WO2018099089A1 - 一种静止状态的判断方法及装置 - Google Patents

一种静止状态的判断方法及装置 Download PDF

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WO2018099089A1
WO2018099089A1 PCT/CN2017/092650 CN2017092650W WO2018099089A1 WO 2018099089 A1 WO2018099089 A1 WO 2018099089A1 CN 2017092650 W CN2017092650 W CN 2017092650W WO 2018099089 A1 WO2018099089 A1 WO 2018099089A1
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standard deviation
static
probability
dynamic
curve
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PCT/CN2017/092650
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English (en)
French (fr)
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张全
牛小骥
付立鼎
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华为技术有限公司
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Priority to EP17876759.6A priority Critical patent/EP3540376B1/en
Priority to JP2019528760A priority patent/JP6849803B2/ja
Publication of WO2018099089A1 publication Critical patent/WO2018099089A1/zh
Priority to US16/424,190 priority patent/US20190277657A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • 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
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • 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

Definitions

  • the present application relates to the field of inertial navigation technology, and in particular, to a method and apparatus for determining a stationary state.
  • the Inertial Navigation System is a dead reckoning system consisting of an Inertial Measurement Unit (IMU) and an inertial navigation machine programming algorithm.
  • IMU Inertial Measurement Unit
  • ZUPT Zero Velocity Update
  • the zero speed correction is based on the data measured by the IMU to determine the period during which the carrier is in a stationary state, that is, the ZUPT detection.
  • the zero-speed correction detection method in the prior art is a threshold method, and a reasonable threshold is set in advance for the data measured by the IMU when the collected INS is in a moving state and in a stationary state; in the INS use process, the data measured by the IMU is used. Or the new data after the data is subjected to the projection transformation is compared with the threshold, and when the data measured by the IMU or the new data is less than or equal to the threshold, it is determined that the INS is in a stationary state period.
  • the accuracy of determining the INS stationary state is low, and how to improve the accuracy of the INS stationary state determination is currently a problem to be solved.
  • Embodiments of the present invention provide a method and apparatus for determining a stationary state to improve the accuracy of determining a stationary state of a target vehicle, device, or device.
  • an embodiment of the present invention provides a method for determining a static state, the method comprising:
  • the inertial navigation system acquires the first operational data of the first set duration by the inertial measurement unit IMU, and the first operational data includes the operation of each of the N axes measured by the IMU at the first set duration Data, N is a positive integer greater than or equal to 1, preferably, the first set duration is 1 second, and the N is 6; the inertial navigation system determines that each axis is at the first Setting a first standard deviation corresponding to the running data of the duration, that is, determining N first standard deviations; the inertial navigation system matching the N first standard deviations with a pre-saved database to determine respectively N Nth standard deviations having the same first standard deviation; the inertial navigation system determines, in the pre-stored database, first information corresponding to the N second standard deviations, the first information The first probability that the second standard deviation is static and the weight corresponding to the second standard deviation are included; specifically, the N first standard deviations are respectively from N axes, and the second standard deviation included in the database is also From N axes
  • the inertial navigation system multiplies a first probability in each of the N pieces of the first information by a corresponding weight, and adds the values obtained by multiplying the N numbers, The added value is determined as a second probability that the N first standard deviations are static; the inertial navigation system determines that the second probability is greater than or equal to the static probability threshold
  • the device in which the inertial navigation system is located is in a stationary state during the first set duration; when the second probability is less than the static probability threshold, determining that the device in which the inertial navigation system is located is in the first set duration Movement state.
  • the inertial navigation system obtains the operation data of the first set duration N axes by the IMU measurement, and calculates the first standard deviation corresponding to the operation data of the N axes, and calculates the N first standards.
  • the difference is queried in the database, and the N second standard deviations identical to the N first standard deviations are determined, and the first probability corresponding to the N second standard deviations in the database and the N second standard deviations are determined.
  • the weights corresponding to the respective axes are determined by the determined N first probabilities and the weights corresponding to the N axes, and the first set duration is determined as the second probability that the device in which the inertial navigation system is located is static.
  • the second probability is compared with a static probability threshold. When the second probability is greater than or equal to the static probability threshold, it is determined that the device in which the inertial navigation system is located is in a stationary state during the first set duration. With the above method, the accuracy of the stationary state judgment is improved.
  • the correspondence between the second standard deviation and the weight of the axis in which the second standard deviation is located is formed by a process in which the inertial navigation system determines an IMU measurement within a second set time period Obtaining sample data of any axis; the inertial navigation system determines each of the first set duration corresponding sample data in the second set duration as static data or dynamic data, and determines according to the static data Determining a dynamic standard deviation according to the dynamic data; wherein the first set duration is performed in the form of a sliding window within the second set duration; The static standard deviations are grouped according to the first set threshold range, and the number of static standard deviations in each of the first set threshold ranges is determined.
  • the first threshold setting range is divided into multiple groups, and the fixed interval values are a group, obtaining a static standard deviation distribution histogram, and grouping the determined plurality of dynamic sample standard deviations according to a second set threshold range, and determining each of the second set threshold ranges
  • the number of sample standard deviations, the first threshold setting range is divided into multiple groups, and the fixed interval values are a group, and a dynamic standard deviation distribution histogram is obtained; the static standard deviation histogram and the dynamic standard deviation distribution are obtained.
  • the histogram is respectively curve-fitted, and the curve obtained by the fitting is normalized to determine the static standard deviation curve and the dynamic standard deviation curve; the static standard deviation curve and the dynamic standard deviation curve are placed in the same coordinate system.
  • the correspondence between the second standard deviation and the first probability is formed by the following process, including: placing the static standard deviation curve and the dynamic standard deviation curve in the same coordinate system to determine When the static standard deviation curve and the dynamic standard deviation curve intersect the area of the portion, the static standard deviation curve and the dynamic standard deviation curve, and the horizontal axis of the coordinate system have two intersection points, and the two intersection points,
  • the intersection point of the coordinate system zero point is the intersection point a
  • the distance from the zero point of the coordinate system is the intersection point b
  • the intersection of the static standard deviation curve and the dynamic standard deviation curve is the intersection point c
  • the probability that the second standard deviation is static is a probability value corresponding to the second standard deviation on the static standard deviation curve
  • the probability that the second standard deviation is static is 0; when the second data is greater than
  • an embodiment of the present invention provides a judging device for a stationary state, the device comprising:
  • Obtaining module configured to acquire first operation data obtained by the inertial measurement unit IMU to obtain a first set duration, where the first operation data includes each of the N axes measured by the IMU at the first set duration Running data, N is a positive integer greater than or equal to 1; a determining module for determining N first standard deviations of the first operational data; and a matching module for using the N first standard deviations Matching with a pre-saved database to determine N second standard deviations respectively different from the N first standard deviations; a searching module, configured to determine the N second standards in the pre-saved database The first information corresponding to the difference, the first information includes a first probability that the second standard deviation is static and a weight corresponding to the second standard deviation; and a processing module, configured to use the N The first probability in each of the first information in the first information is multiplied by a corresponding weight, the N values obtained by multiplication are added, and the added value is determined as the N The first standard deviation is a static second probability; the judgment module As
  • the inertial navigation system obtains the operation data of the first set duration N axes by the IMU measurement, and calculates the first standard deviation corresponding to the operation data of the N axes, and calculates the N first standards.
  • the difference is queried in the database, and the N second standard deviations identical to the N first standard deviations are determined, and the first probability corresponding to the N second standard deviations in the database and the N second standard deviations are determined.
  • the weights corresponding to the respective axes are determined by the determined N first probabilities and the weights corresponding to the N axes, and the first set duration is determined as the second probability that the device in which the inertial navigation system is located is static.
  • the second probability is compared with a static probability threshold. When the second probability is greater than or equal to the static probability threshold, it is determined that the device in which the inertial navigation system is located is in a stationary state during the first set duration. With the above method, the accuracy of the stationary state judgment is improved.
  • the correspondence between the second standard deviation and the weight is formed by determining static data and dynamic data of any axis obtained by the IMU measurement in the second set duration;
  • the dynamic sample data corresponding to the static data corresponding to each of the first set durations and the first set duration is determined in the second set duration, and is determined according to the static data corresponding to the first set duration Determining a static standard deviation, determining a dynamic sample standard deviation according to the dynamic sample data corresponding to the first set duration; and determining the plurality of static standard deviations according to the first set threshold range to determine each of the first Setting a static standard deviation of the threshold range, obtaining a static standard deviation distribution histogram, and grouping the determined plurality of dynamic sample standard deviations according to a second set threshold range to determine each of the second settings
  • the number of dynamic sample standard deviations in the threshold range is obtained, and a dynamic standard deviation distribution histogram is obtained; and the static standard deviation histogram and the dynamic standard deviation distribution histogram are separately simulated And normalizing the curve obtained by the fitting
  • the correspondence between the second standard deviation and the first probability is formed by the following process, including: placing the static standard deviation curve and the dynamic standard deviation curve in the same coordinate system to determine When the static standard deviation curve and the dynamic standard deviation curve intersect the area of the portion, the static standard deviation curve and the dynamic standard deviation a curve, and a horizontal axis of the coordinate system has two intersection points, wherein an intersection point that is smaller than a distance from a zero point of the coordinate system is an intersection point a, and a distance from a zero point of the coordinate system is an intersection point b,
  • the intersection of the static standard deviation curve and the dynamic standard deviation curve is the intersection point c; when the second standard deviation is less than or equal to the value of the intersection point a, the probability that the second standard deviation is static is the second criterion a probability value corresponding to the static standard deviation curve; when the second data is greater than or equal to the value of the intersection b, the probability that the second standard deviation is static is 0; when the second data is When the value is greater than
  • the N is 6.
  • an embodiment of the present invention provides a judging apparatus for a stationary state, including a processor, and a memory connected to the processor, where:
  • a memory for storing program code executed by the processor
  • a processor configured to execute the program code stored by the memory, performs the following process:
  • the inertial measurement unit IMU Acquiring the inertial measurement unit IMU to measure the first operational data of the first set duration, the first operational data comprising operational data of each of the N axes measured by the IMU at the first set duration, N a positive integer greater than or equal to 1; determining N first standard deviations of the first operational data; matching the N first standard deviations with a pre-saved database to determine respectively and the N The first standard deviation is the same as the N second standard deviations; the first information corresponding to the N second standard deviations is determined in the pre-stored database, and the first information includes the second standard The first probability that the difference is static and the weight corresponding to the second standard deviation; multiplying the first probability in each of the N pieces of the first information by a corresponding weight, N values obtained by multiplying are added, and the added value is determined as a second probability that the N first standard deviations are static; and the second probability is greater than or equal to the static
  • the probability threshold is used to determine the device in which the inertial navigation system is
  • the inertial navigation system obtains the operation data of the first set duration N axes by the IMU measurement, and calculates the first standard deviation corresponding to the operation data of the N axes, and calculates the N first standards.
  • the difference is queried in the database, and the N second standard deviations identical to the N first standard deviations are determined, and the first probability corresponding to the N second standard deviations in the database and the N second standard deviations are determined.
  • the weights corresponding to the respective axes are determined by the determined N first probabilities and the weights corresponding to the N axes, and the first set duration is determined as the second probability that the device in which the inertial navigation system is located is static.
  • the second probability is compared with a static probability threshold. When the second probability is greater than or equal to the static probability threshold, it is determined that the device in which the inertial navigation system is located is in a stationary state during the first set duration. With the above method, the accuracy of the stationary state judgment is improved.
  • 1 is a static standard deviation curve and a static standard deviation curve according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of determining a static standard deviation curve and a static standard deviation curve in FIG. 1 according to an embodiment of the present invention
  • FIG. 3 is an original data of an IMU according to an embodiment of the present invention.
  • 5 is a probability curve in which a standard deviation of an overlapping portion of each axis static standard deviation curve and a dynamic standard deviation curve is determined to be static according to an embodiment of the present invention
  • FIG. 6 is a schematic flowchart diagram of a method for determining a static state according to an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of a judging device in a static state according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of hardware of a judging device in a static state according to an embodiment of the present invention.
  • the inertial navigation system INS is a dead reckoning system consisting of an inertial measurement unit IMU and an inertial navigation mechanical programming algorithm.
  • IMU inertial measurement unit
  • the INS error increases with time.
  • the embodiment of the present invention provides a static state determination method for improving the accuracy of the stationary state determination.
  • Step S21 reading sample data.
  • Step S22 Set a window length according to a sample data sampling rate, where the window length is a set time, and the sample data in the set time is determined as static data or dynamic data.
  • Step S23 Calculate a static standard deviation according to the static data, calculate a dynamic standard deviation according to the dynamic data, and obtain a static standard deviation sequence and a dynamic standard deviation sequence respectively.
  • Step S24 setting a group spacing of dynamic and static standard deviations, and determining a static standard deviation distribution histogram and a dynamic standard deviation distribution histogram.
  • the group spacing is set in steps of 5, then divided into groups of 0-4, 5-9, 10-14, ..., 96-100.
  • the division of the group spacing may be determined according to actual needs, which is not limited by the present invention.
  • Step S25 respectively, the static standard deviation histogram and the dynamic standard deviation distribution histogram are respectively curve-fitted, and the normalized area of the curve and the standard deviation (STD) axis are normalized to determine
  • the static standard deviation curve and the dynamic standard deviation curve that is, the standard deviation and its frequency distribution curve
  • the horizontal axis is the standard deviation
  • the vertical axis is the frequency value.
  • the above normalization process may adopt the following method: first, calculate the area S of the curve and the STD axis, and the solution method of the curve area adopts trapezoidal integration, and then the frequency of each STD normalization is f′ std , wherein
  • Step S26 placing the static standard deviation curve and the dynamic standard deviation curve in the same coordinate system, that is, FIG. 1 in an ideal situation is obtained.
  • the probability that any standard deviation is static can be calculated; the static standard deviation curve and the dynamic standard deviation curve, and the horizontal axis of the coordinate system have two intersection points.
  • the intersection point which is smaller than the zero point distance of the coordinate system is the intersection point std_a
  • the distance from the zero point of the coordinate system is the intersection point std_b
  • the intersection point of the static standard deviation curve and the dynamic standard deviation curve is the intersection point std_c
  • the probability that the standard deviation is static is a probability value corresponding to the standard deviation on the static standard deviation curve, that is, the standard deviation is static Probability is 100%; when standard deviation When the value is greater than or equal to the value of the intersection point std_b, the probability that the standard deviation is static is 0; when the standard deviation std0 is greater than the std_a point and less
  • the probability that the standard deviation of the A region is static is 100%, and the probability that the standard deviation of the B region and the C region is static is calculated by the above formula.
  • the probability that the standard deviation of the D region is static is 0. .
  • the reciprocal of the area enclosed by the static standard deviation curve and the dynamic standard deviation curve and the horizontal axis is the weight K of the axis; the determined weight K, the probability that any standard deviation is static, and the standard deviation are determined as a database.
  • the above database is used when there is new data to be judged in the future.
  • the data of the six axes is collected by the vehicle inertial sensor to describe the formation of the database (ie, the standard deviation-probability distribution lookup table), and the sample data is from multiple sports car experiments covering different application scenarios, and the sports car process
  • the parking phenomenon occurs frequently due to traffic jam or intentional reasons, and the original output data is collected.
  • the original data includes the angular rates of the X-axis, Y-axis and Z-axis acquired by the gyroscope, and the X-axis and Y-axis of the accelerometer.
  • the collected data is processed according to steps S21 to S26, respectively, and the static standard deviation curve and the dynamic standard deviation curve of each axis as shown in FIG. 4 are obtained, and determined as shown in FIG. 5 .
  • the standard deviation of the overlap between the static standard deviation curve and the static standard deviation of each axis is judged as a static probability curve, and the reciprocal of the area of each axis overlap and its normalized value, as shown in Table 1 below:
  • the probability that any standard deviation is static is the same for each of the N axes.
  • n is the number of axes and i is the i-th axis.
  • An embodiment of the present invention further provides a method for determining a static state. As shown in FIG. 6, the method includes the following process:
  • the inertial navigation system acquires an inertial measurement unit IMU to obtain first operational data of a first set duration, where the first operational data includes each of the N axes measured by the IMU at the first set duration Run data, N is a positive integer greater than or equal to 1.
  • the inertial navigation system determines N first standard deviations of the first operational data.
  • the inertial navigation system matches the N first standard deviations with a pre-stored database to determine N second standard deviations respectively different from the N first standard deviations.
  • the inertial navigation system determines first information corresponding to the N second standard deviations in the pre-stored database, where the first information includes the first standard deviation being static first The probability and the weight corresponding to the second standard deviation.
  • the inertial navigation system multiplies a first probability in each of the N pieces of the first information by a corresponding weight, and adds the values obtained by multiplying the N numbers. And determining the added value as a second probability that the N first standard deviations are static.
  • the inertial navigation system determines, when the second probability is greater than or equal to the static probability threshold, that the device where the inertial navigation system is located is in a static state during the first set duration.
  • the inertial navigation system obtains the operation data of the first set duration N axes by the IMU measurement, and calculates the first standard deviation corresponding to the operation data of the N axes, and calculates the N first standards.
  • the difference is queried in the database, and the N second standard deviations identical to the N first standard deviations are determined, and the first probability corresponding to the N second standard deviations in the database and the N second standard deviations are determined.
  • the weights corresponding to the respective axes are determined by the determined N first probabilities and the weights corresponding to the N axes, and the first set duration is determined as the second probability that the device in which the inertial navigation system is located is static.
  • the second probability is compared with a static probability threshold. When the second probability is greater than or equal to the static probability threshold, it is determined that the device in which the inertial navigation system is located is in a stationary state during the first set duration. With the above method, the accuracy of the stationary state judgment is improved.
  • a static state judging device 70 includes:
  • the obtaining module 71 is configured to acquire first operation data that is measured by the inertial measurement unit IMU to obtain a first set duration, where the first operation data includes each of the N axes measured by the IMU at the first setting
  • the running data of the duration, N is a positive integer greater than or equal to 1;
  • a determining module 72 configured to determine N first standard deviations of the first operational data
  • the matching module 73 is configured to match the N first standard deviations with a pre-saved database, and determine N second standard deviations respectively different from the N first standard deviations;
  • the searching module 74 is configured to determine first information corresponding to the N second standard deviations in the pre-stored database, where the first information includes a first probability that the second standard deviation is static And a weight corresponding to the second standard deviation;
  • the processing module 75 is configured to: first probability in each of the first information of the N pieces of the first information Multiplying the corresponding weights, adding the N values obtained by multiplication, and determining the added values as a second probability that the N first standard deviations are static;
  • the determining module 76 is configured to determine, when the second probability is greater than or equal to the static probability threshold, that the device where the inertial navigation system is located is in a static state during the first set duration.
  • the inertial navigation system obtains the operation data of the first set duration N axes by the IMU measurement, and calculates the first standard deviation corresponding to the operation data of the N axes, and calculates the N first standards.
  • the difference is queried in the database, and the N second standard deviations identical to the N first standard deviations are determined, and the first probability corresponding to the N second standard deviations in the database and the N second standard deviations are determined.
  • the weights corresponding to the respective axes are determined by the determined N first probabilities and the weights corresponding to the N axes, and the first set duration is determined as the second probability that the device in which the inertial navigation system is located is static.
  • the second probability is compared with a static probability threshold. When the second probability is greater than or equal to the static probability threshold, it is determined that the device in which the inertial navigation system is located is in a stationary state during the first set duration. With the above method, the accuracy of the stationary state judgment is improved.
  • the correspondence between the second standard deviation and the weight is formed by the following process:
  • each of the first set duration corresponding sample data in the second set duration as static data or dynamic data, determining a static standard deviation according to the static data, and determining a dynamic standard according to the dynamic data difference;
  • the plurality of dynamic sample standard deviations are grouped according to the second set threshold range, and the number of dynamic sample standard deviations in each of the second set threshold ranges is determined, and a dynamic standard deviation distribution histogram is obtained;
  • the static standard deviation histogram and the dynamic standard deviation distribution histogram are respectively curve-fitted, and the fitted curve is normalized to determine a static standard deviation curve and a dynamic standard deviation curve;
  • the static standard deviation curve and the dynamic standard deviation curve are placed in the same coordinate system, and the area of the intersection of the static standard deviation curve and the dynamic standard deviation curve is determined;
  • the reciprocal of the area of the intersecting portion is determined as the weight of the plurality of static standard deviations corresponding to the one of the axes and the plurality of dynamic standard deviations.
  • the correspondence between the second standard deviation and the first probability is formed by the following process, including:
  • a horizontal axis of the coordinate system has two intersection points, wherein an intersection point that is smaller than a distance between the coordinate points of the coordinate system is an intersection point a, and a distance from a zero point of the coordinate system is an intersection point b, The intersection of the static standard deviation curve and the dynamic standard deviation curve is the intersection point c;
  • the probability that the second standard deviation is static is a probability value corresponding to the second standard deviation on the static standard deviation curve
  • the probability that the second standard deviation is static is 0;
  • the probability that the second standard deviation is static is: a probability value corresponding to the second standard deviation on the static standard deviation curve, With the second standard deviation a ratio of a corresponding probability value on the static standard deviation curve to a sum of probability values corresponding to the second standard deviation on the dynamic standard deviation curve.
  • the N is 6.
  • the embodiment of the present invention provides a static state determining apparatus 800.
  • a processor 810 As shown in FIG. 8, a processor 810, a memory 820 connected to the processor, and a display 840 connected to the bus 830 for displaying a stationary state are provided.
  • the memory 820 and the processor 810 are connected to each other through a bus 830, where:
  • a memory 820 configured to store program code executed by the processor
  • the processor 810 is configured to execute the program code stored by the memory, and perform any of the step counting methods provided in the foregoing embodiments, for example, perform the following process:
  • the inertial measurement unit IMU Acquiring the inertial measurement unit IMU to measure the first operational data of the first set duration, the first operational data comprising operational data of each of the N axes measured by the IMU at the first set duration, N a positive integer greater than or equal to 1; determining N first standard deviations of the first operational data; matching the N first standard deviations with a pre-saved database to determine respectively and the N The first standard deviation is the same as the N second standard deviations; the first information corresponding to the N second standard deviations is determined in the pre-stored database, and the first information includes the second standard The first probability that the difference is static and the weight corresponding to the second standard deviation; multiplying the first probability in each of the N pieces of the first information by a corresponding weight, N values obtained by multiplying are added, and the added value is determined as a second probability that the N first standard deviations are static; and the second probability is greater than or equal to the static
  • the probability threshold is used to determine the device in which the inertial navigation system is
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

一种静止状态的判断方法及装置，用于解决现有技术中静止状态判断的准确率低的问题。惯性导航***获取IMU测量得到第一设定时长的第一运行数据，确定第一运行数据的N个第一标准差，将N个第一标准差与预先保存的数据库进行匹配，确定分别与N个第一标准差相同的N个第二标准差；在预先保存的数据库中确定出N个第二标准差分别对应的第一信息，将N个第一信息中的每个第一信息中的第一概率与对应的权重相乘，将N个相乘得到的值相加，将相加得到的值确定为N个第一标准差为静态的第二概率；惯性导航***在第二概率大于、或等于静态概率阈值时，确定出惯性导航***所在的设备在第一设定时长处于静止状态。

Description

一种静止状态的判断方法及装置
本申请要求于2016年11月29日提交中国专利局、申请号为201611088239.4、申请名称为“一种静止状态的判断方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及惯性导航技术领域，尤其涉及一种静止状态的判断方法及装置。
背景技术
随着微电子机械***(Micro-Electromechanical System，MEMS)技术的成熟与发展，惯性导航技术应用范围迅速扩大，目前广泛应用于车载导航、行人导航中。惯性导航***(Inertial Navigation System，INS)是一种航位推算***，由惯性测量单元(Inertial Measurement Unit，IMU)和惯性导航机械编排算法组成。但是由于INS中传感器的误差，导致INS误差随时间的增长而增大。为解决INS误差随时间增长而增大的问题，目前采用的解决方法是通过零速修正(Zero Velocity Update，ZUPT)手段，减小INS误差。
零速修正是根据IMU测量出的数据判断出载体处于静止状态的时段，即ZUPT检测。现有技术中的零速修正检测的方法是阈值法，预先为采集的INS处于运动状态和处于静止状态时IMU测量出的数据设置合理的阈值；在INS使用过程中，将IMU测量出的数据或者所述数据经过投影变换后的新数据与所述阈值进行比较，当所述IMU测量出的数据或者所述新数据小于或等于所述阈值时，判定所述INS处于静止状态时段。
综上，现有技术的检测方法中判断INS静止状态的准确率低，如何提高INS静止状态判断的准确率，是目前要解决的问题。
发明内容
本发明实施例提供一种静止状态的判断方法及装置，以提高目标车辆、设备或者装置静止状态判断的准确率。
第一方面，本发明实施例提出了一种静止状态的判断方法，该方法包括：
惯性导航***获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数，优选的，所述第一设定时长为1秒，所述N为6；所述惯性导航***确定出所述每个轴在所述第一设定时长的运行数据对应的第一标准差，即确定出N个第一标准差；所述惯性导航***将所述N个第一标准差与预先保存的数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差；所述惯性导航***在所述预先保存的数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重；具体的，N个第一标准差分别来自N个轴，所述数据库中包括第二标准差也是来自N个轴；所述第一信息中包括所述第二标准差为静态的第一概率，即所述第二标准差所对应的轴为静态的第一概率；所述第二标准差对应的权重，即所述第二标准 差所对应的轴占的权重。所述惯性导航***将所述N个所述第一信息中的每个所述第一信息中的第一概率与对应的权重相乘，将N个所述相乘得到的值相加，将所述相加得到的值确定为所述N个第一标准差为静态的第二概率；所述惯性导航***在所述第二概率大于、或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长处于静止状态；当所述第二概率小于所述静态概率阈值时，确定所述惯性导航***所在的设备在所述第一设定时长处于运动状态。
本发明实施例中，惯性导航***通过IMU测量得到第一设定时长N个轴的运行数据，并计算出N个轴的运行数据对应的第一标准差，将计算出的N个第一标准差在数据库中进行查询，确定出与N个第一标准差相同的N个第二标准差，并确定出数据库中N个第二标准差分别对应的第一概率，以及N个第二标准差所在轴分别对应的权重，通过确定出的N个第一概率以及所述N个轴对应的权重，确定出第一设定时长所述惯性导航***所在的设备为静态的第二概率，将所述第二概率与静态概率阈值进行比较，当所述第二概率大于或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长为静止状态。采用上述方法，提高了静止状态判断的准确率。
在一种可能的设计中，所述第二标准差与所述第二标准差所在的轴的权重的对应关系通过下述过程形成：所述惯性导航***确定出第二设定时长内IMU测量得到的任一轴的样本数据；所述惯性导航***将所述第二设定时长内的每个所述第一设定时长对应样本数据确定为静态数据或者动态数据，根据所述静态数据确定出静态标准差，根据所述动态数据确定出动态标准差；其中，所述第一设定时长是以滑动窗口的形式在所述第二设定时长内进行取值的；将确定出的多个静态标准差按照第一设定阈值范围分组，确定出每个所述第一设定阈值范围内静态标准差的数量，所述第一阈值设定范围内分成多组，固定间隔的数值为一组，得到静态标准差分布直方图，并将确定出的多个动态样本标准差按照第二设定阈值范围分组，确定出每个所述第二设定阈值范围内动态样本标准差的数量，所述第一阈值设定范围内分成多组，固定间隔的数值为一组，得到动态标准差分布直方图；将所述静态标准差直方图与所述动态标准差分布直方图分别进行曲线拟合，将拟合得到的曲线进行归一化处理，确定出静态标准差曲线与动态标准差曲线；将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积；将所述相交部分的面积的倒数确定为所述任一轴对应的所述多个静态标准差与所述多个动态样本标准差的权重。所述N个轴的权重的计算方式都采用上述方式进行确定。
在一种可能的设计中，所述第二标准差与第一概率的对应关系通过下述过程形成，包括：将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积时，所述静态标准差曲线与动态标准差曲线、以及所述坐标系的横轴具有两个交点，所述两个交点中，与所述坐标系零点距离小的交点为交点a，与所述坐标系的零点距离大的为交点b，所述静态标准差曲线与动态标准差曲线的交点为交点c；当所述第二标准差为小于或等于交点a的数值时，所述第二标准差为静态的概率为所述第二标准差在所述静态标准差曲线上对应的概率值；当所述第二数据为大于或等于交点b的数值时，所述第二标准差为静态的概率为0；当所述第二数据为大于a点、小于b点的数值时，所述第二标准差为静态的概率为： 所述第二标准差在所述静态标准差曲线上对应的的概率值，与所述第二标准差在所述静态标准差曲线上对应的概率值和所述第二标准差在所述动态标准差曲线上对应的概率值之和的比值。
第二方面，本发明实施例提出了一种静止状态的判断装置，该装置包括：
获取模块，用于获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数；确定模块，用于确定出所述第一运行数据的N个第一标准差；匹配模块，用于将所述N个第一标准差与预先保存的数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差；查找模块，用于在所述预先保存的数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重；处理模块，用于将所述N个所述第一信息中的每个所述第一信息中的第一概率与对应的权重相乘，将N个所述相乘得到的值相加，将所述相加得到的值确定为所述N个第一标准差为静态的第二概率；判断模块，用于在所述第二概率大于、或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长处于静止状态。
本发明实施例中，惯性导航***通过IMU测量得到第一设定时长N个轴的运行数据，并计算出N个轴的运行数据对应的第一标准差，将计算出的N个第一标准差在数据库中进行查询，确定出与N个第一标准差相同的N个第二标准差，并确定出数据库中N个第二标准差分别对应的第一概率，以及N个第二标准差所在轴分别对应的权重，通过确定出的N个第一概率以及所述N个轴对应的权重，确定出第一设定时长所述惯性导航***所在的设备为静态的第二概率，将所述第二概率与静态概率阈值进行比较，当所述第二概率大于或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长为静止状态。采用上述方法，提高了静止状态判断的准确率。
在一种可能的设计中，所述第二标准差与所述权重的对应关系通过下述过程形成：确定出第二设定时长内IMU测量得到的任一轴的静态数据与动态数据；在所述第二设定时长内确定出每个所述第一设定时长对应的静态数据与所述第一设定时长对应的动态样本数据，根据所述第一设定时长对应的静态数据确定出静态标准差，根据所述第一设定时长对应的动态样本数据确定出动态样本标准差；将确定出的多个静态标准差按照第一设定阈值范围分组，确定出每个所述第一设定阈值范围内静态标准差的数量，得到静态标准差分布直方图，并将确定出的多个动态样本标准差按照第二设定阈值范围分组，确定出每个所述第二设定阈值范围内动态样本标准差的数量，得到动态标准差分布直方图；将所述静态标准差直方图与所述动态标准差分布直方图分别进行曲线拟合，将拟合得到的曲线进行归一化处理，确定出静态标准差曲线与动态标准差曲线；将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积；将所述相交部分的面积的倒数确定为所述任一轴对应的所述多个静态标准差与所述多个动态标准差的权重。
在一种可能的设计中，所述第二标准差与第一概率的对应关系通过下述过程形成，包括：将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积时，所述静态标准差曲线与动态标准差 曲线、以及所述坐标系的横轴具有两个交点，所述两个交点中，与所述坐标系零点距离小的交点为交点a，与所述坐标系的零点距离大的为交点b，所述静态标准差曲线与动态标准差曲线的交点为交点c；当所述第二标准差为小于或等于交点a的数值时，所述第二标准差为静态的概率为所述第二标准差在所述静态标准差曲线上对应的概率值；当所述第二数据为大于或等于交点b的数值时，所述第二标准差为静态的概率为0；当所述第二数据为大于a点、小于b点的数值时，所述第二标准差为静态的概率为：所述第二标准差在所述静态标准差曲线上对应的的概率值，与所述第二标准差在所述静态标准差曲线上对应的概率值和所述第二标准差在所述动态标准差曲线上对应的概率值之和的比值。
在一种可能的设计中，所述N为6。
第三方面，本发明实施例提出了一种静止状态的判断装置，包括处理器、以及与该处理器连接的存储器，其中：
存储器，所述存储器用于存储所述处理器所执行的程序代码；
处理器，用于执行所述存储器所存储的程序代码，执行下列过程：
获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数；确定出所述第一运行数据的N个第一标准差；将所述N个第一标准差与预先保存的数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差；在所述预先保存的数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重；将所述N个所述第一信息中的每个所述第一信息中的第一概率与对应的权重相乘，将N个所述相乘得到的值相加，将所述相加得到的值确定为所述N个第一标准差为静态的第二概率；在所述第二概率大于、或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长处于静止状态。
本发明实施例中，惯性导航***通过IMU测量得到第一设定时长N个轴的运行数据，并计算出N个轴的运行数据对应的第一标准差，将计算出的N个第一标准差在数据库中进行查询，确定出与N个第一标准差相同的N个第二标准差，并确定出数据库中N个第二标准差分别对应的第一概率，以及N个第二标准差所在轴分别对应的权重，通过确定出的N个第一概率以及所述N个轴对应的权重，确定出第一设定时长所述惯性导航***所在的设备为静态的第二概率，将所述第二概率与静态概率阈值进行比较，当所述第二概率大于或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长为静止状态。采用上述方法，提高了静止状态判断的准确率。
附图说明
图1为本发明实施例提供的一种静态标准差曲线与静态标准差曲线；
图2为本发明实施例提供的确定图1中一种静态标准差曲线与静态标准差曲线的流程示意图；
图3为本发明实施例提供的IMU原始数据；
图4为本发明实施例提供的各轴静态标准差曲线与静态标准差曲线；
图5为本发明实施例提供的各轴静态标准差曲线与动态标准差曲线的重叠部分的标准差被判为静态的概率曲线；
图6为本发明实施例提供的一种静止状态的判断方法的流程示意图；
图7为本发明实施例提供的一种静止状态的判断装置的结构示意图；
图8为本发明实施例提供的一种静止状态的判断装置的硬件结构示意图。
具体实施方式
下面结合说明书附图对本发明实施例作进一步详细描述。应当理解，此处所描述的实施例仅用于说明和解释本发明，并不用于限定本发明。
惯性导航***INS是一种航位推算***，由惯性测量单元IMU和惯性导航机械编排算法组成。但是由于INS中传感器的误差，导致INS误差随时间的增长而增大。为解决INS误差随时间增长而增大的问题，减小INS误差，本发明实施例提出了一种静止状态判断方法，用于提高静止状态判断的准确率。
本发明实施例中，以任一轴为例，拟合出如图1所示的静态标准差曲线与动态标准差曲线，具体过程如图2所示：
步骤S21、读取样本数据。
步骤S22、根据样本数据采样率设置窗口长度，所述窗口长度即设定时间，将所述设定时间内的样本数据确定为静态数据或动态数据。
步骤S23、根据静态数据计算出静态标准差、根据动态数据计算出动态标准差，分别得到静态标准差序列、以及动态标准差序列。
步骤S24、设置动态和静态标准差的组间距，确定出静态标准差分布直方图以及动态标准差分布直方图。
举例说明：假设动静和静态标准差的值从0至100，以5为步长设置组间距，则划分为0-4，5-9，10-14，……，96-100的多个组，本发明实施例中，组间距的划分可以根据实际需要进行确定，本发明对其不做限定。
步骤S25、将所述静态标准差直方图与所述动态标准差分布直方图分别进行曲线拟合，并对曲线与标准差(standard deviation，STD)轴所围面积作归一化处理，确定出静态标准差曲线与动态标准差曲线，即标准差及其频率分布曲线，横轴为标准差值，纵轴为频率值。
具体的，上述归一化处理可采用以下方法，首先计算曲线与STD轴所围面积S，曲线面积求解方法采用梯形积分，然后各STD归一化后频率为f′std，其中，
f′std＝fstd/S
步骤S26、将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，即得到了理想情况下的图1。
其中，在图1中根据伯努利分布原理，可以计算出任一标准差为静态的概率；所述静态标准差曲线与动态标准差曲线、以及所述坐标系的横轴具有两个交点，所述两个交点中，与所述坐标系零点距离小的交点为交点std_a，与所述坐标系的零点距离大的为交点std_b，所述静态标准差曲线与动态标准差曲线的交点为交点std_c；当所述标准差为小于或等于交点std_a的数值时，所述标准差为静态的概率为所述标准差在所述静态标准差曲线上对应的概率值，即所述标准差为静态的概率为100％；当标准差 为大于或等于交点std_b的数值时，所述标准差为静态的概率为0；当标准差std0为大于std_a点、小于std_b点的数值时，所述标准差为静态的概率为：所述标准差std0在所述静态标准差曲线上对应的的概率值fs(std0)，与所述标准差在所述静态标准差曲线上对应的概率值fs(std0)和所述标准差std0在所述动态标准差曲线上对应的概率值fs(std0)之和的比值，即std0为静态的概率为：
Figure PCTCN2017092650-appb-000001
举例说明：如图3所示A区域的标准差为静态的概率为100％，B区域和C区域的标准差为静态的概率通过上述公式进行计算，D区域的标准差为静态的概率为0。
所述静态标准差曲线与动态标准差曲线与横轴所围成的面积的倒数为该轴的权重K；将确定出的权重K、任一标准差为静态的概率以及标准差确定为数据库，当后续有新数据需要进行进行状态判断时，采用上述数据库。
本发明实施例中，以图1为了计算权重K，假设静态标准差曲线与动态标准差曲线的重合部分的函数表达式为f(x)，a<x<b；假设所围面积为A，则
Figure PCTCN2017092650-appb-000002
最后确定出归一化后权重K的表达式为：
Figure PCTCN2017092650-appb-000003
本发明实施例中，以车载惯性传感器采集6个轴的数据对数据库(即标准差-概率分布查找表)的形成进行说明，样本数据来自涵盖不同应用场景的多次跑车实验，跑车过程中因堵车或故意等原因频繁出现停车现象，采集到原始输出数据，上述原始数据包括陀螺仪采集的X轴、Y轴和Z轴三个方向的角速率，以及加速度计采集的X轴、Y轴和Z轴三个方向的加速度，将采集到的数据根据步骤S21～S26进行处理，分别得到如图4所示的各轴的静态标准差曲线与动态标准差曲线，并确定出如图5所示的的各轴静态标准差曲线与静态标准差的重叠部分的标准差被判为静态的概率曲线，以及各轴重叠部分面积倒数及其归一化后的值，如下表1所示：
表1
Figure PCTCN2017092650-appb-000004
将上述数据记录到数据库中，当需要判断任一标准差为静态的概率时，采用数据库中的数据进行如下计算：任一标准差为静止状态的概率为N个轴中每个轴的所述任一标准差为静态的概率以及该轴的权重的加权平均，即：
Figure PCTCN2017092650-appb-000005
其中，n为轴数，i表示第i个轴。
本发明实施例还提供了一种静止状态的判断方法，如图6所示，该方法包括以下过程：
S61、惯性导航***获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数。
S62、所述惯性导航***确定出所述第一运行数据的N个第一标准差。
S63、所述惯性导航***将所述N个第一标准差与预先保存的数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差。
S64、所述惯性导航***在所述预先保存的数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重。
S65、所述惯性导航***将所述N个所述第一信息中的每个所述第一信息中的第一概率与对应的权重相乘，将N个所述相乘得到的值相加，将所述相加得到的值确定为所述N个第一标准差为静态的第二概率。
S66、所述惯性导航***在所述第二概率大于、或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长处于静止状态。
本发明实施例中，惯性导航***通过IMU测量得到第一设定时长N个轴的运行数据，并计算出N个轴的运行数据对应的第一标准差，将计算出的N个第一标准差在数据库中进行查询，确定出与N个第一标准差相同的N个第二标准差，并确定出数据库中N个第二标准差分别对应的第一概率，以及N个第二标准差所在轴分别对应的权重，通过确定出的N个第一概率以及所述N个轴对应的权重，确定出第一设定时长所述惯性导航***所在的设备为静态的第二概率，将所述第二概率与静态概率阈值进行比较，当所述第二概率大于或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长为静止状态。采用上述方法，提高了静止状态判断的准确率。
基于同一发明构思，本发明实施例提供的一种静止状态的判断装置70，如图7所示，该装置包括：
获取模块71，用于获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数；
确定模块72，用于确定出所述第一运行数据的N个第一标准差；
匹配模块73，用于将所述N个第一标准差与预先保存的数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差；
查找模块74，用于在所述预先保存的数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重；
处理模块75，用于将所述N个所述第一信息中的每个所述第一信息中的第一概率 与对应的权重相乘，将N个所述相乘得到的值相加，将所述相加得到的值确定为所述N个第一标准差为静态的第二概率；
判断模块76，用于在所述第二概率大于、或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长处于静止状态。
本发明实施例中，惯性导航***通过IMU测量得到第一设定时长N个轴的运行数据，并计算出N个轴的运行数据对应的第一标准差，将计算出的N个第一标准差在数据库中进行查询，确定出与N个第一标准差相同的N个第二标准差，并确定出数据库中N个第二标准差分别对应的第一概率，以及N个第二标准差所在轴分别对应的权重，通过确定出的N个第一概率以及所述N个轴对应的权重，确定出第一设定时长所述惯性导航***所在的设备为静态的第二概率，将所述第二概率与静态概率阈值进行比较，当所述第二概率大于或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长为静止状态。采用上述方法，提高了静止状态判断的准确率。
可选的，所述第二标准差与所述权重的对应关系通过下述过程形成：
确定出第二设定时长内IMU测量得到的任一轴的样本数据；
将所述第二设定时长内的每个所述第一设定时长对应样本数据确定为静态数据或者动态数据，根据所述静态数据确定出静态标准差，根据所述动态数据确定出动态标准差；
将确定出的多个静态标准差按照第一设定阈值范围分组，确定出每个所述第一设定阈值范围内静态标准差的数量，得到静态标准差分布直方图，并将确定出的多个动态样本标准差按照第二设定阈值范围分组，确定出每个所述第二设定阈值范围内动态样本标准差的数量，得到动态标准差分布直方图；
将所述静态标准差直方图与所述动态标准差分布直方图分别进行曲线拟合，将拟合得到的曲线进行归一化处理，确定出静态标准差曲线与动态标准差曲线；
将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积；
将所述相交部分的面积的倒数确定为所述任一轴对应的所述多个静态标准差与所述多个动态标准差的权重。
可选的，所述第二标准差与第一概率的对应关系通过下述过程形成，包括：
将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积时，所述静态标准差曲线与动态标准差曲线、以及所述坐标系的横轴具有两个交点，所述两个交点中，与所述坐标系零点距离小的交点为交点a，与所述坐标系的零点距离大的为交点b，所述静态标准差曲线与动态标准差曲线的交点为交点c；
当所述第二标准差为小于或等于交点a的数值时，所述第二标准差为静态的概率为所述第二标准差在所述静态标准差曲线上对应的概率值；
当所述第二数据为大于或等于交点b的数值时，所述第二标准差为静态的概率为0；
当所述第二数据为大于a点、小于b点的数值时，所述第二标准差为静态的概率为：所述第二标准差在所述静态标准差曲线上对应的的概率值，与所述第二标准差在 所述静态标准差曲线上对应的概率值和所述第二标准差在所述动态标准差曲线上对应的概率值之和的比值。
可选的，所述N为6。
本发明实施例提出一种静止状态的判断装置800，如图8所示，包括处理器810、与该处理器连接的存储器820，以及与所述总线830连接的用于显示静止状态的显示器840，所述存储器820和所述处理器810分别通过总线830相互连接，其中：
存储器820，用于存储所述处理器所执行的程序代码；
处理器810，用于用于执行所述存储器所存储的程序代码，执行上述实施例提供的任一计步方法,例如执行下列过程：
获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数；确定出所述第一运行数据的N个第一标准差；将所述N个第一标准差与预先保存的数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差；在所述预先保存的数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重；将所述N个所述第一信息中的每个所述第一信息中的第一概率与对应的权重相乘，将N个所述相乘得到的值相加，将所述相加得到的值确定为所述N个第一标准差为静态的第二概率；在所述第二概率大于、或等于所述静态概率阈值时，确定出所述惯性导航***所在的设备在所述第一设定时长处于静止状态。
本领域内的技术人员应明白，本发明的实施例可提供为方法、***、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(***)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
尽管已描述了本发明的优选实施例，但本领域内的技术人员一旦得知了基本创造 性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本发明范围的所有变更和修改。
显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。

Claims (8)

  1. 一种静止状态的判断方法，其特征在于，该方法包括：
    惯性导航***获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数；
    所述惯性导航***确定所述第一运行数据的N个第一标准差；
    所述惯性导航***将所述N个第一标准差与数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差；
    所述惯性导航***在所述数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重；
    所述惯性导航***将所述N个所述第一信息中的每个所述第一信息中的第一概率与对应的权重相乘，将N个所述相乘得到的值相加，所述相加得到的值为所述N个第一标准差为静态的第二概率；
    如果所述第二概率大于、或等于所述静态概率阈值，则所述惯性导航***所在的设备在所述第一设定时长处于静止状态。
  2. 根据权利要求1所述的方法，其特征在于，所述第二标准差与所述权重的对应关系通过下述过程形成：
    所述惯性导航***确定出第二设定时长内IMU测量得到的任一轴的样本数据；
    所述惯性导航***将所述第二设定时长内的每个所述第一设定时长对应样本数据确定为静态数据或者动态数据，根据所述静态数据确定出静态标准差，根据所述动态数据确定出动态标准差；
    将确定出的多个静态标准差按照第一设定阈值范围分组，确定出每个所述第一设定阈值范围内静态标准差的数量，得到静态标准差分布直方图，并将确定出的多个动态标准差按照第二设定阈值范围分组，确定出每个所述第二设定阈值范围内动态标准差的数量，得到动态标准差分布直方图；
    将所述静态标准差直方图与所述动态标准差分布直方图分别进行曲线拟合，将拟合得到的曲线进行归一化处理，确定出静态标准差曲线与动态标准差曲线；
    将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积；
    将所述相交部分的面积的倒数确定为所述任一轴对应的所述多个静态标准差与所述多个动态标准差的权重。
  3. 根据权利要求2所述的方法，其特征在于，所述第二标准差与第一概率的对应关系通过下述过程形成，包括：
    将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积时，所述静态标准差曲线与动态标准差曲线、以及所述坐标系的横轴具有两个交点，所述两个交点中，与所述坐标系零点距离小的交点为交点a，与所述坐标系的零点距离大的为交点b，所述静态标准差曲线与动态标准差曲线的交点为交点c；
    当所述第二标准差为小于或等于交点a的数值时，所述第二标准差为静态的概率为所述第二标准差在所述静态标准差曲线上对应的概率值；
    当所述第二数据为大于或等于交点b的数值时，所述第二标准差为静态的概率为0；
    当所述第二数据为大于a点、小于b点的数值时，所述第二标准差为静态的概率为：所述第二标准差在所述静态标准差曲线上对应的的概率值，与所述第二标准差在所述静态标准差曲线上对应的概率值和所述第二标准差在所述动态标准差曲线上对应的概率值之和的比值。
  4. 根据权利要求1所述的方法，其特征在于，所述N为6。
  5. 一种静止状态的判断装置，其特征在于，该装置包括：
    获取模块，用于获取惯性测量单元IMU测量得到第一设定时长的第一运行数据，所述第一运行数据包括所述IMU测量的N个轴中每个轴在所述第一设定时长的运行数据，N为大于、或等于1的正整数；
    确定模块，用于计算所述第一运行数据的N个第一标准差；
    匹配模块，用于将所述N个第一标准差与数据库进行匹配，确定分别与所述N个第一标准差相同的N个第二标准差；
    查找模块，用于在所述数据库中确定出所述N个第二标准差分别对应的第一信息，所述第一信息中包括所述第二标准差为静态的第一概率以及所述第二标准差对应的权重；
    处理模块，用于将所述N个所述第一信息中的每个所述第一信息中的第一概率与对应的权重相乘，将N个所述相乘得到的值相加，所述相加得到的值为所述N个第一标准差为静态的第二概率；
    判断模块，用于判断所述第二概率是否大于所述静态概率阈值，如果所述第二概率大于、或等于所述静态概率阈值，则所述获取惯性测量单元所在的设备在所述第一设定时长处于静止状态。
  6. 根据权利要求5所述的装置，其特征在于，所述查找模块还用于形成所述第二标准差与所述权重的对应关系：
    确定出第二设定时长内IMU测量得到的任一轴的样本数据；
    将所述第二设定时长内的每个所述第一设定时长对应样本数据确定为静态数据或者动态数据，根据所述静态数据确定出静态标准差，根据所述动态数据确定出动态标准差；
    将确定出的多个静态标准差按照第一设定阈值范围分组，确定出每个所述第一设定阈值范围内静态标准差的数量，得到静态标准差分布直方图，并将确定出的多个动态标准差按照第二设定阈值范围分组，确定出每个所述第二设定阈值范围内动态标准差的数量，得到动态标准差分布直方图；
    将所述静态标准差直方图与所述动态标准差分布直方图分别进行曲线拟合，将拟合得到的曲线进行归一化处理，确定出静态标准差曲线与动态标准差曲线；
    将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积；
    将所述相交部分的面积的倒数确定为所述任一轴对应的所述多个静态标准差与所 述多个动态标准差的权重。
  7. 根据权利要求6所述的装置，其特征在于，所述处理模块还用于形成第二标准差与第一概率的对应关系：
    将所述静态标准差曲线与动态标准差曲线放在同一坐标系下，确定出所述静态标准差曲线与动态标准差曲线相交部分的面积时，所述静态标准差曲线与动态标准差曲线、以及所述坐标系的横轴具有两个交点，所述两个交点中，与所述坐标系零点距离小的交点为交点a，与所述坐标系的零点距离大的为交点b，所述静态标准差曲线与动态标准差曲线的交点为交点c；
    当所述第二标准差为小于或等于交点a的数值时，所述第二标准差为静态的概率为所述第二标准差在所述静态标准差曲线上对应的概率值；
    当所述第二数据为大于或等于交点b的数值时，所述第二标准差为静态的概率为0；
    当所述第二数据为大于a点、小于b点的数值时，所述第二标准差为静态的概率为：所述第二标准差在所述静态标准差曲线上对应的的概率值，与所述第二标准差在所述静态标准差曲线上对应的概率值和所述第二标准差在所述动态标准差曲线上对应的概率值之和的比值。
  8. 根据权利要求5所述的装置，其特征在于，所述N为6。
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