Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applie...Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applies this concept to walking persons.The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered.Here,the movement of a foot and the corresponding direction change is measured and summed up,to infer the current position.Measuring and integrating the corresponding physical parameters,e.g.using inertial sensors,introduces small errors that accumulate quickly into large distance errors.Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths.In this paper,we use building maps to improve localization based on a single foot-mounted inertial sensor.We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation.Even though the computation of individual steps is quite accurate,small errors still accumulate in the long term.We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks,such as orientation and displacement drift,and discuss restrictions and disadvantages of these algorithms.We also present a method of deriving the initial position and orientation from GPS measurements.We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building,walking each route three times.Our quantitative results show an endpoint accuracy improvement of up to 60%when using likely paths and 23%when using unlikely paths.However,both approaches can also decrease accuracy in certain scenarios.We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.展开更多
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance....To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.展开更多
A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the...A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.展开更多
For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteri...For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.展开更多
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
In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible ...In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.展开更多
The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estima...The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estimation based on Hopfield network is proposed. Compared with Kalman filter, the algorithm does not require very precise system model and the prior knowledge of noise statistics and does not diverge easily. The simulation results show that the vehicular dead reckoning system based on Hopfield network filtering and estimation has the good position precision, and needn't require the inertial sensors with high precision. Therefore, the algorithm has the good practicability.展开更多
The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing met...The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing methods are solely based on a dynamics-based method or an image-based method,which is susceptible to road excitation limitations and interference from the external environment.Therefore,this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will expe-rience.First,a road feature extraction model based on multi-task learning is conducted,which can simultaneously segment the drivable area and road cast shadow.Second,the optimized candidate regions of interest are classified with confidence levels by ShuffleNet.Considering environmental interference,candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results.Finally,the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels.The performance of the entire framework is verified on a specific dataset with shadow and split curve roads.The results reveal that the proposed method can identify the RSC with accurate predictions in real time.展开更多
The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion entities.This paper propos...The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion entities.This paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models,which can be used to reduce the communication in a distributed interactive simulation.The proposed algorithm perform extrapolation using a number of candidate predictors.Its idea is based on the assumption that a complex trajectory can be decomposed into several simple trajectories.The experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation.展开更多
Controversial 20th-century British politician Enoch Powell once noted that all political careers in Great Britain end in tears. With Theresa May, his words proved true not just figuratively but literally. As the Briti...Controversial 20th-century British politician Enoch Powell once noted that all political careers in Great Britain end in tears. With Theresa May, his words proved true not just figuratively but literally. As the British prime minister announced on May 24 that she would step down on June 7, observers could see her face consumed with emotion and tears well up in her eyes. It was a bathetic note to end a chaotic, and for many, in glorious, period.展开更多
On the second anniversary of the Lehman Brothers’ failure,the world economy finds itself yet again at a "troubling juncture." The U.S.economy shows every sign of heading for a double-dip recession and even ...On the second anniversary of the Lehman Brothers’ failure,the world economy finds itself yet again at a "troubling juncture." The U.S.economy shows every sign of heading for a double-dip recession and even deflation and the European sovereign debt crisis remains far from being resolved,d Desmond Lachman,resident fellow of the American Enterprise Institute,in an article for Beijing Review.Edited excerpts follow:展开更多
The fiber strapdown inertial navigation system （FSINS）/dead reckoning （DR）/Beidou double-star integrated navigation scheme is proposed aiming at the need of land fighting-vehicle independence positioning. The meas...The fiber strapdown inertial navigation system （FSINS）/dead reckoning （DR）/Beidou double-star integrated navigation scheme is proposed aiming at the need of land fighting-vehicle independence positioning. The measurement information fusion technology is studied by introducing the FSINS/DR/Beidou double-star integrated scheme. Several specific methods for the information fusion are discussed, and a Kalman filter is designed for the information fusion. Experimental results show that the design of the integrated scheme can improve the positioning accuracy of the navigation system.展开更多
受室内复杂环境的影响,实现满足各类室内定位需求、准确实时的定位仍有很大的挑战性。提出了一种联合WiFi信息和行人航位推算(pedestrian dead reckoning,PDR)算法的智能手机室内定位方法,并给出了其原理和流程。实验结果表明,该方法适...受室内复杂环境的影响,实现满足各类室内定位需求、准确实时的定位仍有很大的挑战性。提出了一种联合WiFi信息和行人航位推算(pedestrian dead reckoning,PDR)算法的智能手机室内定位方法,并给出了其原理和流程。实验结果表明,该方法适应性较强、定位结果准确。展开更多
This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fin...This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead reckoning.To avoid errors accumulated from dead reckoning,the method uses short-range dead reckoning.The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture inside.The Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them.Random Forests(RF)was used to build regression models to estimate positions from location fingerprints.The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.This error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one location.The results suggest that the proposed method is effective for indoor positioning.展开更多
The indoor positioning system is now an important technique as part of the Internet-of-Things(IoT)ecosystem.Among indoor positioning techniques,multiple Wi-Fi Access Points(APs)-based positioning systems have been res...The indoor positioning system is now an important technique as part of the Internet-of-Things(IoT)ecosystem.Among indoor positioning techniques,multiple Wi-Fi Access Points(APs)-based positioning systems have been researched a lot.There is a lack of research focusing on the scene where only one Wi-Fi AP is available.This work proposes a hybrid indoor positioning system that takes advantage of the Fine-Timing Measurements(FTM)technique that is part of the IEEE 802.11mc standard,introduced back in 2016.The system uses one single Wi-Fi FTM AP and takes advantage of the built-in inertial sensors of the smartphone to estimate the device’s position.We explore both Loosely Coupled(LC)and Tightly Coupled(TC)integration schemes for the sensors’data fusion.Experimental results show that the proposed methods can achieve an average positioning accuracy of about 1 m without knowing the initial position.Compared with the LC integration method,the median error accuracy of the proposed TC fusion algorithm has improved by more than 52%and 67%,respectively,in the two experiments we set up.展开更多
In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even ped...In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.展开更多
文摘Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applies this concept to walking persons.The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered.Here,the movement of a foot and the corresponding direction change is measured and summed up,to infer the current position.Measuring and integrating the corresponding physical parameters,e.g.using inertial sensors,introduces small errors that accumulate quickly into large distance errors.Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths.In this paper,we use building maps to improve localization based on a single foot-mounted inertial sensor.We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation.Even though the computation of individual steps is quite accurate,small errors still accumulate in the long term.We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks,such as orientation and displacement drift,and discuss restrictions and disadvantages of these algorithms.We also present a method of deriving the initial position and orientation from GPS measurements.We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building,walking each route three times.Our quantitative results show an endpoint accuracy improvement of up to 60%when using likely paths and 23%when using unlikely paths.However,both approaches can also decrease accuracy in certain scenarios.We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.
文摘To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
基金Project(60234030) supported by the National Natural Science Foundation of China
文摘A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.
文摘For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.
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.
基金supported by the National Natural Science Foundation of China (61471031)the Fundamental Research Funds for the Central Universities,Beijing Jiaotong University (2013JBZ001)+2 种基金National Science and Technology Major Project (2016ZX03001014006)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (No.2017D14)Shenzhen Peacock Program under Grant No.KQJSCX20160226193545
文摘In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.
文摘The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estimation based on Hopfield network is proposed. Compared with Kalman filter, the algorithm does not require very precise system model and the prior knowledge of noise statistics and does not diverge easily. The simulation results show that the vehicular dead reckoning system based on Hopfield network filtering and estimation has the good position precision, and needn't require the inertial sensors with high precision. Therefore, the algorithm has the good practicability.
基金funded by the National Natural Science Foundation of China under Grant No.52002284the Young Elite Scientists Sponsorship Program by CAST under Grant No.2021QNRC001+1 种基金the Project funded by China Postdoctoral Science Foundation under Grant No.2021M692424the Jiangsu Province Science and Technology Project under Grant No.BE2021006-3.
文摘The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing methods are solely based on a dynamics-based method or an image-based method,which is susceptible to road excitation limitations and interference from the external environment.Therefore,this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will expe-rience.First,a road feature extraction model based on multi-task learning is conducted,which can simultaneously segment the drivable area and road cast shadow.Second,the optimized candidate regions of interest are classified with confidence levels by ShuffleNet.Considering environmental interference,candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results.Finally,the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels.The performance of the entire framework is verified on a specific dataset with shadow and split curve roads.The results reveal that the proposed method can identify the RSC with accurate predictions in real time.
基金the research Project of State Key Laboratory of High Performance computing of National University of Defense Technology(No.201303-05).
文摘The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor,and the forecasting performance would vary with different types of motion entities.This paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models,which can be used to reduce the communication in a distributed interactive simulation.The proposed algorithm perform extrapolation using a number of candidate predictors.Its idea is based on the assumption that a complex trajectory can be decomposed into several simple trajectories.The experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation.
文摘Controversial 20th-century British politician Enoch Powell once noted that all political careers in Great Britain end in tears. With Theresa May, his words proved true not just figuratively but literally. As the British prime minister announced on May 24 that she would step down on June 7, observers could see her face consumed with emotion and tears well up in her eyes. It was a bathetic note to end a chaotic, and for many, in glorious, period.
文摘On the second anniversary of the Lehman Brothers’ failure,the world economy finds itself yet again at a "troubling juncture." The U.S.economy shows every sign of heading for a double-dip recession and even deflation and the European sovereign debt crisis remains far from being resolved,d Desmond Lachman,resident fellow of the American Enterprise Institute,in an article for Beijing Review.Edited excerpts follow:
文摘The fiber strapdown inertial navigation system （FSINS）/dead reckoning （DR）/Beidou double-star integrated navigation scheme is proposed aiming at the need of land fighting-vehicle independence positioning. The measurement information fusion technology is studied by introducing the FSINS/DR/Beidou double-star integrated scheme. Several specific methods for the information fusion are discussed, and a Kalman filter is designed for the information fusion. Experimental results show that the design of the integrated scheme can improve the positioning accuracy of the navigation system.
文摘This paper proposed and evaluated an estimation method for indoor positioning.The method combines location fingerprinting and dead reckoning differently from the conventional combinations.It uses compound location fingerprints,which are composed of radio fingerprints at multiple points of time,that is,at multiple positions,and displacements between them estimated by dead reckoning.To avoid errors accumulated from dead reckoning,the method uses short-range dead reckoning.The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11×5 m with furniture inside.The Received Signal Strength Indicator(RSSI)values of the beacons were collected at 30 measuring points,which were points at the intersections on a 1×1 m grid with no obstacles.A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them.Random Forests(RF)was used to build regression models to estimate positions from location fingerprints.The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.This error is lower than that received with a single-point baseline model,where a feature vector is composed of only RSSI values at one location.The results suggest that the proposed method is effective for indoor positioning.
基金supported by the National Key Research and Development Program of China[grant numbers 2016YFB0502200,2016YFB0502201]the NSFC[grant number 91638203]。
文摘The indoor positioning system is now an important technique as part of the Internet-of-Things(IoT)ecosystem.Among indoor positioning techniques,multiple Wi-Fi Access Points(APs)-based positioning systems have been researched a lot.There is a lack of research focusing on the scene where only one Wi-Fi AP is available.This work proposes a hybrid indoor positioning system that takes advantage of the Fine-Timing Measurements(FTM)technique that is part of the IEEE 802.11mc standard,introduced back in 2016.The system uses one single Wi-Fi FTM AP and takes advantage of the built-in inertial sensors of the smartphone to estimate the device’s position.We explore both Loosely Coupled(LC)and Tightly Coupled(TC)integration schemes for the sensors’data fusion.Experimental results show that the proposed methods can achieve an average positioning accuracy of about 1 m without knowing the initial position.Compared with the LC integration method,the median error accuracy of the proposed TC fusion algorithm has improved by more than 52%and 67%,respectively,in the two experiments we set up.
基金funded by the project“Design of System Integration Construction Scheme Based on Functions of Each Module” (No.XDHT2020169A)the project“Development of Indoor Inspection Robot System for Substation” (No.XDHT2019501A).
文摘In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.
