WO2019033587A1 - 基于智能手表的gps漂移过滤方法和智能手表 - Google Patents

基于智能手表的gps漂移过滤方法和智能手表 Download PDF

Info

Publication number
WO2019033587A1
WO2019033587A1 PCT/CN2017/109858 CN2017109858W WO2019033587A1 WO 2019033587 A1 WO2019033587 A1 WO 2019033587A1 CN 2017109858 W CN2017109858 W CN 2017109858W WO 2019033587 A1 WO2019033587 A1 WO 2019033587A1
Authority
WO
WIPO (PCT)
Prior art keywords
drift
data
speed
gps data
wearer
Prior art date
Application number
PCT/CN2017/109858
Other languages
English (en)
French (fr)
Inventor
戎海峰
饶旋
Original Assignee
东莞市远峰科技有限公司
广东远峰电子科技股份有限公司
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 东莞市远峰科技有限公司, 广东远峰电子科技股份有限公司 filed Critical 东莞市远峰科技有限公司
Publication of WO2019033587A1 publication Critical patent/WO2019033587A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • G01S19/37—Hardware or software details of the signal processing chain
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/52—Determining velocity

Definitions

  • the present disclosure relates to the field of smart wearable devices, for example, to a smart watch based GPS drift filtering method and a smart watch.
  • the present disclosure provides a GPS drift filtering method based on a smart watch and a smart watch, which can combine the movement of the smart watch wearer to perform drift filtering on the GPS trajectory to make the GPS trajectory closer to the real trajectory.
  • the present disclosure provides a global watch system GPS drift filtering method based on a smart watch, including:
  • the currently sampled GPS data is drift data
  • the currently sampled GPS data is discarded, and the wearer's real-time speed is determined based on the motion signal.
  • the method before determining whether the currently sampled GPS data is drift data, according to the motion signal acquired by the smart watch, the method further includes:
  • the current sampled GPS data is discarded
  • the step is performed: determining whether the currently sampled GPS data is drift data according to the motion signal acquired by the smart watch.
  • the step of determining whether the currently sampled GPS data is drift data according to the motion signal acquired by the smart watch includes:
  • the step of determining a real-time speed of the wearer based on the motion signal includes: zeroing the real-time speed.
  • the step of determining whether the currently sampled GPS data is drift data according to the motion signal acquired by the smart watch includes:
  • the state is a moving state, identifying a type of motion of the wearer, the type of motion including a step type and a speed type;
  • the step of determining a real-time speed of the wearer based on the motion signal includes configuring the real-time speed as the reference speed.
  • the step of calculating a reference speed according to the motion type includes:
  • the reference speed is calculated according to the step frequency and the stride of the wearer
  • the reference speed of the wearer is set based on the speed analyzed from the sampled non-drift GPS data.
  • the method further includes:
  • the positions corresponding to the adjacent two non-drift GPS data are connected by a straight line to form a GPS trajectory.
  • the present disclosure also provides a smart watch including a global positioning system GPS module, a nine-axis sensor and a processor, and the GPS module and the nine-axis sensor are respectively connected to the processor
  • the GPS module is configured to perform GPS data sampling by cycle
  • the nine-axis sensor is configured to acquire a motion signal of the wearer
  • the processor is configured to determine whether the currently sampled GPS data is drift data, currently sampled When the GPS data is drift data, the current sampled GPS data is discarded, and the wearer's real-time speed is determined based on the motion signal.
  • the processor includes: a drift filtering unit and a speed configuration unit;
  • the drift filter unit is configured to:
  • the current sampled GPS data is discarded
  • the speed configuration unit is configured to determine a real-time speed of the wearer based on the motion signal.
  • the processor further includes: a motion recognition unit;
  • the motion recognition unit is configured to determine that the wearer is in a stationary state or a moving state according to a motion signal acquired by the smart watch; the drift filtering unit is further configured to: when the wearer is in a stationary state, and the speed and the displacement When at least one is not zero, it is determined that the currently sampled GPS data is drift data;
  • the speed configuration unit is further configured to zero the real-time speed
  • the processor further includes: a motion recognition unit;
  • the motion recognition unit is configured to determine that the wearer is in a stationary state or a moving state according to a motion signal acquired by the smart watch, and to identify a type of motion of the wearer when the wearer is in a moving state, the motion type including a step type and a meter a speed type, the reference speed is calculated according to the type of motion;
  • the drift filtering unit is further configured to determine that the currently sampled GPS data is drift data when the wearer is in a moving state and the speed exceeds a preset error range compared to the reference speed;
  • the speed configuration unit is further configured to configure the real-time speed as the reference speed.
  • the motion recognition unit is configured to:
  • the reference speed is calculated according to the step frequency and the stride of the wearer
  • the reference speed of the wearer is set based on the speed analyzed from the sampled non-drift GPS data.
  • the processor further includes: a trajectory fitting unit configured to, after the step of determining a real-time speed of the wearer according to the motion signal,
  • the positions corresponding to the adjacent two non-drift GPS data are connected by a straight line to form a GPS trajectory.
  • the present disclosure also provides a computer readable storage medium storing computer executable instructions for performing the above method.
  • the present disclosure also provides a smart watch that includes one or more processors, a memory, and one or more programs, the one or more programs being stored in a memory when executed by one or more processors , perform the above method.
  • the present disclosure also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer, Having the computer perform any of the methods described above.
  • the present disclosure filters the GPS trajectory according to the motion signal collected by the smart watch, and thereby calculates the real-time speed of the wearer. Since the data of the smart watch indicates the real motion state of the wearer, the filtered GPS trajectory can be made closer to the real one. The trajectory, the wearer's motion data can also be fully integrated with the GPS trajectory to obtain more accurate trajectory, speed and mileage information.
  • FIG. 1 is a flowchart of a GPS drift filtering method provided in Embodiment 1.
  • Embodiment 2 is a flow chart of a GPS drift filtering method provided in Embodiment 2.
  • Embodiment 3 is a schematic structural view of a smart watch provided in Embodiment 3.
  • FIG. 4 is a schematic diagram showing the hardware structure of a smart watch provided in Embodiment 4.
  • the embodiment provides a smart watch based GPS drift filtering method, which is performed by a smart watch, which can be composed of corresponding software modules and hardware.
  • FIG. 1 is a flowchart of a GPS drift filtering method provided in Embodiment 1. As shown in FIG. 1, the GPS drift filtering method includes the following steps:
  • step 11 GPS data sampling is performed on a periodic basis.
  • the GPS module acquires GPS data according to a preset frequency.
  • the embodiment performs sampling at a frequency of 1 Hz.
  • Step 12 According to the motion signal acquired by the smart watch, determine whether the currently sampled GPS data is drift data; when the currently sampled GPS data is drift data, perform step 13 to perform steps when the currently sampled GPS data is not drift data. 14.
  • the currently sampled GPS data is compared with the last sampled GPS data to obtain the wearer's displacement, velocity, acceleration, and azimuth variation.
  • the three-axis accelerometer of the smart watch acquires the motion signal of the wearer at a frequency of 25 Hz, analyzes the motion signal, and obtains information such as the number of steps, the step frequency, the signal amplitude, and the posture of the device, and determines to wear the information.
  • the state of the person, the above state includes a stationary state and a moving state.
  • Whether the wearer moves can be determined by whether there is a step number in a certain period of time or by the magnitude of the fluctuation of the motion signal. For example, if no new step number is generated within 2 s and the variance of the motion signal is less than the preset threshold, it is determined that the wearer is at a standstill. The wearer is determined to be in a moving state when the variance of the motion signal is greater than or equal to a preset threshold.
  • step 13 When the wearer is in a stationary state, and at least one of the speed and the displacement obtained by analyzing the GPS data is not zero, it is determined that the currently sampled GPS data is the drift data, and step 13 is performed.
  • the type of exercise of the wearer When the wearer is in a moving state, the type of exercise of the wearer is identified, the type of exercise includes a step type and a speed type, the step type exercise includes walking, running, or walking, and the speed type exercise includes riding.
  • the step of calculating the reference speed according to the motion type includes: when the motion type is the step type, calculating the reference speed according to the step frequency and the stride of the wearer; when the motion type is the speed type, according to the sampled The speed of the non-drift GPS data is analyzed (ie, the original speed is used), and the wearer's reference speed is set.
  • reference speeds for different types of motion can also be obtained from the wearer's historical data.
  • step 13 is performed.
  • the preset error range is set to a reference speed of ⁇ 50%, and if the speed is not within the range, that is, the speed has apparently not belonged to a reasonable speed of the corresponding motion type, it is determined that the GPS data is drift data.
  • step 13 the currently sampled GPS data is discarded, and the wearer's real-time speed is determined based on the motion signal.
  • the GPS data of the current sampling is discarded, that is, the GPS data of the current sampling is not considered when generating the GPS track.
  • the wearer's real-time speed is set to zero; in the moving state, the real-time speed is configured as the reference speed, and the reference speed can be calculated from the motion signal in step 12.
  • Step 14 Generate a GPS trajectory according to the GPS data.
  • the GPS trajectory is generated by analyzing the information such as the latitude, longitude, displacement, velocity, acceleration, and azimuth change of the wearer according to step 12.
  • the GPS data is filtered by the motion signal of the smart watch wearer, and the rationality of the GPS data is analyzed according to the motion state and the motion type, thereby filtering out the drift data close to the real trajectory, and overcoming the accuracy of the GPS filtering in the related art.
  • the resulting GPS trajectory is closer to the real trajectory, and the wearer's motion data is more accurate.
  • the embodiment is improved on the basis of the above embodiment. Before the GPS data is accurately filtered according to the motion signal, the GPS data is initially filtered. For some GPS data, it can be judged whether it drifts without performing accurate calculation. .
  • This embodiment only describes the steps that are added, and the steps that are not described are the same as the corresponding steps of the above embodiment.
  • the GPS drift filtering method includes the following steps:
  • step 21 GPS data sampling is performed on a periodic basis.
  • step 22 the currently sampled GPS data and the last sampled GPS data are analyzed to obtain the wearer's displacement, velocity, acceleration, and azimuth variation.
  • Step 23 Compare the displacement, the speed, the acceleration, and the azimuth change amount with a preset criterion to determine whether the currently sampled GPS data is preliminary drift data, and if yes, perform step 25, the currently sampled GPS. When the data is not preliminary drift data, go to step 24.
  • the preset standard setting may be: the displacement is less than 50 m, the speed is less than 130 km/h, the acceleration is less than 4 m/s 2 , and the variation of the azimuth is less than 360 degrees in 6 seconds. If any of the above conditions is not satisfied, the GPS data is judged to be drift data. In practical applications, preset standards can be adjusted according to sampling frequency, filtering accuracy requirements, and the like.
  • step 25 If the currently sampled GPS data can be judged as drift data, you can jump directly to step 25, There is no need to filter with motion signals. If the currently sampled GPS data is determined to be preliminary drift data, then further filtering is performed according to step 24.
  • Step 24 Determine, according to the motion signal acquired by the smart watch, whether the currently sampled GPS data is drift data. If the currently sampled GPS data is drift data, go to step 25. If the currently sampled GPS data is not drift data, go to step 26.
  • step 25 the currently sampled GPS data is discarded, and the wearer's real-time speed is determined based on the motion signal.
  • Step 26 Generate a GPS trajectory according to the collected GPS data.
  • the position of the wearer is determined according to the GPS data, thereby obtaining a GPS trajectory.
  • the positions corresponding to the two adjacent non-drift GPS data are connected by a straight line to form a GPS track.
  • the adjacent two non-drift GPS data include the end point of the previous track and the start point of the next track.
  • the time of the drift is usually short, so the end point of the previous track is directly connected with the starting point of the track.
  • the above step 24 is used to determine whether the currently sampled GPS data is drift data, or whether the currently sampled GPS data is drift data in the following manner: the current sampling is smaller than the GPS data sampled before 4s. 2m judges that the currently sampled GPS data is drift data; or the speed of less than 0.5m/s in 2s continuously determines that the currently sampled GPS data is drift data; or the azimuth changes over 360 degrees within 6 seconds to determine that the currently sampled GPS data is drift data. .
  • the starting point of the trajectory is generated according to the GPS data, the distance is not accumulated, the trajectory is not increased, and the speed is set to zero. The distance, the generated trajectory, and the configured real-time speed are only accumulated when the mobile state is met for a certain period of time.
  • the GPS data is initially filtered, and the GPS data can be judged to be drifted without an accurate calculation, the data processing amount is reduced, and the drift can be generated close to the actual situation.
  • the GPS trajectory of the situation is initially filtered, and the GPS data can be judged to be drifted without an accurate calculation, the data processing amount is reduced, and the drift can be generated close to the actual situation.
  • This embodiment provides a smart watch for performing the GPS drift filtering method described in the foregoing embodiments.
  • the smart watch and the smart watch-based GPS drift filtering method in the above embodiment solve the same technical problem and have the same technical effects.
  • the smart watch includes a GPS module 31, a nine-axis sensor 33, and a processor 32.
  • the GPS module 31 is arranged to perform GPS data sampling on a periodic basis.
  • the nine-axis sensor 33 is arranged to acquire a motion signal of the wearer.
  • the processor 32 is configured to determine whether the currently sampled GPS data is drift data. When the currently sampled GPS data is drift data, the current sampled GPS data is discarded, and the wearer's real-time speed is determined according to the motion signal.
  • the processor 32 includes a drift filtering unit 321 and a speed configuration unit 322.
  • the drift filtering unit 321 is configured to:
  • the current sampled GPS data is discarded; when the currently sampled GPS data is not the preliminary drift data, whether the currently sampled GPS data is determined according to the motion signal acquired by the smart watch For drift data.
  • the speed configuration unit 322 is arranged to determine the real-time speed of the wearer based on the motion signal.
  • the processor 32 further includes: a motion recognition unit 324.
  • the motion recognition unit 324 is configured to determine that the wearer is in a stationary state or a moving state according to a motion signal acquired by the smart watch.
  • the drift filtering unit 321 is configured to determine that the currently sampled GPS data is drift data when the wearer is in a stationary state and at least one of the speed and the displacement is not zero; correspondingly, the Speed configuration unit 322 is arranged to zero the real time speed.
  • the motion recognition unit 324 is further configured to determine that the wearer is in a stationary state or a moving state according to the motion signal acquired by the smart watch; and when the wearer is in the mobile state, identify the type of motion of the wearer, the type of the motion Including the step type and the speed type, the reference speed is calculated according to the type of motion.
  • the drift filtering unit 321 is further configured to determine that the currently sampled GPS data is drift data when the wearer is in a moving state and the speed exceeds a preset error range compared to the reference speed;
  • the speed configuration unit 322 is further configured to configure the real-time speed as the reference speed.
  • the motion recognition unit 324 is configured to:
  • the reference speed is calculated according to the wearer's pitch and stride; when the motion type is the speed type, the wearer is set according to the speed analyzed from the sampled non-drift GPS data. Reference speed.
  • the processor 32 further includes: a trajectory fitting unit 323 configured to: after the step of determining the real-time speed of the wearer according to the motion signal, use a position corresponding to two adjacent non-drift GPS data Lines are connected to form a GPS trajectory.
  • a trajectory fitting unit 323 configured to: after the step of determining the real-time speed of the wearer according to the motion signal, use a position corresponding to two adjacent non-drift GPS data Lines are connected to form a GPS trajectory.
  • the GPS data is initially filtered to exclude drift data, and then the GPS track is further filtered according to the motion data of the smart watch wearer, so that the GPS track is closer to the real track, so that the wearer's motion data is closer to the actual situation. This results in more accurate trajectory, speed and mileage information.
  • the embodiment further provides a computer readable storage medium storing computer executable instructions for performing any of the above methods.
  • FIG. 4 is a schematic diagram showing the hardware structure of a smart watch according to the embodiment. As shown in FIG. 4, the smart watch includes one or more processors 410 and a memory 420. One processor 410 is taken as an example in FIG.
  • the smart watch may further include an input device 430 and an output device 440.
  • the processor 410, the memory 420, the input device 430, and the output device 440 in the smart watch may be connected by a bus or other means, and the bus connection is taken as an example in FIG.
  • the input device 430 can receive input numeric or character information
  • the output device 440 can include a display device such as a display screen.
  • the memory 420 is used as a computer readable storage medium for storing software programs, computers Executable programs and modules.
  • the processor 410 executes various functional applications and data processing by executing software programs, instructions, and modules stored in the memory 420 to implement any of the above-described embodiments.
  • the memory 420 may include a storage program area and an storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to use of the smart watch, and the like.
  • the memory may include volatile memory such as random access memory (RAM), and may also include non-volatile memory such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device.
  • Memory 420 can be a non-transitory computer storage medium or a transitory computer storage medium.
  • the non-transitory computer storage medium such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.
  • memory 420 can optionally include memory remotely located relative to processor 410, which can be connected to the smart watch over a network. Examples of the above networks may include the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • Input device 430 can be used to receive input numeric or character information and to generate key signal inputs related to user settings and function controls of the smart watch.
  • Output device 440 can include a display device such as a display screen.
  • the smart watch of the present embodiment may also include a communication device 440 for transmitting and/or receiving information over a communication network.
  • a person skilled in the art can understand that all or part of the process of implementing the above embodiment method can be completed by executing related hardware by a computer program, and the program can be stored in a non-transitory computer readable storage medium.
  • the program when executed, may include the flow of an embodiment of the method as described above, wherein the non-transitory computer readable storage medium may be a magnetic disk, an optical disk, a read only memory (ROM), or a random access memory (RAM). Wait.
  • the smart watch-based global positioning system GPS drift filtering method and the smart watch provided by the present disclosure can make the filtered GPS trajectory closer to the real trajectory and obtain higher precision trajectory, speed and mileage information.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Electromechanical Clocks (AREA)
  • Electric Clocks (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

一种基于智能手表的GPS漂移过滤方法和智能手表。GPS漂移过滤方法包括：按周期进行GPS数据采样（11）；根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据（12）；若是，舍弃当前采样的GPS数据，根据运动信号确定佩戴者的实时速度（13）。

Description

基于智能手表的GPS漂移过滤方法和智能手表 技术领域
本公开涉及智能穿戴设备领域，例如涉及一种基于智能手表的GPS漂移过滤方法和智能手表。
背景技术
智能穿戴领域中，生成全球定位系统(Global Positioning System，GPS)轨迹时会考虑GPS是否漂移，一般是通过位移、速度、加速度、方位角变化以及精度因子来判断是否存在漂移现象，若确定存在漂移则舍弃该时刻的GPS信息，不继续增加里程，且将速度归零。这样做会造成如下两个问题：第一，在GPS信号弱的情况下，部分漂移轨迹与真实轨迹十分接近，按照相关技术的方法对GPS进行过滤的效果不明显；第二，在运动中出现漂移时，若将速度置零，则实时速度显示将发生错误，对用户的运动状态监控也会不准确，最终导致得到的轨迹、里程、和速度的精度较低。
发明内容
本公开提供一种基于智能手表的GPS漂移过滤方法和智能手表，能够结合智能手表佩戴者的运功情况，对GPS轨迹进行漂移过滤，使GPS轨迹更接近真实轨迹。
本公开提供一种基于智能手表的全球定位系统GPS漂移过滤方法，包括：
按周期进行GPS数据采样；
根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据；
在当前采样的GPS数据是漂移数据时，舍弃所述当前采样的GPS数据，根据所述运动信号确定佩戴者的实时速度。
可选地，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据之前，还包括：
分析当前采样的GPS数据与上一次采样的GPS数据，获得佩戴者的位移、速度、加速度和方位角变化量；
将所述位移、所述速度、所述加速度和所述方位角变化量与预设标准比较，判断当前采样的GPS数据是否为预备漂移数据；
当前采样的GPS数据为预备漂移数据时，舍弃所述当前采样的GPS数据；
当前采样的GPS数据不是预备漂移数据时，则执行步骤：根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据。
可选地，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据的步骤，包括：
根据智能手表获取的运动信号确定佩戴者的状态；
当所述状态为静止状态，且所述速度和所述位移中的至少一个不为零，则判定当前采样的GPS数据为漂移数据；
所述根据所述运动信号确定佩戴者的实时速度的步骤，包括：将所述实时速度置零。
可选地，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据的步骤，包括：
根据智能手表获取的运动信号确定佩戴者的状态；
当所述状态为移动状态，则识别佩戴者的运动类型，所述运动类型包括计步型和计速型；
根据所述运动类型计算参考速度；
当所述佩戴者的速度与所述参考速度相比超出预设误差范围时，则判定当前采样的GPS数据为漂移数据；
所述根据所述运动信号确定佩戴者的实时速度的步骤，包括：将所述实时速度配置为所述参考速度。
可选地，所述根据所述运动类型计算参考速度的步骤，包括：
当所述运动类型为计步型时，根据所述佩戴者的步频和步幅计算参考速度；
当所述运动类型为计速型时，根据从已采样的非漂移的GPS数据中分析出的速度，设置佩戴者的参考速度。
可选地，所述根据所述运动信号确定佩戴者的实时速度的步骤之后，还包括：
将相邻两个非漂移的GPS数据对应的位置用直线连接以形成GPS轨迹。
本公开还提供一种智能手表，包括全球定位系统GPS模块、九轴传感器和处理器，所述GPS模块和所述九轴传感器分别与所述处理器连接
所述GPS模块设置为按周期进行GPS数据采样；
所述九轴传感器设置为获取佩戴者的运动信号；
所述处理器设置为判断当前采样的GPS数据是否为漂移数据，在当前采样的 GPS数据是漂移数据时，舍弃所述当前采样的GPS数据，根据所述运动信号确定佩戴者的实时速度。
可选地，所述处理器包括：漂移过滤单元和速度配置单元；
所述漂移过滤单元设置为：
在根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据的步骤之前，分析当前采样的GPS数据与上一次采样的GPS数据，获得佩戴者的位移、速度、加速度和方位角变化量；
将所述位移、所述速度、所述加速度和所述方位角变化量与预设标准比较，判断当前采样的GPS数据是否为预备漂移数据；
在当前采样的GPS数据为预备漂移数据时，舍弃所述当前采样的GPS数据；
在当前采样的GPS数据不是预备漂移数据时，根据智能手表获取的运动信号，判断所述当前采样的GPS数据是否为漂移数据；
所述速度配置单元设置为根据所述运动信号确定佩戴者的实时速度。
可选地，所述处理器还包括：运动识别单元；
所述运动识别单元设置为根据智能手表获取的运动信号确定佩戴者为静止状态或移动状态；所述漂移过滤单元还设置为，当佩戴者为静止状态，且所述速度和所述位移中的至少一个不为零时，则判定当前采样的GPS数据为漂移数据；
所述速度配置单元还设置为将所述实时速度置零；
可选地，所述处理器还包括：运动识别单元；
所述运动识别单元设置为根据智能手表获取的运动信号确定佩戴者为静止状态或移动状态，当佩戴者为移动状态时，则识别佩戴者的运动类型，所述运动类型包括计步型和计速型，根据所述运动类型计算参考速度；
所述漂移过滤单元还设置为，当佩戴者为移动状态，且所述速度与所述参考速度相比超出预设误差范围时，则判定当前采样的GPS数据为漂移数据；
所述速度配置单元还设置为将所述实时速度配置为所述参考速度。
可选地，所述运动识别单元是设置为：
当所述运动类型为计步型时，根据所述佩戴者的步频和步幅计算参考速度；
当所述运动类型为计速型时，根据从已采样的非漂移的GPS数据中分析出的速度，设置佩戴者的参考速度。
可选地，所述处理器还包括：轨迹拟合单元，设置为在根据所述运动信号确定佩戴者的实时速度的步骤之后，
将相邻两个非漂移的GPS数据对应的位置用直线连接以形成GPS轨迹。
本公开还提供一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行上述方法。
本公开还提供一种智能手表，该智能手表包括一个或多个处理器、存储器以及一个或多个程序，所述一个或多个程序存储在存储器中，当被一个或多个处理器执行时，执行上述方法。
本公开还提供了一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行上述任意一种方法。
本公开根据智能手表采集到的运动信号对GPS轨迹进行过滤，并且由此计算佩戴者的实时速度，由于智能手表的数据表示了佩戴者的真实运动状态，能使过滤后的GPS轨迹更接近真实轨迹，佩戴者的运动数据也能够与GPS轨迹充分结合，获得精度更高的轨迹、速度和里程信息。
附图说明
图1是实施例一提供的GPS漂移过滤方法的流程图。
图2是实施例二提供的GPS漂移过滤方法的流程图。
图3是实施例三提供的智能手表的结构示意图。
图4是实施例四提供的一种智能手表的硬件结构示意图。
具体实施方式
实施例一
本实施例提供一种基于智能手表的GPS漂移过滤方法，该方法由一种智能手表来执行，所述智能手表可以由相应的软件模块和硬件构成。
图1是实施例一提供的GPS漂移过滤方法的流程图。如图1所示，该GPS漂移过滤方法包括如下步骤：
步骤11，按周期进行GPS数据采样。
示例性的，GPS模块按照预设的频率获取GPS数据，为保证轨迹的连续，本实施例按照1Hz的频率进行采样。
步骤12，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据；在当前采样的GPS数据为漂移数据时，执行步骤13，在当前采样的GPS数据不是漂移数据时，执行步骤14。
示例性的，首先，分析当前采样的GPS数据与上一次采样的GPS数据，获得佩戴者的位移、速度、加速度和方位角变化量。
其次，智能手表的三轴加速度计以25Hz的频率获取佩戴者的运动信号，对所述运动信号进行分析，得到步数、步频、信号幅值、和设备姿态等信息，并以此确定佩戴者的状态，上述状态包括静止状态和移动状态。
可以通过某一时间段内是否有步数产生或者通过运动信号的起伏大小来确定佩戴者是否移动。例如，在2s内没有新的步数产生，运动信号的方差小于预设阀值时，判定佩戴者处于静止状态。当运动信号的方差大于等于预设阀值时判定佩戴者处于移动状态。
当佩戴者处于静止状态，且分析GPS数据得到的速度和位移中的至少一个不为零时，则判断当前采样的GPS数据为漂移数据，执行步骤13。
当佩戴者处于移动状态，则识别佩戴者的运动类型，所述运动类型包括计步型和计速型，计步型运动包括步行、跑步、或竞走等，计速型运动包括骑行。
可选的，根据运动类型计算出参考速度的步骤，包括：当运动类型为计步型时，根据佩戴者的步频和步幅计算参考速度；当运动类型为计速型时，根据已采样的非漂移的GPS数据分析出的速度(即沿用原有速度)，设置佩戴者的参考速度。
此外，还可通过该佩戴者的历史数据获得不同运动类型的参考速度。
在移动状态下，当所述速度与所述参考速度相比超出预设误差范围，则判断当前采样的GPS数据为漂移数据，执行步骤13。
例如，将预设误差范围设置为参考速度±50％，若所述速度不在此范围内，即所述速度已经明显不属于对应运动类型的合理速度，则判断该GPS数据为漂移数据。
步骤13，舍弃当前采样的GPS数据，根据运动信号确定佩戴者的实时速度。
舍弃当前采样的GPS数据，即生成GPS轨迹时不考虑当前采样的GPS数据。
在静止状态下，将佩戴者的实时速度置零；在移动状态下，将所述实时速度配置为所述参考速度，参考速度可以从步骤12中根据运动信号计算得到。
步骤14，根据所述GPS数据生成GPS轨迹。
示例性的，当GPS数据为非漂移数据时，根据步骤12分析获得佩戴者的经纬度、位移、速度、加速度和方位角变化量等信息生成GPS轨迹。
本实施例通过智能手表佩戴者的运动信号对GPS数据进行过滤，根据运动状态和运动类型分析GPS数据的合理性，从而滤除那些接近真实轨迹的漂移数据，克服相关技术中GPS过滤的精度问题，使最终得到的GPS轨迹更接近真实轨迹，佩戴者的运动数据也更准确。
实施例二
本实施例在上述实施例的基础上进行改进，在根据运动信号对GPS数据进行精准过滤之前，先对GPS数据进行初步过滤，对于某些GPS数据，可以不用进行精确计算就能判断其是否漂移。本实施例仅对增加的步骤进行说明，未说明的步骤与上述实施例的相应步骤相同。
图2是实施例二提供的GPS漂移过滤方法的流程图。如图2所示，该GPS漂移过滤方法包括如下步骤：
步骤21，按周期进行GPS数据采样。
步骤22，分析当前采样的GPS数据与上一次采样的GPS数据，获得佩戴者的位移、速度、加速度和方位角变化量。
步骤23，将所述位移、所述速度、所述加速度和所述方位角变化量与预设标准比较，判断当前采样的GPS数据是否为预备漂移数据，若是，执行步骤25，当前采样的GPS数据不是预备漂移数据时，执行步骤24。
本实施例中，所述预设标准设置可以为：位移小于50m，速度小于130km/h，加速度小于4m/s2，方位角在6秒内的变化小于360度。以上条件任一项不满足，则判断GPS数据为漂移数据。在实际应用中，预设标准可根据采样频率、过滤精度要求等进行调整。
若当前采样的GPS数据已经可以判断为漂移数据，则可直接跳转到步骤25， 不需要再用运动信号进行过滤。若当前采样的GPS数据判断为预备漂移数据，则根据步骤24做进一步的过滤。
步骤24，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据；若当前采样的GPS数据是漂移数据，执行步骤25，若当前采样的GPS数据不是漂移数据，执行步骤26。
步骤25，舍弃当前采样的GPS数据，根据运动信号确定佩戴者的实时速度。
步骤26，根据采集的GPS数据生成GPS轨迹。
示例性的，当相邻的两个GPS数据均为非漂移数据时，根据GPS数据确定佩戴者的位置，从而得到GPS轨迹。
当出现漂移数据被舍弃时，将相邻两个非漂移的GPS数据对应的位置用直线连接形成GPS轨迹。相邻两个非漂移的GPS数据中包括上一段轨迹的终点和下一段轨迹的起点，漂移出现的时间通常比较短，因此将上一段轨迹的终点与本段轨迹的起点直线连接即可。
当有GPS数据判定为漂移时，则需要通过搜索GPS数据获得非漂移的GPS数据以重新生成轨迹起点。
本实施例中采用上述步骤24判断当前采样的GPS数据是否为漂移数据，或者采用如下方式来判断当前采样的GPS数据是否为漂移数据：当前采样与4s之前的采样的GPS数据相比，位移小于2m判断当前采样的GPS数据为漂移数据；或者连续2s内速度小于0.5m/s判断当前采样的GPS数据为漂移数据；或者方位角6秒内变化超过360度判断当前采样的GPS数据为漂移数据。在当前采样的GPS数据为漂移数据时，根据GPS数据生成轨迹起点，距离不累计、轨迹不增加、速度置零。只有进入移动状态满足一定时长要求时，才会累加距离、生成轨迹、和配置实时速度。
本实施例在根据运动信号对GPS数据进行精准过滤之前，先对GPS数据进行初步过滤，可以不用进行精确计算就能判断GPS数据是否漂移，减少数据处理量，并且出现漂移时也能生成接近实际情况的GPS轨迹。
实施例三
本实施例提供一种智能手表，用于执行上述实施例所述的GPS漂移过滤方法。该智能手表与上述实施例中的基于智能手表的GPS漂移过滤方法，解决相同的技术问题，具有相同的技术效果。
图3是实施例三提供的智能手表的结构示意图。如图3所示，该智能手表包括GPS模块31、九轴传感器33和处理器32。
其中：
所述GPS模块31设置为按周期进行GPS数据采样。
所述九轴传感器33设置为获取佩戴者的运动信号。
所述处理器32设置为判断当前采样的GPS数据是否为漂移数据，在当前采样的GPS数据是漂移数据时，舍弃所述当前采样的GPS数据，根据所述运动信号确定佩戴者的实时速度。
其中，所述处理器32包括：漂移过滤单元321和速度配置单元322。
所述漂移过滤单元321设置为：
在根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据的步骤之前，分析当前采样的GPS数据与上一次采样的GPS数据，获得佩戴者的位移、速度、加速度和方位角变化量；
将所述位移、所述速度、所述加速度和所述方位角变化量与预设标准比较，判断当前采样的GPS数据是否为预备漂移数据；
在当前采样的GPS数据为预备漂移数据时，舍弃所述当前采样的GPS数据；在当前采样的GPS数据不是预备漂移数据时，根据智能手表获取的运动信号，判断所述当前采样的GPS数据是否为漂移数据。
所述速度配置单元322设置为根据所述运动信号确定佩戴者的实时速度。
可选的，所述处理器32还包括：运动识别单元324。
所述运动识别单元324设置为根据智能手表获取的运动信号确定佩戴者为静止状态或移动状态；。
所述漂移过滤单元321设置为，当佩戴者为为静止状态，且所述速度和所述位移中的至少一个不为零时，则判定当前采样的GPS数据为漂移数据；相应的，所述速度配置单元322设置为将所述实时速度置零。
可选的，所述运动识别单元324还设置为根据智能手表获取的运动信号确定佩戴者为静止状态或移动状态；当佩戴者为移动状态时，则识别佩戴者的运动类型，所述运动类型包括计步型和计速型，根据运动类型计算参考速度。
所述漂移过滤单元321还设置为，当佩戴者为移动状态，且所述速度与所述参考速度相比超出预设误差范围时，则判定当前采样的GPS数据为漂移数据；相应的，所述速度配置单元322还设置为将所述实时速度配置为所述参考速度。
其中，所述运动识别单元324是设置为：
当运动类型为计步型时，根据佩戴者的步频和步幅计算参考速度；当运动类型为计速型时，根据从已采样的非漂移的GPS数据中分析出的速度，设置佩戴者的参考速度。
可选的，所述处理器32还包括：轨迹拟合单元323，设置为在根据所述运动信号确定佩戴者的实时速度的步骤之后，将相邻两个非漂移的GPS数据对应的位置用直线连接以形成GPS轨迹。
本实施例先对GPS数据进行初步过滤排除漂移数据，再根据智能手表佩戴者的运动数据对GPS轨迹进行进一步的过滤，使GPS轨迹更接近真实轨迹，使佩戴者的运动数据更贴近实际情况，从而获得精度更高的轨迹、速度和里程信息。
本实施例还提供一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行上述任一方法。
实施例4
图4是本实施例的一种智能手表的硬件结构示意图，如图4所示，该智能手表包括：一个或多个处理器410和存储器420。图4中以一个处理器410为例。
所述智能手表还可以包括：输入装置430和输出装置440。
所述智能手表中的处理器410、存储器420、输入装置430和输出装置440可以通过总线或者其他方式连接，图4中以通过总线连接为例。
输入装置430可以接收输入的数字或字符信息，输出装置440可以包括显示屏等显示设备。
存储器420作为一种计算机可读存储介质，可用于存储软件程序、计算机 可执行程序以及模块。处理器410通过运行存储在存储器420中的软件程序、指令以及模块，从而执行多种功能应用以及数据处理，以实现上述实施例中的任意一种方法。
存储器420可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储根据智能手表的使用所创建的数据等。此外，存储器可以包括随机存取存储器(Random Access Memory，RAM)等易失性存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件或者其他非暂态固态存储器件。
存储器420可以是非暂态计算机存储介质或暂态计算机存储介质。该非暂态计算机存储介质，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器420可选包括相对于处理器410远程设置的存储器，这些远程存储器可以通过网络连接至智能手表。上述网络的实例可以包括互联网、企业内部网、局域网、移动通信网及其组合。
输入装置430可用于接收输入的数字或字符信息，以及产生与智能手表的用户设置以及功能控制有关的键信号输入。输出装置440可包括显示屏等显示设备。
本实施例的智能手表还可以包括通信装置440，通过通信网络传输和/或接收信息。
本领域普通技术人员可理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来执行相关的硬件来完成的，该程序可存储于一个非暂态计算机可读存储介质中，该程序在执行时，可包括如上述方法的实施例的流程，其中，该非暂态计算机可读存储介质可以为磁碟、光盘、只读存储记忆体(ROM)或随机存储记忆体(RAM)等。
工业实用性
本公开提供的基于智能手表的全球定位系统GPS漂移过滤方法和智能手表，能使过滤后的GPS轨迹更接近真实轨迹，获得精度更高的轨迹、速度和里程信息。

Claims (13)

  1. 一种基于智能手表的全球定位系统GPS漂移过滤方法，包括：
    按周期进行GPS数据采样；
    根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据；
    在当前采样的GPS数据是漂移数据时，舍弃所述当前采样的GPS数据，根据所述运动信号确定佩戴者的实时速度。
  2. 根据权利要求1所述的GPS漂移过滤方法，其中，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据之前，还包括：
    分析当前采样的GPS数据与上一次采样的GPS数据，获得佩戴者的位移、速度、加速度和方位角变化量；
    将所述位移、所述速度、所述加速度和所述方位角变化量与预设标准比较，判断当前采样的GPS数据是否为预备漂移数据；
    当前采样的GPS数据为预备漂移数据时，舍弃所述当前采样的GPS数据；
    当前采样的GPS数据不是预备漂移数据时，则执行步骤：根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据。
  3. 根据权利要求2所述的GPS漂移过滤方法，其中，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据的步骤，包括：
    根据智能手表获取的运动信号确定佩戴者的状态；
    当所述状态为静止状态，且所述速度和所述位移中的至少一个不为零，则判定当前采样的GPS数据为漂移数据；
    所述根据所述运动信号确定佩戴者的实时速度的步骤，包括：将所述实时速度置零。
  4. 根据权利要求2所述的GPS漂移过滤方法，其中，根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据的步骤，包括：
    根据智能手表获取的运动信号确定佩戴者的状态；
    当所述状态为移动状态，则识别佩戴者的运动类型，所述运动类型包括计步型和计速型；
    根据所述运动类型计算参考速度；
    当所述佩戴者的速度与所述参考速度相比超出预设误差范围时，则判定当前采样的GPS数据为漂移数据；
    所述根据所述运动信号确定佩戴者的实时速度的步骤，包括：将所述实时速度配置为所述参考速度。
  5. 根据权利要求4所述的GPS漂移过滤方法，其中，所述根据所述运动类型计算参考速度的步骤，包括：
    当所述运动类型为计步型时，根据所述佩戴者的步频和步幅计算参考速度；
    当所述运动类型为计速型时，根据从已采样的非漂移的GPS数据中分析出的速度，设置佩戴者的参考速度。
  6. 根据权利要求1所述的GPS漂移过滤方法，其中，所述根据所述运动信号确定佩戴者的实时速度的步骤之后，还包括：
    将相邻两个非漂移的GPS数据对应的位置用直线连接以形成GPS轨迹。
  7. 一种智能手表，包括全球定位系统GPS模块、九轴传感器和处理器，所述GPS模块和所述九轴传感器分别与所述处理器连接
    所述GPS模块设置为按周期进行GPS数据采样；
    所述九轴传感器设置为获取佩戴者的运动信号；
    所述处理器设置为判断当前采样的GPS数据是否为漂移数据，在当前采样的GPS数据是漂移数据时，舍弃所述当前采样的GPS数据，根据所述运动信号确定佩戴者的实时速度。
  8. 根据权利要求7所述的智能手表，其中，所述处理器包括：漂移过滤单元和速度配置单元；
    所述漂移过滤单元设置为：
    在根据智能手表获取的运动信号，判断当前采样的GPS数据是否为漂移数据的步骤之前，分析当前采样的GPS数据与上一次采样的GPS数据，获得佩戴者的位移、速度、加速度和方位角变化量；
    将所述位移、所述速度、所述加速度和所述方位角变化量与预设标准比较，判断当前采样的GPS数据是否为预备漂移数据；
    在当前采样的GPS数据为预备漂移数据时，舍弃所述当前采样的GPS数据；
    在当前采样的GPS数据不是预备漂移数据时，根据智能手表获取的运动信号，判断所述当前采样的GPS数据是否为漂移数据；
    所述速度配置单元设置为根据所述运动信号确定佩戴者的实时速度。
  9. 根据权利要求8所述的智能手表，其中，所述处理器还包括：运动识别单元；
    所述运动识别单元设置为根据智能手表获取的运动信号确定佩戴者为静止状态或移动状态；所述漂移过滤单元还设置为，当佩戴者为静止状态，且所述 速度和所述位移中的至少一个不为零时，则判定当前采样的GPS数据为漂移数据；
    所述速度配置单元还设置为将所述实时速度置零。
  10. 根据权利要求8所述的智能手表，其中，所述处理器还包括：运动识别单元；
    所述运动识别单元设置为根据智能手表获取的运动信号确定佩戴者为静止状态或移动状态，当佩戴者为移动状态时，则识别佩戴者的运动类型，所述运动类型包括计步型和计速型，根据所述运动类型计算参考速度；
    所述漂移过滤单元还设置为，当佩戴者为移动状态，且所述速度与所述参考速度相比超出预设误差范围时，则判定当前采样的GPS数据为漂移数据；
    所述速度配置单元还设置为将所述实时速度配置为所述参考速度。
  11. 根据权利要求10所述的智能手表，其中，所述运动识别单元是设置为：
    当所述运动类型为计步型时，根据所述佩戴者的步频和步幅计算参考速度；
    当所述运动类型为计速型时，根据从已采样的非漂移的GPS数据中分析出的速度，设置佩戴者的参考速度。
  12. 根据权利要求7所述的智能手表，其中，所述处理器还包括：轨迹拟合单元，设置为在根据所述运动信号确定佩戴者的实时速度的步骤之后，
    将相邻两个非漂移的GPS数据对应的位置用直线连接以形成GPS轨迹。
  13. 一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行权利要求1-6任一项的方法。
PCT/CN2017/109858 2017-08-16 2017-11-08 基于智能手表的gps漂移过滤方法和智能手表 WO2019033587A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710702418.0 2017-08-16
CN201710702418.0A CN107515413A (zh) 2017-08-16 2017-08-16 一种基于智能手表的gps漂移过滤方法和该智能手表

Publications (1)

Publication Number Publication Date
WO2019033587A1 true WO2019033587A1 (zh) 2019-02-21

Family

ID=60723410

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/109858 WO2019033587A1 (zh) 2017-08-16 2017-11-08 基于智能手表的gps漂移过滤方法和智能手表

Country Status (2)

Country Link
CN (1) CN107515413A (zh)
WO (1) WO2019033587A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111399000A (zh) * 2020-04-08 2020-07-10 广州通达汽车电气股份有限公司 Gps漂移过滤方法、gps终端的状态切换方法及切换设备

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109407123B (zh) * 2018-09-29 2023-11-17 深圳市口袋网络科技有限公司 一种信息处理方法、终端及计算机可读存储介质
CN110646824B (zh) * 2019-09-30 2023-09-05 郑州威科姆华大北斗导航科技有限公司 一种多种定位方式实现运动轨迹漂移点过滤计算的方法
CN112346095A (zh) * 2020-10-29 2021-02-09 广东小天才科技有限公司 判断用户处于快速移动状态的方法及装置、智能穿戴设备
CN113791437A (zh) * 2021-09-17 2021-12-14 深圳摩吉智行科技有限公司 过滤定点漂移数据的方法、装置及计算机可读存储介质

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014054351A (ja) * 2012-09-12 2014-03-27 Casio Comput Co Ltd 運動支援装置、運動支援方法及び運動支援プログラム
CN104091053A (zh) * 2014-06-26 2014-10-08 李南君 用于自动检测行为模式的方法和设备
CN104395696A (zh) * 2012-06-28 2015-03-04 皇家飞利浦有限公司 估计设备位置的方法和实施该方法的装置
CN104459736A (zh) * 2014-12-11 2015-03-25 深圳市亲觅科技有限公司 一种基于重力传感器的gps设备及其漂移处理方法
CN106324626A (zh) * 2015-06-19 2017-01-11 杭州海康威视数字技术股份有限公司 一种用于过滤gps位置漂移点的方法和装置
KR20170077572A (ko) * 2015-12-28 2017-07-06 서울과학기술대학교 산학협력단 감정인식 위치 추적 장치 및 방법

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070210957A1 (en) * 2005-09-22 2007-09-13 Brodie Keith J GPS receiver for timekeeping applications
CN102384753A (zh) * 2011-08-03 2012-03-21 东莞市远峰科技有限公司 一种可显示obd信息的导航系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104395696A (zh) * 2012-06-28 2015-03-04 皇家飞利浦有限公司 估计设备位置的方法和实施该方法的装置
JP2014054351A (ja) * 2012-09-12 2014-03-27 Casio Comput Co Ltd 運動支援装置、運動支援方法及び運動支援プログラム
CN104091053A (zh) * 2014-06-26 2014-10-08 李南君 用于自动检测行为模式的方法和设备
CN104459736A (zh) * 2014-12-11 2015-03-25 深圳市亲觅科技有限公司 一种基于重力传感器的gps设备及其漂移处理方法
CN106324626A (zh) * 2015-06-19 2017-01-11 杭州海康威视数字技术股份有限公司 一种用于过滤gps位置漂移点的方法和装置
KR20170077572A (ko) * 2015-12-28 2017-07-06 서울과학기술대학교 산학협력단 감정인식 위치 추적 장치 및 방법

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111399000A (zh) * 2020-04-08 2020-07-10 广州通达汽车电气股份有限公司 Gps漂移过滤方法、gps终端的状态切换方法及切换设备

Also Published As

Publication number Publication date
CN107515413A (zh) 2017-12-26

Similar Documents

Publication Publication Date Title
WO2019033587A1 (zh) 基于智能手表的gps漂移过滤方法和智能手表
CN106705968B (zh) 基于姿态识别和步长模型的室内惯性导航算法
CN111263900B (zh) 用于组合传感器数据的方法和系统
CN111649739B (zh) 定位方法和装置、自动驾驶车辆、电子设备和存储介质
KR20220088387A (ko) 도로의 울퉁불퉁한 구역의 검측방법, 장치, 전자기기, 저장매체, 컴퓨터 프로그램 및 차량
US10508920B2 (en) Pedestrian dead reckoning position tracker
US20160016590A1 (en) Method for detecting driving events of a vehicle based on a smartphone
US20150184348A1 (en) System and Method for Assessing Road Quality Using Data Collected from a Mobile Device
US20170067933A1 (en) Pedometer With Lag Correction
CN109115216A (zh) 行人步长检测方法、装置及系统
CN115556769A (zh) 障碍物状态量确定方法及装置、电子设备和介质
CN110455284A (zh) 一种基于mems-imu的行人运动模式识别方法及装置
CN116952277A (zh) 一种基于人工智能和行为特征的潜在位置估算方法及系统
CN115675528A (zh) 基于相似场景挖掘的自动驾驶方法和车辆
JP6690343B2 (ja) 測定装置、測定方法、及び測定プログラム
CN115727871A (zh) 一种轨迹质量检测方法、装置、电子设备和存储介质
CN111735478B (zh) 基于lstm的行人实时导航零速检测方法
JP2016161313A (ja) 測位システム
CN114120252A (zh) 自动驾驶车辆状态的识别方法、装置、电子设备及车辆
CN116311943B (zh) 交叉路口的平均延误时间的估算方法及装置
CN116311941B (zh) 主要车流路径提取方法、装置、设备及介质
JP6521477B2 (ja) データ解析装置及びデータ解析方法、データ解析プログラム
CN116434041B (zh) 错误感知数据的挖掘方法、装置、设备及自动驾驶车辆
US20230086694A1 (en) Indoor navigation
US20230077619A1 (en) Indoor navigation

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: 17922000

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17922000

Country of ref document: EP

Kind code of ref document: A1