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
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.
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.
