阅读说明：本技术 一种基于gnss的滑坡监测系统及方法 (Landslide monitoring system and method based on GNSS ) 是由 仲志成 赵欧阳 郭健 陈晨 于 2021-08-20 设计创作，主要内容包括：本发明公开了一种基于GNSS的滑坡监测系统及方法,通过GNSS接收机接收GNSS信号和将采集到的数据传入云端服务器,云端服务器对原始GNSS信号数据进行解算,得到GNSS接收机实时的位置坐标,本发明不在本地处理GNSS信号,避免其在野外复杂环境出现问题减少GNSS接收机复杂度,使其更能适应野外复杂环境；云端服务器中提供了一套软件解算流程,通过改进整周模糊度的确定方法和快速最佳卫星选取,减少了计算量,提高了系统的实时性,使其能够及时对滑坡灾害的发生进行预警。(The invention discloses a landslide monitoring system and method based on GNSS, wherein a GNSS receiver receives GNSS signals and transmits acquired data into a cloud server, and the cloud server resolves original GNSS signal data to obtain real-time position coordinates of the GNSS receiver; a set of software resolving process is provided in the cloud server, and through improving the whole-cycle ambiguity determination method and the rapid optimal satellite selection, the calculation amount is reduced, the real-time performance of the system is improved, and the landslide disaster can be warned in time.)

1. A GNSS-based landslide monitoring system, comprising:

the GNSS receiver is used for acquiring GNSS signals, packaging the acquired original GNSS signal data and then transmitting the data to the cloud server in real time;

the network transmission module is used for connecting the GNSS receiver to the cloud server through a network;

and the cloud server is used for resolving the original GNSS signal data to obtain the real-time position coordinate of the GNSS receiver.

2. A landslide monitoring method based on GNSS is characterized by comprising the following steps:

s1, the GNSS receiver collects GNSS signals, packages the collected original GNSS signal data and sends the packed original GNSS signal data to the cloud server in real time through the network transmission module;

s2, the cloud server reads original GNSS signal data, including navigation files, observation files and meteorological files, and extracts the read information to obtain parameters required by resolving;

s3, preprocessing the data;

s4, resolving the satellite position according to the parameters obtained by preprocessing, and selecting the satellite according to the satellite position to obtain the optimal precision factor;

s5, after the satellite is selected, an observation equation is formed, the observation equation and the observation equation of the reference station are differentiated to form a single difference equation, and then the single difference equation and the single difference equation at the previous moment are differentiated to form a double difference equation;

s6, carrying out error correction on the double-difference observation equation, then carrying out linearization processing on the double-difference observation equation, and solving by a least square method to obtain a position parameter;

and S7, displaying the position parameters on the interface in real time after Kalman filtering, and storing the data.

3. The GNSS based landslide monitoring method of claim 2 wherein the step S3 of pre-processing data comprises:

s31, checking the data integrity of the observation file;

s32, detecting whether cycle slip exists in the carrier phase measurement value, if yes, returning to S1, and if not, performing S33;

and S33, detecting whether the measured value of the carrier phase has gross error.

4. The GNSS based landslide monitoring method of claim 2, wherein the satellite selection process in step S4 is:

s41, firstly, searching all satellites detected in an epoch in an observation file in a navigation file, and selecting the satellites with the highest altitude angle, the second highest altitude angle and the lowest altitude angle as the first three satellites;

s42, taking the azimuth angle of the satellite with the lowest altitude angle as the basis, and adding or subtracting 90 degrees on the basis to be used as the basis for judgment;

s43, calculating the difference beta between the azimuth angles of all the rest satellites and the 90 DEG basis_jJ is the satellite number;

s44, selecting beta_jTaking the satellite j corresponding to the minimum value as the 4 th satellite;

and S45, sequentially increasing the azimuth angle of the satellite with the lowest altitude angle by 180 degrees and 270 degrees, and sequentially selecting the rest satellites by repeating the steps S43 and S44.

5. The GNSS-based landslide monitoring method of claim 3, wherein said step S32 comprises the steps of:

the cycle slip is detected by a linear combination of the three frequency observations, the equation for which is as follows:

wherein l, m and n are combination coefficients; l is_lmnThe pseudo range is formed by combining three frequencies; delta N_lmnIs the ambiguity of the difference between the three combined epochs; sigma_lmnIs the three-frequency combined ambiguity variance; λ is the signal wavelength;for the phase value, sigma is the observation noise of the original carrier phase, generally 0.002m is taken, the three-frequency ambiguity of the combined front and rear epochs and the variance thereof are calculated according to the | delta N_lmn|＞l·σ_lmnAnd judging whether the cycle slip occurs or not, and if so, proving that the cycle slip occurs in the epoch.

6. The GNSS based landslide monitoring method of claim 1, wherein the double difference equation formed in step S5 is:

in the formula:is the phase double difference; f is the signal frequency, c is the speed of light; Δ ρ^pq _ijThe pseudo range double differences are obtained; delta N^pq _ijIs the integer ambiguity double difference, V_ionIs an ionospheric error; v_tropFor tropospheric errors, it is seen from this equation that the clock error is eliminated, but ionospheric errors, tropospheric errors and integer ambiguity are still contained therein.

7. The GNSS-based landslide monitoring method according to claim 6, wherein the step S6 specifically comprises:

ionospheric errors are corrected using the Klobuchar model:

wherein I_r ^sThe ionospheric delay is expressed in m, and the calculation formula of each parameter in the formula is as follows:

ψ＝0.0137/(El+0.11)-0.022

t＝4.32×10⁴λ_i+t

F＝1.0+16.0×(0.53-El)³

el is the satellite elevation;is a carrier phase observation; az is the azimuth of the satellite; the navigation data includes the following broadcast ionospheric parameters p_ion＝(α₀，α₁，α₂，α₃，β₀，β₁，β₂，β₃)^TNumber from which the ionospheric delay can be calculated;

tropospheric errors were corrected using the Hopfield model:

in the formula (I), the compound is shown in the specification,

h_d＝40136+148.72(T_s-273.16)；h_w11000; the temperature is absolute temperature and takes K as a unit; t is_s、P_s、e_sRespectively the absolute temperature, the air pressure and the vapor pressure of the atmosphere above the meteorological station; hs is the sea level height (m) of the meteorological station, and the air pressure P and the water vapor pressure e are both in mbar; e is the satellite altitude angle in degrees;

the determination of the integer ambiguity comprises the following two steps:

i, determining an ambiguity floating solution:

and setting a certain epoch to observe k satellites, and linearizing the k satellites to obtain:

wherein i is an epoch number, Ai is a coefficient matrix of a position parameter X, B is an ambiguity coefficient matrix, X is a vector formed by position parameter correction numbers, and X is (delta X, delta y, delta z); n is a k-dimensional integer ambiguity vector, L_iTo observe the vector value, Δ_iIs a k-dimensional error vector; the weight matrix of the observed value is P (here we take the weight matrix as the variance matrix of the measurement error);

II, ambiguity integer solution search:

and if continuous n epochs are set to observe k satellites, the total observation equation after linearization is as follows:

wherein A is a coefficient matrix of the position parameter X; b is an ambiguity coefficient matrix; x is a vector consisting of position parameter correction numbers; x ═ Δ X, Δ y, Δ z; n is an integer ambiguity vector; l is an observation vector; delta is an error vector;

the abbreviation is: GY + Δ; g is an n × m (n is k × n, m is k +4) coefficient matrix, Y is an m-dimensional parameter vector to be solved, L is an n-dimensional observation value vector, and Δ is an n-dimensional error vector;

the position parameters can be found according to the least squares principle:

wherein: p is the weight of the observed value; according toThe parameters to be estimated are divided into two parts, one part is the parameters to be estimated and is marked as X, the other part is the integer ambiguity parameters and is marked as N, correspondingly, the parameter arrays of the parameters are respectively marked as A and B, and the double-difference observation equation after linearization is as follows:

Y＝AX+BN+ε；

wherein Y is a double-difference carrier phase observed value vector; n is a double-difference integer ambiguity vector; x is an unknown baseline component; a and B are parameter matrixes of ambiguity and baseline components, and epsilon is an error vector; solving for the above equation is:

min||Y-AX-BN||²,N∈Z,X∈R；

since N is an integer, the constraint that N is an integer is not considered, and a floating solution N' of N and a covariance matrix Q thereof are solved: (N' -N) Q^-1(N' -N) ═ min, the solution N of which is an integer least squares estimate of the ambiguity; an ideal search space is constructed for it:

(N'-N)^TQ^-1(N'-N)＜χ²；

it is a multi-element ellipsoid with N' as center, its shape is determined by Q and its size is determined by chi²Determining;

and carrying out integer transformation on N to obtain:

(N'-N)^TQ^-1(N'-N)＝(z'-z)^TQ^-1(z'-z)；

thus, the ellipsoid is changed into an approximate sphere by the extrusion of the ambiguity search space, and the volume is unchanged.

Technical Field

The invention relates to the technical field of landslide monitoring, in particular to a landslide monitoring system and method based on GNSS.

Background

China is wide in territory, some regions, particularly southwest regions, have high mountain dangerous peaks, and geological disasters frequently occur, wherein landslide disasters have the largest occupation ratio, a large amount of property loss and casualties are caused every year, and early warning for the geological landslide disasters is very necessary.

At present, landslide is mainly monitored by a landslide body displacement measurement mode and geological information is combined to judge the state of a landslide body, and whether landslide occurs or not is identified. The measuring instruments for the displacement of the landslide body mainly comprise a total station instrument, a displacement instrument and the like, and the stability of the landslide body is judged according to the deformation of the landslide body. Since landslides occur in many mountainous and hilly areas, these manual methods have the disadvantages of high cost and poor safety, and it is difficult to monitor all areas in real time. In response to this situation, a high-precision, low-cost, all-weather monitoring method is needed.

With the advent of Global Navigation Satellite Systems (GNSS), a good solution is provided for landslide monitoring, and the carrier phase difference mode in GNSS ideally has millimeter-scale accuracy, and can completely meet the detection requirement. And can carry out all-weather monitoring, only need regular manual maintenance after once laying, greatly reduced the cost of labor.

Although the GNSS has a good application prospect in landslide monitoring, there are some problems, mainly the following:

due to the complex environment of the landslide occurring region, the observation value has a large amount of environmental noise;

the resolving algorithm has high real-time property while meeting the requirement of precision;

and storing the positioning data for subsequent data analysis.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides a landslide monitoring system and method based on GNSS.

In order to achieve the purpose, the invention is implemented according to the following technical scheme:

a first object of the present invention is to provide a GNSS based landslide monitoring system, comprising:

the GNSS receiver is used for acquiring GNSS signals, packaging the acquired original GNSS signal data and then transmitting the data to the cloud server in real time;

the network transmission module is used for connecting the GNSS receiver to the cloud server through a network;

and the cloud server is used for resolving the original GNSS signal data to obtain the real-time position coordinate of the GNSS receiver.

The second purpose of the present invention is to provide a landslide monitoring method based on GNSS, comprising the following steps:

s1, the GNSS receiver collects GNSS signals, packages the collected original GNSS signal data and sends the packed original GNSS signal data to the cloud server in real time through the network transmission module;

s2, the cloud server reads original GNSS signal data, including navigation files, observation files and meteorological files, and extracts the read information to obtain parameters required by resolving;

s3, preprocessing the data;

s4, resolving the satellite position according to the parameters obtained by preprocessing, and selecting the satellite according to the satellite position to obtain the optimal precision factor;

s5, after the satellite is selected, an observation equation is formed, the observation equation and the observation equation of the reference station are differentiated to form a single difference equation, and then the single difference equation and the single difference equation at the previous moment are differentiated to form a double difference equation;

s6, carrying out error correction on the double-difference observation equation, then carrying out linearization processing on the double-difference observation equation, and solving by a least square method to obtain a position parameter;

and S7, displaying the position parameters on the interface in real time after Kalman filtering, and storing the data.

Further, the data preprocessing in step S3 includes:

s31, checking the data integrity of the observation file;

s32, detecting whether cycle slip exists in the carrier phase measurement value, if yes, returning to S1, and if not, performing S33;

and S33, detecting whether the measured value of the carrier phase has gross error.

Further, the satellite selecting process in step S4 is as follows:

s41, firstly, searching all satellites detected in an epoch in an observation file in a navigation file, and selecting the satellites with the highest altitude angle, the second highest altitude angle and the lowest altitude angle as the first three satellites;

s42, taking the azimuth angle of the satellite with the lowest altitude angle as the basis, and adding or subtracting 90 degrees on the basis to be used as the basis for judgment;

s43, calculating the difference beta between the azimuth angles of all the rest satellites and the 90 DEG basis_jJ is the satellite number;

s44, selecting beta_jTaking the satellite j corresponding to the minimum value as the 4 th satellite;

and S45, sequentially increasing the azimuth angle of the satellite with the lowest altitude angle by 180 degrees and 270 degrees, and sequentially selecting the rest satellites by repeating the steps S43 and S44.

Further, the method for detecting whether cycle slip exists in the carrier phase measurement value in step S32 includes:

the cycle slip is detected by a linear combination of the three frequency observations, the equation for which is as follows:

wherein l, m and n are combination coefficients; l is_lmnThe pseudo range is formed by combining three frequencies; delta N_lmnIs the ambiguity of the difference between the three combined epochs; sigma_lmnIs three frequencyA post-combination ambiguity variance; λ is the signal wavelength;for the phase value, sigma is the observation noise of the original carrier phase, generally 0.002m is taken, the three-frequency ambiguity of the combined front and rear epochs and the variance thereof are calculated according to the | delta N_lmn|＞l·σ_lmnAnd judging whether the cycle slip occurs or not, and if so, proving that the cycle slip occurs in the epoch.

Further, the double difference equation formed in step S4 is:

in the formula:is the phase double difference; f is the signal frequency, c is the speed of light; Δ ρ^pq _ijThe pseudo range double differences are obtained; delta N^pq _ijIs the integer ambiguity double difference, V_ionIs an ionospheric error; v_tropFor tropospheric errors, it is seen from this equation that the clock error is eliminated, but ionospheric errors, tropospheric errors and integer ambiguity are still contained therein. Further, the step S6 specifically includes:

ionospheric errors are corrected using the Klobuchar model:

wherein I_r ^sThe ionospheric delay is expressed in m, and the calculation formula of each parameter in the formula is as follows:

ψ＝0.0137/(El+0.11)-0.022

t＝4.32×10⁴λ_i+t

F＝1.0+16.0×(0.53-El)³

el is the satellite elevation;is a carrier phase observation; az is the azimuth of the satellite; the navigation data includes the following broadcast ionospheric parameters p_ion＝(α₀，α₁，α₂，α₃，β₀，β₁，β₂，β₃)^TFrom which the ionospheric delay can be calculated.

Tropospheric errors were corrected using the Hopfield model:

in the formula (I), the compound is shown in the specification,

h_d＝40136+148.72(T-273.16)；h_w11000; the temperature is absolute temperature and takes K as a unit; t is_s、P_s、e_sRespectively the absolute temperature, the air pressure and the vapor pressure of the atmosphere above the meteorological station; hs is the sea level height (m) of the meteorological station, and the air pressure P and the water vapor pressure e are both in mbar; e is the satellite altitude angle in degrees.

The determination of the integer ambiguity comprises the following two steps:

i, determining an ambiguity floating solution:

and setting a certain epoch to observe k satellites, and linearizing the k satellites to obtain:

wherein i is an epoch number, Ai is a coefficient matrix of a position parameter X, B is an ambiguity coefficient matrix, X is a vector formed by position parameter correction numbers, and X is (delta X, delta y, delta z); n is a k-dimensional integer ambiguity vector, L_iTo observe the vector value, Δ_iIs a k-dimensional error vector; the weight matrix of the observed values is P (here we take the weight matrix as the variance matrix of the measurement errors).

II, ambiguity integer solution search:

and if continuous n epochs are set to observe k satellites, the total observation equation after linearization is as follows:

wherein A is a coefficient matrix of the position parameter X; b is an ambiguity coefficient matrix; x is a vector consisting of position parameter correction numbers; x ═ Δ X, Δ y, Δ z; n is an integer ambiguity vector; l is an observation vector; delta is an error vector;

the abbreviation is: GY + Δ;

the position parameters can be found according to the least squares principle: g is an n × m (n ═ k × n, m ═ k +4) coefficient matrix, Y is an m-dimensional parameter vector to be solved, L is an n-dimensional observation value vector, and Δ is an n-dimensional error vector.

The position parameters can be found according to the least squares principle:

wherein: p is the weight of the observed value; according toThe parameters to be estimated are divided into two parts, one part is the parameters to be estimated and is marked as X, the other part is the integer ambiguity parameters and is marked as N, correspondingly, the parameter arrays of the parameters are respectively marked as A and B, and the double-difference observation equation after linearization is as follows:

Y＝AX+BN+ε；

wherein Y is a double-difference carrier phase observed value vector; n is a double-difference integer ambiguity vector; x is an unknown baseline component; a and B are parameter matrixes of ambiguity and baseline components, and epsilon is an error vector; solving for the above equation is:

min||Y-AX-BN||²,N∈Z,X∈R；

since N is an integer, the constraint that N is an integer is not considered, and a floating solution N' of N and a covariance matrix Q thereof are solved: (N' -N) Q^-1(N' -N) ═ min, the solution N of which is an integer least squares estimate of the ambiguity; an ideal search space is constructed for it:

(N'-N)^TQ^-1(N'-N)＜χ²；

it is a multi-element ellipsoid with N' as center, its shape is determined by Q and its size is determined by chi²Determining;

and carrying out integer transformation on N to obtain:

(N'-N)^TQ^-1(N'-N)＝(z'-z)^TQ^-1(z'-z)；

thus, the ellipsoid is changed into an approximate sphere by the extrusion of the ambiguity search space, and the volume is unchanged.

Compared with the prior art, the invention has the following beneficial effects:

the GNSS receiver only takes charge of receiving GNSS signals and transmitting the acquired data into the cloud server, and the cloud server resolves the original GNSS signal data to obtain the real-time position coordinates of the GNSS receiver.

According to the invention, a set of software resolving process is provided in the cloud server, and through improving the whole-cycle ambiguity determination method and the rapid optimal satellite selection, the calculation amount is reduced, the real-time performance of the system is improved, and the landslide disaster can be early warned in time.

Drawings

FIG. 1 is a diagram illustrating a hardware system of a GNSS receiver according to the present invention.

FIG. 2 is a flow chart of an algorithm for determining integer ambiguities in accordance with the present invention.

Fig. 3 is a flow chart of the fast star selection algorithm of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The specific embodiments described herein are merely illustrative of the invention and do not limit the invention.

Example 1

As shown in fig. 1, the present embodiment provides a GNSS based landslide monitoring system, including:

the GNSS receiver is used for acquiring GNSS signals, packaging the acquired original GNSS signal data and then transmitting the data to the cloud server in real time; it should be noted that, in this embodiment, a GNSS receiver in the prior art is adopted, and as shown in fig. 1, a hardware system structure diagram of the GNSS receiver is shown, where the GNSS receiver includes an antenna, a low noise amplifier, a radio frequency front end, a signal channel, and a control portion; the antenna is responsible for receiving signals; in order to obtain a higher signal-to-noise ratio, a low noise amplifier is used; the radio frequency front end comprises a down converter, a local oscillator, an amplifier, an AD converter and a reference oscillator, wherein radio frequency components of signals received by an antenna continue to be transmitted in the GNSS receiver after passing through the preamplifier, and preparation is prepared for subsequent processing through signal modification in the part, wherein the part comprises down conversion of the signals to intermediate frequency, filtering, sampling and conversion of the signals into digital signals, and the part is the radio frequency front end. The signals received by the receiver rf front-end are a mixture of all satellite signals visible to the GNSS receiver and each signal is composed of components of data codes, pseudo codes, and carriers, which are part of the signal path to identify and separate each individual signal. The control part completes the control of the receiver and the sending work of data, and comprises a memory, a CPU and a power supply module which can be composed of a solar panel and a battery; the SIM card is inserted into the data sending module which sends data to the cloud end through 4G signals;

the network transmission module is used for connecting the GNSS receiver to the cloud server through a network;

and the cloud server is used for resolving the original GNSS signal data to obtain the real-time position coordinate of the GNSS receiver.

Example 2

The embodiment provides a landslide monitoring method based on GNSS, which comprises the following steps:

s1, the GNSS receiver collects GNSS signals, packages the collected original GNSS signal data and sends the packed original GNSS signal data to the cloud server in real time through the network transmission module;

s2, reading original GNSS signal data including navigation files, observation files and meteorological files, extracting the read information, and obtaining parameters required by resolving;

s3, preprocessing the data:

s31, checking the data integrity of the observation file;

s32, detecting whether cycle slip exists in the carrier phase measurement value, if yes, returning to S1, and if not, performing S33;

and S33, detecting whether the measured value of the carrier phase has gross error.

The method for detecting whether cycle slip exists in the carrier phase measurement value comprises the following steps:

the carrier phase observations contain an integer-week portion and a less-than-one-week portion. The whole week part is recorded by a counter inside the receiver through tracking the satellite signal. This period of time, when the receiver device fails or the satellite is occluded, causes the counter to lack its accumulated value, a phenomenon known as cycle slip. When a cycle jump occurs, the whole accumulated value cannot be directly used, and high-precision positioning is influenced.

At present, each system satellite of the GNSS basically has an observed value of three frequencies, and the cycle slip can be detected by linear combination of the observed values of the three frequencies, and the equation is as follows:

wherein l, m and n are combination coefficients; l is_lmnThe pseudo range is formed by combining three frequencies; delta N_lmnIs the ambiguity of the difference between the three combined epochs; sigma_lmnIs the three-frequency combined ambiguity variance; λ is the signal wavelength;for the phase value, sigma is the observation noise of the original carrier phase, generally 0.002m is taken, the three-frequency ambiguity of the combined front and rear epochs and the variance thereof are calculated according to the | delta N_lmn|＞l·σ_lmnAnd judging whether the cycle slip occurs or not, and if so, proving that the cycle slip occurs in the epoch.

S33, detecting whether the carrier phase measurement value has gross error;

s4, resolving the satellite position according to the parameters obtained by preprocessing, and selecting the satellite according to the satellite position to obtain the optimal precision factor; as shown in fig. 3, the satellite selection process is:

s41, firstly, searching all satellites detected in an epoch in an observation file in a navigation file, and selecting the satellites with the highest altitude angle, the second highest altitude angle and the lowest altitude angle as the first three satellites;

s42, taking the azimuth angle of the satellite with the lowest altitude angle as the basis, and adding or subtracting 90 degrees on the basis to be used as the basis for judgment;

s43, calculating the method of all the rest satellitesThe difference beta between the angle of orientation and the 90 deg. basis_jJ is the satellite number;

s44, selecting beta_jTaking the satellite j corresponding to the minimum value as the 4 th satellite;

s45, sequentially increasing the azimuth angle of the satellite with the lowest altitude angle by 180 degrees and 270 degrees, and repeating the steps S43 and S44 to sequentially select the rest satellites;

s5, after the satellite is selected, an observation equation is formed, a single difference equation is formed by difference between the observation equation and the observation equation of the reference station, and a double difference equation is formed by difference between the observation equation and the single difference equation at the previous moment:

in the formula:is the phase double difference; f is the signal frequency, c is the speed of light; Δ ρ^pq _ijThe pseudo range double differences are obtained; delta N^pq _ijIs the integer ambiguity double difference, V_ionIs an ionospheric error; v_tropFor tropospheric errors, it is seen from this equation that the clock error is eliminated, but ionospheric errors, tropospheric errors and integer ambiguity are still contained therein.

S6, error correction is carried out on the double-difference observation equation, then linearization processing is carried out on the double-difference observation equation, and the position parameters are obtained through least square solution:

ionospheric errors are corrected using the Klobuchar model:

wherein I_r ^sThe ionospheric delay is expressed in m, and the calculation formula of each parameter in the formula is as follows:

ψ＝0.0137/(El+0.11)-0.022

t＝4.32×10⁴λ_i+t

F＝1.0+16.0×(0.53-El)³

el is the satellite elevation;is a carrier phase observation; az is the azimuth of the satellite; the navigation data includes the following broadcast ionospheric parameters p_ion＝(α₀，α₁，α₂，α₃，β₀，β₁，β₂，β₃)^TFrom which the ionospheric delay can be calculated.

Tropospheric errors were corrected using the Hopfield model:

in the formula (I), the compound is shown in the specification,

h_d＝40136+148.72(T-273.16)；h_w11000; the temperature is absolute temperature and takes K as a unit; t is_s、P_s、e_sRespectively the absolute temperature, the air pressure and the vapor pressure of the atmosphere above the meteorological station; hs is the altitude (m) of weather station, and the pressure P and the pressure e are equal tombar is a unit; e is the satellite altitude angle in degrees.

The determination of the integer ambiguity comprises the following two steps:

i, determining an ambiguity floating solution:

and setting a certain epoch to observe k satellites, and linearizing the k satellites to obtain:

wherein i is an epoch number, Ai is a coefficient matrix of a position parameter X, B is an ambiguity coefficient matrix, X is a vector formed by position parameter correction numbers, and X is (delta X, delta y, delta z); n is a k-dimensional integer ambiguity vector, L_iTo observe the vector value, Δ_iIs a k-dimensional error vector; the weight matrix of the observed values is P (here we take the weight matrix as the variance matrix of the measurement errors).

II, ambiguity integer solution search:

and if continuous n epochs are set to observe k satellites, the total observation equation after linearization is as follows:

wherein A is a coefficient matrix of the position parameter X; b is an ambiguity coefficient matrix; x is a vector consisting of position parameter correction numbers; x ═ Δ X, Δ y, Δ z; n is an integer ambiguity vector; l is an observation vector; delta is an error vector;

the abbreviation is: GY + Δ;

the position parameters can be found according to the least squares principle:

wherein: p is the weight of the observed value; according toThe parameter to be estimated is divided into two parts, one partDividing into a parameter to be estimated as X, the other part of the parameter to be estimated as integer ambiguity parameter N, correspondingly, the parameter arrays of the parameter to be estimated and integer ambiguity parameter N are respectively marked as A and B, and the double-difference observation equation after linearization is as follows:

Y＝AX+BN+ε；

wherein Y is a double-difference carrier phase observed value vector; n is a double-difference integer ambiguity vector; x is an unknown baseline component; a and B are parameter matrixes of ambiguity and baseline components, and epsilon is an error vector; solving for the above equation is:

min||Y-AX-BN||²,N∈Z,X∈R；

since N is an integer, the constraint that N is an integer is not considered, and a floating solution N' of N and a covariance matrix Q thereof are solved: (N' -N) Q^-1(N' -N) ═ min, the solution N of which is an integer least squares estimate of the ambiguity; an ideal search space is constructed for it:

(N'-N)^TQ^-1(N'-N)＜χ²；

it is a multi-element ellipsoid with N' as center, its shape is determined by Q and its size is determined by chi²Determining;

and carrying out integer transformation on N to obtain:

(N'-N)^TQ^-1(N'-N)＝(z'-z)^TQ^-1(z'-z)；

thus, the ellipsoid is changed into an approximate sphere by the extrusion of the ambiguity search space, and the volume is unchanged; the correlation between ambiguities is reduced and more accurate than the original ambiguities;

and S7, displaying the position parameters on the interface in real time after Kalman filtering, and storing the data.

According to the method, a set of software resolving process is provided in the cloud server, and through improving the whole-cycle ambiguity determination method and the rapid optimal satellite selection, the calculation amount is reduced, the real-time performance of the system is improved, and the landslide disaster can be early warned in time.

The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.