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
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
High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based...High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.展开更多
针对机器人、无人机和其他智能系统的位置信息,研究了非视距(non line of sight,NLOS)环境中基于到达时间(time of arrival,TOA)测距的目标定位问题。在建模过程中,通过引入平衡参数来抑制NLOS误差对定位精度的影响,并成功将定位问题的...针对机器人、无人机和其他智能系统的位置信息,研究了非视距(non line of sight,NLOS)环境中基于到达时间(time of arrival,TOA)测距的目标定位问题。在建模过程中,通过引入平衡参数来抑制NLOS误差对定位精度的影响,并成功将定位问题的形式与一个广义信赖域子问题(generalized trust region subproblem,GTRS)框架进行耦合。与其他凸优化算法不同的是,本文没有联合估计目标节点的位置和平衡参数,而是采用了一种迭代求精的思想,算法可以用二分法高速有效地进行求解。所提算法与已有的算法相比,不需要任何关于NLOS路径的信息。此外,与大多数现有算法不同,所提算法的计算复杂度低,能够满足实时定位的需求。仿真结果表明:该算法具有稳定的NLOS误差抑制能力,在定位性能和算法复杂度之间有着很好的权衡。展开更多
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 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 in part by the National Natural Science Foundation of China under Grant No.61771474in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX212243+2 种基金in part by the Young Talents of Xuzhou Science and Technology Plan Project under Grant No.KC19051in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2021D02in part by the Open Fund of Information Photonics and Optical Communications (IPOC) (BUPT)。
文摘High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.
文摘针对机器人、无人机和其他智能系统的位置信息,研究了非视距(non line of sight,NLOS)环境中基于到达时间(time of arrival,TOA)测距的目标定位问题。在建模过程中,通过引入平衡参数来抑制NLOS误差对定位精度的影响,并成功将定位问题的形式与一个广义信赖域子问题(generalized trust region subproblem,GTRS)框架进行耦合。与其他凸优化算法不同的是,本文没有联合估计目标节点的位置和平衡参数,而是采用了一种迭代求精的思想,算法可以用二分法高速有效地进行求解。所提算法与已有的算法相比,不需要任何关于NLOS路径的信息。此外,与大多数现有算法不同,所提算法的计算复杂度低,能够满足实时定位的需求。仿真结果表明:该算法具有稳定的NLOS误差抑制能力,在定位性能和算法复杂度之间有着很好的权衡。
