Gnss robust positioning and velocity measurement method and system based on original output information of intelligent terminal

Abstract

The application provides a GNSS robust positioning and velocity measurement method and system based on original output information of a smart terminal, and belongs to the technical field of global navigation satellite system positioning. The method comprises the following steps: obtaining GNSS original output information of the smart terminal and converting the GNSS original output information to obtain GNSS observation values; performing quality control on pseudorange observation values according to the GNSS original output information, performing pseudorange positioning and solving, and performing quality control on carrier phase observation values / doppler observation values according to the GNSS original output information, estimating the current epoch displacement through carrier phase inter-epoch difference observation update / doppler observation update; performing one-step prediction through the current epoch displacement according to the result of the pseudorange positioning and solving, performing pseudorange observation update and carrier phase observation update according to the one-step predicted position of the current epoch, and obtaining relative positioning solution. The application performs joint quality control on GNSS observation values based on GNSS original output information of the smart terminal, and effectively improves the precision and robustness of GNSS positioning of the smart terminal.

Description

Technical Field

[0001] This invention relates to the field of global navigation satellite system positioning technology, and in particular to a robust GNSS positioning and velocity measurement method and system based on the raw output information of a smart terminal. Background Technology

[0002] With the continuous development of Global Navigation Satellite Systems (GNSS), smartphones and other terminals have become the mainstream intelligent carriers for public location services due to their low cost and portability. Since 2016, Google has opened the API interface of Android 7.0 and above, allowing developers to obtain raw GNSS output information. By converting this raw output information, GNSS observation values ​​such as pseudorange, phase, Doppler, and carrier-to-noise ratio can be obtained, enabling high-precision GNSS positioning algorithms to be applied to platforms such as smartphones, providing a data foundation and technical support for lane-level positioning accuracy applications. However, limited by complex observation environments (such as urban canyons and forests) and low-cost components (such as low-power GNSS chips and linearly polarized antennas), GNSS observation values ​​from smartphones and other intelligent terminals often suffer from problems such as susceptibility to interference and high environmental sensitivity, making it difficult to provide stable, reliable, and high-precision positioning results. Therefore, only by effectively controlling the quality of GNSS observation values ​​from smartphones can high-precision and robust positioning and velocity measurement be achieved.

[0003] Existing theories often rely on observational information such as carrier-to-noise ratio (CNR) and elevation angle for quality control. For example, by setting cutoff elevation angles and CNR, observations with high signal strength and favorable observation environments are selected to improve positioning accuracy. Building upon this, existing methods frequently employ observational Doppler consistency and pre- and post-approval residuals to further control observational quality. However, relying solely on these indicators is insufficient to directly and accurately reflect observational quality. Even after selection, some observations containing significant gross errors and multidimensional errors still participate in the calculation, contaminating the positioning estimator and affecting positioning accuracy.

[0004] Therefore, the accuracy and reliability of GNSS positioning using current smart terminals, such as smartphones, still urgently need improvement. More precise error characteristic descriptions and more comprehensive data quality control schemes are required to provide a solid guarantee for high-precision, robust GNSS positioning and velocity measurement. Thus, exploring more effective and universally applicable data quality control strategies based on the raw output information of smart terminals is of great significance for effectively improving the accuracy and robustness of GNSS positioning. Summary of the Invention

[0005] This invention provides a GNSS robust positioning and velocity measurement method and system based on the raw output information of a smart terminal, which solves the deficiency of the lack of effective data quality control strategies for the raw output information of smart terminals in the prior art, realizes efficient data quality control of the raw output information of smart terminals, and thus effectively improves the accuracy of GNSS robust positioning and velocity measurement.

[0006] In a first aspect, the present invention provides a GNSS robust positioning and velocity measurement method based on the raw output information of a smart terminal, comprising: The raw GNSS output information of the smart terminal is acquired and converted into GNSS observations; wherein, the GNSS observations include pseudorange observations and carrier phase observations. The pseudorange observations are quality controlled based on the raw GNSS output information, and pseudorange positioning is calculated. The carrier phase observations are quality controlled based on the raw GNSS output information, and extended Kalman filtering is used for inter-epoch differential observation updates of the carrier phase to estimate the current epoch displacement. If the inter-epoch differential observation update of the carrier phase is successful, the current epoch displacement is output; otherwise, the Doppler observations are quality controlled based on the pseudorange rate of change uncertainty in the raw GNSS output information, and extended Kalman filtering is used for Doppler observation updates to estimate the current epoch displacement, which is then output. Based on the pseudorange positioning solution, a one-step prediction is performed using the current epoch displacement to obtain the one-step predicted position of the current epoch. Based on the one-step predicted position of the current epoch, pseudorange observation updates and carrier phase observation updates are performed to obtain the relative positioning solution.

[0007] According to the present invention, a robust GNSS positioning and velocity measurement method based on raw output information of a smart terminal includes the following steps: quality control of the pseudorange observations based on the raw GNSS output information, and pseudorange positioning calculation. The pseudorange observations are filtered by elevation angle, signal-to-noise ratio and pseudorange-Doppler consistency check to obtain the filtered pseudorange observations. Based on the satellite signal transmission time uncertainty in the original GNSS output information, and combined with the original carrier-to-noise ratio stochastic model, a carrier-to-noise ratio stochastic model based on the satellite signal transmission time uncertainty is constructed. The screened pseudorange observations are weighted using the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time, and the pseudorange positioning solution for the current epoch is calculated by combining least squares estimation; wherein, the result of the pseudorange positioning solution is pseudorange difference decomposition or pseudorange single-point solution.

[0008] According to the present invention, a robust GNSS positioning and velocity measurement method based on the raw output information of a smart terminal is provided, wherein the step of weighting the filtered pseudorange observations by means of the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time includes: The variance of the filtered pseudorange observations is determined using the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. The weights of the filtered pseudorange observations are determined based on the variance, and the filtered pseudorange observations are then assigned weights based on these weights.

[0009] According to the GNSS robust positioning and velocity measurement method based on the raw output information of a smart terminal provided by the present invention, the formula for determining the variance of the filtered pseudorange observations through the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time is as follows:

[0010] in, , b , d , , For the fitting parameters, The standard deviation of pseudorange observations. The variance of pseudorange observations. RTUN Represents the uncertainty of the satellite signal transmission time. Where C is the carrier-to-noise ratio and C is the carrier power. This represents the noise power spectral density.

[0011] According to the present invention, a robust GNSS positioning and velocity measurement method based on raw output information of a smart terminal is provided, wherein the quality control of the carrier phase observation value based on the raw GNSS output information includes: Based on the status information of the cumulative change distance of carrier phase in the original GNSS output information, determine whether there are any abnormal phenomena, cycle slip phenomena, or reset phenomena. Carrier phase observations exhibiting cycle slips and reset phenomena are set using LLI; carrier phase observations exhibiting abnormal phenomena such as setting conflicts or unknown setting are discarded to obtain a preliminary selection of carrier phase observations. The carrier phase observations that were initially screened were then subjected to elevation angle, signal-to-noise ratio (SNR) screening, and phase Doppler consistency checks to obtain carrier phase observations that had undergone quality control.

[0012] According to the present invention, a robust GNSS positioning and velocity measurement method based on raw output information of a smart terminal is provided, wherein the quality control of Doppler observations based on the pseudorange change rate uncertainty in the raw GNSS output information includes: Abnormal Doppler observations with pseudorange change rate uncertainty greater than a preset uncertainty threshold are removed to obtain abnormal Doppler observations. After removing anomalies, Doppler observations were screened by elevation angle, signal-to-noise ratio, and Doppler epoch difference consistency check to obtain quality-controlled Doppler observations.

[0013] According to the present invention, a robust GNSS positioning and velocity measurement method based on the raw output information of a smart terminal includes the following steps: updating pseudorange observations and carrier phase observations based on the one-step predicted position of the current epoch, and calculating the relative positioning solution, comprising: Based on the one-step prediction position of the current epoch, calculate the double-difference residual of the previous pseudorange of the current epoch and remove abnormal pseudorange observations to obtain pseudorange observations that pass the test. Based on the sorting according to elevation angle or carrier-to-noise ratio, and combined with the uncertainty of satellite signal transmission time, a reference satellite is selected. The double-difference pseudorange observation value is obtained through the verified pseudorange observation value, inter-satellite difference, and inter-station difference. The pseudorange observations are updated by weighting the double-difference pseudorange observations based on the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. Based on the one-step prediction position of the current epoch, calculate the previous phase double difference residual of the current epoch and remove abnormal carrier phase observations to obtain carrier phase observations that pass the test. The reference satellite is selected based on the elevation angle or carrier-to-noise ratio, and combined with the status information of the cumulative change distance of the carrier phase in the current epoch in the original GNSS output information. The double-difference phase observation value is obtained through the carrier phase observation value that has passed the test, the inter-satellite difference, and the inter-station difference. The carrier phase observations are updated by weighting the double-difference phase observations based on the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. The carrier phase observations used for the carrier phase observation update are subjected to post-hoc residual verification and abnormal ambiguity is reset. The final relative positioning floating-point solution is then calculated iteratively.

[0014] Secondly, the present invention also provides a GNSS robust positioning and velocity measurement system based on the raw output information of a smart terminal, comprising: A pseudorange positioning module is used to acquire the raw GNSS output information of the smart terminal and convert it into GNSS observations; wherein, the GNSS observations include pseudorange observations, carrier phase observations, and Doppler observations; the pseudorange observations in the GNSS observations are subjected to quality control based on the raw GNSS output information, and pseudorange positioning is calculated; The displacement velocity measurement module is used to perform quality control on the carrier phase observations based on the raw GNSS output information, and to perform inter-epoch differential observation updates on the carrier phase using an extended Kalman filter to estimate the displacement at the current epoch. If the inter-epoch differential observation update on the carrier phase is successful, the displacement at the current epoch is output; otherwise, the module performs quality control on the Doppler observations based on the pseudorange rate of change uncertainty in the raw GNSS output information, and to perform Doppler observation updates using an extended Kalman filter to estimate the displacement at the current epoch and output the displacement at the current epoch. The relative positioning module performs a one-step prediction based on the pseudorange positioning solution and the displacement of the current epoch to obtain the one-step predicted position of the current epoch. Based on the one-step predicted position of the current epoch, it performs pseudorange observation update and carrier phase observation update to obtain the relative positioning solution.

[0015] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal as described above.

[0016] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal as described above.

[0017] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal as described above.

[0018] The beneficial effects of the technical solutions provided by some embodiments of the present invention include at least the following: 1) The present invention provides a robust GNSS positioning and velocity measurement method and system based on the original output information of a smart terminal. On the basis of traditional quality control methods based on the characteristics of observations such as elevation angle and signal-to-noise ratio and pre- and post-test consistency checks, the method combines the measurement uncertainty information and status information in the original GNSS output information of the smart terminal to perform joint quality control on the GNSS observations, thereby effectively improving the accuracy and robustness of the GNSS positioning of the smart terminal.

[0019] 2) Based on the uncertainty of satellite signal transmission time in the original GNSS output information, this invention constructs a carrier-to-noise ratio (CNR) stochastic model based on the uncertainty of satellite signal transmission time. By assigning weights to the screened pseudorange observations through the CNR stochastic model based on the uncertainty of satellite signal transmission time, the quality of pseudorange observations can be effectively controlled, thereby improving the pseudorange solution quality.

[0020] 3) Based on the status information of the cumulative change distance of the carrier phase in the original GNSS output information, this invention determines whether there are abnormal phenomena, cycle slips, or reset phenomena, and uses this to perform quality control on the carrier phase observation values. By detecting and processing cycle slips and abnormal phenomena in real time, it ensures the stable output of high-precision positioning, prevents erroneous data from polluting subsequent filtering calculations, and enhances the robustness and reliability of the system.

[0021] 4) When the differential observation update between carrier phase epochs fails, the present invention performs quality control on the Doppler observation value based on the uncertainty of the pseudorange change rate in the original GNSS output information. It can continue to provide high-precision velocity information or position change between epochs even when the carrier phase is unavailable, avoiding data interruption or blanks. It can improve the continuity, reliability and adaptability of GNSS velocity calculation and dynamic positioning in complex environments.

[0022] 5) When performing pseudorange observation updates, this invention considers the uncertainty of satellite signal transmission time to select a reference satellite and assigns weights to the double-difference pseudorange observations based on a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. When performing carrier phase observation updates, the double-difference phase observations are weighted based on a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time, and phase observation updates are performed. This significantly improves the accuracy, consistency, and robustness of pseudorange and phase observation updates, and is especially beneficial for high-reliability positioning in complex environments.

[0023] 6) Field test results of GNSS dynamic positioning of smart terminals in typical urban environments show that the positioning performance of the method proposed in this invention is optimal. The RMS positioning errors in the east and north directions are 0.42m and 0.52m, respectively, and the positioning accuracy in the zenith direction is 0.76m. Compared with the traditional data quality control scheme that combines the original carrier-to-noise ratio random model, the method of this invention improves the positioning accuracy in the east, north, and zenith directions by 26.32%, 36.59%, and 56.57%, respectively, effectively improving the GNSS positioning accuracy and robustness of smart terminals and showing good application prospects. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is one of the flowcharts of the GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal provided by the present invention; Figure 2 This is a schematic diagram of the pseudorange positioning solution provided by the present invention; Figure 3 This is a flowchart illustrating the speed measurement method provided by the present invention; Figure 4 This is the second flowchart of the GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal provided by the present invention. Figure 5 This invention provides the observable number of satellites and the position accuracy attenuation factor throughout the entire vehicle-mounted experimental test process. Figure 6 This is a comparison of the time series of positioning errors in the East-North-Sky directions under the four solution schemes provided by this invention; Figure 7 This is a comparison of the 3D positioning error, the number of observable satellites, and the PDOP value of four solution schemes provided by this invention in the context of high-rise buildings obstructing residential areas; Figure 8 This invention provides a comparison of the 3D positioning error, the number of observable satellites, and the PDOP value of four solution schemes under the obstruction of viaducts and overpasses. Figure 9 This is a schematic diagram showing the comparison results of the cumulative error distribution under the four solution schemes provided by this invention; Figure 10 This is a schematic diagram of the GNSS robust positioning and velocity measurement system based on the original output information of a smart terminal provided by the present invention; Figure 11 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] Existing technologies typically perform basic quality control on GNSS observations based on characteristics such as carrier-to-noise ratio and elevation angle, and further control the quality of observations based on pre- and post-test residuals. Specifically, in the pre-test part, for pseudorange observations, those exceeding error limits are directly discarded; for phase observations, those exceeding limits are marked and their ambiguities are reset. In the post-test part, for pseudorange observations with large post-test residuals and large post-test standardized residuals, the IGG-III scheme is often used for weight reduction or removal; for phase observations with large residuals, they are marked as cycle slips and their ambiguities are reset, and iterative calculations are performed after processing abnormal observations.

[0028] However, there are still significant limitations in using the above indicators to reflect the quality of observations. Specifically, due to hardware limitations, the carrier-to-noise ratio and elevation angle of the observations cannot accurately reflect changes in the observation environment; the consistency of the observations introduces time correlation due to the use of epoch differences, making it difficult to reflect the impact of similar errors of the same magnitude over a short period of time; and the pre- and post-observation residuals of the observations are heavily dependent on the pre- and post-observation position estimates, making it difficult to effectively and independently reflect the error level of the observations themselves.

[0029] The measurement uncertainty and raw output state quantities in the raw output quantities of GNSS observations also provide information on the usability of the observations. Existing research shows that some uncertainty information and raw output state quantity information are correlated with the quality of the observations; furthermore, adjusting the observation generation method according to their variation characteristics can significantly affect the numerical values ​​of the observations, thus impacting their consistency. In addition, using a "nanopsuccessful" approach, combining uncertainty information with ridge regression and machine learning methods to establish a stochastic model, can improve the weight distribution among observations, thereby improving positioning accuracy. However, current correlation analysis between the raw output indicators of smartphones and the residuals of the observations is still relatively lacking. As a quality metric directly output by smart terminals, these indicators are not yet fully utilized in the current quality control process of GNSS positioning.

[0030] Therefore, this invention proposes a robust GNSS positioning and velocity measurement method and system based on the original output information of a smart terminal. It makes full use of the original GNSS output information of the smart terminal to perform joint quality control on GNSS observations, thereby improving the accuracy and robustness of GNSS positioning of the smart terminal.

[0031] Please see Figure 1 , Figure 1 One of the flowcharts for a GNSS robust positioning and velocity measurement method based on raw output information of a smart terminal, provided as an embodiment of the present invention, includes: S101. Obtain the raw GNSS output information from the smart terminal and convert it to obtain GNSS observation values; S102. Perform quality control on pseudorange observations based on the original GNSS output information, and perform pseudorange positioning calculations. S103. Perform quality control on the carrier phase observations based on the original GNSS output information, and use extended Kalman filtering to update the carrier phase epoch differential observations, estimate the current epoch displacement, and output the current epoch displacement if the carrier phase epoch differential observation update is successful; otherwise, perform quality control on the Doppler observations based on the pseudorange change rate uncertainty in the original GNSS output information, and use extended Kalman filtering to update the Doppler observations, estimate the current epoch displacement, and output the current epoch displacement. S104. Based on the pseudorange positioning solution, perform a one-step prediction using the current epoch displacement to obtain the one-step predicted position of the current epoch. Based on the one-step predicted position of the current epoch, perform pseudorange observation update and carrier phase observation update to obtain the relative positioning solution.

[0032] This invention, based on traditional quality control methods such as elevation angle, signal-to-noise ratio, and pre- and post-test consistency checks, combines measurement uncertainty information and status information from the original GNSS output information of the smart terminal to perform joint quality control on GNSS observations, effectively improving the accuracy and robustness of GNSS positioning of the smart terminal.

[0033] In the above implementation, step S101 involves acquiring the original GNSS output information of the smart terminal and converting it into GNSS observations such as pseudorange observations, carrier phase observations, and Doppler observations.

[0034] The raw GNSS output information comprises the raw output quantities used to generate GNSS observations. Besides core observations such as pseudorange, carrier phase, and Doppler correlation, it also includes a large amount of crucial measurement uncertainty information and raw output state information. For example, measurement uncertainties can include time uncertainty, pseudorange rate of change uncertainty, carrier phase uncertainty, and clock drift uncertainty. Specifically, time uncertainty can be the uncertainty of the receiver clock time; pseudorange rate of change uncertainty is the standard deviation (m / s) of the Doppler (pseudorange rate of change) measurement; carrier phase uncertainty is the measurement uncertainty (m) of the cumulative change distance of the carrier phase; this value increases significantly when the state is abnormal; and clock drift uncertainty is the uncertainty of the receiver clock drift rate.

[0035] For example, the system obtains raw GNSS output information through the Android API interface and converts it into GNSS observations such as pseudorange, carrier phase, and Doppler. Simultaneously, it extracts from the raw GNSS output information the satellite signal transmission time uncertainty (RTUN), the current epoch carrier phase cumulative distance state information (ADRS), and the current epoch pseudorange rate of change uncertainty (PRUMPS) measured by the smart terminal. Specifically, the satellite signal transmission time uncertainty (RTUN) reflects the quality of the pseudorange observations; the current epoch carrier phase cumulative distance state information (ADRS) is used to indicate the state of the current phase observation at the current epoch using bit settings; and the current epoch pseudorange rate of change uncertainty (PRUMPS) reflects the quality of the generated Doppler observations.

[0036] Understandably, smart terminals need to be running Android 7.0 or higher and provide Android API interfaces to obtain raw GNSS output information.

[0037] In the above implementation, step S102 involves performing quality control on the pseudorange observations in the GNSS observations based on the measurement uncertainty information in the original GNSS output information.

[0038] Taking smartphones as a representative example of smart terminals, the satellite signal transmission time uncertainty (RTUN) and pseudorange noise in the original output information of smartphones are highly correlated. When the RTUN value is large, the pseudorange noise is also large. RTUN can serve as supplementary information to better reflect the noise level of the observed values ​​and balance the weights among the observed values. However, the RTUN also exhibits large outliers, which are present in most smartphones. At this point, the inter-station and inter-satellite double-difference pseudorange noise exceeds 20m. Therefore, the quality control of pseudorange observations can be achieved through the uncertainty of satellite signal transmission time.

[0039] In some possible embodiments, quality control of pseudorange observations based on raw GNSS output information and pseudorange positioning calculations include: S102-1. Perform elevation angle, signal-to-noise ratio, and pseudorange-Doppler consistency checks on pseudorange observations to obtain the filtered pseudorange observations. S102-2. Based on the uncertainty of satellite signal transmission time in the original GNSS output information, and combined with the original carrier-to-noise ratio stochastic model, construct a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. S102-3. The screened pseudorange observations are weighted by a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time, and the pseudorange positioning solution for the current epoch is calculated by combining least squares estimation.

[0040] Specifically, such as Figure 2 The diagram shown is a schematic flowchart of the pseudorange positioning solution provided by the present invention.

[0041] In step S102-1 above, the pseudorange observations are first filtered by elevation angle and signal-to-noise ratio. For example, a cutoff elevation angle is set to retain only satellites with elevation angles greater than this cutoff elevation angle, and a cutoff signal-to-noise ratio is set to retain only satellites with a signal-to-noise ratio higher than this cutoff signal-to-noise ratio. For example, the cutoff carrier-to-noise ratio can be set to 20 dB-Hz, and the cutoff elevation angle can be set to 15 degrees.

[0042] Then, the pseudorange Doppler consistency condition is set, and the formula for calculating pseudorange Doppler consistency can be:

[0043] In the formula, and These represent pseudorange Doppler consistency and pseudorange Doppler consistency thresholds, respectively. For wavelength, These are the pseudorange observations at time t and time t-1, respectively. These are the Doppler observations at time t and time t-1, respectively. For time intervals.

[0044] When satisfied If the current pseudorange observations are considered abnormal, they are removed during the solution process. The pseudorange Doppler consistency threshold is then applied. It can be set to 15m / s.

[0045] In step S102-2 above, based on the correlation between the satellite signal transmission time uncertainty RTUN and the pseudorange noise variation characteristics in the original output information of the smart terminal, a carrier noise ratio stochastic model (RTUN-C / N0 stochastic model) based on the satellite signal transmission time uncertainty is constructed on the basis of the original carrier noise ratio stochastic model, so as to reduce the influence of observations with large satellite signal transmission time uncertainty RTUN and large pseudorange noise.

[0046] In some possible embodiments, the pseudorange observation variance of the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time is:

[0047] in, , b , d , , For fitting parameters, The standard deviation of pseudorange observations. Where C is the carrier-to-noise ratio and C is the carrier power. This represents the noise power spectral density.

[0048] In the above RTUN-C / N0 stochastic model, through To control the weight of observations and prevent them from becoming too small when the RTUN (Real-Time Uncertainty of Satellite Signal Transmission) has a large outlier, parameters are used. , Adjusting its rate of change, its function value As RTUN increases, it first increases rapidly and then slowly approaches its maximum value. .

[0049] The above fitting parameters , b , d , , It can be obtained by fitting static observation data from various models and types of smart terminals in open environments. For example, in a static open environment, the pseudorange double difference of the current observation value can be calculated using the true location data of a smartphone. Under a short baseline model, the pseudorange double difference can be obtained. Its value can be expressed as:

[0050] Because the pseudorange observations undergo inter-station and inter-satellite double differences, according to the error propagation law, the noise in the observations is actually amplified by approximately [missing information]. Therefore, the standard deviation of the observed values ​​needs to take into account the noise amplification effect during the fitting process. For the remaining variables, the corresponding RTUN and carrier-to-noise ratio should be input during the fitting process. Since the noise levels differ between systems and frequencies, the stochastic model can be fitted separately for each system and frequency. The fitting equation is:

[0051] Considering that the raw GNSS observations from smartphones and other smart terminals are easily affected by environmental factors such as multipath and non-line-of-sight (NLOS) errors in urban environments, and that the quality of the observations is poor due to hardware limitations, the standard deviation ratio of pseudorange, Doppler, and phase observations can be set to 300:30:1.

[0052] In steps S102-3 above, the RTUN-C / N0 stochastic model is used to assign weights to each pseudorange observation, and the weights of each pseudorange observation are controlled to suppress the influence of gross errors or outliers, so as to reasonably reflect the relative accuracy differences of different satellite observations.

[0053] In some possible embodiments, the filtered pseudorange observations are weighted using a carrier-to-noise ratio stochastic model based on the uncertainty of the satellite signal transmission time, including: The variance of the filtered pseudorange observations was determined using a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. The weights of the filtered pseudorange observations are determined based on the variance, and the filtered pseudorange observations are then assigned weights based on these weights.

[0054] Specifically, the variance of each screened pseudorange observation is calculated based on the above RTUN-C / N0 stochastic model, and the weight of each pseudorange observation is determined based on the variance, for example, by taking the reciprocal of the variance as the weight.

[0055] Finally, the pseudorange localization solution for the current epoch is calculated using least squares estimation, and the pseudorange post-hoc residuals are verified through a post-hoc test. After iteratively calculating by removing outlier observations one by one, It can be set to 5m.

[0056] Furthermore, during the process of calculating the pseudorange positioning solution for the current epoch using least squares estimation, when there is base station differential data and the differential correction is solvable, the receiver clock bias is estimated by least squares and subtracted based on the known base station coordinates and broadcast ephemeris. The differential correction for each satellite and each frequency is then calculated and the rover station observations are corrected. In this case, the pseudorange differential solution for the current epoch is output; otherwise, the single-point positioning solution for the current epoch is output.

[0057] This invention constructs a carrier-to-noise ratio (CNR) stochastic model based on the uncertainty of satellite signal transmission time in the original GNSS output information, combined with the original CNR stochastic model. By assigning weights to the screened pseudorange observations using the CNR stochastic model based on the uncertainty of satellite signal transmission time, the quality of pseudorange observations can be effectively controlled, thereby improving the pseudorange solution quality.

[0058] In the above implementation, step S103 first performs quality control on the carrier phase observations based on the status information in the original GNSS output information, and then uses carrier phase epoch differential observation updates for velocity measurement. If this fails, the Doppler observations are then performed quality control based on the pseudorange change rate uncertainty in the original GNSS output information, and then Doppler observation updates are used for velocity measurement.

[0059] In some possible embodiments, quality control of carrier phase observations is performed based on the raw GNSS output information, including: Based on the status information of the cumulative change distance of carrier phase in the original GNSS output information, determine whether there are any abnormal phenomena, cycle slips, or reset phenomena. Carrier phase observations exhibiting cycle slips and reset phenomena are set using LLI; carrier phase observations exhibiting abnormal phenomena such as setting conflicts or unknown setting are discarded to obtain a preliminary selection of carrier phase observations. The carrier phase observations that were initially screened were then filtered for elevation angle, signal-to-noise ratio, and phase Doppler consistency to obtain carrier phase observations that had undergone quality control.

[0060] Specifically, Figure 3 This is a flowchart illustrating the speed measurement method provided by the present invention. When using differential observation between carrier phase epochs for speed measurement, the carrier phase observation values ​​are quality controlled based on the cumulative change distance status information (ADRS) of the carrier phase in the original GNSS output information to ensure speed measurement accuracy.

[0061] The Carrier Phase Cumulative Change Distance Status Information (ADRS) reflects the status information of the Carrier Phase Cumulative Incremental Distance (ADRM) used to generate the phase. It uses bit setting to indicate whether the current phase observation has reached this state. When Bit 0 is set in the ADRS, it indicates that the phase observation is valid; when Bit 1 is set, it indicates that the phase observation has been reset; when Bit 2 is set, it indicates that the phase observation may have detected a cycle slip, and an LLI flag is added to the phase observation; when Bit 3 is set, it indicates that the current epoch may have detected a half-cycle slip and it has been repaired. When Bit 3 is set, Bit 4 should be set; otherwise, a setting mutual exclusion anomaly is considered. Furthermore, for smartphone phase observations, the frequent presence of Bit 4 being set in the ADRS indicates that the receiver internally considers half-cycle slips to occur frequently.

[0062] In this embodiment, during the speed measurement process, abnormal phase observations in the Carrier Phase Cumulative Change Distance Status Information (ADRS) are discarded. If Bit2 of the ADRS is set, a phase cycle slip is considered to have occurred, and the LLI bit is set. Similarly, if frequent phase resets occur when Bit1 of the ADRS is set, or frequent cycle slips occur between phase epochs when Bit0 of the ADRS is set, the carrier phase observations must also be discarded during the speed measurement.

[0063] This invention uses the status information of the cumulative change distance of the carrier phase in the original GNSS output information to determine whether there are abnormal phenomena, cycle slips, or reset phenomena in the carrier phase observation values, and uses this to perform quality control on the carrier phase observation values. By detecting and processing the above phenomena in real time, it ensures the stable output of high-precision velocity measurement and positioning, prevents erroneous data from contaminating subsequent filtering calculations, and enhances the robustness and reliability of the system.

[0064] After quality control of carrier phase observations based on the raw GNSS output information, the quality-controlled carrier phase observations still need to be screened by elevation angle and signal-to-noise ratio thresholds, as well as phase Doppler consistency checks.

[0065] In complex urban environments, due to the generally low carrier-to-noise ratio (CNR), the cutoff CNR for carrier phase observations can be set to 20 dB-Hz, and the cutoff elevation angle to 15 degrees. Simultaneously, the phase observations are checked for consistency. For the phase Doppler consistency condition, the following can be calculated:

[0066] In the formula, and These represent phase Doppler consistency and phase Doppler consistency threshold, respectively. For wavelength, The carrier phase observations at time t and time t-1 are respectively. These are the Doppler observations at time t and time t-1, respectively. For time intervals.

[0067] When satisfied If the current carrier phase observation is considered abnormal, it will be discarded during the velocity measurement process. It can be set to 0.5 m / s.

[0068] Since single-frequency observations have already been verified using the aforementioned Carrier Phase Accumulated Change Distance Status Information (ADRS), no further processing is required. If dual-frequency observations exist, cycle slip detection must be performed based on the Geometry-Free (GF) combination of the dual-frequency observations when they are available.

[0069] Through quality control of the above phase observations, and based on the kinematic state of the carrier's velocity and acceleration, an extended Kalman filter is used to establish a discretized velocity-acceleration (VA) filter model for one-step displacement prediction. Carrier phase epoch differential observation updates are performed, and a velocity measurement observation equation is constructed based on the carrier phase epoch differential observations to update the displacement of the current epoch.

[0070] Specifically, the velocity-acceleration (VA) filtering model performs a one-step prediction based on the kinematic state of the carrier's velocity and acceleration. Then, it constructs the carrier phase inter-epoch differential observation equation using the current epoch observation values ​​and updates the current epoch displacement. The discretized motion model in the one-step prediction is as follows:

[0071] in, and Let k represent the position and acceleration, respectively. and Representing the position and acceleration at time k-1, respectively. and The corresponding process noise spectral density; Δ tRepresents a time interval. Represents the identity matrix.

[0072] The formula for the carrier phase epoch differential observation equation is:

[0073] in, λ Let be the carrier wavelength, Δ be the time difference operator representing the difference between the current epoch and the previous epoch, Δφ be the time difference component of the carrier phase observations at time t and time t-1, and c be the speed of light. These are the time difference components of the satellite-to-Earth distance at time t and time t-1. , These represent receiver clock bias and satellite clock bias, respectively. These represent the time difference components of the receiver clock bias and the satellite clock bias, respectively. This is for phase observation noise and other residual errors.

[0074] The displacement of the current epoch is updated by extended Kalman filtering, and the displacement of the current epoch is estimated.

[0075] Finally, based on the estimated current epoch displacement, the post-hoc residuals and standardized residuals of the differential observations between each carrier phase epoch are calculated. A post-hoc residual test is performed on the carrier phase observations. Quality control is applied to carrier phase observations whose maximum post-hoc residual and maximum standardized post-hoc residual exceed a preset threshold. The formula for this post-hoc residual test is as follows:

[0076] in, , These represent the phase post-approval residual and the phase post-approval residual threshold, respectively, in the VA filter model. , These represent the phase-post-standardized residual and the phase-post-standardized residual threshold, respectively. This represents the standardized residual.

[0077] When the above conditions for post-approval residual verification are met, the carrier phase observation is considered to have experienced a cycle slip. This is then removed from the velocity measurement and marked as a newly detected cycle slip in the TDCP post-approval residual. The displacement is then iteratively calculated repeatedly. It can be set to 0.05 m / s. It can be set to 1.5 m / s. If the carrier phase epoch differential update is successful, the current epoch displacement will be output, and Doppler observation updates will not be performed again.

[0078] like Figure 3As shown, in some possible embodiments, if the carrier phase epoch differential observation update fails, the Doppler observation values ​​in the GNSS observations are quality controlled according to the pseudorange change rate uncertainty in the original GNSS output information, and the Doppler observation is updated. The current epoch displacement is estimated using extended Kalman filtering.

[0079] In some possible embodiments, quality control of Doppler observations is performed based on the pseudorange rate of change uncertainty in the raw GNSS output information, including: Abnormal Doppler observations with pseudorange change rate uncertainty greater than a preset uncertainty threshold are removed to obtain abnormal Doppler observations. After removing anomalies, Doppler observations were screened by elevation angle, signal-to-noise ratio, and Doppler epoch difference consistency check to obtain quality-controlled Doppler observations.

[0080] Specifically, such as Figure 3 As shown, when using Doppler observations for velocity measurement, the quality control of Doppler observations is performed based on the pseudorange rate of change uncertainty information (PRUMPS) in the raw GNSS output information to ensure the accuracy of velocity measurement.

[0081] The pseudorange rate of change uncertainty (PRUMPS) exhibits a high correlation with Doppler residuals. When the PRUMPS value is high, the Doppler residuals are large, resulting in significant Doppler velocities. Furthermore, PRUMPS can exhibit anomalies, with values ​​ranging from the speed of light to one millionth of the speed of light. These anomalies vary depending on the smartphone model, leading to larger residuals in the corresponding double-difference Doppler observations. Therefore, when the following conditions are met... During the velocity measurement process, the current Doppler observations are discarded, taking into account the abnormal conditions of PRUMPS. Desirable .

[0082] After quality control of Doppler observations based on the raw GNSS output information, further screening of Doppler observations using elevation angle and signal-to-noise ratio thresholds is required. Similar to phase observations, the cutoff carrier-to-noise ratio for Doppler observations can be set to 20 dB-Hz, and the cutoff elevation angle can be set to 15 degrees. Simultaneously, phase observations are checked for consistency. For the Doppler epoch difference consistency condition, the following can be calculated:

[0083] In the formula, For wavelength, These are the Doppler observations at time t and time t-1, respectively.

[0084] When satisfied At that time, it was determined that the current Doppler observations were abnormal and were therefore excluded during the velocity measurement process. Set to 2m / s.

[0085] After quality control of the Doppler observations, based on the kinematic state of the carrier's velocity and acceleration, an extended Kalman filter (EVF) model is established for one-step displacement prediction. If the carrier phase inter-epoch differential observation update fails, a velocity measurement equation is constructed based on the Doppler observations. The current epoch displacement is then updated using an EPF to obtain the estimated current epoch displacement. The principle of constructing the velocity measurement equation based on Doppler observations is similar to that of the carrier phase inter-epoch differential observation equation, and will not be elaborated further here.

[0086] Finally, based on the estimated displacement at the current epoch, the posterior residuals and standardized residuals of each Doppler observation are calculated, and a posterior residual test is performed. For observations with the maximum posterior residual and the maximum standardized posterior residual exceeding a preset threshold, quality control is applied, expressed as follows:

[0087] In the formula, and These represent the Doppler post-hoc residual and the Doppler post-hoc residual threshold, respectively, in the VA filter model.

[0088] When the above conditions are met, the Doppler observation residual is considered to be large and is therefore discarded in the velocity measurement. In this case, the displacement is calculated iteratively again. It can be set to 0.5 m / s.

[0089] If the Doppler observation update is successful, the displacement of the current epoch is output. If the Doppler update fails, a one-step prediction recursive displacement is used, recursively for a maximum of five epochs. To ensure the quality of the output displacement, if the carrier phase epoch differential and Doppler updates still fail after five epochs, the velocity measurement is considered a failure.

[0090] In the above implementation, step S104 involves performing a one-step prediction based on the pseudorange positioning solution obtained in step S102 and the current epoch displacement obtained in step S103, to obtain the one-step predicted position for the current epoch. Based on this, pseudorange observation updates and carrier phase observation updates are performed to obtain the relative positioning solution.

[0091] Please see Figure 4 , Figure 4 A second flowchart illustrating a GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal, provided as an embodiment of the present invention. Figure 4The diagram illustrates the complete positioning and velocity measurement process, including pseudorange positioning calculation in step S102, one-step prediction based on the velocity displacement measured in step S103, and pseudorange observation update and carrier phase observation update in step S104.

[0092] Specifically, such as Figure 4 As shown, the pseudorange difference decomposition or single-point positioning solution obtained in step S101 is used for extended Kalman filter initialization. The initial position standard deviation is set to 100m, and the initial ambiguity value is obtained by phase minus pseudorange, with its standard deviation set to 100m.

[0093] Then, based on the position of the previous epoch and the displacement estimated by the VA filter model of the current epoch, the predicted position of the current epoch is calculated using a velocity recursion model. The position variance is obtained by comparing the variance of the floating-point solution of the previous epoch with the variance of the displacement estimated by the VA filter model of the current epoch, thus yielding the predicted position variance of the current epoch.

[0094] Specifically, the one-step prediction of the velocity recursion model can be expressed as:

[0095] in, Here is the state transition matrix. , These represent the positions of the mobile terminal at time k-1 (the previous epoch) and time k (the current epoch), respectively. It is the control input, i.e., the displacement of the current epoch. This displacement is a three-dimensional displacement vector; for The coefficient matrix, i.e., the sampling time interval matrix. I is the identity matrix. For time intervals; This represents the process noise at time k-1.

[0096] If the velocity calculation fails, the extended Kalman filter is re-initialized using pseudorange difference decomposition or single-point localization solution based on the current epoch observations.

[0097] Then, pseudorange observation updates and carrier phase observation updates are performed based on the one-step predicted position of the current epoch.

[0098] In some possible embodiments, pseudorange observation updates and carrier phase observation updates are performed based on the one-step predicted position of the current epoch, and the relative positioning solution is calculated, including: A1. Based on the one-step prediction position of the current epoch, calculate the double difference residual of the previous pseudorange of the current epoch and remove abnormal pseudorange observations to obtain pseudorange observations that pass the test. A2. Sort by elevation angle or carrier-to-noise ratio, and select reference satellites based on the uncertainty of satellite signal transmission time. Obtain double-difference pseudorange observations through verified pseudorange observations, inter-satellite differences, and inter-station differences. A3. Based on the carrier-to-noise ratio stochastic model of satellite signal transmission time uncertainty, the double-difference pseudorange observations are weighted and the pseudorange observations are updated. B1. Based on the one-step prediction position of the current epoch, calculate the double-difference residual of the previous phase at the current epoch and remove abnormal carrier phase observations to obtain carrier phase observations that pass the test. B2. Select reference satellites based on elevation angle or carrier-to-noise ratio, and combine the status information of cumulative carrier phase change distance. Obtain double-difference phase observations through verified carrier phase observations, inter-satellite differences, and inter-station differences. B3. Based on the carrier-to-noise ratio stochastic model of the uncertainty of satellite signal transmission time, the double-difference phase observations are weighted and the carrier phase observations are updated. C. Perform post-hoc residual verification on the carrier phase observations used for carrier phase observation updates and reset the abnormal ambiguity, then iteratively calculate and output the final relative positioning floating-point solution.

[0099] Specifically, such as Figure 4 As shown, steps A1 to A3 are the pseudorange observation update process, and steps B1 to B3 are the phase observation update process.

[0100] In step A1 above, the previous pseudorange double difference residuals of the current epoch are calculated and abnormal pseudorange observations are removed. For example, if the previous pseudorange double difference residuals are greater than 6m, the current pseudorange observation residuals are considered to be large, and these abnormal observations are removed to obtain pseudorange observations that pass the test.

[0101] In step A2 above, reference satellites are selected based on elevation angle or carrier-to-noise ratio, taking into account the RTUN (Real Time Uncertainty) of satellite signal transmission time to avoid selecting satellite observations with high RTUN. For example, when the RTUN meets... If a certain value is selected, it can be used as a reference star. Considering that RTUN may have large outliers, and that its pseudorange residual is large when the value is large, the threshold for RTUN is... It can be set to 100.

[0102] Double-difference pseudorange observations are obtained based on verified pseudorange observations, inter-satellite differences, and inter-station differences.

[0103] In step A3 above, the RTUN-C / N0 stochastic model is used to weight the double-difference pseudorange observations and update the estimates of position parameters and ambiguity parameters.

[0104] Finally, based on the post-approval residuals and standardized residuals, the pseudorange observations are subjected to quality control processing through the post-approval residual test, and iterative calculation is performed. If the final post-approval residual test is passed, the pseudorange update is successful and the phase observation update process begins; otherwise, the extended Kalman filter is re-initialized and position prediction is performed in one step.

[0105] For pseudorange observations, the post-hoc residual test can be performed as follows: Check if the standardized residual of the post-hoc pseudorange double difference exceeds the limit. For example, set the standardized residual threshold to 3m. When the standardized residual of the post-hoc pseudorange double difference exceeds the limit, perform IGG-III robustness testing based on the standardized residual of the pseudorange double difference. When the standardized residual is less than the weighted zone threshold... If the weight remains unchanged, then its weight is constant. Threshold of the preservation zone. It can be set to 3m. It can be set to 6m.

[0106] In step B1 above, the previous phase double difference residual of the current epoch is calculated and abnormal phase observations are removed. For example, if the previous phase double difference residual is greater than 0.6m, the current phase observation residual is considered to be large, and such abnormal observations are removed to obtain the phase observations that pass the test.

[0107] In step B2 above, reference satellites are selected based on elevation angle or carrier-to-noise ratio, and the status information ADRS of the cumulative change distance of the carrier phase at the current epoch is taken into account. When there is no cycle slip between carrier phase epochs, satellites with abnormal ADRS are not selected as reference satellites.

[0108] Then, double-difference phase observations are obtained based on the verified carrier phase observations, inter-satellite differentials, and inter-station differentials.

[0109] In step B3 above, the double-difference phase observations are weighted according to the RTUN-C / N0 stochastic model, and the estimates of position parameters and ambiguity parameters are updated.

[0110] In step C above, the carrier phase observation values ​​used for carrier phase observation updates are first subjected to a post-hoc residual check and the abnormal ambiguity is reset.

[0111] For carrier phase observations, the post-approval residual test can be performed as follows: first, check whether the standardized residual of the post-approval phase double difference exceeds the limit. The standardized residual threshold can be set to 1.75m. Then, check whether the post-approval phase double difference residual exceeds the limit. The residual threshold can be set to 0.1m. When both exceed the limit, it is considered that a new cycle slip has been found in the post-approval, and the ambiguity needs to be reset.

[0112] If the above post-hoc residual test is passed, iterative updates are performed. If the final post-hoc residual test is passed after the iteration, the phase observation update is successful, and the relative positioning RTK floating-point solution is output; if the phase observation update fails, the RTD solution is output.

[0113] Because the intermediate processes of GNSS observations and velocity measurement and positioning in this invention undergo strict data quality control, both the obtained RTK floating-point solution and RTD solution have the advantages of high accuracy and robustness.

[0114] This invention considers the uncertainty of satellite signal transmission time when performing pseudorange observation updates, selects a reference satellite, and assigns weights to the double-difference pseudorange observations based on a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. When performing carrier phase observation updates, it assigns weights to the double-difference phase observations based on a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time, and performs phase observation updates. This significantly improves the accuracy, consistency, and robustness of pseudorange and phase observation updates, and is particularly beneficial for high-reliability positioning in complex environments.

[0115] The effectiveness of the method proposed in this invention is verified by experiments and analysis based on a set of self-collected data.

[0116] The self-collected data is a set of measured data from a vehicle-mounted dynamic smartphone. The test equipment used is a commercially available smartphone. Table 1 shows the frequency information of the observation values ​​that the smartphone (Xiaomi Mi8) used in this experiment can output. In this vehicle-mounted experiment, the smartphone was placed in front of the windshield of the car's driver's cab to simulate normal driving and navigation scenarios. The original GNSS data from the smartphone was collected using GNSS Logger software. The reference device used was the POMS-GI7683 high-precision integrated navigation system. The reference results and trajectory were post-processed using Inertial Explorer 8.9.0 software with RTK / INS tight combination forward and backward filtering and bidirectional smoothing. The base station data used FindCM differential position service to ensure that the relative positioning solution was a short baseline solution. The sampling time interval was set to 1 second.

[0117] Table 1. GNSS systems supported by the smartphones used in this experiment and the frequency information of the observed values. Navigation system frequency GPS L1 / L5 GAL (Galileo) E1 / E5a BDS (BeiDou) B1I GLO (Glonas) L1 QZSS (Quasi-Zenith) L1 / L5 The data collection process for this test lasted approximately 30 minutes. According to this test, in open road areas, the number of available satellites is usually more than 20, and the PDOP value is usually below 3. However, in urban challenging environments, such as when passing through overpasses or urban canyon sections, the number of available satellites suddenly drops to less than 10, and the PDOP value rises sharply, frequently exceeding 5.

[0118] To verify the effectiveness of the quality control strategies proposed in this invention based on raw GNSS output information such as ADRS, PRUMPS, and RTUN, this experiment, based on the original quality control based on pre- and post-application residuals and the original carrier-to-noise ratio stochastic model, incorporates the quality control scheme of this invention strategy by strategy. This verifies the effectiveness of individual raw output-based quality control strategies and the joint quality control scheme after incorporating the three types of raw GNSS output-based quality control strategies proposed in this invention. Therefore, ablation analysis is performed to compare the solution schemes with different raw GNSS output-based quality control strategies incorporated.

[0119] Table 2 Comparison of solution schemes using different quality control strategies Solution Original solution Option 1 Option 2 This invention ADRS × √ √ √ PRUMPS × × √ √ RTUN × × × √ Table 2 compares the solution schemes using different quality control strategies. "×" indicates that the solution scheme did not employ the corresponding quality control strategy, and "√" indicates that the solution scheme employed the corresponding quality control strategy. The original scheme does not use quality control strategies based on GNSS raw output (ADRS, PRUMPS, RTUN). Scheme 1 uses only an ADRS-based quality control strategy, Scheme 2 uses only ADRS and PRUMPS-based quality control strategies, and the scheme of this invention is a joint quality control scheme that simultaneously uses ADRS, PRUMPS, and RTUN-based quality control strategies.

[0120] Before performing the calculation, the data needs to be preprocessed to ensure the quality of the calculation. Table 3 shows the basic configuration of the preprocessing, which includes elevation angle, carrier-to-noise ratio filtering, Doppler consistency check, and cycle slip detection. A velocity recursive model is used for one-step position prediction, and broadcast ephemeris is used uniformly.

[0121] Table 3 Basic configuration of preprocessing Basic processing method Configuration Cutoff elevation angle 15° Cutoff carrier-to-noise ratio 20dB-Hz Doppler consistency 0.5m Cycle slip detection LLL, GF One-step prediction velocity recursion model Satellite ephemeris Broadcast ephemeris After data preprocessing, RTK calculations were performed on the experimental data using different solution schemes listed in Table 2. Figure 6 The table shows the time series comparison results of the positioning errors in the three directions of East, North, and Sky under the four solution schemes in Table 2, and highlights the time periods when passing through high-rise residential areas and severely obstructed environments such as elevated bridges.

[0122] analyze Figure 6It can be seen that the original scheme exhibits relatively obvious positioning error fluctuation characteristics, with large fluctuation amplitude error jumps in the three directions of East, North, and Sky, which are particularly prominent in the periods before and after severe obstruction. This reflects that the traditional quality control strategy is difficult to handle the anomalies in smartphone observations. However, after Scheme 1 and Scheme 2 gradually added ADRS and PRUMPS to filter GNSS observations, the positioning accuracy and velocity measurement accuracy were improved, and the stability of the error sequence was improved. After constructing the RTUN-C / N0 stochastic model using RTUN, the observation weights are more reasonable, effectively reducing the weight of abnormal observations. The positioning error is lower and there are no obvious jumps, which verifies the effectiveness of the quality control strategies based on the original output information and the joint quality control scheme combining the three types of quality control strategies proposed in this invention.

[0123] Therefore, compared with the original scheme that uses traditional quality control strategies, gradually adding GNSS raw output information to perform quality control on GNSS observations can better improve positioning accuracy, while suppressing jump points with large positioning errors and accelerating the positioning convergence process. Figure 7 The results show the comparison of 3D positioning errors, observable satellite counts, and PDOP values ​​for the four solution schemes in Table 2 under the obstruction of tall buildings in residential areas. Figure 8 This section compares the 3D positioning errors, observable satellite counts, and PDOP values ​​of the four solution schemes in Table 2 under conditions of obstruction by viaducts and overpasses. Based on... Figure 7 , Figure 8 It can be seen that when passing through complex urban road sections, the quality control scheme that incorporates the original GNSS output information can effectively suppress the gross errors in observation caused by obstruction and low signal strength. It also performs well in both short-term overpass obstruction and long-term residential high-rise building obstruction environments.

[0124] Figure 9 This is a schematic diagram showing the comparison of the cumulative error distribution under the four solution schemes in Table 2. Analysis Figure 9 It can be seen that with the addition of the quality control strategy based on the original output, the positioning error quantile gradually decreases. Among them, since the displacement is obtained by using the VA filter model as the one-step position prediction and the Doppler observation update is no longer performed after the carrier phase epoch differential observation (TDCP) is successfully updated, the optimization magnitude after adding PRUMPS quality control is smaller compared with the scheme that only adds ADRS quality control.

[0125] Table 4 Comparison of positioning errors under different solution schemes

[0126] Table 4 shows the comparison results of the positioning errors and 3D positioning error quantiles in the East (E), North (N), and U directions under different solution schemes. According to Table 4, after gradually introducing ADRS for quality control of anomalous phase observations, PRUMPS for quality control of anomalous Doppler observations, and RTUN for reasonable weighting of observations, compared with the original scheme, the positioning error RMS was effectively reduced in both the East-North-Universe and three-dimensional directions. At the same time, the positioning accuracy was improved by 12.94%, 13.93%, and 49.25% respectively compared with the original scheme. The joint quality control scheme proposed in this invention, which combines ADRS, PRUMPS, and RTUN three types of raw output information, improved the positioning accuracy in the East, North, and U directions by approximately 26.32%, 36.59%, and 56.57% respectively compared with the original scheme. Meanwhile, it can be observed that, in terms of 3D error quantiles, after gradually introducing ADRS, PRUMPS and RTUN for observation quality control, the 3D error quantiles decrease accordingly. After using the raw output for observation quality control, the 68%, 95% and 99% quantiles show better performance. After adopting the joint quality control scheme combining ADRS, PRUMPS and RTUN three types of raw output information proposed in this invention, the 68% quantile of 3D error is only 1.01m.

[0127] Please see Figure 10 , Figure 10 A schematic diagram of a GNSS robust positioning and velocity measurement system based on raw output information from a smart terminal, provided as an embodiment of the present invention, is shown. The system includes: The pseudorange positioning module 1010 is used to acquire the raw GNSS output information of the smart terminal and convert it into GNSS observations; wherein, the GNSS observations include pseudorange observations, carrier phase observations, and Doppler observations; the pseudorange observations in the GNSS observations are subjected to quality control based on the raw GNSS output information, and pseudorange positioning is calculated. The displacement velocity measurement module 1020 is used to perform quality control on carrier phase observations based on the raw GNSS output information, and to perform inter-epoch differential observation updates on the carrier phase using extended Kalman filtering to estimate the displacement at the current epoch. If the inter-epoch differential observation update on the carrier phase is successful, the displacement at the current epoch is output; otherwise, the Doppler observations are quality controlled based on the pseudorange rate of change uncertainty in the raw GNSS output information, and the Doppler observations are updated using extended Kalman filtering to estimate the displacement at the current epoch and output the displacement at the current epoch. The relative positioning module 1030 performs a one-step prediction based on the pseudorange positioning solution and the displacement of the current epoch to obtain the one-step predicted position of the current epoch. Based on the one-step predicted position of the current epoch, it performs pseudorange observation update and carrier phase observation update to obtain the relative positioning solution.

[0128] The GNSS robust positioning and velocity measurement system based on the original output information of the smart terminal described above and the GNSS robust positioning and velocity measurement method based on the original output information of the smart terminal described above can be referred to each other.

[0129] Figure 11 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 11 As shown, the electronic device may include a processor 1110, a communications interface 1120, a memory 1130, and a communication bus 1140. The processor 1110, communications interface 1120, and memory 1130 communicate with each other via the communication bus 1140. The processor 1110 can call logical instructions in the memory 1130 to execute the GNSS robust positioning and velocity measurement method based on the raw output information of a smart terminal provided in the above-described method embodiments.

[0130] Furthermore, the logical instructions in the aforementioned memory 1130 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal provided in the above-described method embodiments.

[0132] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform a GNSS robust positioning and velocity measurement method based on the raw output information of a smart terminal provided by the methods described above.

[0133] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robust GNSS positioning and velocity measurement method based on raw output information from a smart terminal, characterized in that, include: The raw GNSS output information of the smart terminal is acquired and converted into GNSS observations; wherein, the GNSS observations include pseudorange observations, carrier phase observations, and Doppler observations; The pseudorange observations are quality controlled based on the raw GNSS output information, and pseudorange positioning is calculated. The carrier phase observations are quality controlled based on the raw GNSS output information, and extended Kalman filtering is used for inter-epoch differential observation updates of the carrier phase to estimate the current epoch displacement. If the inter-epoch differential observation update of the carrier phase is successful, the current epoch displacement is output; otherwise, the Doppler observations are quality controlled based on the raw GNSS output information, and extended Kalman filtering is used for Doppler observation updates to estimate the current epoch displacement, and the current epoch displacement is output. Based on the pseudorange positioning solution, a one-step prediction is performed using the current epoch displacement to obtain the one-step predicted position of the current epoch. Based on the one-step predicted position of the current epoch, pseudorange observation updates and carrier phase observation updates are performed to obtain the relative positioning solution. The quality control of the Doppler observations based on the raw GNSS output information includes: Based on the pseudorange change rate uncertainty in the original GNSS output information, abnormal Doppler observations with pseudorange change rate uncertainty greater than a preset uncertainty threshold are removed to obtain the abnormal Doppler observations. After removing anomalies, Doppler observations were screened by elevation angle, signal-to-noise ratio, and Doppler epoch difference consistency check to obtain quality-controlled Doppler observations. The step of updating the pseudorange observation and carrier phase observation based on the one-step predicted position of the current epoch to obtain the relative positioning solution includes: Based on the one-step prediction position of the current epoch, calculate the double-difference residual of the previous pseudorange of the current epoch and remove abnormal pseudorange observations to obtain pseudorange observations that pass the test. Based on the sorting according to elevation angle or carrier-to-noise ratio, and combined with the uncertainty of satellite signal transmission time, a reference satellite is selected. The double-difference pseudorange observation value is obtained through the verified pseudorange observation value, inter-satellite difference, and inter-station difference. The pseudorange observations are updated by weighting the double-difference pseudorange observations based on a random model of carrier-to-noise ratio based on the uncertainty of satellite signal transmission time. Based on the one-step prediction position of the current epoch, calculate the previous phase double difference residual of the current epoch and remove abnormal carrier phase observations to obtain carrier phase observations that pass the test. The reference satellite is selected based on the elevation angle or carrier-to-noise ratio, and combined with the status information of the current epoch carrier phase cumulative change distance in the original GNSS output information. The double-difference phase observation value is obtained through the verified carrier phase observation value, inter-satellite difference, and inter-station difference. The carrier phase observations are updated by weighting the double-difference phase observations based on a carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. The carrier phase observations used for the carrier phase observation update are subjected to post-hoc residual verification and abnormal ambiguity is reset. The final relative positioning floating-point solution is then calculated iteratively.

2. The GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal according to claim 1, characterized in that, The process of quality control of the pseudorange observations based on the raw GNSS output information and performing pseudorange positioning calculations includes: The pseudorange observations are filtered by elevation angle, signal-to-noise ratio and pseudorange-Doppler consistency check to obtain the filtered pseudorange observations. Based on the satellite signal transmission time uncertainty in the original GNSS output information, and combined with the original carrier-to-noise ratio stochastic model, a carrier-to-noise ratio stochastic model based on the satellite signal transmission time uncertainty is constructed. The screened pseudorange observations are weighted using the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time, and the pseudorange positioning solution for the current epoch is calculated by combining least squares estimation; wherein, the result of the pseudorange positioning solution is pseudorange difference decomposition or pseudorange single-point solution.

3. The GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal according to claim 2, characterized in that, The step of assigning weights to the filtered pseudorange observations using the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time includes: The variance of the filtered pseudorange observations is determined using the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time. The weights of the filtered pseudorange observations are determined based on the variance, and the filtered pseudorange observations are then assigned weights based on these weights.

4. The GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal according to claim 3, characterized in that, The formula for determining the variance of the filtered pseudorange observations using the carrier-to-noise ratio stochastic model based on the uncertainty of satellite signal transmission time is as follows: in, , b , d , , For fitting parameters, The standard deviation of pseudorange observations. The variance of pseudorange observations. RTUN Represents the uncertainty of the satellite signal transmission time. Where C is the carrier-to-noise ratio and C is the carrier power. This represents the noise power spectral density.

5. The GNSS robust positioning and velocity measurement method based on the original output information of a smart terminal according to claim 1, characterized in that, The quality control of the carrier phase observations based on the raw GNSS output information includes: Based on the status information of the cumulative carrier phase change distance in the original GNSS output information, determine whether there are any abnormal phenomena, cycle slip phenomena, or reset phenomena. For carrier phase observations exhibiting cycle slips and reset phenomena, set the LLI bit; Carrier phase observations with abnormal phenomena such as position conflicts or unknown positions are discarded to obtain preliminary filtered carrier phase observations. The carrier phase observations that were initially screened were then subjected to elevation angle, signal-to-noise ratio (SNR) screening, and phase Doppler consistency checks to obtain carrier phase observations that had undergone quality control.

6. A GNSS robust positioning and velocity measurement system based on raw output information from a smart terminal, comprising executing the GNSS robust positioning and velocity measurement method based on raw output information from a smart terminal as described in any one of claims 1 to 5, characterized in that, include: A pseudorange positioning module is used to acquire the raw GNSS output information of the smart terminal and convert it into GNSS observations; wherein, the GNSS observations include pseudorange observations, carrier phase observations, and Doppler observations; the pseudorange observations in the GNSS observations are subjected to quality control based on the raw GNSS output information, and pseudorange positioning is calculated; The displacement velocity measurement module is used to perform quality control on the carrier phase observations based on the raw GNSS output information, and to perform inter-epoch differential observation updates on the carrier phase using an extended Kalman filter to estimate the displacement at the current epoch. If the inter-epoch differential observation update on the carrier phase is successful, the displacement at the current epoch is output; otherwise, the module performs quality control on the Doppler observations based on the pseudorange rate of change uncertainty in the raw GNSS output information, and to perform Doppler observation updates using an extended Kalman filter to estimate the displacement at the current epoch and output the displacement at the current epoch. The relative positioning module performs a one-step prediction based on the pseudorange positioning solution and the displacement of the current epoch to obtain the one-step predicted position of the current epoch. Based on the one-step predicted position of the current epoch, it performs pseudorange observation update and carrier phase observation update to obtain the relative positioning solution.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the GNSS robust positioning and velocity measurement method based on the original output information of the smart terminal as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the GNSS robust positioning and velocity measurement method based on the original output information of the smart terminal as described in any one of claims 1 to 5.

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