WO2020233158A1 - 一种基于智能手机的高精度单点定位方法与装置 - Google Patents

一种基于智能手机的高精度单点定位方法与装置 Download PDF

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WO2020233158A1
WO2020233158A1 PCT/CN2020/072411 CN2020072411W WO2020233158A1 WO 2020233158 A1 WO2020233158 A1 WO 2020233158A1 CN 2020072411 W CN2020072411 W CN 2020072411W WO 2020233158 A1 WO2020233158 A1 WO 2020233158A1
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satellite
observations
observation
positioning
point positioning
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高成发
陈波
刘永胜
孙璞玉
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Southeast University
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    • 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
    • G01S19/42—Determining position
    • G01S19/421—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system
    • G01S19/425—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system by combining or switching between signals derived from different satellite radio beacon positioning systems
    • 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
    • G01S19/42—Determining position
    • G01S19/51—Relative positioning
    • 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
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
    • 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
    • G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
    • G01S19/07—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections
    • G01S19/072—Ionosphere corrections
    • 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
    • G01S19/14—Receivers specially adapted for specific applications
    • 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
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/243—Demodulation of navigation message
    • 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
    • G01S19/33—Multimode operation in different systems which transmit time stamped messages, e.g. GPS/GLONASS
    • 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
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry

Definitions

  • the invention relates to a global navigation satellite system (Global Navigation Satellite System, GNSS) satellite positioning method, in particular to a high-precision single-point positioning method and device based on the original GNSS observation value of a smart phone.
  • GNSS Global Navigation Satellite System
  • GNSS Global Navigation Satellite System
  • the PPP Precise Point Positioning
  • a high-precision single-point positioning method based on a smart phone includes the following steps:
  • step (1) use the location-based service API provided by the smartphone operating system to directly obtain part of the GNSS raw data, including time data, GNSS system type and carrier phase observations, and then use the GNSS raw data The time data in the signal propagation time difference is solved to calculate the pseudorange.
  • the attenuated error effects include using precise ephemeris and precise clock offset files for the received observations to eliminate orbit errors and satellite clock offsets, and using ionospheric grid files to weaken the ionosphere Delay, and the multipath effect as observation noise, the resulting non-combined observation model is:
  • the superscripts g, e, c represent GPS system, Galileo system and BDS system, subscripts i, j, and k represent the i, j, and k satellites, and P and ⁇ on the left are pseudorange and carrier observations, respectively Value, ⁇ is the distance from the mobile terminal to the satellite, c is the speed of light, Are the mobile terminal clock errors of the pseudorange and carrier observations, d trop is the tropospheric delay, Is the carrier ambiguity, ⁇ P and ⁇ ⁇ are the residuals of the pseudorange and carrier phase observations respectively; with Respectively represent the time deviation between Galileo and BDS system and GPS system.
  • the weighting scheme for determining the weight of each satellite observation value according to the satellite altitude angle is specifically:
  • the weight of the satellite observation value is 0;
  • the weight of the satellite observation value is sinE, and E is the satellite altitude angle.
  • step (4) the improved static Kalman filter is specifically:
  • the number of observation equations for a satellite is 2, suppose that a certain epoch observes n 1 GPS satellites, n 2 Galileo satellites, and BDS satellites n 3 pieces, satisfying n 1 +n 2 +n 3 ⁇ 8; at this time, the parameter vector to be estimated in the Kalman filter is:
  • x, y and z are the position parameters of the smartphone; the total number of observation equations is 2 ⁇ (n 1 +n 2 +n 3 ), and the parameters to be estimated are n 1 +n 2 +n 3 +8, redundant The number of observations is n 1 +n 2 +n 3 -8;
  • the sixth column is 1, and when the observations are from BDS satellites, the seventh column is 1; MF is the tropospheric wet delay projection coefficient; after the 9th column, it is 1.
  • the carrier phase ambiguity coefficient the odd row is 0, the even row i+8th column is 1, and i is the satellite number; the weight of each satellite observation value in the filtering process is determined by step (3).
  • the present invention provides a high-precision single-point positioning device based on a smart phone, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the computer program is loaded When the processor reaches the processor, the high-precision single-point positioning method based on the smart phone is realized.
  • the present invention proposes a high-precision single-point positioning method based on a smart phone.
  • the non-combined PPP observation model is optimized, which can be In the case of the reference station, the sub-meter positioning accuracy of ordinary smart phones is achieved, and the convergence speed is faster.
  • the high-precision positioning technology of the mobile phone it can provide the mass users with better user experience such as city positioning, car navigation and tourism transportation.
  • higher-precision location services mean unpredictable application prospects
  • the development direction of modern human life such as the Internet of Things, autonomous driving and smart cities, all include low-cost location services, so the present invention has great significance.
  • Figure 1 is a flowchart of a high-precision single-point positioning method based on a smart phone
  • Figure 2 is a comparison diagram of the observed values of the geodetic receiver and the smart phone for simultaneous observation
  • Figure 3 is a positioning result diagram of an embodiment of the present invention.
  • FIG. 1 is a flowchart of a high-precision single-point positioning method based on a smart phone disclosed in an embodiment of the present invention.
  • the location-based service API provided by the Android system (version 7.0 and above) is used to obtain original observation values such as GNSS original pseudoranges and carrier observation values.
  • original observation values such as GNSS original pseudoranges and carrier observation values.
  • a non-combined PPP model is formed from the original observations based on the improved precision single-point positioning algorithm for estimating the double clock error. Then use the satellite altitude angle positioning model to determine the weight of each satellite observation value.
  • the improved static Kalman filtering method is used to obtain high-precision, fast-converging real-time single-point positioning results. The detailed process is described below.
  • the first step is to obtain original observations such as GNSS pseudorange and carrier phase through the location-based service API provided by the Android system.
  • the original data content contained in the GnssMeasurement and GnssClock classes is shown in Table 1 and Table 2.
  • t Tx represents the time when the satellite transmission signal is received
  • c is the speed of light.
  • t Tx can be obtained directly through the getReceivedSvTimeNanos() method, and the value is the number of seconds in the GPS week.
  • TimeNanos is the receiver's local clock value getTimeNanos()
  • TimeOffsetNanos is the local clock offset value getTimeOffsetNanos()
  • FullBiasNanos is the receiver The total local clock deviation getFullBiasNanos()
  • BiasNanos is the receiver's local clock error getBiasNanos().
  • a non-combined observation value model is formed for the original observation value according to the improved PPP algorithm for estimating the double clock error.
  • FIG. 2 shows the comparison between the original observations of the same satellite geodetic receiver and the observations of a smart phone (Huawei P10 mobile phone) at the same time.
  • the receiver carrier and the pseudorange observations coincide.
  • the difference between the two is the ambiguity of the carrier phase x carrier wavelength.
  • the observation value of the geodetic receiver will have a large jump after a period of time, which is caused by the clock jump of the receiver, and does not affect the positioning solution of the original observation value.
  • the difference between the mobile phone pseudorange and the carrier phase observation value is not fixed. This property is different from the geodetic receiver and affects the use of the mobile phone terminal carrier phase data.
  • the positioning scheme is improved, and two clock error parameters are estimated when the parameters are solved.
  • the single-frequency observation equation is:
  • the superscript g represents the GPS system
  • the subscript i represents the i-th satellite
  • Is the pseudorange and carrier observations Is the distance from the mobile phone to the satellite
  • c is the speed of light
  • dTg is the satellite clock error
  • Is the tropospheric delay Is the satellite orbit error
  • Is the ionospheric delay are pseudorange and carrier multipath effect delay
  • Is the carrier ambiguity of the whole week The residuals of the pseudorange and carrier phase observations respectively.
  • the left side is the pseudorange and carrier observation values of each GNSS system
  • the superscripts e and c represent the Galileo system and the BDS system respectively
  • the subscripts j and k represent the jth and kth satellites. with Respectively represent the time deviation between Galileo and BDS system and GPS system.
  • the third step is to determine the weight of each satellite observation value.
  • This method determines the weight of each satellite observation value according to the satellite altitude angle.
  • the specific weighting scheme is:
  • W is the weight
  • E is the altitude angle of the connection from the mobile phone to the satellite.
  • the fourth step is to use Kalman filter to solve the parameters to obtain high-precision positioning results.
  • the number of observation equations for a single epoch is two. Assume that n 1 GPS satellites and 2 Galileo satellites are observed in a certain epoch, BDS There are n 3 satellites. At this time, the parameter vector to be estimated in the Kalman filter is:
  • x, y and z are the location parameters of the smartphone.
  • the total number of observation equations is 2 ⁇ (n 1 +n 2 +n 3 ), the parameters to be estimated are n 1 +n 2 +n 3 +8, and the number of redundant observations is n 1 +n 2 +n 2 -8 .
  • Kalman filtering used in the present invention are:
  • ⁇ k, k-1 is the state transition matrix (because the number of satellites will change, it is necessary to change the nth order X k-1 of the last epoch to this epoch m order X k ).
  • ⁇ k, k-1 is the system noise driving matrix
  • Q k-1 is the system error (error of the model) is a positive definite matrix
  • each epoch must be input, you can directly put ⁇ k, k-1 Q k-1 ⁇ T k, k-1 is regarded as a whole and is defined as a symmetric matrix of order m.
  • the first positioning is the same as P 0.
  • the true values of the three position parameters and n ambiguity parameters should remain unchanged, so the process noise is 0 (the variance corresponding to the system noise), and the phone clock error,
  • the stochastic model of inter-system deviation and tropospheric delay parameters is simulated by a random walk process model.
  • H k is the coefficient matrix of the observation equation. Compared with the conventional static Kalman filter, H k in this method needs to be modified accordingly:
  • the index indicates the satellite number, and the superscript indicates the type of GNSS system; the odd-numbered rows correspond to the pseudorange observations, and the even-numbered rows correspond to the carrier phase observations; the first three columns, ⁇ , ⁇ , and ⁇ are the satellite-mobile direction cosines, and the calculation formula is shown in the formula ( 18);
  • the fourth and fifth columns are the clock error coefficients of the pseudorange observations and the carrier observations, the fourth column of odd rows is 1, and the fifth column of even rows is 1;
  • the sixth and seventh columns are the inter-system deviation coefficients, when When the observation value comes from the Galileo satellite, the sixth column is 1; when the observation value comes from the BDS satellite, the seventh column is 1; in the eighth column, MF is the tropospheric wet delay projection coefficient (the trop
  • x s , y s and z s are the satellite coordinates (obtained through the ephemeris file), x 0 , y 0 and z 0 are the approximate coordinates of the smartphone (obtained through pseudo-range single-point positioning), and ⁇ 0 is the mobile terminal The approximate distance to the satellite (calculated by the satellite coordinates and the approximate coordinates of the mobile phone).
  • R k is the observation noise variance matrix
  • ⁇ P and ⁇ ⁇ are the standard deviation of the zenith direction of the pseudorange observation value and the carrier observation value, respectively, and W is the weight of each satellite, that is, the weight of each satellite observation calculated by equation (12).
  • the variance setting of the observation value is very important. Improper setting can easily cause filtering divergence and seriously affect the positioning result.
  • the standard deviation of the zenith direction of the observation value is determined based on the data quality of the original GNSS observation value of the smartphone measured in the previous experiment.
  • the Mi 8 mobile phone used in the embodiment, the pseudorange observation value and the carrier phase observation value zenith direction standard The difference is set to 3.0m and 0.2m, respectively.
  • L k is the observation value vector, that is, the pseudorange and carrier observation values of each GNSS system in formulas (6) to (11), and I is the unit array.
  • the first 3 items in the X k vector are the precise coordinates of the smartphone calculated in the current epoch. If the current epoch is not the last epoch, return to step (2) for cyclic filtering calculation, and finally obtain a multi-epoch filtering solution.
  • the experiment time is 2018.10.19, using a Huawei Mi 8 smartphone, and the test location is the GE01 control point on the Southeast University campus. 5 observations were made, each time was about 6 minutes, and the sampling interval was 1s. The precise coordinates of this station have been obtained in advance through network RTK.
  • the convergence time is defined as the time elapsed from the time when the positioning starts to the time when the positioning errors in the N and E directions are both less than 1m, and the subsequent epoch errors no longer exceed 1m.
  • Statistics of the convergence time of 5 time periods are shown in Table 4. Each time period can achieve convergence within 30s, indicating that this method can be applied to real-time positioning and provide low-latency and high-precision smart phone positioning results.
  • a high-precision single-point positioning device based on a smart phone disclosed in the embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and running on the processor.
  • the computer program When being loaded into the processor, the described high-precision single-point positioning method based on smart phones is realized.

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Abstract

一种基于智能手机的高精度单点定位方法与装置，属于卫星定位技术领域。该方法改进了常规的PPP非组合定位模型，无需GNSS参考站，仅使用智能手机接收的原始GNSS观测值进行高精度定位。该方法包括以下步骤：获取智能手机原始GNSS伪距和载波相位等观测值；在进行数据预处理削弱部分误差影响之后，根据改进的估计双钟差的精密单点定位方法，对原始观测值形成非组合模型；根据卫星高度角确定各卫星观测值权重；通过改进的卡尔曼滤波方法进行滤波定位，得到高精度单点定位结果。测试实验结果表明，使用该方法能够对智能手机实现平面优于0.9m，高程优于1.7m的定位精度。

Description

一种基于智能手机的高精度单点定位方法与装置 技术领域
本发明涉及全球导航卫星系统(Global Navigation Satellite System，GNSS)卫星定位方法，具体涉及一种基于智能手机原始GNSS观测值的高精度单点定位方法与装置。
背景技术
智能手机等智能手机是如今人们生活中不可或缺的工具，智能手机中的GNSS(Global Navigation Satellite System，全球导航卫星系统)模块已经极大地改善了现代人类生活。在GNSS导航定位技术的发展中，导航或定位精度一直是制约其进一步应用于人类生产生活并发挥巨大作用的关键问题，对于智能手机的GNSS导航定位也是如此。由于一直以来智能手机中的GNSS导航定位模块都封装于手机操作系统之中，研究人员只能获取定位最终结果来进行应用层面的开发，因此相对于传统测地型接收机，智能手机原始观测值的分析与定位算法研究都非常缺乏。
从2016年开始，谷歌公司在Android N操作系统上提供了访问GNSS原始观测数据的接口，学者们开始着手于对智能手机接收的GNSS数据进行质量评估，并对定位性能进行分析。目前国内外学者关于利用原始GNSS观测值实现智能手机高精度定位的研究，主要分为三个方向：1)移动终端GNSS原始观测值数据质量分析；2)综合利用各种类型的GNSS数据，通过滤波的方式提高定位精度；3)利用伪距或载波差分的方式，提升移动终端定位精度。这些研究都是将现有的导航定位增强手段应用到智能手机上来，并且大部分的研究对象都是Android平板电脑，对于普通大众使用的智能手机，目前的定位性能较差。更重要的是，目前还没有根据智能手机原始观测值性质改进现有定位方案的先例。
发明内容
发明目的：基于以上背景技术，针对测试发现的智能手机伪距观测值和载波观测值变化率不一致的性质，改进PPP(Precise Point Positioning，精密单点定位)算法，实现高精度的智能手机实时单点定位。
技术方案：为实现上述发明目的，本发明采用的技术方案为：
一种基于智能手机的高精度单点定位方法，包括以下步骤：
(1)获取智能手机导航定位模块的GNSS伪距和载波相位原始观测值；
(2)在进行数据预处理削弱部分误差影响之后，根据改进的估计双钟差的精密单点定位方法，对原始观测值形成非组合观测值模型；
(3)根据卫星高度角确定各卫星观测值权重；
(4)使用改进的静态卡尔曼滤波进行计算，得到高精度的单点定位结果。
在优选的实施方案中，步骤(1)中，利用智能手机操作系统提供的基于位置服务的API直接获取部分GNSS原始数据，包括时间数据、GNSS系统类型和载波相位观测值，然后根据GNSS原始数据中的时间数据通过信号传播时间差值解算出伪距。
在优选的实施方案中，步骤(2)中，所削弱的误差影响包括对接收到观测值使用精密星历、精密钟差文件消除轨道误差和卫星钟差，使用电离层格网文件削弱电离层延迟，以及将多路径效应视作观测噪声，所形成的非组合观测值模型为：
Figure PCTCN2020072411-appb-000001
Figure PCTCN2020072411-appb-000002
Figure PCTCN2020072411-appb-000003
Figure PCTCN2020072411-appb-000004
Figure PCTCN2020072411-appb-000005
Figure PCTCN2020072411-appb-000006
式中，上标g、e、c分别表示GPS系统、Galileo系统和BDS系统，下标i、j、k表示第i、j、k颗卫星，左侧P、Ф分别为伪距和载波观测值，ρ为手机终端到卫星的距离，c为光速，
Figure PCTCN2020072411-appb-000007
分别为伪距和载波观测值的移动端钟差，d trop为对流层延迟，
Figure PCTCN2020072411-appb-000008
为载波整周模糊度，∈ P、∈ Ф分别为伪距和载波相位观测值的残差；
Figure PCTCN2020072411-appb-000009
和
Figure PCTCN2020072411-appb-000010
分别表示Galileo和BDS系统与GPS系统之间的时间偏差。
在优选的实施方案中，步骤(3)中，根据卫星高度角确定各卫星观测值权重的定权方案具体为：
当卫星高度角小于10°时，该卫星观测值权重为0；
当卫星高度角大于10°时，该卫星观测值权重为sinE，E为卫星高度角。
在优选的实施方案中，步骤(4)中，改进的静态卡尔曼滤波具体为：
对于单个历元，根据步骤(2)所述的非组合观测值模型，一颗卫星的观测方程数为2个，设某历元观测到GPS卫星n 1颗，Galileo卫星n 2颗，BDS卫星n 3颗，满足n 1+n 2+n 3≥8；此时卡尔曼滤波中待估参数向量为：
Figure PCTCN2020072411-appb-000011
式中，x、y和z为智能手机位置参数；则观测方程总数为2×(n 1+n 2+n 3)个，待估参数为n 1+n 2+n 3+8个，多余观测数为n 1+n 2+n 3-8个；
卡尔曼滤波的观测方程系数阵H为：
Figure PCTCN2020072411-appb-000012
矩阵H中，行数为2×(n 1+n 2+n 3)，列数为n 1+n 2+n 3-8，下标1至n表示卫星序号，n＝2×(n 1+n 2+n 3)，上标表示GNSS系统类型，g、e、c分别表示GPS系统、Galileo系统和BDS系统；奇数行对应伪距观测值，偶数行对应载波相位观测值；前3列α、β、γ为卫星-移动端的方向余弦，第4、5列为伪距观测值和载波观测值的钟差系数，奇数行第4列为1，偶数行第5列为1；第6、7列为系统间偏差系数，当观测值来自Galileo卫星时，第6列为1，当观测值来自BDS卫星时，第7列为1；MF为对流层湿延迟投影系数；第9列之后为载波相位模糊度系数，奇数行为0，偶数行第i+8列为1，i为卫星序号；滤波过程中各卫星观测值的权重由步骤(3)确定。
基于相同的发明构思，本发明提供的一种基于智能手机的高精度单点定位装置，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述计算机程序被加载至处理器时实现所述的一种基于智能手机的高精度单点定位方法。
有益效果：本发明提出一种基于智能手机的高精度单点定位方法，在现有的定位算法上，根据手机端原始GNSS观测值独特性质，优化了非组合PPP观测值模型，能够在不需要参考站的情况下，实现普通智能手机的亚米级定位精度，且收敛速度较快。基于手机端的高精度定位技术，可以向大众用户会提供更好的城市定位、车载导航和旅游交通等用户体验，在正在到来智能互联时代，更高精度的位置服务意味着难以预估的应用前景，物联网、自动驾驶和智慧城市等现代人类生活发展方向中，无一不包含着低成本的位置服务，因此本发明具有重大意义。
附图说明
图1是基于智能手机的高精度单点定位方法的流程图；
图2是同步观测的测地型接收机和智能手机的观测值对比图；
图3是本发明实施例的定位结果图。
具体实施方式
下面结合附图对本发明的技术方案作进一步详细说明。
图1为本发明实施例公开的基于智能手机的高精度单点定位方法流程图。本实施例中，首先通过Android系统(7.0及以上版本)提供的基于位置服务的API，获取GNSS原始伪距和载波观测值等原始观测值。在进行数据预处理削弱部分误差影响之后，根据改进的估计双钟差的精密单点定位算法，对原始观测值形成非组合PPP模型。然后采用卫星高度角定位模型，确定各卫星观测值权重。最后，使用改进的静态卡尔曼滤波方式，得到高精度、快速收敛的实时单点定位结果。以下叙述详细过程。
第一步，通过Android系统提供的基于位置服务的API，获取GNSS伪距和载波相位等原始观测值。首先利用Android系统开发中的android.location包中的GnssMeasurement类与GnssClock类直接获取获取部分GNSS原始数据，包括时间数据、GNSS系统类型、载波相位(表1第1行)、伪距率、载噪比，GnssMeasurement类和GnssClock类包含的原始数据内容如表1、表2所示。
表1 GnssMeasurement类主要GNSS原始数据表
Figure PCTCN2020072411-appb-000013
表2 GnssClock类主要GNSS原始数据
Figure PCTCN2020072411-appb-000014
然后根据GNSS原始数据中的时间数据解算出伪距，其实质是通过信号传播时间差值计算伪距：
P＝(t Rx-t Tx)·c    (1)
其中：P表示伪距，t Rx表示接收机接收到信号的时刻，t Tx表示接收到卫星发射信号的时刻，c为光速。t Tx可通过getReceivedSvTimeNanos()方法直接获取，数值为本GPS周内的秒数。
Figure PCTCN2020072411-appb-000015
Figure PCTCN2020072411-appb-000016
其中
Figure PCTCN2020072411-appb-000017
表示从1980.6.1到当前时间的总秒数，GNSS week表示所有整GPS周秒数和，TimeNanos为接收机本地时钟值getTimeNanos()，TimeOffsetNanos为本地时钟偏移值getTimeOffsetNanos()，FullBiasNanos为接收机本地时钟总偏差getFullBiasNanos()，BiasNanos为接收机本地钟差getBiasNanos()。
第二步，在进行数据预处理削弱部分误差影响之后，根据改进的估计双钟差的PPP算法，对原始观测值形成非组合观测值模型。
前期实验将智能手机和测地型接收机置于相同地点进行同步观测，收集原始观测值，并将智能手机原始观测值文件转换为Rinex格式文件。图2为相同时间相同卫星测地型接收机的原始观测值和智能手机(华为P10手机)的观测值对比。图中，接收机载波与伪距观测值重合在一起，实际上二者之差为载波相位的整周模糊度×载波波长。测地型接收机观测值在一段时间之后会有一个大幅度的跳动，这是接收机钟跳引起的，不影响原始观测值的定位解算。而可以明显地观察出手机伪距与载波相位观测值相互之间的差值不固定，这个性质与测 地型接收机是不相同的，影响手机终端载波相位数据的使用。
对同一设备不同卫星原始观测值进行分析，发现同一时刻不同卫星的伪距变化率和载波观测值变化率之间的差异是相同的，故在定位时认为手机终端伪距观测值和载波观测值具有不同的钟差。
根据以上性质改进定位方案，在参数求解时估计两个钟差参数。以GPS系统为例，单频观测方程为：
Figure PCTCN2020072411-appb-000018
Figure PCTCN2020072411-appb-000019
式中，上标g表示GPS系统，下标i表示第i颗卫星，
Figure PCTCN2020072411-appb-000020
为伪距和载波观测值，
Figure PCTCN2020072411-appb-000021
为手机端到卫星的距离，c为光速，
Figure PCTCN2020072411-appb-000022
分别为伪距和载波观测值的钟差，dTg为卫星钟差，
Figure PCTCN2020072411-appb-000023
为对流层延迟，
Figure PCTCN2020072411-appb-000024
为卫星轨道误差，
Figure PCTCN2020072411-appb-000025
为电离层延迟，
Figure PCTCN2020072411-appb-000026
分别为伪距和载波多路径效应延迟，
Figure PCTCN2020072411-appb-000027
为载波整周模糊度，
Figure PCTCN2020072411-appb-000028
分别伪距和载波相位观测值的残差。
实时下载IGS(International GNSS Service，国际GNSS服务组织)提供的预测精密星历、钟差文件和预测电离层格网文件，消除卫星轨道误差和钟差，削弱电离层延迟误差，并通过模型改正消除相对论效应和地球自转等误差影响，将多路径效应视作观测噪声后，简化观测值方程，得到非组合观测值模型：
Figure PCTCN2020072411-appb-000029
Figure PCTCN2020072411-appb-000030
Figure PCTCN2020072411-appb-000031
Figure PCTCN2020072411-appb-000032
Figure PCTCN2020072411-appb-000033
Figure PCTCN2020072411-appb-000034
式中，左侧为各个GNSS系统的伪距和载波观测值，上标e、c分别表示Galileo系统和BDS系统，下标j、k表示第j、k颗卫星，
Figure PCTCN2020072411-appb-000035
和
Figure PCTCN2020072411-appb-000036
分别表示Galileo和BDS系统与GPS系统之间的时间偏差。
第三步，确定各卫星观测值权重。本方法根据卫星高度角确定各卫星观测值权重，定权 方案具体为：
Figure PCTCN2020072411-appb-000037
式中，W为权重，E为手机端到卫星连线的高度角。
第四步，使用卡尔曼滤波进行参数求解，得到高精度定位结果。
对观测值形成第二步所示的非组合模型，此时，单个历元一颗卫星的观测方程数为2个，假设某历元观测到GPS卫星n 1颗，Galileo卫星n 2颗，BDS卫星n 3颗，此时卡尔曼滤波中待估参数向量为：
Figure PCTCN2020072411-appb-000038
式中，x、y和z为智能手机位置参数。
则观测方程总数为2×(n 1+n 2+n 3)个，待估参数为n 1+n 2+n 3+8个，多余观测数为n 1+n 2+n 2-8个。
本发明采用的卡尔曼滤波，具体步骤为：
(1)输入系统状态初始值初值X 0，及其方差P 0。X即为式(13)中的待估参数向量，下标0表示初始时刻，即设置待估参数的初值，此时前3个参数为智能手机近似坐标(通过伪距单点定位获得)，之后的参数分别为伪距钟差、载波钟差、系统间偏差、对流层湿延迟和n 1+n 2+n 3颗卫星的模糊度，全设为0。
(2)计算进一步预测值X k，k-1及预测值的方差-协方差阵P k，k-1(下标k为历元数)：
X k，k-1＝φ k，k-1X k-1      (14)
Figure PCTCN2020072411-appb-000039
式中，φ k，k-1为状态转移矩阵(因为卫星数量会改变，需要将上个历元的n阶X k-1变为这个历元m阶X k)。Γ k，k-1为系统噪声驱动阵，Q k-1为系统误差(模型的误差)为正定矩阵，每个历元都要进行输入，可以直接将Γ k，k-1Q k-1Γ T k，k-1看为一个整体，定为m阶对称矩阵。在首次定位是跟P 0一样，在之后历元的定位中，三个位置参数和n个模糊度参数真值应该不变，所以过程噪声为0(系统噪声对应的方差)，手机钟差、系统间偏差及对流层延迟参数的随机模型采用随机游走过程模型模拟。
(3)计算增益矩阵K k：
Figure PCTCN2020072411-appb-000040
式中，H k为观测方程的系数阵。相对于常规的静态卡尔曼滤波，本方法中的H k需要进行相应的修改：
Figure PCTCN2020072411-appb-000041
H k行数为2×(n 1+n 2+n 3)，列数为n 1+n 2+n 2+8，式中n＝2×(n 1+n 2+n 3)；下标表示卫星序号，上标表示GNSS系统类型；奇数行对应伪距观测值，偶数行对应载波相位观测值；前3列，α、β、γ为卫星-移动端的方向余弦，计算公式见式(18)；第4、5列为伪距观测值和载波观测值的钟差系数，奇数行第4列为1，偶数行第5列为1；第6、7列为系统间偏差系数，当观测值来自Galileo卫星时，第6列为1，当观测值来自BDS卫星时，第7列为1；第8列，MF为对流层湿延迟投影系数(对流层干延迟通过Hopfield模型改正，投影函数为Niell模型)。第9～n 1+n 2+n 2+8列为载波相位模糊度系数，第2×i行，i+8列为1，i为卫星序号。
Figure PCTCN2020072411-appb-000042
式中，x s、y s和z s为卫星坐标(通过星历文件获得)，x 0、y 0和z 0为智能手机近似坐标(通过伪距单点定位获得)，ρ 0为手机端到卫星的近似距离(通过卫星坐标和手机近似坐标计算)。
式(16)中R k为观测噪声方差矩阵：
Figure PCTCN2020072411-appb-000043
式中，σ P和σ Ф分别为伪距观测值和载波观测值的天顶方向标准差，W为各卫星权重，即式(12)计算的各卫星观测值权重。卡尔曼滤波过程中，观测值的方差设置非常重要，设置不当容易造成滤波发散，严重影响定位结果。本发明中，观测值天顶方向标准差根据先前实 验测得的智能手机原始GNSS观测值数据质量来确定，实施例所使用的小米8手机，伪距观测值和载波相位观测值天顶方向标准差分别设置为3.0m和0.2m。
(4)计算当前历元滤波参数估值向量X k和对应的方差-协方差阵P k：
X k＝X k，k-1+K k(L k-H kX k，k-1)       (20)
P k＝(I-K kH k)P k，k-1(I-K kH k) T+K kR kK k T       (21)
式中，L k为观测值向量，即式(6)～(11)中各GNSS系统的伪距和载波观测值，I为单位阵。
计算完成后，X k向量中前3项即为当前历元计算的智能手机精确坐标。若当前历元不是最后一个历元，则返回步骤(2)进行循环滤波计算，最终得到多历元滤波解。
实验例：
实验时间为2018.10.19，使用小米8智能手机，测试地点为东南大学校园内的GE01控制点，进行了5次观测，每次6分钟左右，采样间隔1s。此测站精确坐标已经通过网络RTK的方式事先得到。
使用本发明方法对小米8手机进行定位测试，结果如图3所示。在观测一段时间之后，5个时段数据的N、E方向误差都能收敛到1m以下。
统计5个时段的定位中误差，如表3所示。平均的平面中误差为0.81m，而平均高程中误差为1.65m，这个结果优于目前使用普通智能手机能够获得的最高定位精度。
表3实施例各时段定位中误差
Figure PCTCN2020072411-appb-000044
定义收敛时间为，从定位开始时刻到N和E方向定位误差均小于1m，且后续历元误差不再超过1m的时刻为止经过的时间。统计5个时段的收敛时间如表4所示，各时段均能在30s之内实现收敛，说明本方法能够应用于实时定位，提供低延迟的高精度智能手机定位结果。
表4实施例各时段收敛时间
Figure PCTCN2020072411-appb-000045
Figure PCTCN2020072411-appb-000046
基于相同的发明构思，本发明实施例公开的一种基于智能手机的高精度单点定位装置，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述计算机程序被加载至处理器时实现所述的一种基于智能手机的高精度单点定位方法。

Claims (6)

  1. 一种基于智能手机的高精度单点定位方法，其特征在于，包括以下步骤：
    (1)获取智能手机导航定位模块的GNSS伪距和载波相位原始观测值；
    (2)在进行数据预处理削弱部分误差影响之后，根据改进的估计双钟差的精密单点定位方法，对原始观测值形成非组合观测值模型；
    (3)根据卫星高度角确定各卫星观测值权重；
    (4)使用改进的静态卡尔曼滤波进行计算，得到高精度的单点定位结果。
  2. 根据权利要求1所述的基于智能手机的高精度单点定位方法，其特征在于，步骤(1)中，利用智能手机操作系统提供的基于位置服务的API直接获取部分GNSS原始数据，包括时间数据、GNSS系统类型和载波相位观测值，然后根据GNSS原始数据中的时间数据通过信号传播时间差值解算出伪距。
  3. 根据权利要求1所述的基于智能手机的高精度单点定位方法，其特征在于，步骤(2)中，所削弱的误差影响包括对接收到观测值使用精密星历、精密钟差文件消除轨道误差和卫星钟差，使用电离层格网文件削弱电离层延迟，以及将多路径效应视作观测噪声，所形成的非组合观测值模型为：
    Figure PCTCN2020072411-appb-100001
    Figure PCTCN2020072411-appb-100002
    Figure PCTCN2020072411-appb-100003
    Figure PCTCN2020072411-appb-100004
    Figure PCTCN2020072411-appb-100005
    Figure PCTCN2020072411-appb-100006
    式中，上标g、e、c分别表示GPS系统、Galileo系统和BDS系统，下标i、j、k表示第i、j、k颗卫星，左侧P、Φ分别为伪距和载波观测值，ρ为手机终端到卫星的距离，c为光速，
    Figure PCTCN2020072411-appb-100007
    分别为伪距和载波观测值的移动端钟差，d trop为对流层延迟，
    Figure PCTCN2020072411-appb-100008
    为载波整周模糊度，∈ P、∈ Φ分别为伪距和载波相位观测值的残差；
    Figure PCTCN2020072411-appb-100009
    和
    Figure PCTCN2020072411-appb-100010
    分别表示Galileo和BDS系统与GPS系统之间的时间偏差。
  4. 根据权利要求1所述的基于智能手机的高精度单点定位方法，其特征在于，步骤(3)中，根据卫星高度角确定各卫星观测值权重的定权方案具体为：
    当卫星高度角小于10°时，该卫星观测值权重为0；
    当卫星高度角大于10°时，该卫星观测值权重为sinE，E为卫星高度角。
  5. 根据权利要求1所述的基于智能手机的高精度单点定位方法，其特征在于，步骤(4)中，改进的静态卡尔曼滤波具体为：
    对于单个历元，根据步骤(2)所述的非组合观测值模型，一颗卫星的观测方程数为2个，设某历元观测到GPS卫星n 1颗，Galileo卫星n 2颗，BDS卫星n 3颗，满足n 1+n 2+n 3≥8；此时卡尔曼滤波中待估参数向量为：
    Figure PCTCN2020072411-appb-100011
    式中，x、y和z为智能手机位置参数；则观测方程总数为2×(n 1+n 2+n 3)个，待估参数为n 1+n 2+n 3+8个，多余观测数为n 1+n 2+n 3-8个；
    卡尔曼滤波的观测方程系数阵H为：
    Figure PCTCN2020072411-appb-100012
    矩阵H中，行数为2×(n 1+n 2+n 3)，列数为n 1+n 2+n 3-8，下标1至n表示卫星序号，n＝2×(n 1+n 2+n 3)，上标表示GNSS系统类型，g、e、c分别表示GPS系统、Galileo系统和BDS系统；奇数行对应伪距观测值，偶数行对应载波相位观测值；前3列α、β、γ为卫星-移动端的方向余弦，第4、5列为伪距观测值和载波观测值的钟差系数，奇数行第4列为1，偶数行第5列为1；第6、7列为系统间偏差系数，当观测值来自Galileo卫星时，第6列为1，当观测值来自BDS卫星时，第7列为1；MF为对流层湿延迟投影系数；第9列之后为载波相位模糊度系数，奇数行为0，偶数行第i+8列为1，i为卫星序号；滤波过程中各卫星观测值的权重由步骤(3)确定。
  6. 一种基于智能手机的高精度单点定位装置，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述计算机程序被加载至处理器时实现根据权利要求1-5任一项所述的一种基于智能手机的高精度单点定位方法。
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