A high-frequency solution method for RTK terminals

Abstract

This invention discloses a high-frequency RTK positioning method and system for RTK terminals. The method includes the following steps: setting up two threads, high-frequency and low-frequency; estimating carrier phase ambiguity using the low-frequency thread based on the time-varying characteristics of the parameters to be estimated; correcting the observation model using the high-frequency thread based on the low-frequency thread's solution results to obtain the terminal's position parameters; detecting data update events of the low-frequency thread's solution results and issuing corresponding control signals to schedule the high-frequency and low-frequency threads. This invention, through a high / low-frequency dual-thread solution approach, with the high-frequency thread responsible for position parameter calculation and the low-frequency thread responsible for ambiguity calculation, achieves high-frequency RTK positioning result updates. By separating the estimation methods for time-varying and time-invariant parameters, it reduces the computation time of single-epoch RTK, lowers the frequency of full parameter estimation, improves solution efficiency, and reduces hardware load.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of RTK terminal, and particularly relates to a high-frequency solving method for an RTK terminal. BACKGROUND

[0002] Generally, the process of RTK positioning solution includes three parts of float solution calculation, ambiguity fixing and least square position calculation. Since the hardware resources of GNSS terminal (especially the memory and computing resources of low-cost terminal) are relatively scarce, and the baseband, firmware and scheduler also occupy certain hardware resources, the computing resources available for RTK algorithm are limited. In addition, when the baseline length is long or the ionosphere is active, the spatial atmospheric error cannot be eliminated or weakened, which will seriously affect the RTK positioning accuracy and fixing rate. Therefore, the conventional solution method has the following problems for high-frequency solution of RTK terminal:

[0003] First, the load of terminal hardware is increased seriously, and high-frequency positioning output cannot be met. The processor carried by the terminal device is generally a single-core or dual-core processor, and the main frequency is usually only several hundred MHz. These computing resources are not only allocated to other programs of the periphery, but also need to ensure the stable operation of the algorithm. However, the calculation amount of the conventional solution method is large (mainly concentrated in the float solution filtering algorithm and ambiguity search algorithm, and the more the number of observation values, the lower the solution efficiency). Therefore, in the high-frequency scene, the conventional solution process cannot be completed within the time interval of the epoch, the computing resources cannot be obtained in time for other programs, the solution process is blocked or the epoch data is lost, and the output of high-frequency positioning result cannot be realized.

[0004] Second, the positioning accuracy of least square method will be poor when the observation environment is poor. The least square method only uses the carrier phase observation information of the ambiguity fixed in the current epoch, and the positioning accuracy of RTK is greatly related to the geometric configuration of the satellites participating in the solution. When the observation environment is poor (occlusion / semi-occlusion environment) or the cycle slip occurs frequently, the number of satellites with ambiguity fixed in the current epoch is small, which leads to poor geometric configuration of the satellites participating in the solution, thereby causing the decline of positioning accuracy or gross error.

[0005] Third, it is only suitable for short baseline and cannot meet the application of medium-long baseline and ionosphere active scene. In recent years, the solar activity period tends to peak year, and the activity of ionosphere is also significantly increased. In the ionosphere active area (our southern region) or period, if the influence of ionosphere delay is not considered, it is difficult to ensure the continuous and stable acquisition of high-precision fixed solution of RTK even for medium-short baseline.

[0006] Therefore, it is necessary to improve the existing solution method of RTK terminal to realize the high-frequency positioning solution of RTK terminal. SUMMARY

[0007] In view of the above defects, the technical problem to be solved by the present application is to provide a high-frequency solving method for an RTK terminal, so as to solve the problem that the terminal algorithm cannot support high-frequency RTK positioning solving due to insufficient computing resources or insufficient performance of the GNSS terminal board card or module itself in the prior art.

[0008] To this end, the present application provides a high-frequency solving method for an RTK terminal, comprising the following steps:

[0009] Two threads of high frequency and low frequency are set, the low-frequency thread is used to estimate the carrier phase ambiguity according to the time-varying characteristics of the to-be-estimated parameters, and the high-frequency thread is used to correct the observation model according to the solving result of the low-frequency thread to obtain the position parameters of the terminal;

[0010] The data update event of the solving result of the low-frequency thread is detected, and a corresponding control signal is sent to schedule the high-frequency and low-frequency threads, and the specific steps are as follows:

[0011] Step 211, when the high-frequency thread and the low-frequency thread are started for the first time, register the signal event and the signal processing function;

[0012] Step 212, real-time detection of data update event in the data buffer area;

[0013] Step 213, when the data update event is detected, send a thread suspension signal to the low-frequency thread to suspend the low-frequency thread, send a first signal to the high-frequency thread, and after the first signal processing function receives the first signal, the high-frequency thread is resumed, the high-frequency solving module obtains the updated low-frequency thread solving result from the data buffer area, corrects the observation model, and suspends the high-frequency thread after the current epoch processing is completed;

[0014] Step 214, send a second signal to the low-frequency thread, and after the second signal processing function receives the second signal, the low-frequency thread is resumed, the low-frequency solving module estimates the carrier phase ambiguity, saves the ambiguity to the data buffer area after the solving is completed, generates a data update event, and then suspends the low-frequency thread;

[0015] Step 215, judge whether the terminal stop settlement command is received, if not, turn to step 212; otherwise, end the settlement.

[0016] In the above method, preferably, the high-frequency solving module uses Kalman filtering algorithm for high-frequency solving, and a dynamic model considering speed and acceleration is introduced in the state updating stage to predict and constrain the position of the terminal; the dynamic model is:

[0017] P t =P t-1 +V t-1 dt+0.5a t-1 dt2 ;

[0018] V t = V t-1 + a t-1 dt;

[0019] where P t is the predicted position at time t, V t is the predicted velocity at time t, P t-1 is the position at the previous time, V t-1 is the velocity at the previous time, a t-1 is the acceleration at the previous time, and dt is the sampling interval between the previous and current time.

[0020] In the above method, preferably, the types of observation values used for measurement update in the high-frequency solution module include pseudo-range observation values and non-ambiguity carrier observation values.

[0021] In the above method, preferably, the low-frequency solution module uses an observation model considering spatial atmospheric parameters to estimate ambiguity parameters and atmospheric parameters simultaneously.

[0022] In the above method, preferably, the observation model considering spatial atmospheric parameters is as follows:

[0023]

[0024]

[0025]

[0026]

[0027] where i and j are frequency markers, p and q are satellites, b and r are reference stations and mobile stations, λ is the frequency, and are double-difference pseudo-range and double-difference carrier observation values; is a double-difference satellite-geodetic distance calculated according to the initial position of the mobile station, E p ,q is a single-difference direction cosine of the satellite on the mobile station, {dx, dy, dz} is a receiver position parameter to be estimated, f b and f r are tropospheric wet component projection functions of the reference station and the mobile station, T b and T r are zenith tropospheric delay parameters to be estimated; μ is the coefficient of the inter-frequency ionosphere, is a double-difference ionospheric delay parameter to be estimated; is a double-difference ambiguity parameter to be estimated.

[0028] In the above method, preferably, the sampling frequency of the low-frequency solution module is not higher than 1Hz.

[0029] In the above method, preferably, the state update equation of the Kalman filter is as follows:

[0030]

[0031]

[0032] Wherein: is the state parameter to be estimated, is the state parameter of the previous epoch, T is the state transition matrix, and W is the state transition noise matrix, is the state covariance matrix of the previous epoch.

[0033] The form of the state parameter to be estimated is as follows:

[0034] X={x,y,z,vx,vy,vz,ax,ay,az,clk,vclk}, wherein x, y, z are position parameters, vx, vy, vz are velocity parameters, ax, ay, az are acceleration parameters, clk is a receiver clock difference parameter, and vclk is a receiver clock speed parameter.

[0035] The specific form of the state transition matrix T and its noise matrix W is as follows:

[0036]

[0037]

[0038] Wherein, tt=0.5dt 2 , dt is the sampling interval, the diagonal elements qx, qy, qz are position noise, qvx, qvy, qvz are velocity noise, qax, qay, qaz are acceleration noise, qc is clock difference noise, and qd is clock speed noise. The corresponding non-diagonal elements are the covariances of the corresponding parameter noise.

[0039] The application also provides a high-frequency solution system for an RTK terminal, which is arranged on the RTK terminal and comprises:

[0040] A low-frequency solution module for estimating carrier phase ambiguity;

[0041] A high-frequency solution module for correcting an observation model according to the low-frequency thread solution result to obtain the position parameter of the terminal;

[0042] A thread scheduling module for detecting a data update event of the low-frequency thread solution result and issuing a corresponding control signal to schedule the high-frequency and low-frequency threads, and being configured to:

[0043] registering signal events and signal processing functions when the high-frequency thread and the low-frequency thread are started for the first time;

[0044] detecting data update events in the data buffer in real time;

[0045] when a data update event is detected, sending a thread suspension signal to the low-frequency thread to suspend the low-frequency thread, sending a first signal to the high-frequency thread, resuming execution of the high-frequency thread after the first signal processing function receives the first signal, the high-frequency calculation module obtaining updated low-frequency thread calculation results from the data buffer, correcting the observation model, and suspending the high-frequency thread after the current epoch processing is completed;

[0046] sending a second signal to the low-frequency thread, resuming execution of the low-frequency thread after the second signal processing function receives the second signal, the low-frequency calculation module estimating carrier phase ambiguity, saving the ambiguity to the data buffer after the calculation is completed, generating a data update event, and then suspending the low-frequency thread;

[0047] continuing to detect data update events in the data buffer in real time according to whether a terminal stop settlement command is received, or ending settlement.

[0048] In the above system, preferably, the high-frequency calculation module uses a Kalman filter algorithm for high-frequency calculation, and a dynamic model considering speed and acceleration is introduced in the state update stage to predict and constrain the position of the terminal; the dynamic model is:

[0049] P t =P t-1 +V t-1 dt+0.5a t-1 dt 2 ;

[0050] V t =V t-1 +a t-1 dt;

[0051] wherein P t is the predicted position at time t, V t is the predicted speed at time t, P t-1 is the position at the previous time, V t-1 is the speed at the previous time, a t-1 is the acceleration at the previous time, and dt is the sampling interval between the previous and the current time.

[0052] In the above system, preferably, the low-frequency calculation uses an observation model considering spatial atmospheric parameters to estimate ambiguity parameters and atmospheric parameters simultaneously.

[0053] From the above technical solution can be known, the high frequency solving method for RTK terminal provided by the application solves the problem that the terminal algorithm cannot support high frequency RTK positioning solution due to insufficient or inadequate performance of the GNSS terminal board card or module itself computing resources in the prior art.

[0054] Through the high / low frequency double-threaded solving mode, the high frequency thread is responsible for position parameter solution, and the low frequency thread is responsible for ambiguity solution, high frequency RTK positioning result updating is realized, the separation of time-varying parameter and time-invariant parameter estimation mode reduces the single epoch RTK calculation time consumption, reduces the frequency of full parameter estimation, improves the solving efficiency and reduces the hardware load. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the description of the embodiments of the present application or the prior art will be briefly introduced and described below. Obviously, the drawings in the following description are only part of the embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0056] Figure 1 A flowchart of the high frequency solving method for RTK terminal provided by the present application is shown in the figure.

[0057] Figure 2 A comparison diagram of the solving efficiency of the algorithm of the present application and the conventional algorithm is shown in the figure.

[0058] Figure 3 A comparison diagram of the positioning effect of the algorithm of the present application and the conventional algorithm (U direction) is shown in the figure.

[0059] Figure 4 A comparison diagram of the solving efficiency of the high frequency solving module algorithm in the present application and the conventional least square algorithm is shown in the figure.

[0060] Figure 5 A comparison diagram of the positioning effect of the high frequency solving module algorithm in the present application and the conventional least square algorithm (U direction) is shown in the figure. DETAILED DESCRIPTION

[0061] The technical solutions of the embodiments of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. Obviously, the following described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creating any creative labor belong to the scope of protection of the present application.

[0062] The implementation principle of the present application is:

[0063] According to the characteristics of time-varying parameters and time-invariant parameters, the full parameter estimation of the conventional solution method is decomposed into a low-frequency solution module and a high-frequency solution module, wherein the low-frequency solution module is mainly responsible for carrier phase integer ambiguity estimation, and the high-frequency module is responsible for terminal position parameter estimation, the timeliness of high-frequency solution is ensured through high and low frequency double thread scheduling, the overall solution efficiency of the algorithm is improved, and the calculation load of the terminal is reduced.

[0064] In order to make the technical solutions and implementation modes of the present application clearer and more understandable, the following introduces several preferred specific embodiments for implementing the technical solutions of the present application.

[0065] It should be noted that the orientation words such as "inner", "outer", "front", "rear", "left" and "right" in this paper are described based on the product use state as the reference object. Obviously, the use of corresponding orientation words does not constitute a limitation on the protection scope of the present application.

[0066] Please refer to Figure 1 , Figure 1 The present application provides a high-frequency solution method flow chart for an RTK terminal.

[0067] As Figure 1 shown, the present application provides a high-frequency solution method for an RTK terminal, which comprises the following steps:

[0068] Step 100, the RTK terminal sets up two threads of high-frequency solution and low-frequency solution, estimates the carrier phase ambiguity by using the low-frequency thread according to the time-varying characteristics of the parameters to be estimated, and modifies the observation model by using the high-frequency thread according to the low-frequency thread solution result to obtain the position parameters of the terminal.

[0069] Step 200, detect the data update event of the low-frequency thread solution result, and send corresponding control signals to schedule the high-frequency and low-frequency threads, and the specific steps are as follows:

[0070] Step 211, register the signal event and signal processing function when the high-frequency thread and the low-frequency thread are started for the first time;

[0071] Step 212, real-time detect the data update event in the data buffer area;

[0072] Step 213, when the data update event is detected, send the thread suspension signal to the low-frequency thread to suspend the low-frequency thread, send the first signal to the high-frequency thread, and after the first signal processing function receives the first signal, the high-frequency thread is resumed, the high-frequency solution module obtains the updated low-frequency thread solution result from the data buffer area, modifies the observation model, and suspends the high-frequency thread after the current epoch processing is completed;

[0073] Step 214, a second signal is sent to the low-frequency thread, after the second signal processing function receives the second signal, the low-frequency thread is resumed, the low-frequency solving module estimates the carrier phase ambiguity, after the solving is completed, the ambiguity is saved to the data buffer, a data update event is generated, and then the low-frequency thread is suspended;

[0074] Step 215, it is judged whether a terminal stop settlement command is received, if not, step 212 is turned to; otherwise, the settlement is ended.

[0075] In the method, considering the space atmosphere which cannot be ignored when the medium-long baseline and ionosphere are active, an observation model considering the space atmosphere parameter is introduced in the low-frequency solving module, the ambiguity parameter and the atmosphere parameter are estimated at the same time, and the application in the medium-long baseline and the active atmosphere scene is compatible.

[0076] The observation model considering the space atmosphere parameter is as follows:

[0077]

[0078]

[0079]

[0080]

[0081] Wherein, i and j are frequency marks, p and q are satellites, b and r are reference stations and mobile stations, λ is frequency, And Is the double-difference pseudo-range and double-difference carrier observation; Is the double-difference geodetic distance calculated according to the initial position of the mobile station, E p ,q Is the single-difference direction cosine of the satellite on the mobile station, {dx, dy, dz} is the receiver position parameter to be estimated, f b And f r Is the tropospheric wet component projection function of the reference station and the mobile station, T b And T r Is the zenith tropospheric delay parameter to be estimated; μ is the coefficient of the inter-frequency ionosphere, Is the double-difference ionospheric delay parameter to be estimated; Is the double-difference ambiguity parameter to be estimated.

[0082] For N frequencies and M satellites (excluding reference stars), a total of 1+M+2×N×M observation equations can be obtained, and the parameters to be estimated include 3 coordinate parameters, 2 tropospheric parameters, M ionospheric parameters and N×M ambiguity parameters.

[0083] The above observation equation can be written in the form of the following matrix:

[0084] V = HX - L, wherein:

[0085]

[0086] The low-frequency solution module is responsible for the solution of ambiguity parameters and ionospheric parameters, zenith tropospheric parameters and other atmospheric parameters through a low-frequency thread, and outputs to a data buffer, and the parameter estimation process is consistent with the conventional method.

[0087] In the case where no cycle slip occurs, the ambiguity parameters between epochs remain unchanged, and when the epoch interval is very small (less than 1s in the high-frequency case), the atmospheric parameters can also be approximately considered unchanged, so the solution frequency of the low-frequency solution module can be no higher than 1Hz, or even once every several seconds, greatly reducing the computational load.

[0088] The method of the application, in order to ensure the positioning stability and positioning accuracy in complex environment, the high-frequency solution module adopts Kalman filtering algorithm considering the constraint of dynamic model, and comprehensively uses pseudorange and ambiguity-free carrier observation, which improves the smoothness, stability and reliability of positioning results in complex scenes while ensuring the solution efficiency, and improves the positioning accuracy.

[0089] The dynamic model is as follows:

[0090] P t = P t-1 + V t-1 dt + 0.5a t-1 dt 2 ;

[0091] V t = V t-1 + a t-1 dt.

[0092] Wherein, P t is the predicted position at time t, V t is the predicted velocity at time t, P t-1 is the position at the previous time, V t-1 is the velocity at the previous time, a t-1 is the acceleration at the previous time, and dt is the sampling interval between the previous and the next time.

[0093] The conventional algorithm takes the result of pseudorange single point positioning as the initial value of the position of RTK, and when the dynamic model is introduced, the state update equation of Kalman filtering is adjusted as follows:

[0094]

[0095]

[0096] Wherein: is a state parameter to be estimated, is a state parameter of a previous epoch, T is a state transition matrix, and W is a state transition noise matrix, is a state covariance matrix to be estimated, is a state covariance matrix of a previous epoch;

[0097] The state parameter to be estimated is in the form of:

[0098] X={x,y,z,vx,vy,vz,ax,ay,az,clk,vclk}, wherein x, y, z are position parameters, vx, vy, vz are velocity parameters, ax, ay, az are acceleration parameters, clk is a receiver clock bias parameter, and vclk is a receiver clock rate parameter;

[0099] The specific form of the state transition matrix T and the noise matrix W is as follows:

[0100]

[0101]

[0102] wherein tt=0.5dt 2 , dt is a sampling interval, the diagonal elements qx, qy, qz are position noise, qvx, qvy, qvz are velocity noise, qax, qay, qaz are acceleration noise, qc is clock bias noise, and qd is clock rate noise, and the corresponding non-diagonal elements are the covariances of the corresponding parameter noise.

[0103] The measurement update of the Kalman filter is in the form of:

[0104]

[0105] wherein Z is a new information vector, L is an observation vector, R is a noise matrix of the observation value, and H is a design matrix.

[0106] In the measurement update, the observation value vector of the conventional algorithm only uses the carrier phase observation value with fixed ambiguity, and it is difficult to ensure the accuracy and stability of the positioning result in poor environment. In the present application, in order to ensure the reliability of the positioning calculation, the types of observation values used for measurement update in the high-frequency thread include pseudo-range observation values and carrier observation values without ambiguity.

[0107] Based on the above method, the present application further provides a high-frequency calculation system for an RTK terminal, which is arranged on the RTK terminal and comprises:

[0108] a low-frequency calculation module for estimating carrier phase ambiguity;

[0109] A high-frequency solving module is configured to correct the observation model according to the low-frequency thread solving result to obtain the position parameter of the terminal.

[0110] A thread scheduling module is configured to detect a data update event of the low-frequency thread solving result and send a corresponding control signal to schedule the high-frequency and low-frequency threads, and is configured to:

[0111] Register a signal event and a signal processing function when the high-frequency thread and the low-frequency thread are started for the first time.

[0112] Detect a data update event in the data buffer in real time.

[0113] When the data update event is detected, send a thread suspension signal to the low-frequency thread to suspend the low-frequency thread, send a first signal to the high-frequency thread, and after the first signal processing function receives the first signal, resume the execution of the high-frequency thread, the high-frequency solving module obtains the updated low-frequency thread solving result from the data buffer, corrects the observation model, and suspends the high-frequency thread after the current epoch processing is completed.

[0114] Send a second signal to the low-frequency thread, and after the second signal processing function receives the second signal, resume the execution of the low-frequency thread, the low-frequency solving module estimates the carrier phase ambiguity, saves the ambiguity to the data buffer after the solving is completed, generates a data update event, and then suspends the low-frequency thread.

[0115] According to whether a terminal stop settlement command is received, continue to detect the data update event in the data buffer in real time or end the settlement.

[0116] As shown in Figure 2 , Figure 3 , Figure 4 , Figure 5 , wherein the light color is a conventional algorithm, and the dark color is the algorithm of the application (such as the lower curve in Figure 2 . Compared with the conventional algorithm, the positioning effect of the application is better under the condition of equivalent time consumption.

[0117] According to the description of the above specific embodiments, the intelligent real-time AR engineering measurement method provided by the application has the following advantages compared with the prior art:

[0118] First, the prior art generally solves all the to-be-estimated parameters at one time, due to the large number of observation values and to-be-estimated parameters, the matrix dimension of numerical calculation is very large, which seriously affects the solving efficiency of the algorithm, and cannot meet the requirements of high-frequency positioning. The present application decomposes the conventional RTK algorithm, according to the time-varying characteristics of the to-be-estimated parameters, adopts a double-thread solving scheme of high frequency + low frequency, and schedules the threads through signal interruption + thread locking to ensure the priority of high-frequency solving and the safety of data interaction between threads. Among them, the low-frequency algorithm estimates the carrier phase ambiguity, ionosphere and troposphere and other time-invariant parameters or slowly changing parameters, and the high-frequency algorithm directly modifies the observation model using the parameters solved by the low-frequency algorithm, and obtains the position parameters through the filtering algorithm. The timeliness requirement of high-frequency algorithm to low-frequency parameters is not high, therefore, the frequency of low-frequency algorithm execution can be set very low, and the single solving time can be completed within several seconds. The number of to-be-estimated parameters and observation values of high-frequency algorithm is greatly reduced (the matrix dimension is smaller), and the single solving time is also smaller. The overall solving efficiency is improved, and the computing load of the terminal is reduced.

[0119] Second, the prior art generally uses the least square algorithm to estimate the position parameters, since the least square algorithm only uses the observation information of the current epoch, when the observation environment is poor or the number of fixed satellites is small, the satellite geometry structure involved in the solving is poor, which will lead to unstable or jumping positioning results, and it is difficult to guarantee the continuity and smoothness of the positioning results. The present application uses Kalman filter algorithm for high-frequency solving, introduces a dynamics model considering speed and acceleration in the state update stage to predict and constrain the position of the terminal, and comprehensively uses pseudorange observation values and non-ambiguity carrier phase observation values in the measurement update stage. While ensuring efficient solving, the continuity, smoothness and reliability of the positioning results in complex environment are also improved.

[0120] Third, the prior art generally ignores the influence of atmospheric parameters, and is only suitable for short baseline scenes, but for medium-long baseline or active atmospheric scenes, it is difficult to stably obtain high-precision positioning results. In the process of low-frequency ambiguity solving, the present application considers the influence of atmospheric parameters (the parameter estimation strategy will be dynamically adjusted according to the actual observation situation), and parameterizes the troposphere delay and ionosphere delay together with the ambiguity parameter. In order to speed up the convergence of ambiguity parameters, the present application uses the correlation of space atmosphere to impose prior constraint information on the modeled atmospheric parameters (which can be finely constrained according to external atmospheric information). Since the atmospheric parameters change slowly between epochs, the high-frequency solving thread can directly substitute the atmospheric information obtained by low-frequency solving into the observation equation, therefore, the high-frequency algorithm can be compatible with medium-long baseline high-frequency positioning without increasing the to-be-estimated parameters and reducing the solving efficiency.

[0121] Finally, it should be noted that the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0122] The present application is not limited to the above-described best mode, and any person skilled in the art should know that structural changes made under the inspiration of the present application, any technical solutions with the same or similar to the present application, fall within the protection scope of the present application.

Claims

1. A high-frequency solution method for an RTK terminal, characterized in that, The method comprises the following steps: Two threads of high frequency and low frequency are set, the low frequency thread is used to estimate carrier phase ambiguity according to time-varying characteristics of the parameters to be estimated; the high frequency thread is used to modify the observation model according to the solution result of the low frequency thread to obtain the position parameters of the terminal; A data update event of the solution result of the low frequency thread is detected, and a corresponding control signal is sent to schedule the high frequency thread and the low frequency thread, and the specific steps are as follows: Step 211, when the high frequency thread and the low frequency thread are started for the first time, signal events and signal processing functions are registered; Step 212, a data update event in a data buffer is detected in real time; Step 213, when the data update event is detected, a thread suspension signal is sent to the low frequency thread to suspend the low frequency thread; a first signal is sent to the high frequency thread, after the first signal processing function receives the first signal, the high frequency thread is resumed, the high frequency solution module obtains the updated solution result of the low frequency thread from the data buffer, the observation model is modified, and after the current epoch processing is completed, the high frequency thread is suspended; Step 214, a second signal is sent to the low frequency thread, after the second signal processing function receives the second signal, the low frequency thread is resumed, the low frequency solution module estimates the carrier phase ambiguity, after the solution is completed, the ambiguity is saved to the data buffer, a data update event is generated, and then the low frequency thread is suspended; Step 215, whether a terminal stop settlement command is received is judged, if not, step 212 is turned; otherwise, the settlement is ended.

2. The method of claim 1, wherein, The high frequency solution module uses a Kalman filtering algorithm for high frequency solution, and a dynamic model considering speed and acceleration is introduced in a state update stage to predict and constrain the position of the terminal; the dynamic model is as follows: ； ； wherein, is a predicted position at time t, is a predicted velocity at time t, is a position at a previous time, is a velocity at a previous time, is an acceleration at a previous time, is a sampling interval between previous and current times.

3. The method of claim 2, wherein, The observation value type used for measurement update in the high frequency solution module includes a pseudo-range observation value and a carrier observation value without ambiguity.

4. The method of claim 3, wherein, The low frequency solution module uses an observation model considering spatial atmospheric parameters to estimate ambiguity parameters and atmospheric parameters.

5. The method of claim 4, wherein, The observation model considering spatial atmospheric parameters is as follows: ； ； ； ； wherein and are frequency markers, and are satellites, and are reference stations and mobile stations, is a frequency, and are double-difference pseudo-range and double-difference carrier observations; is a double-difference pseudorange calculated from the initial position of the mobile station, is a single-difference direction cosine of the satellite at the mobile station, is a receiver position parameter to be estimated, and are tropospheric wet component projection functions of the reference stations and the mobile station, and are zenith tropospheric delay parameters to be estimated; is a coefficient of the inter-frequency ionosphere, is a double-difference ionospheric delay parameter to be estimated; is a double-difference ambiguity parameter to be estimated.

6. The method of claim 1, wherein, The sampling frequency of the low frequency solution module is not higher than 1 Hz.

7. The method of claim 2, wherein, The state update equation of the Kalman filtering is as follows: ； ； wherein: is a state parameter to be estimated, is a state parameter of a previous epoch, is a state transition matrix, is a state covariance matrix to be estimated, is a state transition noise matrix, is a state covariance matrix of a previous epoch; The state parameters to be estimated are as follows: is a position parameter, is a velocity parameter, is an acceleration parameter, is a receiver clock bias parameter, is a receiver clock rate parameter; State transition matrix and its noise matrix are as follows: T= ； W= ； wherein, , is a sampling interval, the diagonal elements is a position noise, is a velocity noise, is an acceleration noise, is a clock bias noise, is a clock rate noise, the corresponding off-diagonal elements are the covariance noise of the corresponding parameter noise.

8. A high-frequency solution system for an RTK terminal, provided on an RTK terminal, characterized in that, It comprises: A low frequency solution module is used to estimate carrier phase ambiguity; A high frequency solution module is used to modify the observation model according to the solution result of the low frequency thread to obtain the position parameters of the terminal; A thread scheduling module detects a data update event of the solution result of the low frequency thread, and sends a corresponding control signal to schedule the high frequency thread and the low frequency thread, and is configured as: Signal events and signal processing functions are registered when the high frequency thread and the low frequency thread are started for the first time; A data update event in a data buffer is detected in real time; When the data update event is detected, a thread suspension signal is sent to the low frequency thread to suspend the low frequency thread; A first signal is sent to the high frequency thread, after the first signal processing function receives the first signal, the high frequency thread is resumed, the high frequency solution module obtains the updated solution result of the low frequency thread from the data buffer, the observation model is modified, and after the current epoch processing is completed, the high frequency thread is suspended; A second signal is sent to the low frequency thread, after the second signal processing function receives the second signal, the low frequency thread is resumed, the low frequency solution module estimates the carrier phase ambiguity, after the solution is completed, the ambiguity is saved to the data buffer, a data update event is generated, and then the low frequency thread is suspended; The second signal is sent to the low-frequency thread, and after the second signal processing function receives the second signal, the low-frequency thread is resumed, the low-frequency solution module estimates the carrier phase ambiguity, saves the ambiguity to the data buffer after the solution is completed, generates a data update event, and then suspends the low-frequency thread; According to whether the terminal stops the settlement command is received, the data update event in the data buffer is continuously detected in real time, or the settlement is ended.

9. The system of claim 8, wherein, The high-frequency solution module uses Kalman filtering algorithm for high-frequency solution, and a dynamic model considering speed and acceleration is introduced in the state updating stage to predict and constrain the position of the terminal; the dynamic model is: ； ； wherein, is a predicted position at time t, is a predicted velocity at time t, is a position at a previous time, is a velocity at a previous time, is an acceleration at a previous time, is a sampling interval between previous and current times.

10. The system of claim 8, wherein, The low-frequency solution module uses an observation model considering spatial atmospheric parameters to estimate the ambiguity parameters and atmospheric parameters simultaneously.

Images