[go: up one dir, main page]

CN114779303A - RTK positioning method based on fusion of geometric model algorithm and non-geometric model algorithm - Google Patents

RTK positioning method based on fusion of geometric model algorithm and non-geometric model algorithm Download PDF

Info

Publication number
CN114779303A
CN114779303A CN202210256583.9A CN202210256583A CN114779303A CN 114779303 A CN114779303 A CN 114779303A CN 202210256583 A CN202210256583 A CN 202210256583A CN 114779303 A CN114779303 A CN 114779303A
Authority
CN
China
Prior art keywords
satellite
geometric model
representing
parameters
user
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
CN202210256583.9A
Other languages
Chinese (zh)
Other versions
CN114779303B (en
Inventor
王克志
杨开伟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CETC 54 Research Institute
Original Assignee
CETC 54 Research Institute
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 CETC 54 Research Institute filed Critical CETC 54 Research Institute
Priority to CN202210256583.9A priority Critical patent/CN114779303B/en
Publication of CN114779303A publication Critical patent/CN114779303A/en
Application granted granted Critical
Publication of CN114779303B publication Critical patent/CN114779303B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G01S19/38—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
    • G01S19/38—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
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering

Landscapes

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

Abstract

本发明涉及GNSS卫星RTK定位领域，具体涉及一种基于几何模型算法和无几何模型算法融合的RTK定位方法。实现过程为：对用户的位置参数进行卡尔曼滤波建模，建立几何模型GB；对卫星的状态参数进行卡尔曼滤波建模，建立无几何模型GF；根据卫星载波和伪距观测值对无几何模型GF中的卫星状态参数进行滤波更新；建立几何模型GB的位置参数与无几何模型GF的卫星状态参数之间的联系；进行滤波更新或最小二乘求解，得到用户的位置参数。本发明可以避免质量较差卫星对位置参数解算的影响，对观测质量不佳的卫星及时剔除，且不会对用户状态参数的几何模型产生影响。

Figure 202210256583

The invention relates to the field of GNSS satellite RTK positioning, in particular to an RTK positioning method based on the fusion of a geometric model algorithm and a geometric model-free algorithm. The realization process is: perform Kalman filter modeling on the user's position parameters to establish a geometric model GB; perform Kalman filter modeling on the satellite's state parameters to establish a geometry-free model GF; The satellite state parameters in the model GF are filtered and updated; the relationship between the position parameters of the geometric model GB and the satellite state parameters of the non-geometric model GF is established; the filter update or the least squares solution is performed to obtain the user's position parameters. The present invention can avoid the influence of satellites with poor quality on the calculation of position parameters, promptly eliminate satellites with poor observation quality, and will not affect the geometric model of user state parameters.

Figure 202210256583

Description

基于几何模型算法和无几何模型算法融合的RTK定位方法RTK positioning method based on fusion of geometric model algorithm and geometric model-free algorithm

技术领域technical field

本发明涉及GNSS卫星RTK定位领域，具体涉及一种基于几何模型算法和无几何模型算法融合的RTK定位方法。The invention relates to the field of GNSS satellite RTK positioning, in particular to an RTK positioning method based on the fusion of a geometric model algorithm and a geometric model-free algorithm.

背景技术Background technique

目前，GNSS卫星数量和频点日渐增多，终端可以采用更多的导航卫星信息进行RTK定位，如此多的多余观测信息使得用户获得高精度和高可靠性的定位结果称为可能。但是终端的计算能力有限并不能也不必完全处理所有观测信息，所以需要甄别不同卫星的观测质量，择优挑选进行用户位置解算。At present, the number and frequency of GNSS satellites are increasing day by day, and terminals can use more navigation satellite information for RTK positioning. So much redundant observation information makes it possible for users to obtain high-precision and high-reliability positioning results. However, the computing power of the terminal is limited, and it is not necessary to completely process all the observation information, so it is necessary to screen the observation quality of different satellites, and select the best one for user location calculation.

利用GNSS卫星进行RTK定位解算的算法分为几何模型算法(GB)和无几何模型算法(GF)。几何模型算法直接以用户坐标为滤波参数，但是GNSS卫星组成的双差观测值高度耦合，不能避免质量较差卫星对位置参数解算的影响；无几何模型算法以卫星状态为滤波参数，可以择优选择质量较高的卫星参与解算，但是没有对用户坐标参数进行建模。The algorithm of RTK positioning solution using GNSS satellites is divided into geometric model algorithm (GB) and geometric model algorithm (GF). The geometric model algorithm directly uses the user coordinates as the filter parameter, but the double-difference observations composed of GNSS satellites are highly coupled, which cannot avoid the influence of poor quality satellites on the position parameter calculation; the non-geometry model algorithm uses the satellite state as the filter parameter, which can select the best A higher quality satellite is selected to participate in the solution, but the user coordinate parameters are not modeled.

基于以上原因，提出基于几何模型算法和无几何模型算法融合的RTK定位算法，既兼顾几何模型算法可以对用户坐标参数进行建模，又可以兼顾无几何模型算法择优选择质量较高的卫星参数解算，从而有效处理多模多频GNSS卫星的情况。Based on the above reasons, an RTK positioning algorithm based on the fusion of the geometric model algorithm and the geometric model algorithm is proposed, which not only takes into account the geometric model algorithm that can model the user coordinate parameters, but also takes into account the geometric model algorithm. It can effectively handle the situation of multi-mode and multi-frequency GNSS satellites.

发明内容SUMMARY OF THE INVENTION

本发明的目的是：提供一种基于几何模型算法和无几何模型算法融合的RTK定位方法，从而有效处理多模多频GNSS卫星的情况。The purpose of the present invention is to provide an RTK positioning method based on the fusion of a geometric model algorithm and a geometric model-free algorithm, so as to effectively handle the situation of multi-mode and multi-frequency GNSS satellites.

为了达到上述目的，本发明采用以下技术方案：In order to achieve the above object, the present invention adopts the following technical solutions:

一种基于几何模型算法和无几何模型算法融合的RTK定位方法，包含以下步骤：An RTK positioning method based on the fusion of a geometric model algorithm and a geometric model-free algorithm, comprising the following steps:

步骤一、对用户的位置参数进行卡尔曼滤波建模，建立几何模型GB；Step 1. Perform Kalman filter modeling on the user's position parameters, and establish a geometric model GB;

步骤二、对卫星的状态参数进行卡尔曼滤波建模，建立无几何模型GF；Step 2: Perform Kalman filter modeling on the state parameters of the satellite to establish a geometry-free model GF;

步骤三、根据卫星载波和伪距观测值对无几何模型GF中的卫星状态参数进行滤波更新；Step 3, filter and update the satellite state parameters in the geometry-free model GF according to the satellite carrier and the pseudorange observations;

步骤四、建立几何模型GB的位置参数与无几何模型GF的卫星状态参数之间的联系，得到卫星之间站间单差卫地距参数与用户基线参数之间的关系表达式；Step 4, establishing the relationship between the position parameter of the geometric model GB and the satellite state parameter without the geometric model GF, and obtaining the relationship expression between the single-difference satellite-to-ground distance parameter between the satellites and the user baseline parameter;

步骤五、挑选观测质量高于阈值的卫星状态参数作为几何模型GB中用户位置参数的观测值，对步骤四所得表达式进行滤波更新或最小二乘求解，得到用户的位置参数。Step 5: Select the satellite state parameters whose observation quality is higher than the threshold as the observed value of the user location parameter in the geometric model GB, and filter and update the expression obtained in step 4 or solve the least squares to obtain the user location parameter.

进一步的，步骤四具体实现过程为：Further, the specific implementation process of step 4 is:

需要最终求解的用户基线参数为：The user baseline parameters that need to be finally solved are:

X＝[ΔX，ΔY，ΔZ，ΔTru，w]X=[ΔX, ΔY, ΔZ, ΔT ru, w ]

其中，ΔX，ΔY，ΔZ分别表示用户基线坐标分量，ΔTru，w表示站间单差对流层湿分量；Among them, ΔX, ΔY, and ΔZ represent the user baseline coordinate components, respectively, and ΔT ru, w represent the inter-station single-difference tropospheric humidity component;

卫星之间站间单差卫地距参数与用户基线参数之间的关系表达式为：The relationship expression between the single-difference satellite-to-ground distance parameters between satellites and the user baseline parameters is:

Figure BDA0003548789630000021
Figure BDA0003548789630000021

其中：in:

Figure BDA0003548789630000022
表示双差卫地距参数，j表示参考卫星，k表示其他卫星，r表示基准站，u表示移动站，
Figure BDA0003548789630000023
和
Figure BDA0003548789630000024
分别表示卫星k和卫星j的站间单差卫地距；
Figure BDA0003548789630000022
Indicates the double-difference satellite ground distance parameter, j represents the reference satellite, k represents other satellites, r represents the reference station, u represents the mobile station,
Figure BDA0003548789630000023
and
Figure BDA0003548789630000024
represent the single-difference satellite-to-ground distance between satellite k and satellite j, respectively;

Figure BDA0003548789630000031
和
Figure BDA0003548789630000032
分别表示卫星j和卫星k卫地距对用户基线坐标参数ΔX的偏导，Xk表示卫星k三维X坐标，Xj表示卫星j三维X坐标，Xu表示用户位置三维X坐标；
Figure BDA0003548789630000031
and
Figure BDA0003548789630000032
respectively represent the partial derivation of the satellite j and satellite k satellite-to-ground distances to the user baseline coordinate parameter ΔX, X k represents the three-dimensional X coordinate of satellite k, X j represents the three-dimensional X coordinate of satellite j, and X u represents the three-dimensional X coordinate of the user position;

Figure BDA0003548789630000033
和
Figure BDA0003548789630000034
分别表示卫星j和卫星k卫地距对用户基线坐标参数ΔY的偏导，Yk表示卫星k三维Y坐标，Yj表示卫星j三维Y坐标，Yu表示用户位置三维Y坐标；
Figure BDA0003548789630000033
and
Figure BDA0003548789630000034
respectively represent the partial derivation of satellite j and satellite k satellite-to-ground distance to the user baseline coordinate parameter ΔY, Y k represents the three-dimensional Y coordinate of satellite k, Y j represents the three-dimensional Y coordinate of satellite j, and Yu represents the three-dimensional Y coordinate of the user position;

Figure BDA0003548789630000035
和
Figure BDA0003548789630000036
分别表示卫星j和卫星k卫地距对用户基线坐标参数ΔZ的偏导，Zk表示卫星k三维Z坐标，Zj表示卫星j三维Z坐标，Zu表示用户位置三维Z坐标；
Figure BDA0003548789630000035
and
Figure BDA0003548789630000036
respectively represent the partial derivation of the satellite j and satellite k satellite-to-ground distances to the user baseline coordinate parameter ΔZ, Z k represents the three-dimensional Z coordinate of satellite k, Z j represents the three-dimensional Z coordinate of satellite j, and Z u represents the three-dimensional Z coordinate of the user position;

Figure BDA0003548789630000037
和
Figure BDA0003548789630000038
分别表示卫星j和卫星k对流层湿延迟映射函数。进一步的，步骤五中，若采用最小二乘方法，则根据下式求解位置参数：
Figure BDA0003548789630000037
and
Figure BDA0003548789630000038
are the tropospheric wet delay mapping functions for satellite j and satellite k, respectively. Further, in step 5, if the least squares method is adopted, the position parameter is solved according to the following formula:

X＝(BTPB)-1(BTPl)X=(B T PB) -1 (B T Pl)

其中，B为所挑选卫星所列观测方程组成的系数矩阵，P为双差卫地距参数

Figure BDA0003548789630000039
的方差阵的逆，香为所挑选卫星所列观测方程的残差向量。Among them, B is the coefficient matrix composed of the observation equations listed by the selected satellite, and P is the double-difference satellite-ground distance parameter
Figure BDA0003548789630000039
The inverse of the variance matrix of , is the residual vector of the observation equation listed for the selected satellite.

本发明技术具有以下优点：The technology of the present invention has the following advantages:

1、本发明考虑了传统基于几何模型(GB)算法中对用户坐标参数进行建模的优点，可以兼顾传统模型算法。1. The present invention takes into account the advantages of modeling user coordinate parameters in the traditional geometric model-based (GB) algorithm, and can take into account the traditional model algorithm.

2、本发明在此基础上进一步考虑了无几何模型(GF)算法对卫星有关的状态参数进行建模的优点，可以避免质量较差卫星对位置参数解算的影响，对观测质量不佳的卫星及时剔除，且不会对用户状态参数的几何模型(GB)产生影响。2. On this basis, the present invention further considers the advantages of the geometry-free model (GF) algorithm for modeling satellite-related state parameters, which can avoid the influence of satellites with poor quality on the calculation of position parameters, and the poor observation quality. The satellites are eliminated in time without affecting the geometric model (GB) of the user state parameters.

3、本发明适应导航卫星多模多频GNSS的发展趋势，用户可以根据终端算力自由挑选卫星和频率进行解算。3. The present invention adapts to the development trend of multi-mode and multi-frequency GNSS of navigation satellites, and users can freely select satellites and frequencies for calculation according to the computing power of the terminal.

附图说明Description of drawings

图1是本发明实施例一种基于几何模型算法和无几何模型算法融合的RTK定位方法流程图。FIG. 1 is a flowchart of an RTK positioning method based on the fusion of a geometric model algorithm and a geometric model-free algorithm according to an embodiment of the present invention.

具体实施方式Detailed ways

下面结合附图对本发明作进一步解释说明。The present invention will be further explained below in conjunction with the accompanying drawings.

一种基于几何模型算法和无几何模型算法融合的RTK定位方法，步骤如下：An RTK positioning method based on the fusion of a geometric model algorithm and a geometric model-free algorithm, the steps are as follows:

步骤一、对用户的位置参数进行卡尔曼滤波建模，主要建立几何模型(GB)加以维护和更新。对用户的位置信息建立如下参数：Step 1: Perform Kalman filter modeling on the user's position parameters, and mainly establish a geometric model (GB) to maintain and update. The following parameters are established for the user's location information:

XGB＝[ΔX，ΔY，ΔZ，Vx，Vy，Vz，ΔTru，w]T X GB = [ΔX, ΔY, ΔZ, V x , V y , V z , ΔT ru, w ] T

其中，ΔX，ΔY，ΔZ分别表示用户基线坐标分量，Vx，Vy，Vz分别表示用户速度分量，ΔTru，w表示站间单差对流层湿分量。Among them, ΔX, ΔY, ΔZ represent the user baseline coordinate component, V x , V y , V z represent the user velocity component, respectively, ΔT ru, w represent the inter-station single-difference tropospheric humidity component.

用户位置参数状态转移方程表示为：The user position parameter state transition equation is expressed as:

XGB(k)＝ΦGB(k，k-1)·XGB(k-1)，De，GB(k-1)X GB (k) = Φ GB (k, k-1) X GB (k-1), De , GB (k-1)

其中，ΦGB(k，k-1)为状态转移矩阵，具体形式如下；De，GB(k-1)表示状态转移过程噪声。Among them, Φ GB (k, k-1) is the state transition matrix, the specific form is as follows; De, GB (k-1) represents the state transition process noise.

Figure BDA0003548789630000041
Figure BDA0003548789630000041

Δt表示k-1时刻到k时刻的差值。Δt represents the difference from time k-1 to time k.

步骤二、为卫星的状态参数进行卡尔曼滤波建模，主要建立无几何模型(GF)加以维护和更新。对卫星的状态信息建立如下参数：The second step is to carry out Kalman filter modeling for the state parameters of the satellite, mainly to establish a geometry-free model (GF) to maintain and update. The following parameters are established for the status information of the satellite:

Figure BDA0003548789630000051
Figure BDA0003548789630000051

其中，XGF为所有卫星状态参数，

Figure BDA0003548789630000052
为任一卫星状态参数，卫星个数为m，每颗卫星的状态参数表示为：Among them, X GF is all satellite state parameters,
Figure BDA0003548789630000052
is any satellite state parameter, the number of satellites is m, and the state parameter of each satellite is expressed as:

Figure BDA0003548789630000053
Figure BDA0003548789630000053

其中，r表示基准站，u表示移动站，s表示任一卫星，

Figure BDA0003548789630000054
表示站间单差卫地距，
Figure BDA0003548789630000055
表示站间单差电离层，
Figure BDA0003548789630000056
表示站间单差L1模糊度，
Figure BDA0003548789630000057
表示站间单差宽巷模糊度，
Figure BDA0003548789630000058
表示站间单差超宽巷模糊度。Among them, r represents the base station, u represents the mobile station, s represents any satellite,
Figure BDA0003548789630000054
Indicates the single-difference-to-ground distance between stations,
Figure BDA0003548789630000055
represents the inter-station single-difference ionosphere,
Figure BDA0003548789630000056
represents the inter-station single difference L1 ambiguity,
Figure BDA0003548789630000057
represents the single-difference wide-lane ambiguity between stations,
Figure BDA0003548789630000058
Indicates the ambiguity of the single-difference ultra-wide lane between stations.

卫星状态参数状态转移方程表示为：The state transition equation of satellite state parameters is expressed as:

XGF(k)＝ΦGF(k，k-1)·XGF(k-1)，De，GF(k-1)X GF (k) = Φ GF (k, k-1) · X GF (k-1), De , GF (k-1)

其中，ΦGF(k，k-1)为状态转移矩阵，这里ΦGF(k，k-1)＝I；De，GF(k-1)表示状态转移过程噪声。Among them, Φ GF (k, k-1) is the state transition matrix, where Φ GF (k, k-1)=I; De, GF (k-1) represents the state transition process noise.

步骤三、根据卫星载波和伪距观测值对步骤二中的卫星状态参数进行滤波更新。Step 3: Filter and update the satellite state parameter in Step 2 according to the satellite carrier and the pseudorange observation value.

载波和伪距观测值表示为：The carrier and pseudorange observations are expressed as:

Figure BDA0003548789630000059
Figure BDA0003548789630000059

其中，j表示参考卫星，k表示其他卫星，i表示频率编号(i＝1，w，e)，w表示宽巷，e表示超宽巷，

Figure BDA00035487896300000510
表示任一频率i的双差伪距观测值，
Figure BDA00035487896300000511
为该伪距观测值观测噪声，
Figure BDA00035487896300000512
表示任一频率i的双差载波观测值，
Figure BDA00035487896300000513
为该载波观测值观测噪声，λi表示任意频率i观测值波长，
Figure BDA00035487896300000514
Among them, j represents the reference satellite, k represents other satellites, i represents the frequency number (i=1, w, e), w represents the wide lane, e represents the ultra-wide lane,
Figure BDA00035487896300000510
represents the double-difference pseudorange observations at any frequency i,
Figure BDA00035487896300000511
Observe the noise for this pseudorange observation,
Figure BDA00035487896300000512
represents the double-difference carrier observation at any frequency i,
Figure BDA00035487896300000513
is the observation noise for the carrier observation, λ i represents the wavelength of the observation at any frequency i,
Figure BDA00035487896300000514

步骤四、建立步骤一几何模型(GB)位置参数与步骤二无几何模型(GF)状态参数之间的联系。Step 4: Establish the relationship between the position parameters of the geometric model (GB) in step 1 and the state parameters of the geometric model (GF) in step 2.

需要最终求解的用户基线参数为：The user baseline parameters that need to be finally solved are:

X＝[ΔX，ΔY，ΔZ，ΔTru，w]X=[ΔX, ΔY, ΔZ, ΔT ru, w ]

在步骤三中得到的两颗卫星j，k的站间单差卫地距参数与用户基线参数之间的关系表示为：The relationship between the inter-station single-difference satellite-to-ground distance parameters of the two satellites j, k obtained in step 3 and the user baseline parameters is expressed as:

Figure BDA0003548789630000061
Figure BDA0003548789630000061

其中：in:

Figure BDA0003548789630000062
表示双差卫地距参数；
Figure BDA0003548789630000062
Indicates the double-difference satellite ground distance parameter;

Figure BDA0003548789630000063
和
Figure BDA0003548789630000064
分别表示卫星j，k卫地距对用户基线坐标参数ΔX的偏导，Xk表示卫星k三维X坐标，Xj表示卫星j三维X坐标，Xu表示用户位置三维X坐标；
Figure BDA0003548789630000063
and
Figure BDA0003548789630000064
Respectively represent the partial derivation of satellite j, k satellite-to-ground distance to the user baseline coordinate parameter ΔX, X k represents the three-dimensional X coordinate of satellite k, X j represents the three-dimensional X coordinate of satellite j, and X u represents the three-dimensional X coordinate of the user position;

Figure BDA0003548789630000065
和
Figure BDA0003548789630000066
分别表示卫星j，k卫地距对用户基线坐标参数ΔY的偏导，Yk表示卫星k三维Y坐标，Yj表示卫星j三维Y坐标，Yu表示用户位置三维Y坐标；
Figure BDA0003548789630000065
and
Figure BDA0003548789630000066
respectively represent the partial derivation of satellite j, k satellite-to-ground distance to the user baseline coordinate parameter ΔY, Y k represents the three-dimensional Y coordinate of satellite k, Y j represents the three-dimensional Y coordinate of satellite j, and Yu represents the three-dimensional Y coordinate of the user position;

Figure BDA0003548789630000067
和
Figure BDA0003548789630000068
分别表示卫星j，k卫地距对用户基线坐标参数ΔZ的偏导，Zk表示卫星k三维Z坐标，Zj表示卫星j三维Z坐标，Zt表示用户位置三维Z坐标；
Figure BDA0003548789630000067
and
Figure BDA0003548789630000068
respectively represent the partial derivation of satellite j, k satellite-to-ground distance to the user baseline coordinate parameter ΔZ, Z k represents the three-dimensional Z coordinate of satellite k, Z j represents the three-dimensional Z coordinate of satellite j, and Z t represents the three-dimensional Z coordinate of the user position;

Figure BDA0003548789630000069
和
Figure BDA00035487896300000610
分别表示卫星j，k对流层湿延迟映射函数。
Figure BDA0003548789630000069
and
Figure BDA00035487896300000610
are the tropospheric wet delay mapping functions for satellites j and k, respectively.

步骤五、根据步骤三可以获得所有卫星的卫地距状态参数，但由于GNSS导航卫星较多，用户可以挑选观测质量较高卫星的状态参数作为步骤一几何模型(GB)中用户位置参数的观测值，对其进行滤波更新或最小二乘求解。Step 5. According to Step 3, the satellite-to-ground distance state parameters of all satellites can be obtained. However, due to the large number of GNSS navigation satellites, the user can select the state parameters of the satellites with higher observation quality as the observation of the user's position parameters in the geometric model (GB) of Step 1. value, perform a filter update or least squares solution on it.

采用步骤四所列观测方程，对其进行卡尔曼滤波更新；若采用最小二乘方法，根据下式求解位置参数：The observation equation listed in step 4 is used to update it by Kalman filter; if the least squares method is used, the position parameter is solved according to the following formula:

X＝(BTPB)-1(BTPl)X=(B T PB) -1 (B T Pl)

其中，B为所挑选卫星所列观测方程组成的系数矩阵，P为双差卫地距参数

Figure BDA0003548789630000071
的方差阵的逆，香为所挑选卫星所列观测方程的残差向量。Among them, B is the coefficient matrix composed of the observation equations listed by the selected satellite, and P is the double-difference satellite-ground distance parameter
Figure BDA0003548789630000071
The inverse of the variance matrix of , is the residual vector of the observation equation listed for the selected satellite.

由此解算基线向量X后，根据基准站坐标进一步得到流动站用户的最终位置坐标。After solving the baseline vector X, the final position coordinates of the rover user are further obtained according to the coordinates of the reference station.

Claims (3)

1. An RTK positioning method based on fusion of a geometric model algorithm and a non-geometric model algorithm is characterized by comprising the following steps:
firstly, performing Kalman filtering modeling on position parameters of a user, and establishing a geometric model GB;
secondly, performing Kalman filtering modeling on state parameters of the satellite to establish a non-geometric model GF;
step three, filtering and updating satellite state parameters in the non-geometric model GF according to the satellite carrier and the pseudo-range observation values;
establishing a relation between the position parameters of the geometric model GB and the satellite state parameters of the non-geometric model GF to obtain a relational expression between the inter-station single-difference satellite distance parameters and the user baseline parameters;
and step five, selecting satellite state parameters with observation quality higher than a threshold value as observed values of user position parameters in the geometric model GB, and performing filtering updating or least square solving on the expression obtained in the step four to obtain the user position parameters.
2. An RTK positioning method based on the fusion of geometric model algorithm and non-geometric model algorithm according to claim 1, characterized in that the step four is realized by the following steps:
the user baseline parameters that need to be finally solved are:
X＝[ΔX，ΔY，ΔZ，ΔTru，w]
where Δ X, Δ Y, Δ Z represent the user baseline coordinate components, Δ T, respectivelyru，wRepresenting the single difference tropospheric wet component between stations;
the relational expression between the inter-station single-difference satellite distance parameters and the user baseline parameters is as follows:
Figure FDA0003548789620000021
wherein:
Figure FDA0003548789620000022
representing a double-difference range parameter, j representing a reference satellite, k representing other satellites, r representing a reference station, u representing a mobile station,
Figure FDA0003548789620000023
and
Figure FDA0003548789620000024
respectively representing the single difference between stations of the satellite k and the satellite j;
Figure FDA0003548789620000025
Figure FDA0003548789620000026
and
Figure FDA0003548789620000027
respectively representing the partial derivatives of satellite j and satellite k satellite range to user baseline coordinate parameter delta X, XkRepresenting the three-dimensional X coordinate, X, of satellite kjRepresenting the three-dimensional X coordinate, X, of the satellite juRepresenting a three-dimensional X coordinate of the user position;
Figure FDA0003548789620000028
Figure FDA0003548789620000029
and
Figure FDA00035487896200000210
respectively representing the partial derivatives of satellite j and satellite k-satellite distance to user baseline coordinate parameter delta YkRepresenting the three-dimensional Y coordinate of satellite k, YjRepresenting the three-dimensional Y coordinate, Y, of satellite juA three-dimensional Y coordinate representing a user position;
Figure FDA00035487896200000211
Figure FDA00035487896200000212
and
Figure FDA00035487896200000213
respectively representing the partial derivatives of satellite j and satellite k-satellite distance to user baseline coordinate parameter delta ZkRepresenting the three-dimensional Z coordinate of satellite k, ZjRepresenting the three-dimensional Z coordinate, Z, of the satellite juThree-dimensional Z coordinates representing the user's position;
Figure FDA00035487896200000214
Figure FDA00035487896200000215
and
Figure FDA00035487896200000216
representing the tropospheric wet delay mapping functions for satellite j and satellite k, respectively.
3. An RTK positioning method based on the fusion of geometric model algorithm and non-geometric model algorithm according to claim 2 characterized by that in step five, if the least square method is used, the position parameters are solved according to the following formula:
X＝(BTPB)-1(BTPl)
wherein B is a coefficient matrix formed by observation equations listed by the selected satellites, and P is a double-difference range parameter
Figure FDA0003548789620000031
Is the inverse of the variance matrix of (a), l is the residual vector of the observation equation listed for the chosen satellite.
CN202210256583.9A 2022-03-16 2022-03-16 RTK positioning method based on the fusion of geometric model algorithm and non-geometric model algorithm Active CN114779303B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210256583.9A CN114779303B (en) 2022-03-16 2022-03-16 RTK positioning method based on the fusion of geometric model algorithm and non-geometric model algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210256583.9A CN114779303B (en) 2022-03-16 2022-03-16 RTK positioning method based on the fusion of geometric model algorithm and non-geometric model algorithm

Publications (2)

Publication Number Publication Date
CN114779303A true CN114779303A (en) 2022-07-22
CN114779303B CN114779303B (en) 2024-11-08

Family

ID=82426191

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210256583.9A Active CN114779303B (en) 2022-03-16 2022-03-16 RTK positioning method based on the fusion of geometric model algorithm and non-geometric model algorithm

Country Status (1)

Country Link
CN (1) CN114779303B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008008099A2 (en) * 2006-03-07 2008-01-17 Trimble Navigation Limited Gnss signal processing methods and apparatus
CN103197326A (en) * 2013-03-25 2013-07-10 东南大学 Multi-constellation single base station receiver clock difference estimation method
CN109581455A (en) * 2019-01-24 2019-04-05 东南大学 A kind of three bandwidth lane tight integration localization methods of BDS and GPS fusion
JP2021525373A (en) * 2018-06-04 2021-09-24 北京未来導航科技有限公司 Navigation reinforcement methods and systems
CN114002719A (en) * 2021-10-12 2022-02-01 广州市城市规划勘测设计研究院 Single-frequency dual-mode multi-antenna fusion RTK positioning method, device, equipment and medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008008099A2 (en) * 2006-03-07 2008-01-17 Trimble Navigation Limited Gnss signal processing methods and apparatus
CN103197326A (en) * 2013-03-25 2013-07-10 东南大学 Multi-constellation single base station receiver clock difference estimation method
JP2021525373A (en) * 2018-06-04 2021-09-24 北京未来導航科技有限公司 Navigation reinforcement methods and systems
CN109581455A (en) * 2019-01-24 2019-04-05 东南大学 A kind of three bandwidth lane tight integration localization methods of BDS and GPS fusion
CN114002719A (en) * 2021-10-12 2022-02-01 广州市城市规划勘测设计研究院 Single-frequency dual-mode multi-antenna fusion RTK positioning method, device, equipment and medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘扬;程鹏飞;徐彦田;: "BDS/GPS非差多频实时动态定位算法研究", 测绘科学, no. 12, 11 September 2018 (2018-09-11) *

Also Published As

Publication number Publication date
CN114779303B (en) 2024-11-08

Similar Documents

Publication Publication Date Title
CN106970404B (en) Multi-redundancy network RTK atmospheric error interpolation method based on Delaunay triangulation network
CN107797126B (en) BDS/GPS broadcast type network RTK algorithm based on star network
CN105842721B (en) The method for improving medium-long baselines GPS Carrier Phase Ambiguity Resolution success rates
CN101464149B (en) POS-assisted aerial image matching method
CN108828626B (en) Network RTK ionosphere delay interpolation method and system based on real-time grid
CN105629279B (en) A kind of wide lane ambiguity fixing means between Fiducial station of the network
CN104680008B (en) A kind of network RTK regional atmospheric error modeling methods based on many reference stations
CN111239787A (en) GNSS dynamic Kalman filtering method in cluster autonomous coordination
CN108317949A (en) A kind of RTK high-precision differences positioning deformation monitoring system and method
CN106338738A (en) Pierce point-based regional augmentation system real-time ionosphere modeling method
WO2019218766A1 (en) Inertial navigation assisted beidou triple-frequency carrier phase whole-cycle ambiguity resolution method
CN113805212B (en) Self-adaptive GNSS carrier phase differential landslide monitoring method
CN106772478A (en) The localization method of difference constraint between a kind of star based on epoch
CN112991436B (en) Monocular vision SLAM method based on object size prior information
CN115856973A (en) GNSS calculation method, device, positioning system, electronic equipment and storage medium
CN110646822A (en) A Kalman Filtering Algorithm for Integer Ambiguity Based on Inertial Navigation Aid
CN109669196A (en) A kind of multiple antennas GNSS carrier phase precision survey attitude positioning method for taking baseline deformation into account
Li et al. Enhancing RTK performance in urban environments by tightly integrating INS and LiDAR
CN115144878A (en) Short-distance large-altitude-difference NRTK troposphere delay correction method based on PPP
CN114779303B (en) RTK positioning method based on the fusion of geometric model algorithm and non-geometric model algorithm
Xia et al. Accurate and rapidly-convergent GNSS/INS/LiDAR tightly-coupled integration via invariant EKF based on two-frame group
CN105929430A (en) GNSS (global navigation satellite system) zero-baseline inter-reference station ambiguity quick fixation method
CN104035112B (en) Utilize the method for satellite navigation location under the auxiliary urban environment of virtual elevation model
CN115578447A (en) Carbon block polishing method and device
CN114739404A (en) High-precision positioning method and device and positioning system of hot-line work robot

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant