GNSS (Global Navigation Satellite System) and 5G fusion positioning method and system for intelligent terminal

Abstract

The invention belongs to the technical field of positioning, and discloses a GNSS and 5G fusion positioning method and system for an intelligent terminal, and the method achieves the joint smoothing of pseudo-range observation data through introducing a GNSS carrier phase and Doppler frequency shift in combination with auxiliary observation values such as a 5G TOA observation value. The observation continuity and the data stability in a low signal-to-noise ratio and dynamic environment can be remarkably improved. According to the method, the clock error of the intelligent terminal is effectively eliminated by using an inter-satellite single-difference and inter-base-station single-difference method, the clock error drift of the intelligent terminal is effectively inhibited while a low-complexity lightweight fusion positioning model is constructed, the positioning precision is improved, the dependence on the hardware synchronization capability of the intelligent terminal is reduced, and the positioning accuracy is improved. The method improves the positioning precision, continuity and environmental adaptability in a dynamic scene and a shielding environment, and is suitable for intelligent terminal positioning tasks in a complex urban environment, a shielding region and a mobile scene.

Description

Technical Field

[0001] The present invention belongs to the field of positioning technology, and specifically relates to a GNSS and 5G integrated positioning method and system for smart terminals. Background Art

[0002] The Global Navigation Satellite System (GNSS), with its global coverage, high precision, and all-weather capabilities, has been widely used in transportation navigation, emergency rescue, mobile communications, and other fields. In recent years, smart devices such as mobile phones have gradually acquired the ability to receive multi-frequency GNSS signals, providing the technical foundation for achieving sub-meter and even higher precision positioning services.

[0003] However, in actual applications, smart terminals still face many challenges in GNSS high-precision positioning, such as low antenna gain leading to severe signal attenuation; unstable clock errors of smart terminals introducing systematic errors; frequent GNSS signal jumps affecting the availability of carrier phase data, and other issues that seriously limit their positioning accuracy and continuity.

[0004] At the same time, 5G communication networks, with their high bandwidth, low latency, and high-density deployment, offer excellent local ranging capabilities, making them a powerful complement to GNSS. However, existing research on GNSS-5G fusion positioning is largely based on high-end geodetic-grade receivers, making it difficult to apply to smart devices with limited computing power and significant observation noise. Therefore, there is an urgent need to develop a lightweight and high-precision GNSS-5G fusion positioning method and system for smart devices.

[0005] Existing GNSS and 5G converged positioning technologies face the following key challenges in smart device applications: First, GNSS observations from smart devices are limited by antenna gain and RF structure design, resulting in high noise and frequent lock loss. Traditional GNSS data processing and quality control methods are difficult to apply in low signal-to-noise ratio environments. Second, the local clock error within smart devices is significant and unstable, leading to non-negligible systematic drift in GNSS pseudorange, carrier phase, and Doppler data, seriously affecting positioning accuracy. Third, there is a lack of methods to fully integrate 5G TOA observations (e.g., TOA ranging values) with GNSS pseudoranges from 5G communication base stations to constrain and smooth GNSS pseudoranges, making it difficult to effectively improve positioning stability. Fourth, the high complexity of existing positioning solution models makes real-time operation difficult on computing resource-constrained platforms such as smart devices. These issues make it difficult for smart devices to achieve stable sub-meter positioning accuracy or even higher, limiting the widespread adoption of high-precision location services on mass-market smart devices. Summary of the Invention

[0006] The purpose of this invention is to propose a GNSS and 5G fusion positioning method for smart terminals. The method covers heterogeneous positioning observation data fusion, error modeling and correction, and lightweight solution model construction technology, and is particularly suitable for the demand for high-precision positioning services for smart terminals in complex environments.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: A GNSS and 5G integrated positioning method for smart terminals includes the following steps: Step 1. Use smart terminals to collect heterogeneous positioning observation data, including GNSS observation data and 5G observation data, in real time. Step 2. Screen the collected heterogeneous positioning observation data for validity and eliminate gross errors; Step 3. Model and correct the systematic errors in the heterogeneous positioning observation data, and smooth the pseudorange based on the Doppler frequency shift of the GNSS observation data; Step 4. Based on the GNSS inter-satellite single-difference model and the 5G inter-base station single-difference model, construct a GNSS and 5G single-difference non-combined model and a random model. Step 5. Based on the GNSS and 5G single-difference non-combined model and the random model, use the iterative least squares method to solve the 3D coordinates of the smart terminal at each epoch, obtaining the preliminary GNSS and 5G positioning results and the observation residuals. Step 6. Determine whether joint pseudorange smoothing has been performed; If joint pseudorange smoothing has not been performed, perform joint pseudorange smoothing based on the carrier phase and Doppler shift of the GNSS observation data and the preliminary positioning results of GNSS and 5G, and go to step 4; If joint pseudorange smoothing has been performed, go to step 7; Step 7. Calculate the root mean square error of the observation residuals for each epoch and determine whether there are any abnormal data points based on the preset threshold; If there are no abnormal data points, go to step 8; if there are abnormal data points, remove the abnormal data points and go to step 4; if the observed data continues to be abnormal, trigger the fault-tolerant relocation mechanism and reselect an available data source; Step 8. Output the three-dimensional coordinate position of the smart terminal at the current epoch.

[0008] In addition, based on the GNSS and 5G integrated positioning method for smart terminals, the present invention also proposes a corresponding GNSS and 5G integrated positioning system for smart terminals, the technical solution of which is as follows: A GNSS and 5G integrated positioning system for smart terminals, including smart terminals for receiving GNSS observation data and 5G observation data; A readable storage medium is provided in the smart terminal, and when the readable storage medium is executed, it is used to implement the steps of the GNSS and 5G fusion positioning method for smart terminals mentioned above.

[0009] In addition, based on the above-mentioned GNSS and 5G integrated positioning method for smart terminals, the present invention also proposes a computer-readable storage medium on which a program is stored; when the program is executed by the processor, it is used to implement the steps of the above-mentioned GNSS and 5G integrated positioning method for smart terminals.

[0010] The present invention has the following advantages: As described above, the present invention describes a GNSS and 5G fusion positioning method for smart terminals. This method introduces the carrier phase and Doppler frequency shift of GNSS observation data, and combines it with auxiliary observation values ​​such as the TOA ranging value of 5G observation data to achieve joint smoothing of pseudo-range observation data, which can significantly improve the observation continuity and data stability in low signal-to-noise ratio and dynamic environments. The method of the present invention also uses the GNSS inter-satellite single-difference model and the 5G inter-base station single-difference model to effectively eliminate the clock error of the smart terminal. While realizing the construction of a low-complexity lightweight fusion positioning model, namely the GNSS and 5G single-difference non-combined model, it effectively suppresses the clock error drift of the smart terminal, improves positioning accuracy, and reduces dependence on the hardware synchronization capability of the smart terminal, thereby improving positioning accuracy, continuity and environmental adaptability in dynamic scenes and occluded environments. The method of the present invention also designs an error correction and residual quality control mechanism suitable for smart terminals, which can achieve efficient removal of gross errors and invalid observation values, thereby ensuring the reliability of the positioning results. The GNSS and 5G fusion positioning method for smart terminals of the present invention can be applied to smart terminal positioning tasks in complex urban environments, obscured areas and mobile scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flowchart of the GNSS and 5G integrated positioning method for smart terminals in an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 This embodiment provides a GNSS and 5G fusion positioning method for smart terminals. This method addresses the problem that smart terminals are susceptible to signal obstruction and clock instability in complex environments, and designs a fusion positioning process that combines accuracy, adaptability, and lightness.

[0013] like Figure 1 As shown in FIG, the GNSS and 5G integrated positioning method for smart terminals includes the following steps: Step 1. Data collection: Heterogeneous positioning observation data, including GNSS observation data and 5G observation data, is collected in real time through smart terminals.

[0014] The smart terminal can be a smartphone or other smart terminal device with corresponding hardware capabilities. Heterogeneous positioning observation data is collected in real time through the smart terminal. The heterogeneous positioning observation data includes GNSS observation data and 5G observation data.

[0015] GNSS observation data includes the original observation values ​​of GNSS pseudorange, carrier phase, Doppler frequency shift, and carrier-to-noise ratio; 5G observation data is the arrival time observation value from the 5G communication base station, namely the TOA ranging value.

[0016] At the same time, the local clock of the smart terminal and the timestamp of the 5G network reference time are synchronized and recorded to ensure the consistency of GNSS and 5G multi-epoch observation data in the time domain.

[0017] Continuously collect GNSS and 5G observation data from multiple consecutive epochs to build a complete joint observation sequence, which is used to smooth the pseudorange.

[0018] Step 1 lays the data foundation for subsequent fusion positioning by constructing a joint observation sequence.

[0019] Step 2. Data preprocessing: Screen the collected heterogeneous positioning observation data for validity and eliminate gross errors.

[0020] The collected raw observation data, including GNSS and 5G observation data, is screened for validity and gross error removal to ensure the reliability of subsequent model input. This method uses conventional GNSS methods such as dual-frequency verification and inter-epoch double-difference testing to identify and remove gross errors. It also combines the AccumulatedDeltaRangeState field information (the original observation field information of the smart terminal) with Doppler information (Doppler frequency shift) to detect and mark cycle slips, thereby improving data continuity and stability.

[0021] Specifically, gross errors are eliminated using dual-frequency verification and inter-epoch double-difference verification. Dual-frequency verification involves performing inter-frequency single-difference calculations on the GNSS pseudoranges of the same satellite at two frequencies. If the difference exceeds a preset threshold, the error is considered gross and eliminated. Inter-epoch double-difference verification involves performing inter-epoch double-difference calculations on the GNSS pseudoranges, carrier phase, and Doppler shift. If the difference exceeds a threshold, the error is considered gross and eliminated.

[0022] Single-frequency cycle slip detection is used for validity screening, and the original observation field information and Doppler frequency shift of the smart terminal are combined to determine and mark the cycle slips of single-frequency GNSS observations.

[0023] Step 2 can ensure that the data entering the error modeling and positioning model has high accuracy and credibility.

[0024] Step 3. Error correction: Model and correct the systematic errors in the heterogeneous positioning observation data, and smooth the pseudorange based on the Doppler frequency shift of the GNSS observation data to reduce the random noise of the GNSS pseudorange observation values.

[0025] Establish a systematic error modeling and correction mechanism to compensate for common systematic errors in GNSS observation data and 5G observation data, including relativistic effects, tidal influences, ionospheric and tropospheric delays, antenna phase center deviations, and errors caused by the Earth's rotation.

[0026] Modeling and correcting the systematic errors in GNSS observation data and 5G observation data to improve the accuracy of the positioning model. The systematic errors modeled and corrected in this embodiment specifically include: The model in IERS Conventions 2010 is used to calculate the corrections for relativistic effects and tides.

[0027] For the antenna phase center deviation, the igs14.atx antenna model is used for correction.

[0028] The improved Saastamoinen model is used to correct the tropospheric dry delay.

[0029] The Earth rotation error is corrected based on the IERS EOP C04 Earth parameter model.

[0030] Ionospheric delay, precise clock error, and hardware delay are corrected using precise ionospheric products, satellite clock error, and differential code bias (DCB) products released by IGS.

[0031] In addition, the present invention is also based on Doppler frequency shift smoothing pseudorange, and its process is shown in formula (1): (1) in, Indicates the GNSS pseudorange observations after smoothing of epochs, Indicates the GNSS pseudorange observations after smoothing of epochs; The wavelength representing the carrier phase; represents the smoothing window time difference; Indicates the The Doppler shift observation value of epochs, Respectively represent The Doppler shift observation value of epoch.

[0032] Step 3 enhances the quality of observation information and is an important step before fusion of the method of the present invention.

[0033] Step 4. Model construction: Based on the GNSS inter-satellite single-difference model and the 5G inter-base station single-difference model, a GNSS and 5G single-difference non-combined model and a random model are constructed.

[0034] To address the limited resources of smart terminals, we constructed a single-differenced, non-combined GNSS and 5G model, as well as a random model, suitable for smart terminals. The random model includes a variance-covariance matrix. To address the instability of local clocks in smart terminals, the GNSS component uses an inter-satellite single-difference model to eliminate clock errors. For the 5G component, based on known base station locations and network time synchronization, a single-differenced model between 5G base stations was designed to offset receiver clock errors. The constructed single-differenced, non-combined GNSS and 5G model achieves unified modeling in terms of structure, with parameter weights controlled by observed variance-covariance.

[0035] Combining GNSS observation data and 5G observation data, a GNSS and 5G lightweight fusion positioning model adapted to smart terminals is constructed, namely a GNSS and 5G single-difference non-combined model, which includes a GNSS inter-satellite single-difference model for the GNSS part and a 5G inter-base station single-difference model for the 5G part.

[0036] Since the local hardware clock of the smart terminal is unstable and prone to jumps, the inter-satellite single difference method is used to eliminate the clock error of the smart terminal. The GNSS inter-satellite single difference model adopts a full-frequency non-combined model. The GNSS inter-satellite single difference model is expressed as: .

[0037] The 5G inter-base station single-difference model is based on the premise that the base station locations are known and the network time synchronization is accurate. 5G geometric constraints are established. Similar to satellites, considering the instability of the local clock of smart terminals, a 5G inter-base station single-difference model is constructed to eliminate the clock error at the receiving end. The 5G inter-base station single-difference model is expressed as: .

[0038] For the sake of intuition, the errors corrected by the existing model are not listed one by one. Construct a GNSS and 5G single-difference non-combined model as shown in formula (2): (2) in, is the frequency point number, The value of depends on the observation error at different frequencies. After smoothing The GNSS pseudo-range inter-satellite single difference observation value of the frequency point, is the geometric distance from the intelligent terminal to the satellite after inter-satellite single difference, is the tropospheric wet delay after intersatellite single difference, is the intersatellite single difference operator, Indicates the GNSS pseudorange observation errors other than the systematic error correction, For the The wavelength of the frequency point, For the The carrier phase inter-satellite single difference observation value of the frequency point, For the The inter-satellite single-difference carrier phase ambiguity of the frequency point, Indicates the other observation errors of the carrier phase except the systematic error correction, is the speed of light in vacuum, It is the single difference observation value between 5G base stations. The TOA observation value of 5G base station 1 received by the smart terminal is subtracted from the TOA observation value of 5G base station 2 to obtain the single difference observation value between 5G base stations. is the geometric distance from the 5G base station to the smart terminal after single difference between 5G base stations, Indicates other 5G observation errors except for the systematic error correction, is the single difference operator between 5G base stations.

[0039] (3) (4) in, Indicates the geometric distance from the smart terminal to the satellite, represents the three-dimensional coordinates of the satellite, Represents the three-dimensional coordinates of the smart terminal, Indicates the geometric distance from the 5G base station to the smart terminal. Indicates the three-dimensional coordinates of the 5G base station.

[0040] Depend on Doing intersatellite single difference, we get Depend on It is obtained by performing single difference between 5G base stations.

[0041] The random model adopts the carrier-to-noise ratio weighting model, and the satellite carrier-to-noise ratio weighting strategy is shown in formula (5): (5) in, represents the standard deviation of the observation error in satellite carrier-to-noise ratio determination, represents the satellite carrier-to-noise ratio; Indicates the standard deviation of the error of the zenith direction observation; is the carrier-to-noise ratio threshold.

[0042] The variance-covariance matrix used to construct the observation values ​​in formulas (6) to (8) is used to assign weights to different observation values, affecting the accuracy and stability of the final least squares solution.

[0043] In order to achieve weighted solution of different types of observations, it is necessary to construct the corresponding variance-covariance matrix, which is used to construct the weight matrix in the least squares solution and is the core part of the model solution.

[0044] The variance-covariance matrix is ​​shown in formulas (6) to (8): (6) (7) (8) in, represents the GNSS pseudorange variance-covariance matrix after inter-satellite single difference processing, 、 and They represent the standard deviation of the error of the GNSS pseudorange carrier-to-noise ratio weighting for frequency points 1, 2, and 3, respectively. represents the carrier phase variance-covariance matrix after inter-satellite single-difference processing, 、 and They represent the standard deviation of the carrier phase noise ratio weighting of frequency points 1, 2, and 3, respectively. represents the variance-covariance matrix of GNSS and 5G combined positioning; Indicates the error standard deviation of the 5G ranging observation value, namely the TOA ranging value.

[0045] Step 5. Model solution: Based on the GNSS and 5G single-difference non-combined model and the random model, the iterative least squares method is used to solve the three-dimensional coordinates of the smart terminal at each epoch to obtain the preliminary positioning results of GNSS and 5G and the observation value residuals.

[0046] An iterative least squares method is used to solve a non-combined model based on single-difference GNSS and 5G, obtaining the three-dimensional coordinates of the smart terminal at the current epoch. The solution utilizes the variance-covariance matrix constructed in step 4 based on the carrier-to-noise ratio weighting strategy to achieve optimal fusion of different types of observations. Step 5, which outputs a preliminary positioning result, is the core computational stage of the invented method.

[0047] The model solution is based on the GNSS and 5G single-difference non-combined model shown in formula (2) and the variance-covariance matrix shown in formulas (6) to (8). The iterative least squares method is used to solve the three-dimensional coordinates of the smart terminal at each epoch. The three-dimensional coordinates of the smart terminal are obtained to adapt to the lightweight processing capabilities of the smart terminal.

[0048] The parameters to be solved are shown in formula (9): (9) in, represents all parameters to be solved, represents the inter-satellite single-difference carrier phase ambiguity, .

[0049] Step 6. Joint pseudorange smoothing: Determine whether joint pseudorange smoothing has been performed. If not, perform joint pseudorange smoothing based on the carrier phase and Doppler shift of the GNSS observation data and the preliminary positioning results of GNSS and 5G, and proceed to step 4. If joint pseudorange smoothing has been performed, proceed to step 7.

[0050] The process of joint pseudorange smoothing based on the carrier phase, Doppler frequency shift of GNSS observation data and the preliminary positioning results of GNSS and 5G is shown in formula (10): (10) in, represents the smoothing coefficient of Doppler shift, represents the smoothing coefficient of the carrier phase, Indicates the smoothing coefficient of the preliminary positioning results of GNSS and 5G; Indicates the GNSS pseudorange observations of epochs; Indicates the epochs of carrier phase observations, Indicates the Carrier phase observations for epochs; Indicates the Preliminary positioning results of GNSS and 5G for epochs, Indicates the Preliminary positioning results of GNSS and 5G for epochs.

[0051] Taking into account that the existing 5G positioning method can usually only use the 5G positioning method to obtain a simple 5G position estimation result when the number of 5G base stations reaches 4 or more, and then perform pseudorange smoothing, the method of the present invention first uses only Doppler frequency shift to smooth the pseudorange in step 3, and after obtaining the preliminary positioning results of GNSS and 5G in step 5, it uses the joint smoothed pseudorange method to calculate once in step 6. In this way, even if the received 5G base station observation values ​​are less than 4, combined positioning can be performed and the pseudorange can be smoothed again.

[0052] In step 6, a joint pseudorange smoothing method is performed based on the carrier phase, Doppler frequency shift of the GNSS observation data and the preliminary positioning results of GNSS and 5G, which can effectively reduce the random noise of the pseudorange observation value and improve the robustness of the positioning model.

[0053] Step 7. Data quality control: Calculate the root mean square error (RMS) of the observation residuals for each epoch and determine whether there are any abnormal data points based on a preset threshold. If no abnormal data points exist, proceed to Step 8. If abnormal data points exist, remove them and proceed to Step 4 to rebuild the GNSS and 5G single-difference non-combined model. If the observed data continues to be abnormal, trigger the fault-tolerant relocation mechanism and reselect an available data source.

[0054] First, the observation accuracy is calculated based on the model's posterior residuals, and data with residuals exceeding the limit are removed. Residuals are generated during the model solution process, which are the observation residuals obtained after step 5. If the observation residual is determined to be greater than a preset threshold, the corresponding observation is removed, thus eliminating the excess data. The GNSS and 5G single-difference non-combined model and the random model are reconstructed and solved.

[0055] Specifically, the calculation of the root mean square error of the observation residual for each epoch is shown in formula (11): (11) in, Indicates the The root mean square error of the residuals of all observations for epochs, For the The observed residuals for observations, , is the number of observations in the current epoch.

[0056] Eliminate abnormal data points based on the set threshold: (12) in, is the experience multiplier, The general value range is (2.5, 3).

[0057] If formula (12) holds true, then eliminate the Observation values ​​are obtained, and the algorithm returns to step 4 to rebuild the GNSS and 5G single-difference non-combined model, and then performs the model solution in step 5.

[0058] Furthermore, if a particular type of observation fails or becomes abnormal, the system removes the outlier and triggers a fault-tolerant repositioning mechanism. Specifically, if a particular type of observation remains abnormal, such as a 5G base station signal failure, the system automatically removes that data source and restarts the positioning solution based solely on GNSS or other available sources, ensuring uninterrupted positioning and enhancing system adaptability.

[0059] In step 7, the method of the present invention evaluates the accuracy of each epoch observation based on the model's posterior residuals, calculates the residual root mean square (RMS), and removes anomalous data points based on a set threshold. The method then returns to steps 4 and 5, rebuilds the model, and solves the problem. If a certain type of observation data, such as 5G TOA observations, is found to be persistently anomalous, the system triggers a fault-tolerant relocation mechanism, reselects available data sources, and iterates the solution to ensure the system's continuous positioning capability in complex environments. Step 7 enables self-feedback and correction of the model output, enhancing the system's applicability.

[0060] Step 8. Result output: Output the three-dimensional coordinate position of the smart terminal at the current epoch.

[0061] Output the final 3D coordinate solution for the current epoch and optionally output position information quality indicators such as DOP, residual value, and weight coefficient for reference by the application layer. Step 8 not only completes the positioning target but also provides support for subsequent continuous positioning. The final output results depend on the quality control and model optimization of Step 7.

[0062] The preliminary GNSS and 5G positioning results output in step 5 are three-dimensional coordinate solutions without quality control; step 7 evaluates reliability through observation residuals, eliminates observations with excessive influence, and re-solves the coordinates; step 8 finally outputs a high-confidence positioning result after quality control, which can be accompanied by an error assessment indicator, namely the position information quality indicator.

[0063] The method of the present invention covers heterogeneous observation data fusion, error modeling and correction in step 3, and lightweight solution model construction technology in step 4, and is particularly suitable for the demand for high-precision positioning services for smart terminals in complex environments.

[0064] Furthermore, when 5G TOA observations are unavailable, other types of 5G information, such as TDOA time difference of arrival, RTT round-trip ranging, or AOA angle measurement, can be used as auxiliary observations. In addition to eliminating clock errors through single-difference analysis between satellites and 5G base stations, clock error parameters can be introduced through modeling and estimation, and a joint solution can be performed. In addition to the least squares method, alternative positioning methods such as the extended Kalman filter (EKF), the unscented Kalman filter (UKF), or the factor graph optimization (FGO) can also be used. For different smart terminal chip platforms, an adaptive parameter adjustment mechanism can be introduced to adjust filter weights and processing strategies based on the quality of real-time observations to enhance model compatibility.

[0065] The method of the present invention realizes joint smoothing of pseudorange observation data and improves data stability by introducing GNSS carrier phase and Doppler frequency shift, combined with auxiliary observation values ​​such as 5G TOA observation values; effectively eliminates the clock error of the intelligent terminal by using the single difference method between satellites and base stations, and realizes the construction of a low-complexity lightweight fusion positioning model; thereby improving positioning accuracy, continuity and environmental adaptability in dynamic scenes and occluded environments.

[0066] Specifically, in step 4, the method of the present invention constructs a non-combined model and a random model of GNSS and 5G single differences based on inter-satellite single differences and inter-5G base station single differences, which effectively suppresses the terminal clock error drift, improves positioning accuracy, and reduces dependence on the terminal hardware synchronization capability. In step 6, it is proposed to integrate the carrier phase, Doppler frequency shift of GNSS observation data and the preliminary positioning results of GNSS and 5G for joint pseudo-range smoothing, which significantly improves the observation continuity and stability in low signal-to-noise ratio and dynamic environments. In steps 3 and 7, error correction and residual quality control mechanisms suitable for smart terminal platforms are designed respectively to achieve efficient removal of gross errors and invalid observation values ​​and ensure the reliability of positioning results. An overall architecture of a fusion positioning system that takes into account high precision, low power consumption and high portability is constructed, which is suitable for smart terminal positioning tasks in complex urban environments, blocked areas and mobile scenarios.

[0067] Compared with the existing technology, the method of the present invention improves positioning accuracy and anti-interference capability. By integrating carrier phase, Doppler and 5G TOA observations to smooth the GNSS pseudorange, it effectively enhances the stability and anti-obstruction capability of pseudorange observations, and significantly improves positioning accuracy and continuity in complex environments. The method of the present invention also enhances the environmental adaptability of the system. Through the model construction in step 4 and the joint solution of GNSS and 5G heterogeneous observation data in step 5, it has observation redundancy and complementary characteristics. It can still maintain stable output when some observations fail or signal quality degrades, thereby improving the positioning availability of the system in weak signal scenarios such as urban canyons and semi-indoors. The method of the present invention also reduces the deployment threshold and terminal load. The designed fusion model fully considers the computing resource limitations of smart terminals and adopts lightweight least squares solution. It does not require dedicated hardware support and can output observation values ​​and then solve them at other terminals, or it can be solved directly at the smart terminal, thus achieving high-precision positioning and facilitating its promotion to mass smart terminals.

[0068] Example 2 This embodiment 2 describes a GNSS and 5G integrated positioning system for smart terminals, which is based on the same inventive concept as the GNSS and 5G integrated positioning method for smart terminals in embodiment 1.

[0069] Specifically, the GNSS and 5G integrated positioning system for smart terminals includes a smart terminal for receiving GNSS observation data and 5G observation data.

[0070] A readable storage medium is provided in the smart terminal, and when the readable storage medium is executed, it is used to implement the steps of the GNSS and 5G fusion positioning method for smart terminals described in Example 1.

[0071] Example 3 This embodiment 3 describes a computer-readable storage medium on which a program is stored. When the program is executed by a processor, it is used to implement the steps of a GNSS and 5G fusion positioning method for smart terminals.

[0072] The computer-readable storage medium can be an internal storage unit of any device or apparatus with data processing capabilities, such as a hard disk or memory, or an external storage device of any device with data processing capabilities, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc. equipped on the device.

[0073] Of course, the above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above-mentioned embodiments. It should be noted that all equivalent substitutions and obvious deformation forms made by any technician familiar with this field under the guidance of this specification fall within the substantive scope of this specification and should be protected by the present invention.

Claims

1. A GNSS and 5G integrated positioning method for smart terminals, characterized in that: The steps include: Step 1. Use smart terminals to collect heterogeneous positioning observation data, including GNSS observation data and 5G observation data, in real time. Step 2. Screen the collected heterogeneous positioning observation data for validity and eliminate gross errors; Step 3. Model and correct the systematic errors in the heterogeneous positioning observation data, and smooth the pseudorange based on the Doppler frequency shift of the GNSS observation data; Step 4. Based on the GNSS inter-satellite single-difference model and the 5G inter-base station single-difference model, construct a GNSS and 5G single-difference non-combined model and a random model. Step 5. Based on the GNSS and 5G single-difference non-combined model and the random model, use the iterative least squares method to solve the 3D coordinates of the smart terminal at each epoch, obtaining the preliminary GNSS and 5G positioning results and the observation residuals. Step 6. Determine whether joint pseudorange smoothing has been performed; If joint pseudorange smoothing has not been performed, perform joint pseudorange smoothing based on the carrier phase and Doppler shift of the GNSS observation data and the preliminary positioning results of GNSS and 5G, and go to step 4; If joint pseudorange smoothing has been performed, go to step 7; Step 7. Calculate the root mean square error of the observation residuals for each epoch and determine whether there are any abnormal data points based on the preset threshold; If there are no abnormal data points, go to step 8; If there are abnormal data points, remove them and go to step 4; If the observed data continues to be abnormal, the fault-tolerant relocation mechanism will be triggered to reselect an available data source; Step 8. Output the three-dimensional coordinate position of the smart terminal at the current epoch.

2. The GNSS and 5G integrated positioning method for smart terminals according to claim 1 is characterized in that: In step 1, the GNSS observation data includes the original observation values ​​of GNSS pseudorange, carrier phase, Doppler frequency shift, and carrier-to-noise ratio; the 5G observation data is the arrival time observation value from the 5G communication base station, namely the TOA ranging value.

3. The GNSS and 5G integrated positioning method for smart terminals according to claim 2 is characterized in that: In step 2, the double-frequency check and the double-difference check between epochs are used to eliminate gross errors; Single-frequency cycle slip detection is used for validity screening, and the original observation field information and Doppler frequency shift of the smart terminal are combined to determine and mark the cycle slips of single-frequency GNSS observations.

4. The GNSS and 5G integrated positioning method for smart terminals according to claim 2, characterized in that: In step 3, the systematic errors include relativistic effects, tidal effects, ionosphere and troposphere delays, antenna phase center deviations, and errors caused by the Earth's rotation; The process of smoothing pseudorange based on Doppler frequency shift is shown in formula (1): (1) in, Indicates the GNSS pseudorange observations after smoothing of epochs, Indicates the GNSS pseudorange observations after smoothing of epochs; The wavelength representing the carrier phase; represents the smoothing window time difference; Indicates the The Doppler shift observation value of epochs, Respectively represent The Doppler shift observation value of epoch.

5. The GNSS and 5G integrated positioning method for smart terminals according to claim 4 is characterized in that: The step 4 is specifically as follows: Construct a GNSS and 5G single-difference non-combined model as shown in formula (2): (2) in, is the frequency point number, After smoothing The GNSS pseudo-range inter-satellite single difference observation value of the frequency point, is the geometric distance from the intelligent terminal to the satellite after inter-satellite single difference, is the tropospheric wet delay after intersatellite single difference, is the intersatellite single difference operator, Indicates the GNSS pseudorange observation errors other than the systematic error correction, For the The wavelength of the frequency point, For the The carrier phase inter-satellite single difference observation value of the frequency point, For the The inter-satellite single-difference carrier phase ambiguity of the frequency point, Indicates the other observation errors of the carrier phase except the systematic error correction, is the speed of light in vacuum, is the single-difference observation value between 5G base stations, is the geometric distance from the 5G base station to the smart terminal after single difference between 5G base stations, Indicates other 5G observation errors except for the systematic error correction, is the single difference operator between 5G base stations; (3) (4) in, Indicates the geometric distance from the smart terminal to the satellite, represents the three-dimensional coordinates of the satellite, Represents the three-dimensional coordinates of the smart terminal, Indicates the geometric distance from the 5G base station to the smart terminal. Indicates the three-dimensional coordinates of the 5G base station; The random model adopts the carrier-to-noise ratio weighting model, and the satellite carrier-to-noise ratio weighting strategy is shown in formula (5): (5) in, represents the standard deviation of the observation error in satellite carrier-to-noise ratio determination, represents the satellite carrier-to-noise ratio; Indicates the standard deviation of the error of the zenith direction observation; is the carrier-to-noise ratio threshold; Construct a variance-covariance matrix that implements a weighted solution for different types of observations: (6) (7) (8) in, represents the GNSS pseudorange variance-covariance matrix after inter-satellite single difference processing, 、 and They represent the standard deviation of the error of the GNSS pseudorange carrier-to-noise ratio weighting for frequency points 1, 2, and 3, respectively. represents the carrier phase variance-covariance matrix after inter-satellite single-difference processing, 、 and They represent the standard deviation of the error of the carrier phase carrier-to-noise ratio weighting for frequency points 1, 2, and 3, respectively. represents the variance-covariance matrix of GNSS and 5G combined positioning; Indicates the standard deviation of the TOA ranging value.

6. The GNSS and 5G integrated positioning method for smart terminals according to claim 5, characterized in that: The step 5 is specifically as follows: The solution of the GNSS and 5G single-difference non-combined model shown in formula (2) is based on the variance-covariance matrix shown in formulas (6) to (8), and the iterative least squares method is used to solve the three-dimensional coordinates of the smart terminal at each epoch; The parameters to be solved are shown in formula (9): (9) in, represents all parameters to be solved, represents the inter-satellite single-difference carrier phase ambiguity, .

7. The GNSS and 5G integrated positioning method for smart terminals according to claim 6, characterized in that: In step 6, the process of joint pseudorange smoothing based on the carrier phase and Doppler shift of GNSS observation data and the preliminary positioning results of GNSS and 5G is shown in formula (10): (10) in, represents the smoothing coefficient of Doppler shift, represents the smoothing coefficient of the carrier phase, Indicates the smoothing coefficient of the preliminary positioning results of GNSS and 5G; Indicates the GNSS pseudorange observations of epochs; Indicates the epochs of carrier phase observations, Indicates the Carrier phase observations for epochs; Indicates the Preliminary positioning results of GNSS and 5G for epochs, Indicates the Preliminary positioning results of GNSS and 5G for epochs.

8. The GNSS and 5G integrated positioning method for smart terminals according to claim 7, characterized in that: In step 7, the root mean square error of the observation residuals for each epoch is calculated as shown in formula (11): (11) in, Indicates the The root mean square error of the residuals of all observations for epochs, For the The observed residuals for observations, , is the number of observations in the current epoch; Eliminate abnormal data points based on the set threshold: (12) in, is the experience multiplier; If formula (12) holds true, then eliminate the Observation values ​​are obtained, and the algorithm returns to step 4 to rebuild the GNSS and 5G single-difference non-combined model, and then performs the model solution in step 5.

9. The GNSS and 5G integrated positioning system for smart terminals is characterized by: Including smart terminals for receiving GNSS observation data and 5G observation data; A readable storage medium is provided in the smart terminal, and when the readable storage medium is executed, it is used to implement the steps of the GNSS and 5G fusion positioning method for smart terminals according to any one of claims 1 to 8.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by the processor, the steps of the GNSS and 5G fusion positioning method for smart terminals as described in any one of claims 1 to 8 are implemented.

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