Real-time precise positioning method and system based on the characteristics of smartphone GNSS observation values

Abstract

The present invention discloses a real-time precise positioning method and system based on the characteristics of GNSS observation values ​​of smartphones. The present invention solves the problems of difficulty in detecting cycle slips of GNSS observation values ​​of smartphones, difficulty in weighting observation values, and difficulty in positioning single-frequency and dual-frequency mixed data, thereby improving the precise positioning performance of smartphones. The present invention does not directly adopt traditional altitude angle weighting and carrier-to-noise ratio weighting, but utilizes the ReceivedSvTimeUncertaintyNanos characteristic variable that has a stronger correlation with pseudorange residuals, and combines the carrier-to-noise ratio and the synchronization state of satellite signals, and adopts a ridge regression model to learn the mapping relationship between these characteristic variables and the pseudorange residuals of mobile phones, thereby realizing the reasonable weighting of GNSS observation values ​​in smartphone positioning. On this basis, the smartphone GNSS cycle slip detection is performed by integrating Doppler observation values, geometric distance-free combined observation values ​​and time differential carrier phase, and a single-frequency and dual-frequency mixed GNSS precise positioning model for smartphones is established, making full use of the smartphone GNSS observation data, and greatly improving the precise positioning performance of smartphones.

Description

Technical Field

[0001] The present invention belongs to the field of GNSS data processing and high-precision navigation positioning, and specifically relates to a real-time precision positioning method and system based on the GNSS observation value characteristics of a smartphone. Background Art

[0002] The global navigation satellite system has been widely used in all aspects of human life. Modern life necessities represented by smart wearable devices (such as smartphones, watches, shared bicycles, etc.) also urgently need precise positioning services. Since the release of Android 7.0, Android smart devices have begun to provide GNSS raw observation data such as pseudorange and phase, which has attracted widespread attention in the academic community. How to achieve high-precision positioning of smart devices has become a new research hotspot.

[0003] Since the navigation chip integrated in smartphones is low-cost and the antenna uses a polarized antenna, the quality of the GNSS observation data output by them is very different from that of geodetic reception, so that its positioning accuracy has always been maintained within the range of 5-10m. However, with the rapid development of emerging fields such as smart travel, the Internet of Things, and artificial intelligence, many mobile phone users also need real-time high-precision navigation and positioning services. The original observation data output by smartphones has the characteristics of excessive observation noise, poor data continuity, and difficulty in determining the phase center of mobile phone GNSS. Under normal circumstances, the traditional geodetic receiver determines the random model of observation values ​​mainly by using the method of determining the weight of the elevation angle, that is, the accuracy of the observation value changes with the elevation angle. The smaller the elevation angle, the greater the error of the observation value. However, the accuracy of mobile phone GNSS observation values ​​is not the same. Studies by scholars have shown that the correlation between the noise of smartphone observation values ​​and the satellite elevation angle is low, and it is not suitable for the construction of random models of smartphone GNSS observation values. Compared with the satellite elevation angle, the signal-to-noise ratio (C / N0) has a stronger correlation with pseudorange noise and is more suitable for building random models of smartphones. On the other hand, due to the complex observation environment and low cost of navigation chips, smartphone GNSS data has frequent cycle slips, many gross errors, and missing data of some frequencies. Therefore, how to build a random model of GNSS observation values ​​for smartphones and solve problems such as observation data preprocessing and single-frequency and dual-frequency hybrid positioning models is the key to achieving high-precision navigation and positioning of smartphones.

[0004] In summary, the problems existing in the prior art are:

[0005] 1. Currently, the random model of smartphone GNSS observations is mainly determined by the signal-to-noise ratio (C / N0) data output by the mobile phone, or the signal-to-noise ratio (C / N0) and satellite altitude angle information are used to determine the accuracy of the observations. However, the signal-to-noise ratio (C / N0) is easily affected by the multipath effect, and the observations affected by the multipath will not necessarily have large errors. In other words, the signal-to-noise ratio is not the most correlated with the accuracy of smartphone observations.

[0006] 2. The observation environment of smartphones is complex, and the carrier phase observation values ​​frequently cycle slip. Cycle slips may occur in different systems, different satellites, and at different times, and there are problems such as missing observation values ​​of some frequencies. The traditional single cycle slip detection method is difficult to effectively detect cycle slips, resulting in unsatisfactory positioning accuracy. On the other hand, due to the frequent missing of some frequency observation data, the observation data is mixed with single-frequency and dual-frequency data. If the precise point positioning model (PPP) of the ionospheric elimination combination is used, it may often have too few effective satellites to complete precise positioning. Summary of the invention

[0007] In view of the fact that existing observation value weighting methods, cycle slip processing methods, and precise positioning methods are not completely applicable to smartphone GNSS positioning, the present invention provides a real-time precise positioning method and system based on the characteristics of smartphone GNSS observation values, which can effectively solve the problems of poor positioning accuracy and low reliability caused by large smartphone observation noise, few dual-frequency satellites, frequent cycle slips, etc. The present invention provides a smartphone GNSS precise positioning technical solution from data preprocessing to positioning solution.

[0008] Traditional geodetic receivers usually use altitude angle models to weight observation values ​​due to the high quality of GNSS observation data. Some scholars use carrier-to-noise ratio to weight smartphone GNSS observation values. The present invention analyzes the correlation between the noise of smartphone GNSS observation values ​​and the original observation information of many different states output by the mobile phone, screens out the feature parameters with the highest correlation, and trains the relationship between them and pseudo-range noise through a ridge regression model, constructs a random model of smartphone observation values, and realizes accurate weighting between smartphone GNSS observation values.

[0009] In the present invention, the pre-processing cycle slip of the joint Doppler observation value and the GF combination observation value is used, and the TDCP cycle slip detection method is combined to detect the carrier phase cycle slip in the case of single-frequency and dual-frequency mixing. Then the single-frequency and dual-frequency mixed PPP positioning model is used to realize the precise positioning of the smart phone, and the positioning accuracy and reliability are improved.

[0010] The first aspect of the embodiment provides a real-time precise positioning method based on the characteristics of GNSS observation values ​​of smartphones, including: detecting phase cycle slips of GNSS observation values; inputting ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio and satellite signal synchronization status into a trained ridge regression model, the ridge regression model predicts pseudorange noise; using pseudorange noise to calculate phase noise; a single-frequency and dual-frequency hybrid PPP positioning model jointly detects phase cycle slips of GNSS observation values ​​and the determined pseudorange noise and phase noise, and solves the precise position of the smartphone based on the real-time precise product broadcast by IGS or Beidou satellite-based PPP-B2b, wherein phase observation values ​​without cycle slips will all participate in the adjustment solution of the single-frequency and dual-frequency hybrid PPP positioning model, and phase observation values ​​confirmed as cycle slips will not participate in the solution of the current epoch, and if two consecutive epochs are confirmed as cycle slips, the ambiguity parameters of the single-frequency and dual-frequency hybrid PPP positioning model are reset.

[0011] The second aspect of the embodiment provides a real-time precise positioning system based on the characteristics of GNSS observation values ​​of smartphones, including: a data preprocessing module, which is configured to detect GNSS observation value phase cycle jumps; a noise determination module, which is configured to input ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio and satellite signal synchronization status into a trained ridge regression model, so that the ridge regression model predicts pseudorange noise, and is configured to use pseudorange noise to calculate phase noise; a single-frequency and dual-frequency hybrid PPP positioning model, which combines the detection of GNSS observation value phase cycle jumps and the determined pseudorange noise and phase noise, and solves the precise position of the smartphone based on the real-time precise product broadcast by IGS or Beidou satellite-based PPP-B2b, wherein the phase observation values ​​without cycle jumps will all participate in the adjustment solution of the single-frequency and dual-frequency hybrid PPP positioning model, and the phase observation values ​​confirmed as cycle jumps will not participate in the solution of the current epoch, if two consecutive epochs are confirmed as cycle jumps, the ambiguity parameters of the single-frequency and dual-frequency hybrid PPP positioning model are reset.

[0012] According to a third aspect of the embodiment, a smart phone is provided, comprising: a processor; a memory comprising one or more computer program modules; wherein the one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for implementing the real-time precise positioning method based on the GNSS observation value characteristics of the smart phone.

[0013] In a fourth aspect of the embodiment, a computer-readable storage medium is provided for storing non-temporary computer-readable instructions, which, when executed by a computer, can implement any of the real-time precise positioning methods based on the characteristics of smartphone GNSS observation values.

[0014] In some examples, the pseudorange noise is multiplied by a scaling factor to obtain the phase noise, which is the same as the coefficient between the carrier and the pseudorange of the carrier-to-noise ratio random model.

[0015] In some examples, the training method of the ridge regression model includes: selecting a base station closest to a smartphone; using the true coordinate value of the base station, the pseudorange observation value of the base station, the true coordinate value of the mobile phone, and the pseudorange observation value of the mobile phone, and combining the averaging of single differences between stations to calculate the pseudorange residuals; the pseudorange residuals and ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, and satellite signal synchronization status form a training set; using the training set to train the ridge regression model, so that the ridge regression model learns the mapping relationship between ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, satellite signal synchronization status and pseudorange residuals.

[0016] In some examples, a method for detecting phase cycle slips of GNSS observation values ​​includes: pre-marking phase observation values ​​suspected of cycle slips; performing cycle slip detection on the pre-marked phase observation values ​​using a TDCP method to confirm whether a cycle slip actually exists; and performing cycle slip detection on the remaining unmarked phase observation values ​​using a robust TDCP method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention provides an overall process framework for random model construction and real-time precise positioning based on the characteristics of smartphone GNSS observation values.

[0018] Figure 2 A flow chart of a method for detecting single- and dual-frequency cycle slips of GNSS in a smart phone provided in an embodiment of the present invention.

[0019] Figure 3 A flow chart of a method for constructing a random model based on the characteristics of GNSS observation values ​​of a smartphone provided in an embodiment of the present invention.

[0020] Figure 4 A set of experimental positioning results in Wuchang District, Wuhan City on November 4, 2020 provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The invention discloses a random model construction and real-time precise positioning method based on the GNSS observation value characteristics of a smart phone. Figure 1This is the overall process framework of the method. The process is generally divided into three steps: data preprocessing, random model construction, and single- and dual-frequency hybrid PPP. Using the ReceivedSvTimeUncertaintyNanos feature variable that has a stronger correlation with the pseudorange residual, combined with the carrier-to-noise ratio (SNR) and the satellite signal synchronization state (State), the ridge regression model is used to learn the mapping relationship between these feature variables and the mobile phone pseudorange residual, so as to achieve the reasonable weighting of the smartphone GNSS observations. The method of combining Doppler observations, geometry-free distance (GF) combined observations and time differential carrier phase (TDCP) is used to detect the phase cycle jump of the smartphone GNSS observations, and a single- and dual-frequency hybrid precise positioning model is established to improve the precise positioning performance of smartphones. The method is further explained below.

[0022] Step 1: Analyze the noise level of Doppler observations and phase observations of smartphone GNSS, determine the cycle slip detection threshold of Doppler observations and geometry-free distance (GF) combined observations, calculate the Doppler cycle slip test quantity and GF combined cycle slip test quantity, and pre-mark the phase observations suspected of cycle slips.

[0023] (1) Doppler cycle slip test quantity:

[0024]

[0025] Among them, ΔN k is the cycle slip test quantity; and D are the phase observation value and Doppler observation value respectively; Δt is the sampling rate.

[0026] The mean error of the cycle slip detection threshold can be calculated by the error propagation law:

[0027]

[0028] (2) GF combination cycle slip test quantity:

[0029] L GF =L1-L5=ΔI+λ1N1-λ5N5

[0030] Among them, L1 and L2 represent the phase observation values ​​of L1 band and L5 band respectively, ΔI represents the difference of ionospheric delay at two frequencies, N1 and N5 represent the ambiguity parameters of L1 band and L5 band respectively, λ1 and λ5 represent the wavelengths of L1 band and L5 band respectively.

[0031] The GF combination observation values ​​are differentiated between epochs. The specific formula is as follows:

[0032] ΔL GF (i) = L GF (i)-L GF(i-1)

[0033] =ΔI′+λ1ΔN1-λ5ΔN5

[0034] Among them, ΔI′=ΔI(i)-ΔI(i-1) is the change in the difference in ionospheric delay.

[0035] For single-frequency observations, if the absolute value of its Doppler cycle slip test value is greater than a predetermined threshold (determined according to the specific mobile phone model, the same below), it will be pre-marked as a suspected cycle slip; for dual-frequency observations, if the absolute value of its GF combination cycle slip test value is greater than the predetermined threshold, it will be determined in turn whether the absolute value of the Doppler cycle slip test value of each frequency point is greater than the predetermined threshold, and the frequency points greater than the threshold will be pre-marked as suspected cycle slips.

[0036] Step 2: First, the robust TDCP method is used to detect cycle slips on the remaining unmarked phase observations, and then the TDCP method is used to detect cycle slips on the pre-marked phase observations to confirm whether there are real cycle slips.

[0037] (1)TDCP method

[0038] The original observation model with known phase observations:

[0039]

[0040] The subscript r and superscript s represent the serial numbers of the GNSS receiver and satellite, respectively, and the subscript i represents the frequency. is the phase observation value along the line of sight (LoS) direction between the satellite and the receiver, represents the geometric distance between the satellite and the receiver, c is the speed of light in a vacuum, dt r and dt s Represent the clock errors of GNSS receiver and satellite respectively, is the tropospheric delay along the LoS direction, is the ionospheric delay at frequency i, b r,i and are the fractional phase deviations between the GNSS receiver and the satellite at frequency i, λ i is the wavelength, is the phase integer ambiguity, It is a combination of observation noise and various other errors.

[0041] Subtract the phase observation at the current epoch from the phase observation at the previous epoch:

[0042]

[0043] Satellite clock error, ionosphere, troposphere, receiver-side phase hardware delay, and satellite-side phase hardware delay change slowly, and the receiver (mobile phone) sampling interval is generally 1s, so they can be eliminated.

[0044] The above equation has three coordinates and N (number of satellite constellations) receiver clock error parameters to be estimated. After linearization, each parameter to be estimated is solved. Then, the post-validation unit weighted mean error of the model is calculated. When there is no cycle slip, the post-validation unit weighted mean error is at the centimeter level or even the millimeter level; when a cycle slip occurs, the post-validation unit weighted mean error will increase significantly.

[0045] (2) Robust TDCP method

[0046] The robust least squares method is used to estimate the satellites that have cycle slips in real time, and the observations that are difficult to determine whether they have cycle slips are downgraded. The robust least squares method uses an equivalent weight function to iteratively update the weights of the observations. The termination condition of the iterative calculation is that the change in the parameters between the two calculations is very small and less than a certain threshold. Equivalent weight function:

[0047]

[0048] Among them, k0 is 1, k1 is 8, W i The weights calculated by traditional weighting methods such as carrier-to-noise ratio or altitude angle, the standardized residual σ is the unit weight error.

[0049] In fact, because the threshold for terminating the iterative calculation is difficult to determine, it is more appropriate to use the appearance of 0 weight as the termination condition.

[0050] Finally, all phase observations without cycle slips will participate in the adjustment solution of single-frequency and dual-frequency hybrid PPP, while phase observations confirmed as cycle slips will not participate in the solution of the current epoch. If two consecutive epochs are confirmed as cycle slips, their ambiguity will be reset.

[0051] Figure 3 The flow chart of the GNSS carrier phase cycle slip detection method is shown. As mentioned above, the method first pre-marks the GNSS phase observations for cycle slips through Doppler cycle slip detection and GF combined cycle slip detection, and then uses the robust TDCP method to further detect the observations that are uncertain whether there are cycle slips. The observations suspected of cycle slips and observations without cycle slips are input into the TDCP method to finally confirm the suspected cycle slips.

[0052] Step 3: Analyze the correlation between the original observation information of the smartphone GNSS (such as carrier-to-noise ratio, ReceivedSvTimeUncertaintyNanos, satellite signal synchronization state State, etc.) and the pseudorange single difference residual value, and calculate their correlation coefficient.

[0053] Step 4: Select highly correlated information as feature variables to determine the weights of observation values, and use the ridge regression model to learn the mapping relationship between these feature variables and the single-difference residuals of the mobile phone pseudorange, so as to achieve reasonable weighting of GNSS observation values ​​in smartphone positioning.

[0054] Select smartphone GNSS feature variables, such as ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, satellite signal synchronization state, etc. The ridge regression model used is as follows:

[0055] Y=XW+ε

[0056]

[0057] Among them, Y represents the target value pseudorange residual, X is a vector, X(1) represents the first element of this vector (representing the bias term), X(2:n) represents the remaining elements (representing each characteristic variable), W is the coefficient to be determined, ε is the interference term, and I represents the unit matrix.

[0058] After training with a large amount of data, the coefficients W in the ridge regression model are determined, which reflect the mapping relationship between pseudorange noise and each characteristic variable. The observation value random model based on ridge regression is:

[0059]

[0060] The ridge regression model is a biased estimation regression method, and the random model mentioned here is a model that uses this method to estimate the observation noise, where σ P is the pseudorange observation noise, σ L is the phase observation noise, x is the characteristic variable, Ridge() is the trained ridge regression model, and the value of k is the same as that of the carrier-to-noise ratio random model. The carrier-to-noise ratio random model is a conventional model and will not be described in detail in this invention.

[0061] The entire GNSS observation weighting process based on ridge regression is to first collect or collect a large amount of observation information, including base station observations and true coordinate values, mobile phone observations and true coordinate values, etc., and calculate the pseudorange residuals by averaging the single differences between stations. The ridge regression model is used to learn the mapping relationship between the feature variables and the mobile phone pseudorange residuals. During positioning, the ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, and State state value of each pseudorange observation in the current epoch output by the smartphone in real time are used as the input of the trained ridge regression model to predict the pseudorange noise. The phase noise is obtained by multiplying the pseudorange noise by a proportional coefficient, which is the same as the coefficient between the carrier and the pseudorange in the carrier-to-noise ratio random model.

[0062] Figure 2The flowchart of the random model construction method is shown. As mentioned above, the method is mainly divided into two parts: offline model training and online model prediction. The offline model training part mainly uses the mobile phone data and the nearest base station data to calculate the pseudorange single difference residual, and forms a training set with the original observation information of the mobile phone, and then learns the mapping relationship through the ridge regression model. The online model prediction part mainly uses the original observation information of the mobile phone to predict the noise of the observation value in real time, which is used for the weighting of the GNSS observation value in PPP positioning.

[0063] Step 5: Build a single-frequency and dual-frequency hybrid PPP positioning model, combine the cycle slip detection in step 2 with the weight determination method in step 4, and calculate the precise location of the smartphone based on the real-time precision products broadcast by IGS or Beidou satellite-based PPP-B2b.

[0064] The GNSS signal consists of two parts: pseudorange and carrier phase observations. Its observation can be expressed as:

[0065]

[0066] The subscript r and superscript s represent the serial numbers of the GNSS receiver and satellite, respectively, and the subscript i represents the frequency. and are the pseudorange and phase observations along the line of sight (LoS) between the satellite and the receiver, represents the geometric distance between the satellite and the receiver, c is the speed of light in a vacuum, dt r and dt s Represent the clock errors of GNSS receiver and satellite respectively, is the tropospheric delay along the LoS direction, is the ionospheric delay of frequency i, B r,i and are the pseudorange hardware delays of the GNSS receiver and the satellite at frequency i, and b r,i and are the fractional phase deviations between the GNSS receiver and the satellite at frequency i, λ i is the wavelength, is the phase integer ambiguity, and It is a combination of observation noise and various other errors.

[0067] The above observations are corrected with precise clock error products. The introduced pseudorange hardware delay and the original pseudorange hardware delay or phase hardware delay of the observations will be absorbed by the receiver clock error parameters and ionospheric parameters, and the reorganized observation equation can be obtained:

[0068]

[0069] in, Represents the receiver clock error parameter after absorbing part of the pseudorange and phase hardware delay, Represents the ionospheric parameters after absorbing some pseudorange and phase hardware delays.

[0070] Because the single-frequency observation equation is rank-deficient, it is necessary to add the ionospheric constraint equation:

[0071]

[0072] in, Represents the satellite's ionospheric delay prior information, which can be obtained from the ionospheric real-time product or empirical model. Represents the error in the a priori information of ionospheric delay.

[0073] The observation matrix of the single-frequency and dual-frequency hybrid PPP positioning model that uses single-frequency and dual-frequency observations and adds ionospheric constraint equations is:

[0074]

[0075]

[0076] Where u represents the direction vector of the line connecting the satellite and the receiver, Mw is the projection function of the tropospheric delay, X represents the receiver coordinates to be estimated, τ represents the wet delay parameter in the tropospheric zenith direction, I represents the ionospheric delay parameter vector, N1 represents the ambiguity parameter vector of the L1 band, N5 represents the ambiguity parameter vector of the L5 band, Represents the observation matrix coefficients of satellite j-band i-related observations at the ionospheric parameters, represents the observation matrix coefficients of the observation values ​​associated with satellite j band i at the L1 band ambiguity parameters, Represents the observation matrix coefficients of the observations associated with satellite j band i at the L5 band ambiguity parameters.

[0077] The random model includes an observation value random model and a parameter random model. The observation value model is determined according to the method described in step 4. Different parameters of the parameter random model adopt different models according to their characteristics. In the present invention, the station coordinate parameters are estimated epoch by epoch using white noise, and the tropospheric delay parameters are estimated using a random walk process; the receiver clock difference parameters are estimated epoch by epoch as white noise; the ambiguity parameters are estimated as constants in the arc segment where cycle slips do not occur, and if cycle slips occur, new ambiguity parameters need to be set; the ionospheric delay parameters adopt a random walk model.

[0078] The first-order Gauss-Markov process in discrete form can be expressed as:

[0079]

[0080] Where p(t) represents a random process; u k (t) is Gaussian white noise, and its variance is σ 2 ; β = 1 / τ is the inverse of the correlation time τ; t i ,t j are two moments. p(t j ) and p(t i ) is related to the numerical selection of parameters σ and β. When β approaches 0, the first-order Gauss-Markov model degenerates into a random walk model, and when β approaches infinity, the first-order Gauss-Markov model degenerates into a white noise model.

[0081] The present invention also provides an embodiment of a real-time precise positioning system based on the GNSS observation value characteristics of a smart phone. The system includes a data preprocessing module, a noise determination module and a single-frequency and dual-frequency hybrid PPP positioning model.

[0082] The data preprocessing module is configured to detect GNSS observation phase cycle jumps. The noise determination module is configured to input ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio and satellite signal synchronization status into the trained ridge regression model, so that the ridge regression model predicts pseudorange noise, and is configured to calculate phase noise using pseudorange noise. The single-frequency and dual-frequency hybrid PPP positioning model combines the detection of GNSS observation phase cycle jumps and the determined pseudorange noise and phase noise, and solves the precise position of the smartphone based on the real-time precision product broadcast by IGS or Beidou satellite-based PPP-B2b, where all phase observations without cycle jumps will participate in the adjustment solution of the single-frequency and dual-frequency hybrid PPP positioning model, and the phase observations confirmed as cycle jumps will not participate in the solution of the current epoch. If two consecutive epochs are confirmed as cycle jumps, the ambiguity parameters of the single-frequency and dual-frequency hybrid PPP positioning model are reset. For more detailed information about GNSS observation phase cycle jump detection, pseudorange noise and phase noise calculation, single-frequency and dual-frequency hybrid PPP positioning model and position solution, please refer to steps 1 to 5 in the above method section. The system described is coupled to the method described.

[0083] The present invention also provides an embodiment of a smart phone. The smart phone includes a processor and a memory. The memory is used to store non-transitory computer-readable instructions (such as one or more computer program modules). The processor is used to run the non-transitory computer-readable instructions, and when the non-transitory computer-readable instructions are run by the processor, one or more steps in the real-time precise positioning method described above can be executed. The memory and the processor can be interconnected through a bus system and / or other forms of connection mechanisms.

[0084] For example, the processor may be a central processing unit (CPU), a graphics processing unit (GPU), or other forms of processing units with data processing capabilities and / or program execution capabilities. For example, the central processing unit (CPU) may be an X86 or ARM architecture, etc. The processor may be a general-purpose processor or a special-purpose processor, and may control other components in the computer to perform desired functions.

[0085] For example, the memory may include any combination of one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer program modules may be stored on the computer-readable storage medium, and the processor may run one or more computer program modules to implement various functions of the computer.

[0086] The present invention also provides an embodiment of a computer-readable storage medium. The computer-readable storage medium is used to store non-temporary computer-readable instructions. When the non-temporary computer-readable instructions are executed by a computer, one or more steps in the above-mentioned real-time precision positioning method can be implemented. That is, when the real-time precision positioning method and system based on the characteristics of smartphone GNSS observation values ​​provided in the embodiment of the present application are implemented in the form of software and sold or used as an independent product, they can be stored in a computer-readable storage medium. For relevant instructions on the storage medium, please refer to the corresponding description of the memory in the computer above, which will not be repeated here.

[0087] Figure 4 The PPP positioning error sequences of the weighting method of the present invention and the traditional carrier-to-noise ratio weighting method are shown. Old and New represent the traditional carrier-to-noise ratio weighting method and the weighting method of the present invention, respectively. Compared with the traditional carrier-to-noise ratio weighting method, the weighting method of the present invention has a significant improvement in positioning accuracy. Compared with the traditional carrier-to-noise ratio weighting method, the PPP positioning result of the weighting method of the present invention is closer to the reference value, greatly reducing the deviation in each direction. The RMS of the weighting method of the present invention in the three directions of E, N, and U is 2.241m, 1.724m, and 5.194m, while the RMS of the traditional carrier-to-noise ratio weighting method in the three directions of E, N, and U is 3.064m, 1.868m, and 7.935m.

Claims

1. A real-time precise positioning method based on the characteristics of smartphone GNSS observation values, characterized in that: include: Detecting GNSS observation value phase cycle slips, the method includes: for a single-frequency observation value, if the absolute value of its Doppler cycle slip test quantity is greater than a threshold, pre-marking it as a suspected cycle slip; for a dual-frequency observation value, if the absolute value of its GF combination cycle slip test quantity is greater than the threshold, then judging in turn whether the absolute value of the Doppler cycle slip test quantity of each frequency point is greater than the threshold, and pre-marking the frequency point greater than the threshold as a suspected cycle slip; using the anti-error TDCP method to perform cycle slip detection on the remaining unmarked phase observation values; using the TDCP method to perform cycle slip detection on the pre-marked phase observation value to confirm whether it actually has a cycle slip; The ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, and satellite signal synchronization status are input into the trained ridge regression model, which predicts the pseudorange noise; The phase noise is calculated using the pseudorange noise; The single-frequency and dual-frequency hybrid PPP positioning model jointly detects the phase cycle slips of GNSS observations and the determined pseudorange noise and phase noise, and solves the precise position of the smartphone based on the real-time precision products broadcast by IGS or Beidou satellite-based PPP-B2b. The phase observations without cycle slips will all participate in the adjustment and solution of the single-frequency and dual-frequency hybrid PPP positioning model, while the phase observations confirmed as cycle slips will not participate in the solution of the current epoch. If two consecutive epochs are confirmed as cycle slips, the ambiguity parameters of the single-frequency and dual-frequency hybrid PPP positioning model will be reset.

2. The real-time precise positioning method based on the GNSS observation value characteristics of a smartphone according to claim 1, characterized in that: The pseudorange noise is multiplied by a scaling factor to obtain the phase noise, which is the same as the coefficient between the carrier and the pseudorange in the carrier-to-noise ratio random model.

3. The real-time precise positioning method based on the GNSS observation value characteristics of a smartphone according to claim 1, characterized in that: The training methods for the ridge regression model include: Select the base station closest to your smartphone; The pseudorange residual is calculated by using the true value of the base station coordinates, the pseudorange observation value of the base station, the true value of the coordinates of the mobile phone and the pseudorange observation value of the mobile phone, combined with the average of the single difference between stations; The pseudorange residuals and ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, and satellite signal synchronization status constitute the training set; The training set is used to train the ridge regression model so that the ridge regression model can learn the mapping relationship between ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, satellite signal synchronization state and pseudorange residuals.

4. A real-time precise positioning system based on the GNSS observation value characteristics of a smartphone, characterized in that: include: A data preprocessing module is configured to detect a phase cycle slip of a GNSS observation value. For a single-frequency observation value, if the absolute value of its Doppler cycle slip test value is greater than a threshold, it is pre-marked as a suspected cycle slip; For dual-frequency observations, if the absolute value of the GF combined cycle slip test value is greater than the threshold, then the absolute value of the Doppler cycle slip test value of each frequency point is determined in turn to be greater than the threshold, and the frequency points greater than the threshold are pre-marked as suspected cycle slips; The robust TDCP method is used to detect cycle slips on the remaining unmarked phase observations; The TDCP method is used to detect cycle slips on the pre-marked phase observations to confirm whether cycle slips actually exist. A noise determination module, configured to input ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio and satellite signal synchronization status into a trained ridge regression model, so that the ridge regression model predicts pseudorange noise, and configured to calculate phase noise using pseudorange noise; The single-frequency and dual-frequency hybrid PPP positioning model combines the detection of GNSS observation phase cycle slips and the determined pseudorange noise and phase noise, and solves the precise position of the smartphone based on the real-time precision products broadcast by IGS or Beidou satellite-based PPP-B2b. The phase observations without cycle slips will all participate in the adjustment solution of the single-frequency and dual-frequency hybrid PPP positioning model, while the phase observations confirmed as cycle slips will not participate in the solution of the current epoch. If two consecutive epochs are confirmed as cycle slips, the ambiguity parameters of the single-frequency and dual-frequency hybrid PPP positioning model will be reset.

5. The real-time precise positioning system based on the GNSS observation value characteristics of a smartphone according to claim 4, characterized in that: The pseudorange noise is multiplied by a scaling factor to obtain the phase noise, which is the same as the coefficient between the carrier and the pseudorange in the carrier-to-noise ratio random model.

6. The real-time precise positioning system based on the GNSS observation value characteristics of a smartphone according to claim 4, characterized in that: The training methods for the ridge regression model include: Select the base station closest to your smartphone; The pseudorange residual is calculated by using the true value of the base station coordinates, the pseudorange observation value of the base station, the true value of the coordinates of the mobile phone and the pseudorange observation value of the mobile phone, combined with the average of the single difference between stations; The pseudorange residuals and ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, and satellite signal synchronization status constitute the training set; The training set is used to train the ridge regression model so that the ridge regression model can learn the mapping relationship between ReceivedSvTimeUncertaintyNanos, carrier-to-noise ratio, satellite signal synchronization state and pseudorange residuals.

7. A smart phone, characterized in that: include: processor; a memory including one or more computer program modules; Wherein, the one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for implementing the real-time precise positioning method based on the characteristics of smartphone GNSS observation values ​​as described in any one of claims 1-3.

8. A computer-readable storage medium for storing non-transitory computer-readable instructions, characterized in that: When the non-temporary computer-readable instructions are executed by a computer, the real-time precise positioning method based on the smartphone GNSS observation value characteristics as described in any one of claims 1 to 3 can be implemented.

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