CN104502935B - A kind of network RTK Ambiguity Solution Methods based on the non-combined model of non-difference - Google Patents
A kind of network RTK Ambiguity Solution Methods based on the non-combined model of non-difference Download PDFInfo
- Publication number
- CN104502935B CN104502935B CN201410837313.2A CN201410837313A CN104502935B CN 104502935 B CN104502935 B CN 104502935B CN 201410837313 A CN201410837313 A CN 201410837313A CN 104502935 B CN104502935 B CN 104502935B
- Authority
- CN
- China
- Prior art keywords
- difference
- ambiguity
- satellite
- delta
- station
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 239000011159 matrix material Substances 0.000 claims description 17
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 238000013461 design Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 4
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005295 random walk Methods 0.000 description 1
- 238000005309 stochastic process Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
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
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
本发明公布了一种基于非差非组合模型的网络RTK模糊度解算方法。使用非差非组合模型对全网的单站非差模糊度进行估计,并将卫星钟作为未知参数,实时卫星钟差产品作为伪观测量加入。根据用户位置,提取用户相邻站点的非差模糊度,在相邻站点中选择一个作为基准站点,利用差分算子还原得到双差模糊度的整数特性,单历元获得具有整数特性的双差模糊度结果。使用本发明提出的方法,在大规模的基准站网络中可有效提升计算性能,同时减弱大气误差和长度对双差模糊度固定的影响,降低双差模型的病态性,提高模型抗差能力和模糊度固定成功率。
The invention discloses a network RTK ambiguity resolution method based on a non-difference and non-combination model. The undifferenced uncombined model is used to estimate the undifferenced ambiguity of the whole network, and the satellite clock is used as an unknown parameter, and the real-time satellite clock difference product is added as a pseudo-observation. According to the user's location, extract the non-difference ambiguity of the user's adjacent sites, select one of the adjacent sites as the reference site, and use the difference operator to restore the integer characteristics of the double-difference ambiguity, and obtain the double-difference with integer characteristics in a single epoch Blur results. Using the method proposed by the present invention can effectively improve the calculation performance in a large-scale reference station network, and at the same time weaken the influence of atmospheric errors and lengths on the fixed double-difference ambiguity, reduce the ill-conditionedness of the double-difference model, and improve the model's ability to resist errors and Ambiguity fixed success rate.
Description
技术领域technical field
本发明涉及全球导航卫星系统(GNSS)卫星定位领域,特别涉及GNSS非差非组合精密单点定位。The invention relates to the field of global navigation satellite system (GNSS) satellite positioning, in particular to GNSS non-difference and non-combined precise single point positioning.
背景技术Background technique
随着基准站数量的逐渐增多以及多系统多频信号的兼容,大范围CORS(Continuous Operational Reference System:连续运行参考系统)系统已被广泛建设并逐渐成为国家重要基础设施以支持高精度定位应用,用户利用多参考站网络可实时获取厘米级的定位精度。With the gradual increase in the number of reference stations and the compatibility of multi-system and multi-frequency signals, a large-scale CORS (Continuous Operational Reference System: Continuous Operational Reference System) system has been widely constructed and has gradually become an important national infrastructure to support high-precision positioning applications. Users can use the multi-reference station network to obtain centimeter-level positioning accuracy in real time.
在当前网络RTK应用中,必须保证双差模糊度的正确计算,以便全网的差分改正信息以及VRS观测值得以生成,从而确保定位效果。因此,大部分CORS系统软件均采用基线解算方法以实现全网模糊度解算。基线解算方法虽然减少了未知参数个数,一定程度上解决了求解的秩亏问题,但通常存在复杂而难以确切解决的相关性问题。同时,由于大气误差和卫星轨道误差的影响,基线解算过程中,基线长度受到限制,不得不考虑新的大气误差估计模型以扩宽基线长度。而且随着基准站数量的增大,所需解算的基线数量呈指数级增长。因此基于基线解算方法的全网模糊度解算不利于大范围CORS系统的分布式架构的搭建及运行,限制了大范围CORS系统的应用。In the current network RTK application, it is necessary to ensure the correct calculation of the double difference ambiguity, so that the difference correction information of the whole network and the VRS observation value can be generated, so as to ensure the positioning effect. Therefore, most CORS system software adopts the baseline solution method to achieve network-wide ambiguity resolution. Although the baseline solution method reduces the number of unknown parameters and solves the rank deficiency problem to a certain extent, it usually has complex and difficult to solve correlation problems. At the same time, due to the influence of atmospheric error and satellite orbit error, the baseline length is limited during the baseline calculation process, and a new atmospheric error estimation model has to be considered to expand the baseline length. Moreover, as the number of reference stations increases, the number of baselines to be solved increases exponentially. Therefore, the network-wide ambiguity resolution based on the baseline solution method is not conducive to the construction and operation of the distributed architecture of the large-scale CORS system, and limits the application of the large-scale CORS system.
与此同时,随着非差PPP的成功应用,一种创新的PPP-RTK方法得到了广泛研究。在非组合观测方程中,接收机及卫星的载波伪距偏差项被模糊度所吸收,使得非差模糊失去整数特性。但利用固定的双差模糊度以及正确的基准模糊度,可还原非差模糊度的整数特性,而且与双差方程具有数值等价性,但不需要面对相关性问题。因此,全网的模糊度固定值得深入研究,并将大大有利于大范围CORS系统的发展。Meanwhile, with the successful application of non-differenced PPP, an innovative PPP-RTK method has been extensively studied. In the uncombined observation equation, the receiver and satellite carrier pseudo-range bias items are absorbed by the ambiguity, which makes the undifferenced ambiguity lose the integer property. But using fixed double-difference ambiguity and correct reference ambiguity, the integer characteristics of non-difference ambiguity can be restored, and it has numerical equivalence with double-difference equation, but it does not need to face the correlation problem. Therefore, the ambiguity fixation of the whole network is worthy of in-depth study, and will greatly benefit the development of large-scale CORS systems.
发明内容Contents of the invention
发明目的:针对上述现有技术,提出一种基于非差非组合模型的网络RTK模糊度解算方法,能够解决目前网络RTK受相关性问题影响以及基线长度、数量限制的问题,并将大大有利于大范围CORS系统的发展。Purpose of the invention: Aiming at the above existing technologies, a network RTK ambiguity resolution method based on the non-difference and non-combination model is proposed, which can solve the problems that the current network RTK is affected by the correlation problem and the baseline length and quantity are limited, and will greatly benefit Conducive to the development of a wide range of CORS systems.
技术方案:一种基于非差非组合模型的网络RTK模糊度解算方法,首先使用非差非组合模型对全网的单站非差模糊度进行估计,其中将卫星钟作为未知参数,实时卫星钟差产品作为伪观测量加入;然后根据用户位置,提取用户相邻站点的非差模糊度,在相邻站点中选择一个作为基准站点,利用差分算子还原得到双差模糊度的整数特性,单历元获得具有整数特性的双差模糊度结果。Technical solution: A network RTK ambiguity resolution method based on the non-difference and non-combination model. Firstly, the non-difference and non-combination model is used to estimate the single-station non-difference ambiguity of the whole network. The satellite clock is used as an unknown parameter, and the real-time satellite The clock difference product is added as a pseudo-observation; then according to the user's location, the non-difference ambiguity of the user's adjacent sites is extracted, one of the adjacent sites is selected as the reference site, and the integer characteristics of the double-difference ambiguity are obtained by using the difference operator to restore, Single epochs obtain double-differenced ambiguity results with integer properties.
进一步的,所述的基于非差非组合模型的网络RTK模糊度解算方法包括如下具体步骤:Further, the described network RTK ambiguity resolution method based on the non-difference non-combination model includes the following specific steps:
步骤1),使用各站点的原始观测值,对各站点的的单站非差模糊度进行估计,并将卫星钟差作为未知参数进行估计,如式(1.1)所示:Step 1), use the original observations of each station to estimate the single-station indifferent ambiguity of each station, and estimate the satellite clock error as an unknown parameter, as shown in formula (1.1):
其中:s表示卫星,k表示基准站接收机,j代表各观测值频率,j=1、2;表示卫星s与站点k间在频率j上的伪距观测量,表示卫星s与站点k间在频率j上的相位观测量,表示卫星到接收机天线相位中心的几何距离,δtk表示接收机钟差,δts表示卫星钟差,表示对流层延迟,表示电离层延迟;αj是频率比值,表示频率j的值;分别表示频率j上的接收机和卫星的硬件延迟;是其它可模型化的误差;分别表示频率j上的接收机和卫星的相位小数偏差;λj是频率j上的载波波长,Nj是频率j上的相位模糊度;是频率j上的伪距观测值和相位观测值噪声;c表示光速;Among them: s represents the satellite, k represents the receiver of the base station, j represents the frequency of each observation value, and j=1, 2; Indicates the pseudo-range observations at frequency j between satellite s and station k, Indicates the phase observation at frequency j between satellite s and station k, Indicates the geometric distance from the satellite to the receiver antenna phase center, δt k indicates the receiver clock error, δt s indicates the satellite clock error, represents the tropospheric delay, Indicates the ionospheric delay; α j is the frequency ratio, Indicates the value of frequency j; Denote the hardware delay of the receiver and the satellite on frequency j, respectively; are other modelable errors; Respectively represent the phase fractional deviation of the receiver and satellite on frequency j; λ j is the carrier wavelength on frequency j, and N j is the phase ambiguity on frequency j; is the pseudorange observation value and phase observation value noise at frequency j; c represents the speed of light;
根据式(1.1),使用卡尔曼滤波对未知参数进行估计,得到非差浮点模糊度,包括如下具体步骤:According to the formula (1.1), use the Kalman filter to estimate the unknown parameters to obtain the non-difference floating-point ambiguity, including the following specific steps:
步骤a),设置未知参数向量Xi,如式(1.2)所示:Step a), setting the unknown parameter vector X i , as shown in formula (1.2):
其中,未知参数Xi分为时变和时不变两个部分,时变部分包括天顶湿延迟ZTDw,k;吸收接收机无电离层硬件延迟的接收机钟差δtk′,其中吸收接收机及卫星频率相关硬件延迟部分的n维电离层倾斜延迟 其中n为该历元观测卫星数,吸收卫星无电离层硬件延迟的n维卫星钟差δts′,时不变部分包括n维非差初始N1整周模糊度和n维非差初始N2整周模糊度 Among them, the unknown parameter X i is divided into two parts: time-varying and time-invariant, and the time-varying part includes the zenith wet delay ZTD w,k ; the absorbing receiver has no ionospheric hardware delay The receiver clock error δt k ′, in n-Dimensional Ionospheric Slope Delay of Absorbing Receiver and Satellite Frequency Dependent Hardware Delay Part in n is the number of satellites observed in this epoch, and the absorbing satellite has no ionospheric hardware delay The n-dimensional satellite clock error δt s ′, The time-invariant part includes n-dimensional non-differenced initial N1 integer ambiguities and n-dimensional non-differenced initial N2 integer ambiguities
步骤b),设置卡尔曼滤波的设计矩阵Bi和观测向量Li,如式(1.3)所示:Step b), set the Kalman filter design matrix B i and observation vector L i , as shown in formula (1.3):
其中,所述观测向量Li包含四个非差观测值以及一个实时卫星钟差产品结果作为伪观测值,分别表示卫星s与站点k间分别在频率1,2上的相位观测量,分别表示卫星s与站点k间分别在频率1,2上的伪距观测量;Wherein, the observation vector L i contains four non-difference observation values and a real-time satellite clock product result As pseudo observations, represent the phase observations between satellite s and station k at frequencies 1 and 2 respectively, represent the pseudo-range observations between satellite s and station k at frequencies 1 and 2, respectively;
n表示该历元观测卫星数,θn表示第n个卫星该历元的卫星高度角,MFw(θ)为天顶湿延迟与卫星高度角相关的映射函数,c表示光速,λ1,λ2分别代表频率1,2的波长;n represents the number of observed satellites in this epoch, θ n represents the satellite altitude angle of the nth satellite in this epoch, MF w (θ) is the mapping function related to the zenith wet delay and satellite altitude angle, c represents the speed of light, λ 1 , λ 2 represents the wavelength of frequency 1, 2 respectively;
根据所述未知参数向量Xi以及设计矩阵Bi和观测向量Li,进行卡尔曼滤波得到非差浮点模糊度 According to the unknown parameter vector X i and the design matrix B i and observation vector L i , Kalman filtering is performed to obtain the non-difference floating-point ambiguity
步骤2),根据用户位置与站点的距离,提取至少三个用户相邻站点的非差模糊度,各用户相邻站点的非差模糊度如式(1.5)所示:Step 2), according to the distance between the user's location and the site, extract the non-difference ambiguity of at least three user adjacent sites, and the non-difference ambiguity of each user's adjacent site As shown in formula (1.5):
其中,分别为对应相邻站点中第1到第n颗卫星的非差基础模糊度N1;分别为对应相邻站点中第1到第n颗卫星的非差基础模糊度N2;in, are the non-difference basic ambiguities N1 of the 1st to nth satellites corresponding to adjacent stations; are the non-difference basic ambiguities N2 of the 1st to nth satellites corresponding to adjacent stations;
步骤3),首先在所述各用户相邻站点中选择一个站点作为基准站点,其他相邻站点作为非基准站点;然后设置所述基准站点与非基准站点的站间差分算子Ckd和参考卫星与非参考卫星的星间差分算子Csd,根据所述差分算子Ckd和Csd还原站间星间具有整数特性的双差模糊度,所述还原步骤如式(1.6)所示:Step 3), first select a site in each user's adjacent sites as a reference site, and other adjacent sites as non-reference sites; then set the inter-station difference operator C kd and reference The inter-satellite difference operator C sd of the satellite and the non-reference satellite, according to the difference operator C kd and C sd restores the double-difference ambiguity with integer characteristics between the stations, and the restoration steps are shown in formula (1.6) :
其中,k1、k2分别为基准站点与非基准站点,r为参考卫星,s为非参考卫星,为站间星间双差模糊度,分别为站点k1和k2的非差模糊度,分别为双差基础模糊度N1、N2;Among them, k1 and k2 are the reference station and non-reference station respectively, r is the reference satellite, s is the non-reference satellite, is the inter-satellite double-difference ambiguity, are the undifferenced ambiguities of sites k1 and k2, respectively, Respectively, double-difference basic ambiguity N1, N2;
步骤4),使用LAMBDA算法进行模糊度搜索固定,具体为:先将所述双差基础模糊度N1、N2转换为宽巷模糊度和N1模糊度部分再采用LAMBDA算法得到固定的整数宽巷模糊度,最后使用宽巷模糊度作为已知值,确定N1模糊度整数值。Step 4), use the LAMBDA algorithm to search and fix the ambiguities, specifically: first convert the double-difference basic ambiguities N1, N2 into wide-lane ambiguities and N1 ambiguity part Then use the LAMBDA algorithm to obtain a fixed integer wide-lane ambiguity, and finally use the wide-lane ambiguity as a known value to determine the integer value of N1 ambiguity.
有益效果:本发明提出的一种基于非差非组合模型的网络RTK模糊度解算方法,融入实时卫星钟差产品作为伪观测量,提高了实时PPP的解算效果,提高了模型的稳健性程度。利用差分算子将非差模糊度还原得到双差整数模糊度,过程中仅引入了时不变的模糊度参数进行全网平差,降低了基线长度和大气延迟对结果的影响,提高了计算效率,有利于大规模CORS系统的发展。Beneficial effects: a network RTK ambiguity resolution method based on the non-difference and non-combination model proposed by the present invention incorporates real-time satellite clock difference products as pseudo observations, which improves the real-time PPP solution effect and improves the robustness of the model degree. The difference operator is used to restore the non-difference ambiguity to obtain the double-difference integer ambiguity. In the process, only the time-invariant ambiguity parameters are introduced for the whole network adjustment, which reduces the influence of the baseline length and atmospheric delay on the results, and improves the calculation efficiency. Efficiency is conducive to the development of large-scale CORS systems.
附图说明Description of drawings
图1是本发明方法流程图;Fig. 1 is a flow chart of the method of the present invention;
图2是实施例基准站点网分布图;Fig. 2 is the distribution diagram of embodiment benchmark site network;
图3是实施例实时卫星钟差产品精度统计图;Fig. 3 is embodiment real-time satellite clock difference product accuracy statistical diagram;
图4是PRN8号卫星和PRN14号卫星的卫星钟差偏差图;Figure 4 is the satellite clock deviation diagram of the PRN8 satellite and the PRN14 satellite;
图5是不同卫星产品钟精度下的条件数变化图;Fig. 5 is the change diagram of the condition number under different satellite product clock precisions;
图6是不同卫星产品钟精度下的ADOP变化图;Figure 6 is a graph of ADOP variation under different satellite product clock precisions;
图7是无电离层组合模型、非组合模型、估计卫星钟的非组合模型的观测值残差图;Fig. 7 is the observation value residual diagram of the ionosphere-free combination model, the non-combination model, and the non-combination model of the estimated satellite clock;
图8是采用非差方式和采用双差方式得到的双差模糊度的条件数变化图;Fig. 8 is the change diagram of the condition number of the double-difference ambiguity obtained by adopting the non-difference method and the double-difference method;
图9是采用非差方式和采用双差方式得到的双差宽巷模糊度的浮点偏差;Fig. 9 is the floating-point deviation of the double-difference wide-lane ambiguity obtained by using the non-difference method and the double-difference method;
图10是采用非差方式和采用双差方式得到的双差N1模糊度的浮点偏差。Fig. 10 shows the floating-point deviation of the double-difference N1 ambiguity obtained by using the non-difference method and the double-difference method.
具体实施方式detailed description
下面结合附图对本发明作更进一步的说明。The present invention will be further described below in conjunction with the accompanying drawings.
如图1所示,一种基于非差非组合模型的网络RTK模糊度解算方法,首先使用非差非组合模型对全网的单站非差模糊度进行估计,其中将卫星钟作为未知参数,实时卫星钟差产品作为伪观测量加入;然后根据用户位置,提取用户相邻站点的非差模糊度,在相邻站点中选择一个作为基准站点,利用差分算子还原得到双差模糊度的整数特性,单历元获得具有整数特性的双差模糊度结果。本发明方法包括如下具体步骤:As shown in Figure 1, a network RTK ambiguity resolution method based on the non-difference and non-combination model first uses the non-difference and non-combination model to estimate the single-station non-difference ambiguity of the entire network, and the satellite clock is used as an unknown parameter , the real-time satellite clock difference product is added as a pseudo-observation; then according to the user's position, extract the non-difference ambiguity of the user's adjacent sites, select one of the adjacent sites as the reference site, and use the difference operator to restore the double-difference ambiguity Integer properties, single epoch Obtain double-differenced ambiguity results with integer properties. The inventive method comprises the following concrete steps:
步骤1),使用各站点的原始观测值,对各站点的的单站非差模糊度进行估计,并将卫星钟差作为未知参数进行估计;各站点的非组合观测方程如式(1.1)所示:Step 1), use the original observation values of each station to estimate the single-station undifference ambiguity of each station, and estimate the satellite clock error as an unknown parameter; the non-combined observation equation of each station is as shown in formula (1.1) Show:
其中:s表示卫星,k表示基准站接收机,j代表各观测值频率,j=1、2;表示卫星s与站点k间在频率j上的伪距观测量,表示卫星s与站点k间在频率j上的相位观测量,表示卫星到接收机天线相位中心的几何距离,δtk表示接收机钟差,δts表示卫星钟差,表示对流层延迟,表示电离层延迟;αj是频率比值,表示频率j的值;分别表示频率j上的接收机和卫星的硬件延迟;是其它可模型化的误差,如潮汐改正,极移误差;分别表示频率j上的接收机和卫星的相位小数偏差;λj是频率j上的载波波长,Nj是频率j上的相位模糊度;是频率j上的伪距观测值和相位观测值噪声;c表示光速;Among them: s represents the satellite, k represents the receiver of the base station, j represents the frequency of each observation value, and j=1, 2; Indicates the pseudo-range observations at frequency j between satellite s and station k, Indicates the phase observation at frequency j between satellite s and station k, Indicates the geometric distance from the satellite to the receiver antenna phase center, δt k indicates the receiver clock error, δt s indicates the satellite clock error, represents the tropospheric delay, Indicates the ionospheric delay; α j is the frequency ratio, Indicates the value of frequency j; Denote the hardware delay of the receiver and the satellite on frequency j, respectively; are other modelable errors, such as tidal corrections, pole shift errors; Respectively represent the phase fractional deviation of the receiver and satellite on frequency j; λ j is the carrier wavelength on frequency j, and N j is the phase ambiguity on frequency j; is the pseudorange observation value and phase observation value noise at frequency j; c represents the speed of light;
根据式(1.1),使用卡尔曼滤波对未知参数进行估计,得到非差浮点模糊度,包括如下具体步骤:According to the formula (1.1), use the Kalman filter to estimate the unknown parameters to obtain the non-difference floating-point ambiguity, including the following specific steps:
步骤a),设置未知参数向量Xj,如式(1.2)所示:Step a), setting the unknown parameter vector X j , as shown in formula (1.2):
其中,未知参数Xi分为时变和时不变两个部分,时变部分包括天顶湿延迟ZTDw,k;吸收接收机无电离层硬件延迟的接收机钟差δtk′,其中吸收接收机及卫星频率相关硬件延迟部分的n维电离层倾斜延迟 其中n为该历元观测卫星数,吸收卫星无电离层硬件延迟的n维卫星钟差δts′,时不变部分包括n维非差初始N1整周模糊度和n维非差初始N2整周模糊度 Among them, the unknown parameter X i is divided into two parts: time-varying and time-invariant, and the time-varying part includes the zenith wet delay ZTD w,k ; the absorbing receiver has no ionospheric hardware delay The receiver clock error δt k ′, in n-Dimensional Ionospheric Slope Delay of Absorbing Receiver and Satellite Frequency Dependent Hardware Delay Part in n is the number of satellites observed in this epoch, and the absorbing satellite has no ionospheric hardware delay The n-dimensional satellite clock error δt s ′, The time-invariant part includes n-dimensional non-differenced initial N1 integer ambiguities and n-dimensional non-differenced initial N2 integer ambiguities
步骤b),设置卡尔曼滤波的设计矩阵Bi和观测向量Li,如式(1.3)所示:Step b), set the Kalman filter design matrix B i and observation vector L i , as shown in formula (1.3):
其中,所述观测向量Li包含四个非差观测值以及一个实时卫星钟差产品结果作为伪观测值,分别表示卫星s与站点k间分别在频率1,2上的相位观测量,分别表示卫星s与站点k间分别在频率1,2上的伪距观测量。需要指出的是,由于实时卫星钟差产品作为伪观测值,受内插精度、产品精度影响明显,将卫星钟差作为未知参数进行估计,同时伪观测方程的加入可以降低方程的病态性。其中:Wherein, the observation vector L i contains four non-difference observation values and a real-time satellite clock product result As pseudo observations, represent the phase observations between satellite s and station k at frequencies 1 and 2 respectively, Represent the pseudo-range observations between satellite s and station k at frequencies 1 and 2, respectively. It should be pointed out that since the real-time satellite clock error product is a pseudo-observation value, which is significantly affected by the interpolation accuracy and product accuracy, the satellite clock error is estimated as an unknown parameter, and the addition of the pseudo-observation equation can reduce the ill-conditionedness of the equation. in:
n表示该历元观测卫星数,θn表示第n个卫星该历元的卫星高度角,MFw(θ)为天顶湿延迟与卫星高度角相关的映射函数,c表示光速,λ1,λ2分别代表频率1,2的波长;n represents the number of observed satellites in this epoch, θ n represents the satellite altitude angle of the nth satellite in this epoch, MF w (θ) is the mapping function related to the zenith wet delay and satellite altitude angle, c represents the speed of light, λ 1 , λ 2 represents the wavelength of frequency 1, 2 respectively;
根据所述未知参数向量Xi以及设计矩阵Bi和观测向量Li,进行卡尔曼滤波得到非差浮点模糊度其中,卡尔曼滤波形式如式(1.5)所示:According to the unknown parameter vector X i and the design matrix B i and observation vector L i , Kalman filtering is performed to obtain the non-difference floating-point ambiguity Among them, the form of Kalman filtering is shown in formula (1.5):
式中,Φi,i-1是(4n+2)×(4n+2)的状态转移矩阵,如式(1.6)所示:In the formula, Φ i, i-1 is the state transition matrix of (4n+2)×(4n+2), as shown in formula (1.6):
Qi是(4n+2)×(4n+2)动态噪声协方差阵,表示如下:Q i is a (4n+2)×(4n+2) dynamic noise covariance matrix, expressed as follows:
其中, 分别为相应随机过程的相关时间,En为n×n的单位矩阵。in, are the relative times of the corresponding stochastic process, respectively, E n is an n×n unit matrix.
式1.7中,天顶湿延迟,接收机、卫星钟差和电离层倾斜延迟的动态模型取决于各自的动态特性。对于天顶湿延迟参数,可以认为是一阶马尔可夫过程:qZWD=1~9cm2/h,Δt是历元时间间隔。而白噪声可以简单有效地描述接收机、卫星钟差的随机过程。对于电离层倾斜延迟参数,也可以认为是与电离层穿刺点位置的天顶角z′有关的随机游走:模糊度被认为是时不变参数: 表示克罗内克积。In Equation 1.7, the dynamic models for zenith wet delay, receiver, satellite clock bias and ionospheric tilt delay depend on their respective dynamic characteristics. For the zenith wet delay parameter, it can be considered as a first-order Markov process: q ZWD =1~9cm 2 /h, Δt is the time interval of epoch. White noise can simply and effectively describe the random process of receiver and satellite clock error. For the ionospheric tilt delay parameter, it can also be considered as a random walk related to the zenith angle z′ of the position of the ionospheric puncture point: Ambiguity is considered as a time-invariant parameter: represents the Kronecker product.
步骤c),从模糊度协方差条件数、ADOP值和观测值残差三个指标,验证模糊度解算模型,具体步骤如下:Step c), verify the ambiguity resolution model from the three indicators of ambiguity covariance condition number, ADOP value and observation value residual, the specific steps are as follows:
模糊度协方差条件数反应的是模糊度间的内部关系,条件数越小,其抗差能力越强,模型结构更加稳定,除模型本身系数矩阵结构特征外,不同的载波伪距精度比值及伪观测方程精度均会条件数结果产生影响。对模型法方程进行评定,由于其模糊度法方程为对称正定矩阵,采用条件数作为度量指标,计算如式(1.8)所示:The condition number of ambiguity covariance reflects the internal relationship between ambiguities. The smaller the condition number is, the stronger the tolerance is and the more stable the model structure is. The accuracy of the pseudo-observation equation will affect the result of the condition number. To evaluate the model method equation, since the ambiguity method equation is a symmetric positive definite matrix, the condition number is used as the measurement index, and the calculation is shown in formula (1.8):
其中,是模糊度浮点解的协方差阵;||·||表示范数符号;λmax和λmin分别是特征值的最大值和最小值。in, is the covariance matrix of the floating-point solution of the ambiguity; ||·|| represents the norm symbol; λ max and λ min are the maximum and minimum values of the eigenvalues, respectively.
ADOP值是用于评定解算模糊度成功率的内部的模型强度,类似于PDOP用于判定卫星接收机几何位置对定位结果的影响,ADOP用于评定模糊度滤波解的本身精度值。其受各项指标影响综合得到的结果,本方法中,除受历元数、卫星数影响外,还受到伪观测方程精度及待估参数数量影响。其计算如式(1.9)所示:The ADOP value is used to evaluate the internal model strength of the ambiguity resolution success rate, similar to the PDOP used to determine the influence of the geometric position of the satellite receiver on the positioning result, and the ADOP is used to evaluate the accuracy value of the ambiguity filter solution itself. It is affected by various indicators to obtain comprehensive results. In this method, in addition to being affected by the number of epochs and satellites, it is also affected by the accuracy of pseudo-observation equations and the number of parameters to be estimated. Its calculation is shown in formula (1.9):
观测值残差直接反应的是模型的后验误差,可直观体现不同方法下观测值噪声对结果的影响,同时需考虑不同高度角情况下载波噪声的特点。Observation residuals directly reflect the posterior error of the model, which can intuitively reflect the influence of observation noise on the results under different methods, and at the same time, the characteristics of wave noise at different altitude angles need to be considered.
步骤2),根据用户位置与站点的距离,提取至少三个用户相邻站点的非差模糊度,各用户相邻站点的非差模糊度及其协方差阵如式(1.10)所示:Step 2), according to the distance between the user's location and the site, extract the non-difference ambiguity of at least three user adjacent sites, and the non-difference ambiguity of each user's adjacent site and its covariance matrix As shown in formula (1.10):
其中,分别为对应相邻站点中第1到第n颗卫星的非差基础模糊度N1;分别为对应相邻站点中第1到第n颗卫星的非差基础模糊度N2;in, are the non-difference basic ambiguities N1 of the 1st to nth satellites corresponding to adjacent stations; are the non-difference basic ambiguities N2 of the 1st to nth satellites corresponding to adjacent stations;
步骤3),首先在所述各用户相邻站点中选择一个站点作为基准站点,其他相邻站点作为非基准站点;然后设置所述基准站点与非基准站点的站间差分算子Ckd和参考卫星与非参考卫星的星间差分算子Csd,如式(1.11)所示:Step 3), first select a site in each user's adjacent sites as a reference site, and other adjacent sites as non-reference sites; then set the inter-station difference operator C kd and reference The inter-satellite difference operator C sd between the satellite and the non-reference satellite is shown in formula (1.11):
在星间差分算子中,″-1″列的位置由参考卫星在观测卫星中的排序位置决定;In the inter-satellite difference operator, the position of the "-1" column is determined by the sorting position of the reference satellite in the observation satellite;
根据所述差分算子Ckd和Csd还原站间星间具有整数特性的双差模糊度,所述还原步骤如式(1.12)、(1.13)所示:According to the difference operators C kd and C sd restore the double-difference ambiguity with integer characteristics between stations and stars, the restoration steps are shown in formulas (1.12) and (1.13):
其中,k1、k2分别为基准站点与非基准站点,r为参考卫星,s为非参考卫星;为站间星间双差模糊度,对应的协方差阵为分别为站点k1和k2的非差模糊度,分别为双差基础模糊度N1、N2;Among them, k1 and k2 are reference sites and non-reference sites respectively, r is a reference satellite, and s is a non-reference satellite; is the double-difference ambiguity between stations and satellites, and the corresponding covariance matrix is are the undifferenced ambiguities of sites k1 and k2, respectively, Respectively, double-difference basic ambiguity N1, N2;
步骤4),使用LAMBDA算法进行模糊度搜索固定,包括如下步骤:Step 4), use the LAMBDA algorithm to search and fix the ambiguity, including the following steps:
a),将所述双差基础模糊度N1、N2转换为宽巷模糊度和N1模糊度部分用于转换观测阵和协方差阵的线性变换矩阵Clinear如式(1.14)所示:a), converting the double-difference basic ambiguities N1, N2 into wide-lane ambiguities and N1 ambiguity part Used to convert the observation array and covariance matrix The linear transformation matrix C linear is shown in formula (1.14):
其中,分别为和对应的协方差阵。in, respectively with The corresponding covariance matrix.
b),采用LAMBDA算法得到固定的整数宽巷模糊度,使用宽巷模糊度作为已知值,确定N1模糊度整数值,如式(1.15)所示:b) Use the LAMBDA algorithm to obtain a fixed integer wide-lane ambiguity, use the wide-lane ambiguity as a known value, and determine the integer value of the N1 ambiguity, as shown in formula (1.15):
式(1.15)中,分别为N1模糊度、宽巷模糊度的整数值,为对应的协方差阵。In formula (1.15), are the integer values of N1 ambiguity and widelane ambiguity respectively, for The corresponding covariance matrix.
得到用户位置相邻站点间的整数特性双差模糊度,其中采用分步法以减少模糊度的数量,提高了LAMBDA算法的全网搜索效率。The double-difference ambiguities of integer characteristics between adjacent sites of the user's location are obtained, and the step-by-step method is used to reduce the number of ambiguities, which improves the network-wide search efficiency of the LAMBDA algorithm.
本实施例采用美国CORS的24小时RINEX观测文件和导航文件,以及匹配观测文件的IGS实时产品。实验中全网包括九个参考站,参考站分布如图2所示的网图。This embodiment adopts the 24-hour RINEX observation file and navigation file of CORS in the United States, and the IGS real-time product matching the observation file. In the experiment, the whole network includes nine reference stations, and the distribution of reference stations is shown in Figure 2.
为了模拟稀疏网络RTK,设p332站为基准站点,组成了平均基线长度约为150km的八条基线,如表1所示:In order to simulate the sparse network RTK, station p332 is set as the reference station, and eight baselines with an average baseline length of about 150 km are formed, as shown in Table 1:
表1 美国CORS网中九个基站组成的八条基线Table 1 Eight baselines composed of nine base stations in the US CORS network
从图3可以看出,大多数卫星钟差的均方差在0.25s以内,然而仍有部分卫星误差较大,与此同时,从图4可以看出,实时数据中钟差存在系统偏差与数据丢失,因此在非差非组合模型中需要对卫星钟差进行估计以确保模糊度的时不变特性。图5-7给出了对卫星钟差不同的估计策略下条件数、ADOP值和观测残差的对比,可以看出,对卫星钟差进行估计虽然需要牺牲较多时间以确保模糊度解算的可靠性,但是减小了条件数与相位残差,确保了模型的稳定性与抗差能力。从图8可以看出,非差方法还原双差模糊度相比双差方法,对浮点模糊度有更好的估计效果。从图9、10可以看出,相比传统的MW方法,非差非组合方法对宽巷模糊度和N1模糊度的固定成功率显著提高。It can be seen from Figure 3 that the mean square error of most satellite clock errors is within 0.25s, but there are still some satellites with large errors. Therefore, in the non-difference and non-combination model, the satellite clock bias needs to be estimated to ensure the time-invariant property of the ambiguity. Figure 5-7 shows the comparison of the condition number, ADOP value and observation residual under different estimation strategies for the satellite clock error. It can be seen that although the estimation of the satellite clock error needs to sacrifice more time to ensure the ambiguity resolution Reliability, but the condition number and phase residual are reduced to ensure the stability and tolerance of the model. It can be seen from Figure 8 that the non-difference method restores the double-difference ambiguity, and has a better estimation effect on the floating-point ambiguity than the double-difference method. From Figures 9 and 10, it can be seen that compared with the traditional MW method, the non-difference and non-combination method has significantly improved the fixing success rate of wide-lane ambiguity and N1 ambiguity.
以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, and it should be pointed out that for those of ordinary skill in the art, some improvements and modifications can be made without departing from the principle of the present invention. It should be regarded as the protection scope of the present invention.
Claims (2)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410837313.2A CN104502935B (en) | 2014-12-29 | 2014-12-29 | A kind of network RTK Ambiguity Solution Methods based on the non-combined model of non-difference |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410837313.2A CN104502935B (en) | 2014-12-29 | 2014-12-29 | A kind of network RTK Ambiguity Solution Methods based on the non-combined model of non-difference |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104502935A CN104502935A (en) | 2015-04-08 |
CN104502935B true CN104502935B (en) | 2017-03-15 |
Family
ID=52944346
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201410837313.2A Expired - Fee Related CN104502935B (en) | 2014-12-29 | 2014-12-29 | A kind of network RTK Ambiguity Solution Methods based on the non-combined model of non-difference |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104502935B (en) |
Families Citing this family (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105158782B (en) * | 2015-05-29 | 2017-07-28 | 东南大学 | A kind of wide lane ambiguity calculation method of BDS and GPS observation informations fusion |
CN104898145B (en) * | 2015-06-25 | 2017-05-31 | 和芯星通科技(北京)有限公司 | A kind of fuzziness fixing means and system based on half cycle fuzziness |
CN105301618A (en) * | 2015-10-22 | 2016-02-03 | 北京理工大学 | Method of fixing integer ambiguity when carrier phase generates half cycle slip |
CN105549055A (en) * | 2015-11-24 | 2016-05-04 | 航天恒星科技有限公司 | Virtual observation data generation method and device |
CN106814376B (en) * | 2015-12-02 | 2022-03-04 | 成都联星技术股份有限公司 | Rapid and accurate centimeter-level single-point positioning method |
CN105738934B (en) * | 2016-02-06 | 2017-11-28 | 武汉大学 | The quick fixing means of URTK fuzzinesses of additional atmospheric information dynamic constrained |
CN105891864B (en) * | 2016-04-29 | 2018-03-30 | 辽宁工程技术大学 | A kind of BDS is with mixing double difference fuzziness fixing means between GPS system |
CN106249256B (en) * | 2016-07-08 | 2018-08-14 | 辽宁工程技术大学 | Real-time GLONASS phase deviation estimation methods based on particle swarm optimization algorithm |
CN106814380B (en) * | 2017-01-19 | 2019-07-05 | 湖南北云科技有限公司 | A kind of cellular network cooperation RTK localization method and system |
CN107561568A (en) * | 2017-08-22 | 2018-01-09 | 中国科学院国家授时中心 | The non-combined PPP RTK localization methods of the non-difference of the Big Dipper based on unified model |
CN108226976B (en) * | 2017-11-17 | 2021-10-19 | 北京自动化控制设备研究所 | An Adaptive Fading Kalman Filtering Algorithm for RTK |
CN108196287B (en) * | 2018-02-02 | 2019-11-12 | 东南大学 | A Tight Combined RTK Positioning Method Without Considering Reference Satellite Transformation |
CN108535749B (en) * | 2018-03-19 | 2022-05-31 | 千寻位置网络有限公司 | Positioning enhancement method and system based on CORS and positioning system |
CN108254774A (en) * | 2018-03-29 | 2018-07-06 | 千寻位置网络有限公司 | Single base station long range real-time location method based on GNSS multi-frequency signal |
CN108562917B (en) * | 2018-04-09 | 2021-09-28 | 东南大学 | Constraint filtering resolving method and device for additional orthogonal function fitting condition |
CN108415049B (en) * | 2018-04-19 | 2022-05-06 | 千寻位置网络有限公司 | Method for improving network RTK double-difference wide lane ambiguity fixing accuracy |
CN108508470A (en) * | 2018-05-16 | 2018-09-07 | 武汉大学 | Towards the instantaneous decimeter grade navigation locating method that the whole world is seamless |
CN110398762A (en) * | 2019-07-15 | 2019-11-01 | 广州中海达卫星导航技术股份有限公司 | Fuzziness fixing means, device, equipment and medium in real-time clock bias estimation |
CN111796309B (en) * | 2020-06-24 | 2023-04-18 | 中国科学院精密测量科学与技术创新研究院 | Method for synchronously determining atmospheric water vapor and total electron content by navigation satellite single-frequency data |
CN114355420B (en) * | 2021-12-15 | 2023-05-09 | 中国科学院国家授时中心 | A distributed Beidou location service center PPP product positioning method and device |
CN114545470B (en) * | 2022-02-15 | 2024-04-05 | 国汽大有时空科技(安庆)有限公司 | Cross-network continuous network RTK positioning service method |
CN115225245B (en) * | 2022-09-20 | 2022-12-09 | 中国科学院国家授时中心 | A non-difference and non-combination PPP-RTK long-baseline real-time time transfer method |
CN118859269B (en) * | 2024-09-24 | 2024-12-03 | 长江三峡集团实业发展(北京)有限公司 | Method, device and system for fixing wide lane ambiguity network based on ionospheric correlation |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2003002977A1 (en) * | 2001-06-29 | 2003-01-09 | Protea Biosciences, Inc. | Specimen-linked g protein coupled receptor database |
US20090093959A1 (en) * | 2007-10-04 | 2009-04-09 | Trimble Navigation Limited | Real-time high accuracy position and orientation system |
CN101295014B (en) * | 2008-05-19 | 2011-01-05 | 中国测绘科学研究院 | Distant-range high-precision real-time/fast positioning method and system based on GNSS |
CN101770033B (en) * | 2010-02-08 | 2013-04-03 | 东南大学 | Fixing method of ambiguity network between CORS and system station |
-
2014
- 2014-12-29 CN CN201410837313.2A patent/CN104502935B/en not_active Expired - Fee Related
Also Published As
Publication number | Publication date |
---|---|
CN104502935A (en) | 2015-04-08 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104502935B (en) | A kind of network RTK Ambiguity Solution Methods based on the non-combined model of non-difference | |
US20220299652A1 (en) | Gnss standard point positioning method based on spherical harmonics | |
CN101770033B (en) | Fixing method of ambiguity network between CORS and system station | |
CN103941272B (en) | The localization method of GPS, GLONASS and BDS Combined Calculation | |
CN104102822B (en) | A kind of multifrequency GNSS observations stochastic behaviour modeling method | |
CN105158783B (en) | A kind of real time dynamic differential localization method and its equipment | |
CN105182388B (en) | A kind of accurate one-point positioning method of Fast Convergent | |
CN103675835B (en) | A kind of Big Dipper three is signal carrier phase integer ambiguity single epoch defining method frequently | |
CN104459745B (en) | A kind of many constellation Long baselines network RTK obscure portions degree fast resolution algorithms | |
CN103728643B (en) | With the Big Dipper three network RTK blur level single epoch fixing means frequently that wide lane retrains | |
CN108802782B (en) | An Inertial Navigation Aided Method for Solving the Phase Integer Ambiguity of Beidou Three-Frequency Carrier | |
CN108549095B (en) | Non-differential parallel enhancement method and system for regional CORS network | |
CN105842720B (en) | A wide-area precise real-time positioning method based on carrier phase | |
CN107561568A (en) | The non-combined PPP RTK localization methods of the non-difference of the Big Dipper based on unified model | |
CN108445518A (en) | A kind of GNSS chronometer time transmission methods based on the constraint of double difference fuzziness fixed solution | |
CN104483691B (en) | A kind of GNSS combines accurate one-point positioning method | |
CN111308528A (en) | Beidou/GPS tightly-combined virtual reference station positioning method | |
CN103728876A (en) | Satellite clock error real-time estimation method based on area multi-reference-station joint resolving | |
CN105549056A (en) | Relative positioning device and carrier wave integer ambiguity calculation method thereof | |
CN105044741A (en) | Solution method of pseudo range phase comprehensive wide-area differential correction value | |
CN108061911A (en) | A kind of GLONASS carrier waves list difference residual error method of estimation | |
CN104597465A (en) | Method for improving convergence speed of combined precise point positioning of GPS (Global Position System) and GLONASS | |
CN112526564A (en) | Precise single-point positioning re-convergence method | |
CN105699999A (en) | Method for fixing narrow lane ambiguity of Beidou ground based augmentation system base station | |
CN103217698A (en) | Determining method of three frequency signal ambiguity based on Beidou navigation system |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170315 |