NLoS) error which seriously affects the localization accuracy and robustness in complex indoor environment,a novel Iterative Minimum Residual (IMR) based on the consistency hypothesi...To mitigate the Non-Line-of-Sight (NLoS) error which seriously affects the localization accuracy and robustness in complex indoor environment,a novel Iterative Minimum Residual (IMR) based on the consistency hypothesis of the residual and the error is proposed in this paper.It chooses the best subset of measurements to calculate the coordinates of the unknown node by comparing the residuals obtained with different subsets of beacons.To reduce the time complexity of the IMR algorithm,Spatial Correlation Filter (SCF) is also proposed,which can remove the most serious NLoS distance with low calculation cost.Combined with the proposed SCF and IMR algorithm,nodes can be localized with high accuracy and low time complexity.Experimental results with real dataset demonstrate that the proposed algorithm can identify the NLoS range effectively with about 50% time cost of employing SCF only.展开更多
In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.Howeve...In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.However,the existing algorithms still suffer from two disadvantages:1)The algorithms strongly depend on prior information;2)The approaches do not satisfy the mean square error(MSE)optimal criterion of the measurement noise.To tackle the troubles,we first formulate an MSE minimization model for measurement noise by taking the source and the NLOS biases as variables.To obtain stable solutions,we introduce a penalty function to avoid abnormal estimates.We further tackle the nonconvex locating problem with semidefinite relaxation techniques.Finally,we incorporate mixed constraints and variable information to improve the estimation accuracy.Simulations and experiments show that the proposed method achieves consistent performance and good accuracy in dynamic NLOS environments.展开更多
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
Integrating Global Navigation Satellite Systems(GNSS)in Simultaneous Localization and Mapping(SLAM)systems draws increasing attention to a global and continuous localization solution.Nonetheless,in dense urban environ...Integrating Global Navigation Satellite Systems(GNSS)in Simultaneous Localization and Mapping(SLAM)systems draws increasing attention to a global and continuous localization solution.Nonetheless,in dense urban environments,GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight(NLOS)measurements,which might lead to a sharp deterioration in localization results.In this paper,we propose to detect the sky area from the up-looking camera to improve GNSS measurement reliability for more accurate position estimation.We present Sky-GVINS:a sky-aware GNSS-Visual-Inertial system based on a recent work called GVINS.Specifically,we adopt a global threshold method to segment the sky regions and non-sky regions in the fish-eye sky-pointing image and then project satellites to the image using the geometric relationship between satellites and the camera.After that,we reject satellites in non-sky regions to eliminate NLOS signals.We investigated various segmentation algorithms for sky detection and found that the Otsu algorithm reported the highest classification rate and computational efficiency,despite the algorithm's simplicity and ease of implementation.To evaluate the effectiveness of Sky-GVINS,we built a ground robot and conducted extensive real-world experiments on campus.Experimental results show that our method improves localization accuracy in both open areas and dense urban environments compared to the baseline method.Finally,we also conduct a detailed analysis and point out possible further directions for future research.For detailed information,visit our project website at https://github.com/SJTU-ViSYS/Sky-GVINS.展开更多
In order to improve the performance of the traditional hybrid time-of-arrival（TOA）/angle-of-arrival（AOA）location algorithm in non-line-of-sight（NLOS）environments,a new hybrid TOA/AOA location estimation algorith...In order to improve the performance of the traditional hybrid time-of-arrival（TOA）/angle-of-arrival（AOA）location algorithm in non-line-of-sight（NLOS）environments,a new hybrid TOA/AOA location estimation algorithm by utilizing scatterer information is proposed.The linearized region of the mobile station（MS）is obtained according to the base station（BS）coordinates and the TOA measurements.The candidate points（CPs）of the MS are generated from this region.Then,using the measured TOA and AOA measurements,the radius of each scatterer is computed.Compared with the prior scatterer information,true CPs are obtained among all the CPs.The adaptive fuzzy clustering（AFC）technology is adopted to estimate the position of the MS with true CPs.Finally,simulations are conducted to evaluate the performance of the algorithm.The results demonstrate that the proposed location algorithm can significantly mitigate the NLOS effect and efficiently estimate the MS position.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
基金supported by the National Natural Science Foundation of China under Grants No.60973110,No.61003307the Natural Science Foundation of Beijing City of China under Grant No.4102059the Major Projects of Ministry of Industry and Information Technology under Grants No.2010ZX03006-002-03,No.2011ZX03005-005
文摘To mitigate the Non-Line-of-Sight (NLoS) error which seriously affects the localization accuracy and robustness in complex indoor environment,a novel Iterative Minimum Residual (IMR) based on the consistency hypothesis of the residual and the error is proposed in this paper.It chooses the best subset of measurements to calculate the coordinates of the unknown node by comparing the residuals obtained with different subsets of beacons.To reduce the time complexity of the IMR algorithm,Spatial Correlation Filter (SCF) is also proposed,which can remove the most serious NLoS distance with low calculation cost.Combined with the proposed SCF and IMR algorithm,nodes can be localized with high accuracy and low time complexity.Experimental results with real dataset demonstrate that the proposed algorithm can identify the NLoS range effectively with about 50% time cost of employing SCF only.
基金supported by the National Natural Science Foundation of China under Grant No.62101370。
文摘In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.However,the existing algorithms still suffer from two disadvantages:1)The algorithms strongly depend on prior information;2)The approaches do not satisfy the mean square error(MSE)optimal criterion of the measurement noise.To tackle the troubles,we first formulate an MSE minimization model for measurement noise by taking the source and the NLOS biases as variables.To obtain stable solutions,we introduce a penalty function to avoid abnormal estimates.We further tackle the nonconvex locating problem with semidefinite relaxation techniques.Finally,we incorporate mixed constraints and variable information to improve the estimation accuracy.Simulations and experiments show that the proposed method achieves consistent performance and good accuracy in dynamic NLOS environments.
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 National Key R&D Plan of China[2022YFB3903800]and NSFC[62073214].
文摘Integrating Global Navigation Satellite Systems(GNSS)in Simultaneous Localization and Mapping(SLAM)systems draws increasing attention to a global and continuous localization solution.Nonetheless,in dense urban environments,GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight(NLOS)measurements,which might lead to a sharp deterioration in localization results.In this paper,we propose to detect the sky area from the up-looking camera to improve GNSS measurement reliability for more accurate position estimation.We present Sky-GVINS:a sky-aware GNSS-Visual-Inertial system based on a recent work called GVINS.Specifically,we adopt a global threshold method to segment the sky regions and non-sky regions in the fish-eye sky-pointing image and then project satellites to the image using the geometric relationship between satellites and the camera.After that,we reject satellites in non-sky regions to eliminate NLOS signals.We investigated various segmentation algorithms for sky detection and found that the Otsu algorithm reported the highest classification rate and computational efficiency,despite the algorithm's simplicity and ease of implementation.To evaluate the effectiveness of Sky-GVINS,we built a ground robot and conducted extensive real-world experiments on campus.Experimental results show that our method improves localization accuracy in both open areas and dense urban environments compared to the baseline method.Finally,we also conduct a detailed analysis and point out possible further directions for future research.For detailed information,visit our project website at https://github.com/SJTU-ViSYS/Sky-GVINS.
基金The National High Technology Research and Development Program of China(863Program)(No.2008AA01Z227)the National Natural Science Foundation of China(No.60872075)
文摘In order to improve the performance of the traditional hybrid time-of-arrival（TOA）/angle-of-arrival（AOA）location algorithm in non-line-of-sight（NLOS）environments,a new hybrid TOA/AOA location estimation algorithm by utilizing scatterer information is proposed.The linearized region of the mobile station（MS）is obtained according to the base station（BS）coordinates and the TOA measurements.The candidate points（CPs）of the MS are generated from this region.Then,using the measured TOA and AOA measurements,the radius of each scatterer is computed.Compared with the prior scatterer information,true CPs are obtained among all the CPs.The adaptive fuzzy clustering（AFC）technology is adopted to estimate the position of the MS with true CPs.Finally,simulations are conducted to evaluate the performance of the algorithm.The results demonstrate that the proposed location algorithm can significantly mitigate the NLOS effect and efficiently estimate the MS position.
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.
