Navigation systems play an important role in many vital disciplines. Determining the location of a user relative to its physical environment is an important part of many indoor-based navigation services such as user n...Navigation systems play an important role in many vital disciplines. Determining the location of a user relative to its physical environment is an important part of many indoor-based navigation services such as user navigation, enhanced 911 (E911), law enforcement, location-based and marketing services. Indoor navigation applications require a reliable, trustful and continuous navigation solution that overcomes the challenge of Global Navigation Satellite System (GNSS) signal unavailability. To compensate for this issue, other navigation systems such as Inertial Navigation System (INS) are introduced, however, over time there is a significant amount of drift especially in common with low-cost commercial sensors. In this paper, a map aided navigation solution is developed. This research develops an aiding system that utilizes geospatial data to assist the navigation solution by providing virtual boundaries for the navigation trajectories and limits its possibilities only when it is logical to locate the user on a map. The algorithm develops a Pedestrian Dead Reckoning (PDR) based on smart-phone accelerometer and magnetometer sensors to provide the navigation solution. Geospatial model for two indoor environments with a developed map matching algorithm was used to match and project navigation position estimates on the geospatial map. The developed algorithms were field tested in indoor environments and yielded accurate matching results as well as a significant enhancement to positional accuracy. The achieved results demonstrate that the contribution of the developed map aided system enhances the reliability, usability, and accuracy of navigation trajectories in indoor environments.展开更多
The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algor...The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algorithm for the inertial positioning of personnel. The historical moment position constraint and feasible region constraint of particles were introduced in this paper. A resampling method based on multi-stage backtracking of particles was proposed. Therefore, the effectiveness of newly generated particles could be guaranteed. The utilization rate of map information could be improved, thus enhancing the accuracy of personnel localization. The walking experiment results showed that, compared with the traditional PDR algorithm, the proposed method had higher localization accuracy and better repeatability of the localization trajectory for multi-turn paths. Under the total travel of 480 meters, the deviation of the starting end point was less than 2 meters, which was about 0.4% of the total travel.展开更多
针对室内定位系统中现有的行人航位推算(pedestrian dead reckoning,PDR)方法存在加速度计适用性较差,以及基于惯性和磁传感器的航向估计易受器件误差和磁场环境的影响,导致精度较低的问题,在不增加基础设施成本和现场勘察工作的前提下...针对室内定位系统中现有的行人航位推算(pedestrian dead reckoning,PDR)方法存在加速度计适用性较差,以及基于惯性和磁传感器的航向估计易受器件误差和磁场环境的影响,导致精度较低的问题,在不增加基础设施成本和现场勘察工作的前提下,提出一种调频(frequencymodulation,FM)广播信号辅助PDR的室内行人定位技术:在传播模型理论基础上探究FM信号接收信号强度指数(RSSI)与步长的关系,将其与加速度组合以提升步长估计的适用性;然后通过分析FM信号在直线/转弯运动模式下的变化,将其与角速度组合以提升模式识别准确率,并使用模式识别结果约束航向漂移误差;最后,综合步长与航向估计结果实现定位。实验结果表明,引入FM信号后定位误差均值可分别减小36.1%、78.9%。展开更多
For the purpose of positioning in various scenes, including indoors, on open road, and side street buildings, a low-cost personal navigation unit is put forward. The unit consists of a low-cost MEMS(micro-electro-mech...For the purpose of positioning in various scenes, including indoors, on open road, and side street buildings, a low-cost personal navigation unit is put forward. The unit consists of a low-cost MEMS(micro-electro-mechanical system) accelerometer, a gyroscope, a magnetometer and a GPS(global positioning system) chip, and it is capable of switching modes between indoor and outdoor situations seamlessly. The outdoor mode is MIMU(MEMS-inertial measurement unit)/GPS/magnetometer integrated mode and the indoor mode is MIMU/magnetometer integrated mode. The outdoor algorithm uses the extended Kalman filter to fuse data and provide optimum parameters. The indoor algorithm is dead reckoning, which uses vertical and forward accelerations to judge steps and uses a magnetometer to define heading. The two-axis acceleration data is used to calculate the adaptive threshold and estimate the confidence value of the steps, and when the confidence of both two axes data meet the conditions, the steps can be detected in the adaptive time windows. The detection precision is more than 95%. An experiment was conducted in complex situations. The experiment participant wearing the unit walked about 1 600 m in the experiment. The results show that the positioning error is less than 0.2% of the total route distance.展开更多
文摘Navigation systems play an important role in many vital disciplines. Determining the location of a user relative to its physical environment is an important part of many indoor-based navigation services such as user navigation, enhanced 911 (E911), law enforcement, location-based and marketing services. Indoor navigation applications require a reliable, trustful and continuous navigation solution that overcomes the challenge of Global Navigation Satellite System (GNSS) signal unavailability. To compensate for this issue, other navigation systems such as Inertial Navigation System (INS) are introduced, however, over time there is a significant amount of drift especially in common with low-cost commercial sensors. In this paper, a map aided navigation solution is developed. This research develops an aiding system that utilizes geospatial data to assist the navigation solution by providing virtual boundaries for the navigation trajectories and limits its possibilities only when it is logical to locate the user on a map. The algorithm develops a Pedestrian Dead Reckoning (PDR) based on smart-phone accelerometer and magnetometer sensors to provide the navigation solution. Geospatial model for two indoor environments with a developed map matching algorithm was used to match and project navigation position estimates on the geospatial map. The developed algorithms were field tested in indoor environments and yielded accurate matching results as well as a significant enhancement to positional accuracy. The achieved results demonstrate that the contribution of the developed map aided system enhances the reliability, usability, and accuracy of navigation trajectories in indoor environments.
文摘The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algorithm for the inertial positioning of personnel. The historical moment position constraint and feasible region constraint of particles were introduced in this paper. A resampling method based on multi-stage backtracking of particles was proposed. Therefore, the effectiveness of newly generated particles could be guaranteed. The utilization rate of map information could be improved, thus enhancing the accuracy of personnel localization. The walking experiment results showed that, compared with the traditional PDR algorithm, the proposed method had higher localization accuracy and better repeatability of the localization trajectory for multi-turn paths. Under the total travel of 480 meters, the deviation of the starting end point was less than 2 meters, which was about 0.4% of the total travel.
文摘针对室内定位系统中现有的行人航位推算(pedestrian dead reckoning,PDR)方法存在加速度计适用性较差,以及基于惯性和磁传感器的航向估计易受器件误差和磁场环境的影响,导致精度较低的问题,在不增加基础设施成本和现场勘察工作的前提下,提出一种调频(frequencymodulation,FM)广播信号辅助PDR的室内行人定位技术:在传播模型理论基础上探究FM信号接收信号强度指数(RSSI)与步长的关系,将其与加速度组合以提升步长估计的适用性;然后通过分析FM信号在直线/转弯运动模式下的变化,将其与角速度组合以提升模式识别准确率,并使用模式识别结果约束航向漂移误差;最后,综合步长与航向估计结果实现定位。实验结果表明,引入FM信号后定位误差均值可分别减小36.1%、78.9%。
基金The National Natural Science Foundation of China(No.61773113)International Special Projects for Scientific and Technological Cooperation(No.2014DFR80750)the National Key Research and Development Program of China(No.2016YFC0303006,2017YFC0601601)
文摘For the purpose of positioning in various scenes, including indoors, on open road, and side street buildings, a low-cost personal navigation unit is put forward. The unit consists of a low-cost MEMS(micro-electro-mechanical system) accelerometer, a gyroscope, a magnetometer and a GPS(global positioning system) chip, and it is capable of switching modes between indoor and outdoor situations seamlessly. The outdoor mode is MIMU(MEMS-inertial measurement unit)/GPS/magnetometer integrated mode and the indoor mode is MIMU/magnetometer integrated mode. The outdoor algorithm uses the extended Kalman filter to fuse data and provide optimum parameters. The indoor algorithm is dead reckoning, which uses vertical and forward accelerations to judge steps and uses a magnetometer to define heading. The two-axis acceleration data is used to calculate the adaptive threshold and estimate the confidence value of the steps, and when the confidence of both two axes data meet the conditions, the steps can be detected in the adaptive time windows. The detection precision is more than 95%. An experiment was conducted in complex situations. The experiment participant wearing the unit walked about 1 600 m in the experiment. The results show that the positioning error is less than 0.2% of the total route distance.
