WO2019061949A1 - Procédé et appareil de positionnement avec fusion en intérieur assistée par un comportement de mouvement et support de stockage - Google Patents

Procédé et appareil de positionnement avec fusion en intérieur assistée par un comportement de mouvement et support de stockage Download PDF

Info

Publication number
WO2019061949A1
WO2019061949A1 PCT/CN2018/072114 CN2018072114W WO2019061949A1 WO 2019061949 A1 WO2019061949 A1 WO 2019061949A1 CN 2018072114 W CN2018072114 W CN 2018072114W WO 2019061949 A1 WO2019061949 A1 WO 2019061949A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
behavior
pedestrian
motion
indoor
Prior art date
Application number
PCT/CN2018/072114
Other languages
English (en)
Chinese (zh)
Inventor
周宝定
李清泉
朱家松
涂伟
Original Assignee
深圳大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳大学 filed Critical 深圳大学
Publication of WO2019061949A1 publication Critical patent/WO2019061949A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
    • G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Definitions

  • the invention relates to the field of indoor positioning, in particular to an indoor fusion positioning method and device and a storage medium assisted by an activity behavior.
  • indoor positioning has become a research hotspot in the field of location services.
  • the indoor positioning based on the smart phone does not require the user to carry additional equipment, which is beneficial to the promotion and popularization of the indoor positioning technology.
  • Multi-source data acquired by a variety of sensors built into the smartphone can be used for indoor positioning.
  • the accuracy of the built-in sensors of smart phones is not high at present, and the positioning method (Wifi positioning, inertial positioning, Bluetooth positioning, etc.) relying on a single sensor is inferior in accuracy, and cannot meet the needs of indoor positioning.
  • the data collected by the multi-sensor of the smart phone can recognize the behavior of the pedestrian, and the relative motion trajectory and the multi-source observation data acquired by the associated smart phone can be estimated through the motion behavior recognition.
  • the behavior of pedestrians in the indoor environment contains rich situational information. Due to the constraints of the indoor building structure, pedestrians will present different movement states in special indoor locations, resulting in position-related behaviors, such as overweight when riding an elevator. And weightlessness.
  • the indoor scenes are complex and diverse, with different topologies, and may have different indoor positioning signal sources (WiFi routers, etc.) with different geomagnetic field distributions; smartphone users have different physical characteristics (height, step size, etc.); different In the smart phone usage mode (handheld, phone, swing, pocket, etc.), the mobile phone is in different postures, which makes the mobile phone coordinate system and the pedestrian coordinate system have a heading deviation; the smart phone device is diverse and affected by hardware conditions, based on The WiFi and geomagnetic signal strength received by the smartphone is related to the device.
  • the above factors require that the multi-source data fusion indoor positioning method based on smart phones has strong adaptability, and needs to adapt to indoor scenes of different signal environments, different users, different usage modes, and different types of mobile phone devices.
  • an object of the present invention is to provide an indoor behavior locating method and apparatus for assisting motion behavior, and a storage medium, aiming at solving the problem of inaccurate positioning of the existing indoor positioning method.
  • An exercise behavior-assisted indoor fusion positioning method comprising the steps of:
  • the motion behavior-assisted indoor fusion positioning method wherein the wireless signal data includes Wifi signal data and geomagnetic signal data.
  • step B specifically includes:
  • step C specifically includes:
  • the motion behavior-assisted indoor fusion positioning method wherein the smart terminal comprises a smart phone, a smart tablet, a smart bracelet or a smart watch.
  • An activity-assisted indoor fusion positioning device which comprises:
  • a storage device adapted to store a plurality of instructions adapted to be loaded by the processor and to perform the following steps:
  • the current location of the pedestrian is estimated according to the multi-source data fusion model and the transition probability formula.
  • the online target space dividing device wherein the wireless signal data includes Wifi signal data and geomagnetic signal data.
  • the online target space dividing device wherein the step of parameterizing the motion trajectory data and the wireless signal data, constructing a multi-source data fusion model, and obtaining a transition probability formula between pedestrians in consecutive positions comprises:
  • the feature function of behavior recognition is calculated by combining the motion trajectory data with the indoor map model
  • the multi-source data fusion model formula is obtained by using the conditional random field model to fuse the behavior recognition feature function, Wifi positioning feature function and geomagnetic localization feature function.
  • S k represents the indoor position of the pedestrian at time k
  • ⁇ i is the characteristic function Weight parameter
  • a storage medium wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor and to perform the steps of the indoor fusion positioning method assisted by the motion behavior of any of the above.
  • the present invention provides an indoor behavior locating method for assisting an activity behavior, and by acquiring motion behavior recognition of a smart terminal in a multi-use mode, acquiring trajectory data of a pedestrian and wireless signal data received by the intelligent terminal, based on The motion trajectory data and the wireless signal data construct a multi-source data fusion model for indoor positioning, which integrates multi-source observation data in the technique of motion behavior recognition, reduces the dependence on single-mode data, and improves the intelligent terminal-based The accuracy of indoor positioning.
  • FIG. 1 is a flow chart of a preferred embodiment of a motion-assisted indoor fusion positioning method according to the present invention.
  • FIG. 2 is a schematic diagram of a multi-source data fusion model for indoor positioning according to the present invention.
  • FIG. 3 is a structural block diagram of a preferred embodiment of a motion-assisted indoor fusion positioning device according to the present invention.
  • the present invention provides a method and a device for locating a motion-assisted indoor fusion, and a storage medium.
  • the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
  • FIG. 1 is a flow chart of a preferred embodiment of a motion behavior assisted indoor fusion positioning method provided by the present invention. As shown in Figure 1, the following steps are included:
  • the sensor signals generated by different actions of the smart terminal in different usage modes are analyzed, and the characteristics of each behavior in different usage modes are extracted; wherein the usage modes of the smart terminal include a handheld mode, a call mode, a swing mode, and In the pocket mode, the sensor includes an acceleration sensor, an angular velocity sensor, a barometric pressure sensor, etc.; the classifier parameters are obtained through a large number of sample training, and finally, based on the behavior characteristics, a machine learning method is used to implement behavior classification, and the position behavior is used for behavior map matching, walking. Behavior is used to estimate the motion trajectory.
  • the trajectory data is obtained by the method of pedestrian dead reckoning.
  • the pedestrian dead reckoning has three parts: step detection, step estimation and heading estimation. Step detection and step estimation are used for walking distance estimation.
  • the acceleration signal generated by the pedestrian during walking has a periodic characteristic, and the present invention uses the peak detection algorithm to implement the step detection.
  • the smart terminal since the smart terminal is in different postures in different usage modes, there is a heading deviation between the intelligent terminal coordinate system and the pedestrian coordinate system, and the heading deviation is set using the empirical value in the initial positioning, and then the model parameter adaptive learning method is adopted. Adjust the heading deviation in real time.
  • the heading change is calculated by the data of the magnetometer and the gyroscope, wherein the magnetometer outputs the heading angle and the gyroscope outputs the angular velocity.
  • Kalman filtering method is proposed to fuse the data of magnetometer and gyroscope.
  • the step S20 parameterizing the motion trajectory data and the wireless signal data, constructing a multi-source data fusion model and obtaining a transition probability formula of the pedestrian between consecutive positions, specifically including :
  • S24 Using the conditional random field model to fuse the behavior recognition feature function, the Wifi positioning feature function, and the geomagnetic positioning feature function to obtain a multi-source data fusion model formula, Where S k represents the indoor position of the pedestrian at time k, Indicates the nth multi-source observation data at time k, Indicates the probability that the nth observation data is observed at the S k position at time k , and ⁇ i is the characteristic function Weight parameter
  • the motion behavior recognition based on the multi-use mode can obtain the motion track data of the pedestrian, and the wireless terminal can also receive the wireless signal data of the indoor location of the user, where the wireless signal data includes the Wifi signal data and the geomagnetic signal. data.
  • the pedestrian's motor behavior can be described as a hidden Markov process.
  • the hidden Markov factor graph is used to parameterize the observation factors and construct the characteristic function of each observation data.
  • the observation factors include motion trajectory data, Wifi positioning, and geomagnetic positioning.
  • the indoor map is modeled to obtain the "dotted line” model of the indoor map, that is, the indoor map model, wherein the "point” is a position where special behavior may occur, for example, a corner, an elevator, a staircase, etc., and the "line” is a connection "point”"Between the sides.
  • the feature function of behavior recognition is calculated by combining the motion trajectory data with the indoor map model, expressed as
  • the Wifi positioning is implemented based on the location fingerprint method, that is, the probability of the pedestrian being in the current position is estimated by the received Wifi signal data, and the Euclidean distance between the Wifi signal strength and the signal intensity of each position in the location fingerprint database is first calculated, and then the Euclidean distance is passed.
  • the reciprocal normalization gives the Wifi positioning feature function, expressed as Similar to Wifi positioning, the geomagnetic positioning obtains the characteristic function of geomagnetic positioning by calculating the Euclidean distance between the geomagnetic signal data and the signal intensity of each position in the geomagnetic database, and is represented by the reciprocal normalization of the Euclidean distance.
  • the conditional random field model is used to fuse the behavior recognition feature function, Wifi positioning feature function and geomagnetic localization feature function to obtain the initial multi-source data fusion model formula. Then according to the Bayesian principle P(Z k
  • S k ) P(S k
  • Z k ) of the pedestrian at each position is calculated according to the multi-source data fusion model formula, and then the motion trajectory is estimated by the motion behavior recognition to obtain the pedestrian's transfer between successive positions.
  • S k-1 ) since the observation results of the pedestrian dead reckoning include distance observation and angle observation, the two are independent of each other and can be described by a normal Gaussian distribution, and the state transition probability formula is Where d is the observed distance, ⁇ is the observed distance, and ⁇ d and ⁇ ⁇ are the standard deviations of the direction and distance estimates, respectively.
  • the current location of the pedestrian is estimated according to the multi-source data fusion model and the transition probability formula, and specifically includes:
  • the process of indoor positioning is based on the feature function obtained by the multi-source data fusion model and the state transition probability derived from the pedestrian behavior estimation, and the current position of the pedestrian is estimated.
  • the pedestrian initial position S 0 is estimated based on the observation data, and then the probability P(S 0 ) is calculated according to the feature function.
  • the probability of each trajectory is calculated according to the multiple sets of initial positions and their probabilities, and then the trajectories of each trajectory are calculated based on the plurality of sets of initial positions, and the state with the largest posterior probability is the current estimation result.
  • the empirical model parameters are first used, and then based on the motion trajectories and observation data generated during the positioning process, adaptive parameter learning methods are used to obtain real-time dynamic model parameters.
  • each of the perceptual results includes multiple observation model parameters, including heading deviation of the intelligent terminal coordinate system and the pedestrian coordinate system, Wifi and geomagnetic observation error, and step estimation.
  • the weight parameter ⁇ i of a single feature function in the constructed multi-source data fusion model is also one of the model data.
  • the parameter adaptive learning method of multi-source data fusion model is as follows: Firstly, based on the "point-line" model obtained by indoor map modeling and the relative trajectory between positional behavior and positional behavior obtained by motion behavior recognition, behavior map matching is adopted. The method estimates the user's historical trajectory as a reference trajectory for parameter learning. Since there is only one trajectory in the indoor environment that matches the trajectory obtained by the multi-source data observed by the intelligent terminal, the probability of matching the trajectory and the indoor is the greatest when the model parameters are adapted to the current environment, the user, the usage mode, and the device.
  • the optimization problem is solved by the Expectation Maximization (EM) algorithm.
  • the EM algorithm is a heuristic iterative algorithm, which can be used to implement the maximum likelihood estimation of the parameters with hidden variables, and iterative approximation is used to solve the internal parameters of the model.
  • the smart terminal includes a smart phone, a smart tablet, a smart wristband or a smart watch, etc., but is not limited thereto.
  • the present invention also provides an indoor behavior locating device for assisting an exercise behavior, as shown in FIG. 3, wherein the processor 10 is adapted to implement each instruction;
  • the storage device 20 is adapted to store a plurality of instructions adapted to be loaded by the processor and to perform the following steps:
  • the current location of the pedestrian is estimated according to the multi-source data fusion model and the transition probability formula.
  • the online target space dividing device wherein the wireless signal data includes Wifi signal data and geomagnetic signal data.
  • the online target space dividing device wherein the step of parameterizing the motion trajectory data and the wireless signal data, constructing a multi-source data fusion model, and obtaining a transition probability formula between pedestrians in consecutive positions comprises:
  • the feature function of behavior recognition is calculated by combining the motion trajectory data with the indoor map model
  • the multi-source data fusion model formula is obtained by using the conditional random field model to fuse the behavior recognition feature function, Wifi positioning feature function and geomagnetic localization feature function.
  • S k represents the indoor position of the pedestrian at time k
  • ⁇ i is the characteristic function Weight parameter
  • the present invention also provides a storage medium in which a plurality of instructions are stored, the instructions being adapted to be loaded by a processor and performing the steps of the indoor behavior locating method assisted by the motion behavior of any of the above.
  • the present invention provides a motion-assisted indoor fusion positioning method, which acquires the motion track data of the pedestrian and the wireless signal data received by the intelligent terminal by studying the motion behavior recognition of the intelligent terminal in the multi-use mode.
  • a multi-source data fusion model for indoor positioning is constructed based on the motion trajectory data and wireless signal data.
  • the model combines multi-source observation data in the technique of motion behavior recognition, reduces the dependence on single-mode data, and improves the accuracy of indoor positioning based on intelligent terminal; meanwhile, the present invention combines behavioral map matching to calculate historical motion trajectory Adaptive model parameter adaptive learning of multi-source data fusion model can realize adaptive model parameter adjustment and increase the adaptability of indoor algorithm to scene, user, usage mode and mobile device.

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Traffic Control Systems (AREA)

Abstract

La présente invention concerne un procédé et un appareil de positionnement avec fusion en intérieur assistée par un comportement de mouvement et un support de stockage, ledit procédé comportant les étapes consistant: d'après un comportement de mouvement d'un terminal intelligent en mode multi-usage, à obtenir des données de trajectoire de mouvement d'un piéton et des données de signal sans fil reçues par un ordiphone; à paramétrer lesdites données de trajectoire de mouvement et les données de signal sans fil, et à construire un modèle de fusion de données multi-source et à obtenir une équation de la probabilité de transition entre les positions continues d'un piéton; d'après ledit modèle de fusion de données multi-source et l'équation de probabilité de transition, à estimer la position à laquelle le piéton se situe actuellement. Le modèle de fusion de données multi-source construit par la présente invention combine des données d'observation multi-source dans une technologie de reconnaissance de comportement de mouvement, réduisant le degré de dépendance vis-à-vis de données monomode et améliorant la précision d'un positionnement en intérieur basé sur un terminal intelligent.
PCT/CN2018/072114 2017-09-27 2018-01-10 Procédé et appareil de positionnement avec fusion en intérieur assistée par un comportement de mouvement et support de stockage WO2019061949A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710891775.6 2017-09-27
CN201710891775.6A CN107635204B (zh) 2017-09-27 2017-09-27 一种运动行为辅助的室内融合定位方法及装置、存储介质

Publications (1)

Publication Number Publication Date
WO2019061949A1 true WO2019061949A1 (fr) 2019-04-04

Family

ID=61104098

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/072114 WO2019061949A1 (fr) 2017-09-27 2018-01-10 Procédé et appareil de positionnement avec fusion en intérieur assistée par un comportement de mouvement et support de stockage

Country Status (2)

Country Link
CN (1) CN107635204B (fr)
WO (1) WO2019061949A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11009583B1 (en) 2019-12-23 2021-05-18 At&T Intellectual Property I, L.P. Refining and selecting timing data to increase accuracy of timing-based location for user equipment
CN113114850A (zh) * 2021-03-18 2021-07-13 电子科技大学 一种基于监控视频和pdr在线融合定位方法

Families Citing this family (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108322889A (zh) * 2018-02-01 2018-07-24 深圳市交投科技有限公司 一种多源数据融合室内定位的方法、存储介质及智能终端
CN108362289B (zh) * 2018-02-08 2020-12-08 浙江大学城市学院 一种基于多传感器融合的移动智能终端pdr定位方法
CN108426582B (zh) * 2018-03-03 2021-07-30 北京工业大学 行人室内三维地图匹配方法
CN109218983B (zh) * 2018-06-28 2020-09-18 中国人民解放军国防科技大学 一种定位方法及定位系统
CN109029429B (zh) * 2018-09-04 2022-02-11 电子科技大学 基于WiFi和地磁指纹的多分类器全局动态融合定位方法
CN111561921B (zh) * 2019-02-14 2022-05-10 华为技术有限公司 定位方法及装置
CN109781094A (zh) * 2018-12-24 2019-05-21 上海交通大学 基于循环神经网络的地磁定位系统
CN109945865B (zh) * 2019-02-25 2023-02-28 天津大学 基于WiFi与地磁融合的室内定位方法
CN110082716B (zh) * 2019-04-29 2023-07-11 徐州医科大学 一种医院复杂环境室内定位系统及定位方法
CN110619662B (zh) * 2019-05-23 2023-01-03 深圳大学 一种基于单目视觉的多行人目标空间连续定位方法及系统
CN110322067A (zh) * 2019-07-03 2019-10-11 中山大学 基于因子图模型的移动用户位置预测方法
CN111025269B (zh) * 2019-12-19 2022-11-15 哈尔滨工程大学 一种水下机器人传感器安装偏差估计方法
US11743678B2 (en) * 2020-08-24 2023-08-29 The Hong Kong University Of Science And Technology Generic signal fusion framework for multi-modal localization
CN113358117B (zh) * 2021-03-09 2023-05-26 北京工业大学 一种利用地图的视觉惯性室内定位方法
CN112985392B (zh) * 2021-04-19 2021-07-30 中国人民解放军国防科技大学 基于图优化框架的行人惯性导航方法和装置

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105910601A (zh) * 2016-05-31 2016-08-31 天津大学 一种基于隐马尔科夫模型的室内地磁定位方法
CN106054125A (zh) * 2016-05-05 2016-10-26 南京邮电大学 一种基于线性链条件随机场的融合室内定位方法
CN106123897A (zh) * 2016-06-14 2016-11-16 中山大学 基于多特征的室内融合定位方法
US20170171718A1 (en) * 2015-12-09 2017-06-15 Research & Business Foundation Of Sungkyunkwan University Location estimation method for indoor device

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170171718A1 (en) * 2015-12-09 2017-06-15 Research & Business Foundation Of Sungkyunkwan University Location estimation method for indoor device
CN106054125A (zh) * 2016-05-05 2016-10-26 南京邮电大学 一种基于线性链条件随机场的融合室内定位方法
CN105910601A (zh) * 2016-05-31 2016-08-31 天津大学 一种基于隐马尔科夫模型的室内地磁定位方法
CN106123897A (zh) * 2016-06-14 2016-11-16 中山大学 基于多特征的室内融合定位方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11009583B1 (en) 2019-12-23 2021-05-18 At&T Intellectual Property I, L.P. Refining and selecting timing data to increase accuracy of timing-based location for user equipment
CN113114850A (zh) * 2021-03-18 2021-07-13 电子科技大学 一种基于监控视频和pdr在线融合定位方法

Also Published As

Publication number Publication date
CN107635204B (zh) 2020-07-28
CN107635204A (zh) 2018-01-26

Similar Documents

Publication Publication Date Title
WO2019061949A1 (fr) Procédé et appareil de positionnement avec fusion en intérieur assistée par un comportement de mouvement et support de stockage
Gu et al. Accurate step length estimation for pedestrian dead reckoning localization using stacked autoencoders
WO2020083110A1 (fr) Procédé et appareil de reconnaissance de la parole et d'apprentissage de modèle de reconnaissance de la parole
WO2017088196A1 (fr) Dispositif et procédé de navigation par fusion en fonction d'un lecteur d'empreintes digitales sans fil et à système microélectromécanique (mems)
CN108632761B (zh) 一种基于粒子滤波算法的室内定位方法
Bae et al. Large-scale indoor positioning using geomagnetic field with deep neural networks
WO2012035707A1 (fr) Dispositif et procédé de génération de carte, procédé de déplacement d'un corps mobile et dispositif robot
CN110100150B (zh) 利用地磁场的有效室内定位
WO2021012879A1 (fr) Procédé et appareil de reconstruction de trajectoire, dispositif informatique et support de stockage
CN106441302B (zh) 大型开放式区域内的室内定位方法
CN111595344B (zh) 一种基于地图信息辅助的多姿态下行人行位推算方法
Wang et al. Pedestrian heading estimation based on spatial transformer networks and hierarchical LSTM
Al-Homayani et al. Improved indoor geomagnetic field fingerprinting for smartwatch localization using deep learning
Wu et al. Indoor positioning based on walking-surveyed Wi-Fi fingerprint and corner reference trajectory-geomagnetic database
Wang et al. Deep neural network‐based Wi‐Fi/pedestrian dead reckoning indoor positioning system using adaptive robust factor graph model
Ding et al. OGI-SLAM2: A hybrid map SLAM framework grounded in inertial-based SLAM
CN106028450B (zh) 室内定位方法及设备
Yu et al. Neural networks-based Wi-Fi/PDR indoor navigation fusion methods
Wei et al. MM-Loc: Cross-sensor indoor smartphone location tracking using multimodal deep neural networks
CN108322889A (zh) 一种多源数据融合室内定位的方法、存储介质及智能终端
US10551195B2 (en) Portable device with improved sensor position change detection
Wu et al. CWIWD-IPS: A Crowdsensing/Walk-surveying Inertial/Wi-Fi Data-driven Indoor Positioning System
Li et al. An indoor positioning approach using smartphone based on PDR and EKF
Xu et al. An indoor pedestrian navigation algorithm based on smartphone mode recognition
Huang et al. VariFi: Variational Inference for Indoor Pedestrian Localization and Tracking Using IMU and WiFi RSS

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18862702

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 25.09.2020)

122 Ep: pct application non-entry in european phase

Ref document number: 18862702

Country of ref document: EP

Kind code of ref document: A1