Global satellite navigation terminal and its navigation and positioning method

Abstract

The present invention relates to a global satellite navigation terminal and its navigation and positioning method. The global satellite navigation terminal includes a satellite navigation antenna, first and second satellite navigation receivers; and a processing module. The processing module performs the following operations: calculating zero-baseline double-difference observations based on the satellite navigation data of the two satellite navigation receivers, and calculating an observation random noise matrix based on the zero-baseline double-difference observations; calculating single-difference observations based on the observations in the satellite navigation data of one satellite navigation receiver and the corrections obtained from a navigation server, and performing robust adaptive filtering iterative estimation of a robust adaptive parameter vector based on the calculated single-difference observations and the observation random noise matrix; searching for and fixing the integer ambiguity based on the floating-point ambiguity in the robust adaptive parameter vector, and re-solving the robust adaptive parameter vector based on the fixed ambiguity. In this way, high-precision positioning can be achieved based on a more reliable observation random noise model.

Description

Technical Field

[0001] The present invention relates to the field of navigation, and in particular, to a global satellite navigation terminal and a navigation and positioning method thereof. Background Art

[0002] Real Time Kinematic (RTK) positioning of the Global Navigation Satellite System (GNSS) is a low-cost high-precision positioning technology route. The Global Navigation Satellite System includes GPS of the United States, GLONASS (Gelonas) of Russia, BEIDOU of China, and GALILEO of the European Union. Among them, the navigation signals of GPS, BEIDOU, and GALILEO are based on Code Division Multiple Access (CDMA). Therefore, the frequencies of the signals in the same frequency band of different satellite signals are the same. While the GLONASS navigation signal is based on Frequency Division Multiple Access (FDMA).

[0003] GNSS RTK positioning technology is a high-precision positioning technology widely used in the current field of autonomous driving. Compared with GNSS differential positioning (DGNSS), RTK also relies on the observation data from the base station, which can effectively eliminate the ionospheric error, tropospheric error, satellite clock error, and receiver clock error; RTK also uses carrier observations for position solution. Starting from the modeling of pseudorange and carrier observations, to the linear optimization solution of the float solution, then using the popular LAMBDA algorithm to solve the ambiguity, and finally the ambiguity fixed solution can be obtained.

[0004] The solution of RTK positioning is actually quite mature. In an open environment, with a good receiver, it is quite possible to obtain a fixed solution for RTK. Therefore, an EKF (Extended Kalman Filter) can be used to achieve an observable centimeter-level accuracy. However, the key problem is still the use of low-cost GNSS receivers (which can also be called satellite navigation receivers in this article) in an urban environment (where GNSS signals are severely blocked). Due to the multipath effect, there are obvious errors in the original observations. In addition, problems such as loss of lock and cycle slip occur in phase observations. These problems are more serious in low-cost GNSS receivers. This is also one of the main reasons why RTK cannot achieve correct ambiguity fixation in an urban environment, resulting in unsatisfactory positioning accuracy. Currently, widely studied autonomous driving is another application with extremely high safety requirements. However, GNSS RTK positioning technology is a very crucial part of autonomous driving. Therefore, how to effectively monitor whether the integrated positioning of autonomous driving meets the requirements of current autonomous driving systems is a very important issue. This leads to the concept of GNSS RTK integrity.

[0005] Integrity is a criterion used to measure whether the information provided by the entire system can be trusted. An integrity system needs to promptly send an alarm message to inform the user that the system has an error when the system sends an incorrect message, so as to prevent the user from using the incorrect information. A complete GNSS receiver integrity system includes the detection, identification, and exclusion of errors, as well as the availability detection of navigation results. The core step of the integrity system is the calculation of the protection level of the estimated parameters, which has the following three characteristics. First, when the protection level is less than the alert limit, the positioning result is considered available; otherwise, the system determines that the positioning result is unavailable. Second, the protection level should be as close as possible to the true error and slightly greater than the true error, so as to ensure that the real-time integrity detection reflects the real RTK positioning result to the greatest extent. Finally, the calculation of the protection level of the position is usually based on the least squares or Kalman filter residuals of GNSS positioning. This means that the accuracy of GNSS integrity detection also depends to a large extent on the accuracy of the positioning result and the random noise model of the observations. The traditional integrity system calculates the protection level based on the residuals obtained from the traditional RTK positioning result. In an urban environment where GNSS signals are severely blocked, the residuals cannot truly reflect the real positioning error.

[0006] Therefore, it is necessary to propose a solution to solve the above problems. Summary of the Invention

[0007] The object of the present invention is to provide a global satellite navigation terminal and its navigation and positioning method. The method uses two satellite navigation receivers connected to the same satellite navigation antenna, and through zero-baseline solution, it can realize real-time evaluation of the influence of the surrounding environment on the quality of GNSS observation values, and can establish a more reliable observation value random noise model.

[0008] To solve the above technical problems, according to one aspect of the present invention, a global satellite navigation terminal is provided, which includes: a satellite navigation antenna; a first satellite navigation receiver connected to the satellite navigation antenna, which receives satellite navigation data in a first frequency band and a second frequency band, and the satellite navigation data includes ephemeris data and observation values, and the observation values include pseudorange observation values, phase observation values and Doppler observation values; a second satellite navigation receiver connected to the satellite navigation antenna, which receives satellite navigation data in the first frequency band and the second frequency band; a processing module connected to the first satellite navigation receiver and the second satellite navigation receiver; and a communication module connected to the processing module. The processing module performs the following operations: calculating a single-point positioning solution based on the satellite navigation data of one satellite navigation receiver, sending the single-point positioning solution to a navigation server, and receiving a correction number obtained by the navigation server according to the single-point positioning solution; calculating zero-baseline double-difference observation values based on the satellite navigation data of the two satellite navigation receivers, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation values; calculating single-difference observation values based on the observation values in the satellite navigation data of one satellite navigation receiver and the correction number obtained from the navigation server, and performing robust adaptive filtering iterative estimation of a robust adaptive parameter vector based on the calculated single-difference observation values and the observation value random noise matrix, and the robust adaptive parameter vector includes position, velocity, receiver clock offset and floating-point ambiguity; searching and fixing the integer ambiguity based on the floating-point ambiguity in the estimated robust adaptive parameter vector, and re-solving the robust adaptive parameter vector based on the fixed ambiguity.

[0009] According to another aspect of the present invention, the present invention provides a navigation and positioning method for a global satellite navigation terminal. The global satellite navigation terminal includes: a satellite navigation antenna; a first satellite navigation receiver connected to the satellite navigation antenna, which receives satellite navigation data in a first frequency band and a second frequency band. The navigation method includes: calculating a single-point positioning solution based on the satellite navigation data of one satellite navigation receiver, sending the single-point positioning solution to a navigation server, and receiving a correction number obtained by the navigation server based on the single-point positioning solution; calculating a zero-baseline double-difference observation value based on the satellite navigation data of two satellite navigation receivers, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation value; calculating a single-difference observation value based on the observation value in the satellite navigation data of one satellite navigation receiver and the correction number obtained from the navigation server, and performing robust adaptive filtering iteration to estimate a robust adaptive parameter vector based on the calculated single-difference observation value and the observation value random noise matrix. The robust adaptive parameter vector includes position, velocity, receiver clock error, and floating-point ambiguity; searching and fixing the integer ambiguity based on the floating-point ambiguity in the estimated robust adaptive parameter vector, and re-solving the robust adaptive parameter vector based on the fixed ambiguity.

[0010] Compared with the prior art, the present invention uses two satellite navigation receivers connected to the same satellite navigation antenna, and realizes real-time evaluation of the influence of the surrounding environment on the quality of GNSS observation values (i.e., observation value random noise) through zero-baseline solution, and can establish a more reliable observation value random noise model. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic structural diagram of the global satellite navigation system in an embodiment of the present invention;

[0012] Figure 2 It is a schematic structural diagram of the global satellite navigation terminal in an embodiment of the present invention;

[0013] Figure 3 is Figure 2 a schematic flow diagram of the navigation method of the global satellite navigation terminal in an embodiment of ; and

[0014] Figure 4 It is a schematic diagram of the normal distribution introducing deviation to the residual of the i-th observation value. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features, and effects according to the present invention.

[0016] Figure 1FIG. 0 is a schematic structural diagram of the global satellite navigation system 100 in an embodiment of the present invention. The global satellite navigation system 100 includes a global satellite navigation terminal device 102 and a navigation server 106.

[0017] There can be many navigation terminal devices 102. The navigation terminal device 102 can be installed on a motor vehicle to perform high-precision navigation for the driving navigation of the motor vehicle, especially for driverless driving. The navigation terminal device 102 can communicate with the navigation server 106 through a wireless network 104. The wireless network 104 can be a 2G, 3G, 4G or 5G network, or a combination of multiple networks, such as Bluetooth + 4G, Wifi + 4G, Wifi + Internet + 5G, etc. The present invention has no specific requirements for the specific type of the wireless network 104, as long as it can support stable communication.

[0018] Figure 2 FIG. 7 is a schematic structural diagram of the global satellite navigation terminal device 102 in an embodiment of the present invention. The global satellite navigation terminal device 102 includes a satellite navigation antenna 210, a first satellite navigation receiver 220 connected to the satellite navigation antenna 210, a second satellite navigation receiver 221 connected to the satellite navigation antenna 210, a processing module 230 connected to the first satellite navigation receiver 220 and the second satellite navigation receiver 221, and a communication module 240 connected to the processing module 230.

[0019] The first satellite navigation receiver 220 receives satellite navigation data in a first frequency band and a second frequency band. The second satellite navigation receiver 221 also receives satellite navigation data in the first frequency band and the second frequency band. The satellite side of satellite navigation systems such as GPS / Beidou / Galileo / GLONASS will broadcast signals with different code formats in multiple frequency bands at the same time. The first frequency band and the second frequency band can be two of the multiple frequency bands supported by GNSS. The satellite navigation data includes satellite navigation ephemeris data (abbreviated as ephemeris data) and satellite navigation observations (abbreviated as observations, sometimes also referred to as observation data, satellite navigation observation data, etc.). The observations include pseudorange observations, phase observations, and Doppler observations. The communication module 240 can be a wireless communication module or a wired communication module. The wireless communication module can communicate through mobile communication (such as 5G, 4G, etc.), Wifi wireless communication, Bluetooth and other networks. The communication module 240 can communicate with the navigation server 106 through the wireless network 104.

[0020] The processing module 230 performs high-precision navigation positioning based on the satellite navigation data of the first frequency band and the second frequency band received by the first satellite navigation receiver 220 and the second satellite navigation receiver 221, that is, real-time kinematic positioning RTK of GNSS.

[0021] Figure 3 For Figure 2 is a schematic flowchart of the navigation method 300 of the global satellite navigation terminal in an embodiment. The processing module 230 executes the navigation method 300. As Figure 3 shown, the navigation method 300 includes the following steps.

[0022] Step 310, calculate a single-point positioning solution based on the satellite navigation data of one of the satellite navigation receivers, send the single-point positioning solution to the navigation server 106, and receive the correction number (or RTK correction number) obtained by the navigation server 106 according to the single-point positioning solution.

[0023] One of the satellite navigation receivers here can be the first satellite navigation receiver 220 or the second satellite navigation receiver 221. Specifically, the satellite orbital clock error is calculated based on the satellite navigation ephemeris data in the satellite navigation data, and the single-point positioning solution is calculated in combination with the satellite navigation observation data. The global satellite navigation terminal 102 sends the obtained single-point positioning solution to the navigation server 106, and the navigation server 106 provides RTK correction numbers according to the obtained single-point positioning solution.

[0024] Step 320, calculate the zero-baseline double-difference observation value based on the satellite navigation data of the two satellite navigation receivers 220 and 221, and calculate the observation value random noise matrix based on the zero-baseline double-difference observation value.

[0025] Specifically, the calculating the zero-baseline double-difference observation value based on the satellite navigation data of the two satellite navigation receivers includes the following steps:

[0026] Use the phase observation value time difference and Doppler observation value of the first satellite navigation receiver 220 and the second satellite navigation receiver 221 to perform phase observation value cycle slip detection and repair;

[0027] Calculate the single-difference observation values of the first satellite navigation receiver and the second satellite navigation receiver, select the satellite with the highest satellite elevation angle as the reference satellite according to the satellite system to form the zero-baseline double-difference observation value, and use the phase observation value without cycle slip to calculate the zero-baseline double-difference observation value.

[0028] More specifically, calculate the zero-baseline double-difference observation value according to formulas (1)-(3):

[0029]

[0030]

[0031]

[0032] where is the double-difference operator, is the zero-baseline double-difference observation value of the pseudorange, is the zero-baseline double-difference observation value of the phase, is the zero-baseline double-difference observation value of the Doppler. P, L, and D represent the pseudorange, phase, and Doppler observation values in satellite navigation observation data respectively; λ is the wavelength; the subscripts i and j are receiver numbers; the subscript f is the frequency; the superscripts s and t are satellite PRN (pseudo random noise) numbers, N is the ambiguity, and ε is the observation value error.

[0033] Specifically, calculating the observation value random noise matrix R based on the zero-baseline double-difference observation value k includes:

[0034] Assume that there are n n observation values within the moving time window [t - t i , t] and the noises of each observation value are synchronous. Then, the unit weight variances of the undifferenced pseudorange observation value, phase observation value, and Doppler observation value within the moving time window are respectively:

[0035]

[0036]

[0037]

[0038] where i represents the satellite navigation system and j represents the observation epoch number, is the estimated mean square error of the unit weight of the observation value;

[0039] Subsequently, the initial variance σ 2 of the GNSS observation value is obtained by using the height angle-based weighting method:

[0040]

[0041]

[0042]

[0043] where s represents the satellite number, σ i represents the standard deviation of the observation value noise of satellite i, and E s represents the elevation angle of satellite s;

[0044] Finally, the observed value random noise matrix R k is

[0045]

[0046] where k is the observation epoch, and R P,k , R D,k , R L,k are the random noise matrices of the pseudorange, Doppler, and phase observations respectively. The observed value random noise matrix is the observed value random noise model. In the present invention, based on two low-cost dual-frequency satellite navigation receivers, real-time evaluation of the influence of the surrounding environment on the quality of GNSS observed values (observed value random noise) can be achieved through zero-baseline solution, and a more reliable observed value random noise model R k is established.

[0047] Step 330: Calculate the single-difference observed values based on the observed values in the satellite navigation data of a satellite navigation receiver and the corrections obtained from the navigation server, and perform robust adaptive filtering iteration estimation of the robust adaptive parameter vector based on the calculated single-difference observed values and the observed value random noise matrix. The robust adaptive parameter vector includes position, velocity, receiver clock offset, and floating-point ambiguity.

[0048] Specifically, calculate the robust adaptive parameter vector according to formula (10)

[0049]

[0050] where is the robust adaptive parameter vector, which includes parameters such as position, velocity, receiver clock offset, and ambiguity, H k is the observed value design matrix, L k is the observed value vector, is the equivalent weight matrix of the observed quantities; is the predicted state vector, is the weight matrix of the predicted state vector; 0 ≤ a k ≤ 1 is the adaptive factor.

[0051] Step 340: Search for and fix the integer ambiguity based on the floating-point ambiguity in the estimated robust adaptive parameter vector, and re-calculate the robust adaptive parameter vector based on the fixed ambiguity

[0052] Specifically, the search for and fixation of the integer ambiguity based on the floating-point ambiguity in the estimated robust adaptive parameter vector includes:

[0053] Substitute the floating-point ambiguity and its covariance matrix in the estimated adaptive robust parameter vector, use the LAMBDA method to search for the integer ambiguity and verify whether it passes the detection. If the ambiguity detection is passed, continue to verify whether the ambiguity is fixed correctly through the dual-frequency combined phase observation value and the ambiguity-fixed residual value, and eliminate the ambiguities with incorrect fixes.

[0054] Based on the re-solved adaptive robust parameter vector The position and velocity in can be used for positioning and navigation. So far, the navigation method 300 in the present invention can already achieve RTK navigation positioning.

[0055] As described in the background, in order to more accurately evaluate the integrity of the navigation method 300. The navigation method 300 in the present invention further includes the following steps.

[0056] Step 350: Calculate the observation residual of the current epoch by using the calculated observation value random noise matrix and the observation value vector calculated in the robust adaptive filtering process.

[0057] Specifically, the observation residual r k of the current epoch is:

[0058]

[0059] Where is the variance of the observation residual, and L k is the observation value vector;

[0060] Step 360: Calculate the non-centrality parameters of different parameters according to the distribution of the observation residual of the current epoch after introducing gross errors. The different parameters include position and velocity.

[0061] Specifically, assuming that there is a gross error in the observation value then L in (11) k is rewritten as:

[0062]

[0063]

[0064] Without considering the introduction of gross errors, assuming that r k is unbiased r k ~N(0, C r ), M is the projection function of the gross error and the observation value, and the residual r k The bias introduced due to the existence of the gross error is Δr k , combining formula (11) gives the residual distribution after introducing the gross error as:

[0065]

[0066] After standardization, the residual of the i-th observation value introduces a deviation that follows a normal distribution as follows:

[0067]

[0068] Among them, the mean of the observation value residual is called the centrality parameter, which represents the translation value of the standardized residual of the normal distribution, as follows Figure 4 shown, Figure 4 The regions indicated by α / 2 in total represent the regions where type I errors occur (i.e., wrongly rejecting the null hypothesis), with a probability of occurrence of α, and the region indicated by β represents the occurrence of type II errors (i.e., wrongly accepting the null hypothesis), with a probability of occurrence of β. Thus, the non-centrality parameter δ0 is approximately:

[0069] δ0 = N 1-α / 2 +N 1-β (16)

[0070] Among them, N 1-α / 2 is the quantile of type I error, and N 1-β is the quantile of type II error. Given a confidence interval, the quantiles of the two types of errors are obtained, and then the non-centrality parameters of different parameters are calculated.

[0071] Step 370: Based on the non-centrality parameters of different parameters, calculate the minimum detectable gross error under the current confidence interval.

[0072] Specifically, based on the non-centrality parameters of different parameters, calculate the minimum detectable gross error according to formula (17) under the current confidence interval as:

[0073]

[0074] Step 380: Calculate the protection levels of the parameters in the adaptive robust parameter vector based on the minimum detectable gross error.

[0075] Specifically, calculate the protection levels of the parameters in the adaptive robust parameter vector according to formula (18) based on the minimum detectable gross error

[0076]

[0077] including the protection levels of position, velocity, receiver clock offset, and ambiguity. It reflects the influence of the gross error (the maximum residual value) that cannot be detected in the existing filtering on each parameter.

[0078] Step 390: Compare the protection levels of the calculated parameters with the preset alarm limit values. When the protection level of each parameter is less than the preset alarm limit value, recognize the positioning result in the adaptive robust parameter vector; otherwise, do not recognize the positioning result in the adaptive robust parameter vector and issue an alarm to the user.

[0079] The present invention realizes the refinement of the random noise model of real-time navigation satellite observation data through a GNSS dual-satellite navigation receiver. While improving the accuracy of the RTK positioning result, it calculates a more accurate position protection level through the residuals calculated from the dual-frequency combined phase observations and the optimized random noise model, thereby improving the accuracy and reliability of the integrity system.

[0080] The adaptive robust Kalman filter is introduced in detail below.

[0081] The robust Kalman filter is developed on the basis of the standard Kalman filter, focusing on the actual anti-interference ability and reliability of the filtering estimation, and can correspondingly adjust the weight of the observation information, which is more suitable for positioning calculations in complex dynamic environments. The error equations of the Kalman filter state model and observation model are as follows:

[0082]

[0083]

[0084] In the above formula, V k respectively represent the state prediction residual and the observation residual. In order to control the influence of observation anomalies on the filtering estimation, the following extreme value equation is constructed:

[0085]

[0086] Taking the extreme value of the state parameter vector, the solution of the robust adaptive filter can be obtained as:

[0087]

[0088] In the formula, is the equivalent weight matrix of the observed quantity; R k is the random noise matrix of the navigation satellite observation value calculated in Step 7, is the weight matrix of the predicted state vector; 0 ≤ a k ≤ 1 is the adaptive factor. It can be seen from the above formula that when there are gross error observations in the observed quantity L k , the influence of the observations containing gross errors on the estimation of the state parameters can be controlled through the equivalent weight matrix or the equivalent covariance matrix ; when there are anomalies in the state prediction information, through the adaptive factor a kThe influence of the predicted state information on the estimation of the state parameters can be controlled. Therefore, the equivalent covariance matrix and the adaptive factor a k are crucial for implementing robust adaptive filtering.

[0089] In robust adaptive filtering, usually, the abnormal observations containing gross errors are downweighted, and the adopted equivalent covariance matrix is:

[0090]

[0091] In the formula, is a diagonal matrix, and the elements p i on its diagonal can be determined by the IGG-III three-segment weight function model, that is

[0092]

[0093] In the formula, k0 and k1 are test thresholds, generally taking values of k0 = 1.0 - 2.5, k1 = 3.5 - 8.0; is the standardized residual, and its calculation method is:

[0094]

[0095] In the formula, v i is the observation residual; R i is the noise variance of the i-th observation; is the estimated value of the unit weight variance factor.

[0096] For the adaptive factor a k , it is usually constructed based on the three-segment function of the state misfit statistic, that is:

[0097]

[0098] In the formula, c0 and c1 are detection thresholds, generally taking values of c0 = 1.0 - 1.5, c1 = 3.0 - 8.5; is the state misfit statistic, and its calculation formula is:

[0099]

[0100] In the formula, ||·|| represents the modulus operation; tr represents the trace operation; represents the predicted state; is solved by the least squares method according to the current epoch observation information, that is:

[0101]

[0102] When performing robust adaptive filtering, first diagnose the motion model information of the carrier. If an abnormality in the motion model is detected, construct the adaptive factor a according to the piecewise function formula in Equation Three. k Adjust the predicted state covariance matrix; then diagnose the observation information of the carrier. If gross errors are detected in a certain observation quantity, construct a weight scaling factor according to the IGG-III piecewise weight function model to adjust the weight of the observation quantity.

[0103] In this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion. In addition to the listed elements, it may also include other elements not expressly listed.

[0104] In this article, the front, back, up, down and other orientation words are defined based on the positions of the components in the drawings and the positions of the components relative to each other, only for the sake of clarity and convenience in expressing the technical solution. It should be understood that the use of the orientation words should not limit the scope of protection claimed in this application.

[0105] Without conflict, the above-mentioned embodiments and the features in the embodiments in this article can be combined with each other.

[0106] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A global satellite navigation terminal, characterized in that, It includes: A satellite navigation antenna; A first satellite navigation receiver connected to the satellite navigation antenna, which receives satellite navigation data in a first frequency band and a second frequency band. The satellite navigation data includes ephemeris data and observations, and the observations include pseudorange observations, phase observations, and Doppler observations; A second satellite navigation receiver connected to the satellite navigation antenna, which receives satellite navigation data in the first frequency band and the second frequency band; A processing module connected to the first satellite navigation receiver and the second satellite navigation receiver; And A communication module connected to the processing module; Wherein, the processing module performs the following operations: Calculate a single-point positioning solution based on the satellite navigation data of one of the first satellite navigation receiver and the second satellite navigation receiver, send the single-point positioning solution to a navigation server, and receive a correction number obtained by the navigation server according to the single-point positioning solution; Calculate zero-baseline double-difference observations based on the satellite navigation data of the two satellite navigation receivers, and calculate an observation random noise matrix based on the zero-baseline double-difference observations; Calculate single-difference observations based on the observations in the satellite navigation data of one of the first satellite navigation receiver and the second satellite navigation receiver and the correction number obtained from the navigation server, and perform robust adaptive filtering iterative estimation of a robust adaptive parameter vector based on the calculated single-difference observations and the observation random noise matrix. The robust adaptive parameter vector includes position, velocity, receiver clock offset, and floating-point ambiguity; Calculate the adaptive robust parameter vector according to formula (10) wherein is the adaptive robust parameter vector, which includes position, velocity, receiver clock error, and ambiguity, and H k is the observation design matrix, and L k is the observation vector, is the equivalent weight matrix of the observed quantity; is the predicted state vector, is the weight matrix of the predicted state vector; 0 ≤ a k ≤ 1 is the adaptive factor; Search for and fix the integer ambiguity based on the floating-point ambiguity in the estimated robust adaptive parameter vector, and re-solve the robust adaptive parameter vector based on the fixed ambiguity.

2. The global satellite navigation terminal according to claim 1, characterized in that, Calculating a single-point positioning solution based on the satellite navigation data of one of the first satellite navigation receiver and the second satellite navigation receiver includes: Calculating a satellite orbit clock offset based on the satellite navigation ephemeris data of one of the first satellite navigation receiver and the second satellite navigation receiver, and combining the satellite navigation observation data of this satellite navigation receiver to calculate a single-point positioning solution.

3. The global satellite navigation terminal according to claim 1, characterized in that, The calculating zero-baseline double-difference observations based on the satellite navigation data of the two satellite navigation receivers includes: Using the time difference of the phase observations and the Doppler observations of the first satellite navigation receiver and the second satellite navigation receiver to detect and repair the cycle slips of the phase observations; Calculating the single-difference observations of the first satellite navigation receiver and the second satellite navigation receiver, selecting the satellite with the highest satellite elevation angle as a reference satellite according to the satellite system to form zero-baseline double-difference observations, wherein the zero-baseline double-difference observations are calculated using the cycle-slip-free phase observations.

4. The global satellite navigation terminal according to claim 3, characterized in that, Calculate zero-baseline double-difference observations according to formulas (1)-(3): wherein is a double-difference operator, and P, L, and D respectively represent pseudorange, phase, and Doppler observations in satellite navigation observation data; is the zero-baseline double-difference observation value of the pseudorange, is the zero-baseline double-difference observation value of the phase, is the zero-baseline double-difference observation value of the Doppler; λ is the wavelength; the subscripts i and j are receiver numbers; the subscript f is the frequency; the superscripts are the satellite PRN numbers s and t respectively, N is the ambiguity, and ε is the observation error.

5. The global satellite navigation terminal according to claim 1, characterized in that, The calculating the observation random noise matrix based on the zero-baseline double-difference observations includes: Suppose there are n n observations within the moving time window [t - t i , t], and the noises of each observation are synchronous. Then, the unit weight variances of the undifferenced pseudorange observations, phase observations, and Doppler observations within the moving time window are respectively: where \(i\) represents the satellite navigation system and \(j\) represents the observation epoch number, is the mean error of unit weight of the estimated observation value; Adopting a weight determination method based on elevation angle to obtain the initial variance of GNSS observations: where s represents the satellite number, and σ i represents the standard deviation of the observation noise of satellite i, and E s represents the elevation angle of satellite s; Observed value random noise matrix R k is where \(k\) is the observation epoch, \(R\) P,k , \(R\) D,k , \(R\) L,k are the random noise matrices of the pseudorange, Doppler, and phase observations, respectively.

6. The global satellite navigation terminal according to claim 1, characterized in that, Searching for and fixing the integer ambiguity based on the floating-point ambiguity in the estimated adaptive robust parameter vector includes: Substitute the floating-point ambiguity and its covariance matrix in the estimated adaptive robust parameter vector, use the LAMBDA method to search for the integer ambiguity and verify whether it passes the detection. If the ambiguity detection is passed, continue to check whether the ambiguity is correctly fixed through the dual-frequency combined phase observations and the ambiguity-fixed residual values, and eliminate the ambiguities with incorrect fixes.

7. The global satellite navigation terminal according to claim 1, characterized in that, The processing module also performs the following operations: Calculate the current epoch observation residual using the calculated observation random noise matrix and the observation vector calculated during the robust adaptive filtering process; Calculate the non-centrality parameters of different parameters according to the distribution of the current epoch observation residual after introducing outliers, where the different parameters include position and velocity; Calculate the minimum detectable outlier under the current confidence interval based on the non-centrality parameters of different parameters; Calculate the protection levels of the parameters in the adaptive robust parameter vector based on the minimum detectable outlier; Compare the calculated protection levels of each parameter with the preset alarm limit values. When the protection levels of each parameter are less than the preset alarm limit values, recognize the positioning result in the adaptive robust parameter vector; otherwise, do not recognize the positioning result in the adaptive robust parameter vector.

8. The global satellite navigation terminal according to claim 7, characterized in that, The residual r of the current epoch observation k is as follows: wherein, is the variance of the observation value residuals, and L k is the observation value vector; Calculating the non-centrality parameters of different parameters according to the distribution of the current epoch observation residual after introducing outliers includes: Assume that there is a gross error in the observed values Then L in (11) k is rewritten as: Ignoring the introduction of gross errors, assuming that r k is unbiased r k ~N(0,C r ), M is the projection function of the gross error and the observed value, and the residual r k The bias introduced due to the existence of gross errors is Δr k . Combining with formula (11), the residual distribution after the introduction of gross errors can be obtained as follows: After standardization, the deviation introduced by the i-th observation residual follows a normal distribution as follows: where the mean of the observed value residuals is called the centrality parameter, representing the standardized residual translation value of the normal distribution. The non-centrality parameter δ0 is approximately: δ0 = N 1-α / 2 +N 1-β (16) where N 1-α / 2 is the cut-off position for type I error, and N 1-β is the cut-off position for type II error. Given a confidence interval, the cut-off positions for the two types of errors are obtained, and then the non-centrality parameters for different parameters are calculated; Based on the non-centrality parameters with different parameters, the minimum detectable gross error is calculated according to formula (17) under the current confidence interval as follows: Calculate the protection level of each parameter in the adaptive robust parameter vector according to the minimum detectable gross error based on formula (18). Including the protection levels of position, velocity, receiver clock bias, and ambiguity, where is the adaptive robust parameter vector, H k is the observation design matrix, is the equivalent weight matrix of the observables, and the observation random noise matrix R k , and k is the observation epoch.

9. A navigation and positioning method for a global satellite navigation terminal, characterized in that, The global satellite navigation terminal includes: a satellite navigation antenna; a first satellite navigation receiver connected to the satellite navigation antenna, which receives satellite navigation data in a first frequency band and a second frequency band; a second satellite navigation receiver connected to the satellite navigation antenna, which receives satellite navigation data in the first frequency band and the second frequency band; The navigation positioning method includes: Calculate a single-point positioning solution based on the satellite navigation data of one of the first satellite navigation receiver and the second satellite navigation receiver, send the single-point positioning solution to the navigation server, and receive the correction obtained by the navigation server based on the single-point positioning solution; Calculate zero-baseline double-difference observations based on the satellite navigation data of the two satellite navigation receivers, and calculate the observation random noise matrix based on the zero-baseline double-difference observations; Calculate single-difference observations based on the observations in the satellite navigation data of one of the first satellite navigation receiver and the second satellite navigation receiver and the correction obtained from the navigation server, and perform robust adaptive filtering iteration based on the calculated single-difference observations and the observation random noise matrix to estimate the adaptive robust parameter vector, where the adaptive robust parameter vector includes position, velocity, receiver clock offset, and floating-point ambiguity; Calculate the adaptive robust parameter vector according to formula (10) wherein is an adaptive robust parameter vector, which includes position, velocity, receiver clock bias and ambiguity, H k is the observation design matrix, L k is the observation vector, is the equivalent weight matrix of the observed quantity; is the predicted state vector, is the weight matrix of the predicted state vector; 0 ≤ a k ≤ 1 is the adaptive factor; Search for and fix the integer ambiguity based on the floating-point ambiguity in the estimated adaptive robust parameter vector, and re-solve the adaptive robust parameter vector based on the fixed ambiguity.

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