Precise positioning acceleration method and device based on low earth orbit satellite / LSTM neural network

Abstract

The embodiment of the invention discloses a precision positioning acceleration method and device based on a low earth orbit satellite / LSTM neural network. The method comprises the steps that a terminal obtains a wide lane phase decimal deviation product, a narrow lane phase decimal deviation product and a first GNSS combination observation value which are generated by a server based on an LSTM method and are subjected to coding processing; determining a first GNSS wide lane real number ambiguity according to the first GNSS wide lane real number Based on a precise point positioning algorithm, determining initial GNSS ionosphere elimination combination real ambiguity and other parameters; performing inter-satellite single difference on the first GNSS wide-lane real number ambiguity, and obtaining the integer ambiguity by combining wide-lane phase decimal deviation product fixation; performing inter-satellite single difference on the initial GNSS ionosphere-free combination real ambiguity, determining a first GNSS narrow-lane real ambiguity by combining the wide-lane integer ambiguity, and fixing the first GNSS narrow-lane real ambiguity by using a narrow-lane phase decimal deviation product through a partial ambiguity fixing method to obtain the integer ambiguity of the narrow-lane phase decimal deviation product; and calculating a user coordinate fixed solution in combination with related parameters.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation and positioning technology, and is related to but not limited to a precision positioning acceleration method and device based on low-orbit satellite / LSTM neural network. Background Art

[0002] The Global Navigation Satellite System (GNSS) utilizes radio ranging signals transmitted by a global network of navigation satellites to determine the precise location and time of users on the ground, providing positioning, navigation, and timing (PNT) services. Positioning is the core function and service of the GNSS system, and the algorithms and models involved in precise positioning technology are a hot topic of long-term research and discussion.

[0003] Precise Point Positioning (PPP) is one of the most representative precision positioning technologies. It utilizes pre-estimated precise orbits and clock errors, comprehensively considers various error corrections for GNSS signals, and achieves global, high-precision absolute positioning with a single receiver. Due to its high positioning accuracy, simple structure, and flexible operation, it has been widely used in many fields, such as precision agriculture, precision mapping, deformation monitoring, water vapor inversion, and precise navigation and positioning. However, due to the high orbit altitude of GNSS satellites (typically above 20,000 kilometers), the satellite geometry changes slowly relative to the ground station, resulting in the typical requirement of tens of minutes to achieve centimeter-level positioning accuracy. To shorten initialization time and improve positioning accuracy, ambiguity resolution (AR) has been proposed. However, a reliable ambiguity resolution also requires a long observation time, which limits the real-time application of PPP.

[0004] As commercial spaceflight costs decrease, low Earth orbit (LEO) communications constellations such as SpaceX and OneWeb are booming. LEO satellites typically orbit at altitudes between 400 and 1500 kilometers, and their geometric changes are approximately 40 times faster than those of GNSS medium Earth orbit (MEO) satellites, accelerating PPP convergence. Some studies have shown that LEO constellations can significantly improve PPP and PPPAR performance, but few have examined the contribution of LEO satellite float solutions to PPP AR. Summary of the Invention

[0005] In view of this, an embodiment of the present application provides a precise positioning acceleration method and device based on low-orbit satellite / LSTM neural network to solve the problems of low positioning efficiency and slow convergence time.

[0006] The technical solution of the embodiment of the present application is implemented as follows: In a first aspect, an embodiment of the present application provides a precise positioning acceleration method based on a low-orbit satellite / LSTM neural network, the method comprising: The server generates wide-lane fractional phase deviation products and narrow-lane fractional phase deviation products based on the LSTM method, encodes them using a specific method, and sends them to the terminal. The terminal obtains the encoded widelane phase fractional deviation product, the narrowlane phase fractional deviation product, and the first GNSS combined observation value; and determines the first GNSS widelane real ambiguity based on the first GNSS combined observation value; The terminal determines a user coordinate floating-point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error based on a precise point positioning algorithm that integrates low-orbit satellites (LEO) and GNSS. The terminal performs inter-satellite single-difference processing on the first GNSS wide-lane real ambiguity, and fixes the GNSS wide-lane real ambiguity after the inter-satellite single-difference processing using the wide-lane phase fractional deviation product to obtain a first GNSS wide-lane integer ambiguity; The terminal performs inter-satellite single-difference processing on the initial GNSS ionospheric-free combined real ambiguity, determines a first GNSS narrowlane real ambiguity based on the inter-satellite single-differenced GNSS ionospheric-free combined real ambiguity and the first GNSS widelane integer ambiguity, and fixes the first GNSS narrowlane real ambiguity using a partial ambiguity fixing method using the narrowlane phase fractional deviation product to obtain a first GNSS narrowlane integer ambiguity; The terminal calculates a user coordinate fixed solution based on the first GNSS combined observation value, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, the first GNSS widelane integer ambiguity, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

[0007] In a second aspect, an embodiment of the present application provides a precise positioning acceleration device based on a low-orbit satellite / LSTM neural network, comprising: Server module and terminal module, including: The server module includes a generation and encoding module, and the terminal module includes an acquisition module, a determination module, a first fixing module, a second fixing module and a calculation module; The generation and encoding module is used to generate wide-lane fractional phase deviation products and narrow-lane fractional phase deviation products based on the LSTM method, and encode them using a specific method and then send them to the terminal; The acquisition module is configured to acquire the encoded widelane phase fractional deviation product, the narrowlane phase fractional deviation product, and the first GNSS combined observation value; and determine the first GNSS widelane real ambiguity based on the first GNSS combined observation value; The determination module is configured to determine a user coordinate floating-point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error based on a precise point positioning algorithm that integrates low-orbit satellites (LEO) and GNSS. The first fixing module is configured to perform inter-satellite single-difference processing on the first GNSS wide-lane real ambiguity, and fix the GNSS wide-lane real ambiguity after the inter-satellite single-difference processing using the wide-lane phase fractional deviation product to obtain a first GNSS wide-lane integer ambiguity; The second fixing module is configured to perform inter-satellite single-difference processing on the initial GNSS ionospheric-free combined real ambiguity, determine a first GNSS narrowlane real ambiguity based on the inter-satellite single-differenced GNSS ionospheric-free combined real ambiguity and the first GNSS widelane integer ambiguity, and fix the first GNSS narrowlane real ambiguity using a partial ambiguity fixing method using the narrowlane phase fractional deviation product to obtain a first GNSS narrowlane integer ambiguity. The calculation module is configured to calculate a user coordinate fixed solution based on the first GNSS combined observation value, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, the first GNSS widelane integer ambiguity, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present application include at least: By integrating low-Earth orbit satellites (LEO) with the GNSS system and combining wide-lane and narrow-lane phase fractional deviation products to fix ambiguities, high-precision calculation of user coordinate fixed solutions is achieved, improving the efficiency, accuracy and reliability of precision positioning. In particular, through inter-satellite single-difference processing and ambiguity fixing strategies, the impact of errors is effectively reduced and positioning results are optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which: Figure 1 A flowchart of a precise positioning acceleration method based on a low-orbit satellite / LSTM neural network provided in an embodiment of the present application; Figure 2 A schematic diagram of the basic framework of GNSS data and product messages provided in an embodiment of the present application; Figure 3 A schematic diagram of a specific data protocol for satellite phase deviation encoding provided in an embodiment of the present application; Figure 4 A schematic diagram of a satellite phase deviation encoding data protocol provided in an embodiment of the present application; Figure 5 Schematic diagram of the first fixed time of PPP of a single-system with 1, 2, 3, 4, or all LEO satellites in different latitudes provided in an embodiment of the present application; Figure 6 A schematic diagram of the composition structure of a precision positioning acceleration device based on a low-orbit satellite / LSTM neural network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. The following examples are used to illustrate the present application, but are not intended to limit the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0011] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0012] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0013] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as generally understood by those skilled in the art in the art to which the embodiments of the present application belong. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0014] The embodiment of the present application provides a method for accelerating precise positioning based on a low-orbit satellite / LSTM neural network, which is applied to electronic devices. The electronic devices include but are not limited to mobile phones, laptops, tablet computers, handheld internet devices, multimedia devices, streaming media devices, mobile Internet devices, wearable devices, or other types of electronic devices. The functions implemented by the method can be implemented by calling program code by a processor in the electronic device. Of course, the program code can be stored in a computer storage medium. It can be seen that the electronic device includes at least a processor and a storage medium. The processor can be used to process the precise positioning acceleration process based on the low-orbit satellite / LSTM neural network, and the memory can be used to store the data required and the data generated in the precise positioning acceleration process based on the low-orbit satellite / LSTM neural network.

[0015] Figure 1 A flowchart of a precise positioning acceleration method based on a low-orbit satellite / LSTM neural network is provided in an embodiment of the present application, such as Figure 1 As shown, the method comprises at least the following steps: In step S110, the server generates widelane fractional phase deviation products and narrowlane fractional phase deviation products based on the LSTM method, encodes them using a specific method, and then sends them to the terminal; the terminal obtains the encoded widelane fractional phase deviation products, narrowlane fractional phase deviation products, and a first GNSS combined observation value; and determines a first GNSS widelane real ambiguity based on the first GNSS combined observation value. Here, the method includes two parts: a server and a user side (i.e., a terminal). The server can calculate and generate satellite-specific wide-lane (WL) phase fractional cycle bias (FCB) products (i.e., satellite-side wide-lane FCB) and narrow-lane (NL) phase fractional cycle bias products (i.e., satellite-side narrow-lane FCB) for each satellite based on multiple satellites. Then, the server sends the encoded wide-lane FCB and narrow-lane FCB products of all in-orbit satellites to the terminal at a fixed period. When the terminal receives a signal (e.g., the first GNSS combined observation value) from a satellite (e.g., a satellite in a GNSS system), it can obtain the wide-lane FCB and narrow-lane FCB products corresponding to this satellite based on the satellite's unique identifier and use them for ambiguity positioning (i.e., achieving integer ambiguity resolution by correcting errors).

[0016] Step S120, the terminal determines a user coordinate floating-point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error based on a precise point positioning algorithm that integrates low-orbit satellites (LEO) and GNSS. Here, simultaneous LEO / GNSS PPP can obtain the user coordinate floating-point solution and parameters such as the LEO and GNSS ionospheric-free combined real ambiguity.

[0017] Step S130: The terminal performs inter-satellite single-difference processing on the first GNSS wide-lane real ambiguity, and fixes the GNSS wide-lane real ambiguity after the inter-satellite single-difference processing using the wide-lane phase fractional deviation product to obtain a first GNSS wide-lane integer ambiguity. Here, the first GNSS wide-lane real ambiguity is subjected to inter-satellite single difference to eliminate the wide-lane FCB at the receiving end, and the wide-lane FCB product at the satellite end (i.e., the wide-lane phase fractional deviation product) is used to fix the wide-lane ambiguity by rounding.

[0018] Step S140: The terminal performs inter-satellite single-difference processing on the initial GNSS ionospheric-free combined real ambiguity, determines a first GNSS narrowlane real ambiguity based on the inter-satellite single-differenced GNSS ionospheric-free combined real ambiguity and the first GNSS widelane integer ambiguity, and fixes the first GNSS narrowlane real ambiguity using a partial ambiguity fixing method using the narrowlane phase fractional deviation product to obtain a first GNSS narrowlane integer ambiguity. Here, the GNSS ionospheric-free combined real ambiguity is used as an inter-satellite single difference and combined with the fixed wide-lane ambiguity to calculate the real narrow-lane ambiguity. The narrow-lane FCB product (i.e., the narrow-lane phase fractional deviation product) is used to fix the narrow-lane ambiguity using the LAMBDA (Least-square AM Biguity Decorrelation Adjustment) algorithm.

[0019] In step S150, the terminal calculates a user coordinate fixed solution based on the first GNSS combined observation value, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, the first GNSS widelane integer ambiguity, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

[0020] In the above embodiment, by integrating low-Earth orbit satellites (LEO) with the GNSS system and combining wide-lane and narrow-lane phase fractional deviation products to fix ambiguities, high-precision calculation of user coordinate fixed solutions is achieved, thereby improving the efficiency, accuracy, and reliability of precision positioning. In particular, through inter-satellite single-difference processing and ambiguity fixing strategies, the impact of errors is effectively reduced and the positioning results are optimized.

[0021] In some embodiments, the method further comprises: Step S101: The server obtains GNSS raw observation values, which include GNSS dual-frequency carrier frequency, dual-frequency carrier phase, dual-frequency pseudorange, wide-lane carrier phase observation value, and narrow-lane pseudorange observation value; Here, the GNSS observations mainly include code pseudorange and carrier phase There are two types of observation equations, which can be expressed as the following formulas (1) and (2): Formula (1); Formula (2); Among them, the subscript is the station number, Indicates frequency, superscript is the satellite number, Represents GNSS systems, such as GPS, BDS-3 and other Code Division Multiple Access (CDMA) systems. Indicates the geometric distance between the station (receiver, i.e. observation station) and the star, is the speed of light in vacuum, is the receiver clock error, is the satellite clock error, is the total tropospheric delay in the direction of signal propagation, which can be divided into dry delay and wet delay , and are the tropospheric dry and wet components in the zenith direction, and are the dry delay and wet delay projection functions, for Ionospheric delay at the frequency point (GNSS frequency point), and are the hardware delay deviations of the code pseudorange measurement at the receiver and satellite ends, and are the Uncalibrated Phase Delay (UPD) of the carrier phase hardware delay at the receiver and satellite ends respectively. for The integer ambiguity at the frequency point, the corresponding wavelength is , 、 are the observation noise errors of the code pseudorange and carrier phase, respectively. Other error terms, such as Earth rotation, satellite and receiver antenna phase center deviations and variations, antenna phase wraparound, solid tide, ocean tide, atmospheric tide, and relativistic effects, can be corrected based on existing models and are therefore not listed in the above equation.

[0022] In the embodiment of the present application, the frequencies of the GNSS dual-frequency carrier can be expressed as f1 and f2. 、 express For different frequency points of the system, such as L1 and L2 of GPS, B1 and B3 of BDS-3, and E1 and E5a of Galileo, the dual-frequency carrier phase can be expressed as and , the dual-frequency pseudorange can be expressed as and , the wide-lane carrier phase observation value can be expressed as , the narrow lane pseudorange observation value can be expressed as .

[0023] Step S102: The server performs a weighted operation on the dual-frequency carrier phase based on the frequency of the GNSS dual-frequency carrier to obtain a first GNSS ionospheric-free carrier phase combined observation value; Step S103: The server performs a weighted operation on the dual-frequency pseudorange based on the frequency of the GNSS dual-frequency carrier to obtain a first GNSS ionospheric-free pseudorange combined observation value; In order to eliminate the first-order ionospheric delay, the original observations are usually combined to form the ionospheric-eliminating combined observations (the first GNSS ionospheric-eliminating pseudorange combined observations). and the first GNSS ionospheric-free carrier phase combined observation value ), the combination can be expressed by the following formula (3) and formula (4): ; Among them, the combined observation equation can be expressed as shown in the following formula (5): Formula (5); in, Indicates the geometric distance between the station and the satellite, is the speed of light in vacuum, is the receiver clock error, is the satellite clock error, is the total tropospheric delay in the direction of signal propagation, and are the hardware delay bias of the ionosphere-free combined code measurement pseudorange at the receiver and satellite ends, respectively. and are the hardware delay deviations of the ionospheric-free combined carrier phase at the receiver and satellite, respectively. and is the carrier wavelength and ambiguity of the ionosphere-free combination, 、 are the observation noise errors of the ionospheric-free combined code pseudorange and carrier phase, respectively. The IF combination ambiguity, the receiver-side and satellite-side pseudorange and phase hardware delay bias are shown in the following formulas (6) to (10): Formula (6); Formula (7); Formula (8); Formula (9); Formula (10); in, and are the hardware delay bias of the ionosphere-free combined code measurement pseudorange at the receiver and satellite ends, respectively. and is the hardware delay deviation of the code pseudorange measurement at the receiver end at frequencies f1 and f2, and is the hardware delay deviation of the code pseudorange measurement at the receiver end at frequencies f1 and f2, and are the hardware delay deviations of the ionospheric-free combined carrier phase at the receiver and satellite, respectively. and is the carrier phase hardware delay deviation at the receiver end at frequencies f1 and f2, and is the carrier phase hardware delay deviation of the satellite at frequencies f1 and f2, and is the carrier wavelength and ambiguity of the ionosphere-free combination, and The carrier phase ambiguity when the receiver observes satellite s at frequencies f1 and f2.

[0024] Step S104: The server analyzes the coupling relationship between the ionospheric-free combined real ambiguity and the hardware delay based on the first GNSS ionospheric-free carrier phase combined observation value and the first GNSS ionospheric-free pseudorange combined observation value, and re-parameterizes the ionospheric-free combined real ambiguity and the hardware delay to obtain the GNSS ionospheric-free combined real ambiguity containing the hardware delay bias. In order to reduce satellite orbit errors and satellite clock errors, PPP generally uses precise ephemeris and precise satellite clock error products. Since precise satellite clock errors are generated by ionosphere-free combined pseudo-range observation data from the ground tracking network, the IGS (International GNSS Service) provides Absorbs the satellite-side ionosphere-free combined pseudo-range hardware delay bias . The pseudorange hardware delay bias at the receiver end varies with different satellite systems (this difference is called inter-system bias (ISB)), and it will be absorbed by the receiver clock error, so the receiver clock errors of different satellite systems are different. The ambiguity parameter is closely related to the phase hardware delay bias and is difficult to separate from each other. At the same time, due to the introduction of the satellite clock based on pseudorange observation, the pseudorange hardware delay bias at the satellite end is introduced into the phase observation equation and absorbed by the ambiguity parameter; since the pseudorange observation equation and the phase observation equation jointly estimate the receiver clock error, the pseudorange hardware delay bias at the receiver end is also introduced into the phase observation equation and absorbed by the ambiguity parameter. Therefore, after the satellite clock error is corrected by the precision product, formula (5) can be rewritten as shown in the following formula (11): Formula (11); Where, the re-parameterized receiver clock error and ambiguity are shown in the following formulas (12) to (15). In addition, the parameters to be estimated also include the three-dimensional position of the receiver and the tropospheric wet delay in the zenith direction, that is, the parameter vector to be estimated is .

[0025] Formula (12); Formula (13); Formula (14); Formula (15); in, is the reparameterized receiver-side UPD, is the re-parameterized satellite UPD, is the re-parameterized receiver clock error that incorporates the receiver hardware delay. is the re-parameterized GNSS ionospheric elimination combined real ambiguity containing hardware delay bias. As can be seen from the above formula, the IF combined ambiguity parameter It contains hardware delay deviations at the receiver and satellite ends, and loses its integer characteristics. However, PPP uses single-station positioning and cannot directly separate them. Usually, a real number solution is used in parameter estimation to combine the two for estimation.

[0026] In formula (13), the re-parameterized IF combined real ambiguity (i.e., the GNSS ionospheric-free combined real ambiguity including hardware delay bias) is ) can be expressed as the combination of the second GNSS wide lane real ambiguity and the second GNSS narrow lane real ambiguity in formula (16): ; in, is the second GNSS wide lane real ambiguity, is the second GNSS narrow lane real ambiguity.

[0027] Step S105: the server determines a second GNSS widelane real ambiguity based on the widelane carrier phase observation value and the narrowlane pseudorange observation value; In some embodiments, step S105 “the server determines a second GNSS widelane real ambiguity based on the widelane carrier phase observation value and the narrowlane pseudorange observation value” includes the following steps: Step S1051: The server constructs a first HMW combined carrier phase observation value based on the difference between the widelane carrier phase observation value and the narrowlane pseudorange observation value; In step S1052, the server performs smoothing on the first HMW combined carrier phase observation value using a multi-epoch LSTM-assisted neural network method, and determines a second GNSS wide-lane real ambiguity based on the smoothed first HMW combined carrier phase observation value and the wide-lane combined wavelength.

[0028] The second GNSS wide-lane real ambiguity can be calculated using the first HMW combined carrier phase observation, which is defined as the difference between the wide-lane carrier phase observation and the narrow-lane pseudorange observation, as shown in the following formulas (17) to (19): Formula (17); Formula (18); Formula (19); in, represents the second GNSS wide-lane real ambiguity, represents the first HMW combined carrier phase observation value after smoothing, represents the wide-lane carrier phase observation value, represents the narrow lane pseudorange observation value, represents the wide-lane combination wavelength, represents the wide lane integer ambiguity including the wide lane hardware delay integer effect (i.e. the second GNSS wide lane integer ambiguity), and Indicates the fractional part FCB of UPD at the receiver and satellite ends, and represents the wavelength corresponding to f1 and f2. The integer part of the UPD is absorbed by the wide-lane integer ambiguity and does not affect its integer characteristics. Due to the large noise of the HMW combination observation, multi-epoch smoothing is required to reduce the influence of observation noise and multipath effects. The smoothing method is shown in the following formula (20): Formula (20); Where, represents the epoch number, Represents the first smoothed HMW combined carrier phase observation. To increase the accuracy of the smoothing process, this application introduces a neural network-assisted algorithm to assist in multi-epoch smoothing. Long short-term memory (LSTM) is an improved model in neural networks that introduces memory units. With the help of gated units, it solves gradient explosion and vanishing problems, can capture long-term effects, and has the ability to perform highly nonlinear dynamic mapping and store past information. The steps of LSTM-assisted smoothing are as follows: Determine the model input and output: take the smoothed observations before epoch i and the observations of the i-th epoch as the parameters of the LSTM input model; Perform standardization: Calculate the mean and standard deviation of the training data set, use normalization methods to unify the dimensions of the training data set, and train under the same standard to effectively reduce prediction errors; Set LSTM network parameters: The accuracy of LSTM training is closely related to network parameters, such as the number of hidden layers, the number of hidden layer neurons, etc. The training speed and training error are related to the number of hidden layers. In this application, one hidden layer is set in the training. For the number of hidden layer neurons, this application adopts The empirical formula sets the number of neurons, where represents the number of input layer features, The number of output layer features is represented by the number of neurons, based on which the number of neurons is adjusted according to the training results. The Adam algorithm uses moment estimation and has advantages such as adaptability, fast computation speed, and small memory usage. Therefore, Adam is chosen to speed up training and meet the real-time requirements of practical applications.

[0029] Training the network sets the error threshold and selects RMSE as the loss function, which means , where The output features are obtained after the input features are normalized. For the corresponding output features, iteratively adjust the weights and stop training when the RMSE is less than the threshold.

[0030] LSTM prediction: The smoothed observations before epoch i and the observations at the i-th epoch are fed into the trained network, which outputs a standardized prediction sequence.

[0031] Destandardization: restore the sequence obtained in the previous step to its original dimension, and denormalize to obtain the smoothed observation value of the predicted epoch i .

[0032] The HMW combined observations are constructed based on the wide-lane carrier phase and narrow-lane pseudorange observations, and are smoothed by a multi-epoch LSTM-assisted neural network to reduce the influence of observation noise and multipath effects and improve the calculation accuracy of the second GNSS wide-lane real ambiguity.

[0033] Step S106: The server generates the widelane fractional phase deviation product based on the linear relationship between the fractional part of the second GNSS widelane real ambiguity and the fractional phase deviation; and fixes the second GNSS widelane real ambiguity based on the widelane fractional phase deviation product to obtain a second GNSS widelane integer ambiguity. Step S107: The server determines a second GNSS narrowlane real ambiguity based on the GNSS ionospheric-free combined real ambiguity including the hardware delay bias and the second GNSS widelane integer ambiguity. Among them, when the wide lane phase fractional deviation product is obtained, the fixed second GNSS wide lane integer ambiguity can be substituted into formula (16) to obtain the second GNSS narrow lane real ambiguity, as shown in the following formulas (21) to (23): Formula (21); Formula (22); Formula (23); Where, represents the second GNSS narrow lane real ambiguity, is the re-parameterized GNSS ionospheric-free combined real ambiguity with hardware delay bias, represents the second GNSS wide lane integer ambiguity, Indicates that it contains narrow lane integer ambiguity (i.e., the second GNSS narrow lane integer ambiguity), and Indicates narrow lane FCB at the receiver and satellite ends.

[0034] In step S108 , the server generates the narrowlane phase fractional deviation product based on a linear relationship between the fractional part of the second GNSS narrowlane real ambiguity and the phase fractional deviation.

[0035] From formula (21) to formula (23), it can be seen that the narrow lane FCB absorbs the wide lane FCB. The narrow lane FCB can be obtained by using an estimation process similar to that of the wide lane FCB.

[0036] In the above embodiment, by obtaining GNSS raw observation values ​​and performing combined operations, the ionospheric combined ambiguity and hardware delay are re-parameterized, the hardware delay deviation is separated and processed, and wide-lane and narrow-lane phase fractional deviation products are generated simultaneously, providing reliable data support for subsequent ambiguity fixation and enhancing the robustness of the positioning model.

[0037] In some embodiments, in step S106, "the server generating the widelane phase fractional deviation product based on the linear relationship between the fractional part of the second GNSS widelane real ambiguity and the phase fractional deviation" includes the following steps: Step S1061: The server constructs a first virtual observation equation based on a linear relationship between a fractional part of the second GNSS wide lane real ambiguity and a fractional phase deviation; Assume that the ground observation network consists of Each station can observe satellites, then according to formula (17), the first virtual observation equation shown in formula (24) can be obtained:

[0038] Step S1062: The server sets the fractional phase deviation of a certain receiver or satellite to 0 as a reference, and solves the widelane fractional phase deviation product based on the reference and the first virtual observation equation; in, It is a virtual observation, that is, the fractional part of the wide-lane ambiguity. The normal equation in the above formula is rank-deficient. In order to solve the equation, it is necessary to take the FCB=0 of a certain receiver or satellite as a reference and treat it as a virtual observation, and then perform wide-lane FCB product estimation. In this embodiment of the application, the FCB of the receiver of the first measuring station can be selected as the reference.

[0039] In step S108, "the server generates the narrowlane phase fractional deviation product based on the linear relationship between the fractional part of the second GNSS narrowlane real ambiguity and the phase fractional deviation", includes the following steps: Step S1081: The server constructs a second virtual observation equation based on the linear relationship between the fractional part of the second GNSS narrow lane real ambiguity and the fractional phase deviation; In step S1082, the server sets the phase fractional deviation of a certain receiver or satellite to 0 as a reference, and solves the narrowlane phase fractional deviation product based on the reference and the second virtual observation equation.

[0040] The second virtual observation equation can be constructed by a similar process to that of constructing the first virtual observation equation, and then the narrow lane FCB product estimation can be performed.

[0041] In the above embodiment, by constructing a virtual observation equation and setting a benchmark (the phase fractional deviation of a certain receiver or satellite is 0), the rank deficiency problem of the normal equation is solved, and the stable solution of the wide-lane and narrow-lane phase fractional deviation products is achieved, ensuring product accuracy and laying the foundation for ambiguity fixation.

[0042] In some embodiments, the method further comprises: In step S1091, the server compresses the widelane fractional phase deviation product and the narrowlane fractional phase deviation product in binary format; and performs structured packaging on the compressed widelane fractional phase deviation product and the compressed narrowlane fractional phase deviation product based on a preset message basic framework. The message basic framework includes a synchronization header, a reserved flag bit, a message length, and a data message. The data message includes an information type, an information reference time, a data synchronization flag, an information body, and data verification information. In step S1092, the server determines a transmission rule based on a preset coding protocol, and transmits the encapsulated widelane fractional phase deviation product and the encapsulated narrowlane fractional phase deviation product based on the transmission rule. The transmission rule includes splitting the broadcast by satellite system and satellite, differentiating the transmission by frequency and signal type, deducting the integer part of the widelane fractional phase deviation product and the narrowlane fractional phase deviation product greater than a preset number of weeks from the widelane fractional phase deviation product and the narrowlane fractional phase deviation product, and assigning unique message numbers to the widelane fractional phase deviation products and the narrowlane fractional phase deviation products of different systems.

[0043] The design of the message for the widelane and narrowlane ambiguity corrections mentioned above is a crucial step in the practical implementation of this technology. In real-time engineering applications, the amount of transmitted data must be minimized while maintaining a certain level of accuracy. Therefore, a message format must be designed based on theoretical and practical application to compress the network-side product code into a binary format. GNSS real-time data is transmitted or stored in a machine-readable binary format. This format effectively compresses data size compared to the ASCII format, facilitating the network transmission of large amounts of data.

[0044] To ensure simple program implementation and maximize data compression, thereby increasing data transmission stability, this embodiment of the application utilizes a binary format with bits as the minimum storage unit, based on the PPP-B2b data format, the QZSS Centimeter-Level Augmentation Service (CLAS) message format, and the IGS Status Domain Real-Time Precision Correction Product format. A binary phase fractional deviation message data format based on RTCM3 was designed.

[0045] Figure 2 This is the basic framework of GNSS data and product messages, primarily consisting of a synchronization header, reserved flags, message length, and data message. The synchronization header identifies the message type. The machine program identifies the synchronization header to determine the message encoding format. Different message types within the same information channel must have different synchronization headers. The message length records the length of the data message and can be omitted for fixed-length messages. Figure 2 The first line of the data message contains the information type, information reference time (corresponding to Figure 2 'time' in the data synchronization mark (corresponding to Figure 2 'Synchronization flag' in the Figure 2 The second line of the 'data message') and data verification information (corresponding to Figure 2 The message type is used to distinguish different data or product types; the message reference time records information time information, which usually includes seconds within the week or seconds within the day to save broadcast traffic; the data synchronization mark is used to connect and divide telegrams; and the data verification verifies whether the telegram is complete and usable.

[0046] To minimize the size of transmitted data packets, corrections are broadcast system by system, with a maximum of 20 satellites per system. A synchronization flag is also set to ensure the integrity of each system's product. Different observation types at the same frequency have the same phase accuracy, with a difference of 1 / 4 wavelength or an integer multiple of 1 / 4 wavelength. This information is common to all satellites and can be provided by the receiver. This deviation has already been pre-corrected for the MSM signal in the RTCM. Therefore, wide-lane FCBs and narrow-lane FCBs are broadcast for each satellite at each frequency, supporting a maximum of 5 frequencies. These deviations are coded in the message for user reference. The message primarily addresses wide-lane FCBs and narrow-lane FCBs.

[0047] Figure 3 The specific data protocol for satellite phase deviation encoding is given. Figure 4 For the satellite phase deviation encoding digitization protocol, it should be noted that: signals of the same frequency but different types differ from each other by 1 / 4 or an integer multiple of 1 / 4 wavelength. This information will be recorded in the observation value. Therefore, when broadcasting satellite phase deviation products, two sets of reference type products need to be broadcast at each frequency point; the satellite phase deviation is deducted by an integer greater than 100 weeks.

[0048] The embodiment of the present application defines the phase deviation encoding message number of each system, which is 1601 for GPS, 1602 for GALILEO, 1603 for BDS, and 1604 for GLONASS. Other systems can continue to number the message numbers.

[0049] In the above embodiment, binary compression and structured encapsulation are used to process the phase fractional deviation product, combined with preset transmission rules (splitting by system / satellite, distinguishing by frequency, etc.), to reduce the data transmission volume while ensuring accuracy, improve transmission efficiency and stability, and meet the needs of real-time engineering applications.

[0050] In some embodiments, the first GNSS combined observation value includes a widelane carrier phase combined observation value and a narrowlane pseudorange combined observation value, and the step S110 of "the terminal determining a first GNSS widelane real ambiguity based on the first GNSS combined observation value" includes the following steps: Step S1101: The terminal constructs a second HMW combined carrier phase observation value by taking the difference between the widelane carrier phase combined observation value and the narrowlane pseudorange combined observation value; In step S1102, the terminal smoothes the second HMW combined carrier phase observation value using a multi-epoch recursive averaging method, and determines the first GNSS wide-lane real ambiguity based on the smoothed second HMW combined carrier phase observation value and the wide-lane combined wavelength.

[0051] Here, the first GNSS wide lane real ambiguity can be expressed by the following formula (25): Formula (25); in, represents the first GNSS wide-lane real ambiguity, represents the second HMW combined carrier phase observation value after smoothing, represents the wide-lane combination wavelength, represents the wide-lane carrier phase combined observation value, represents the narrow lane pseudorange combined observation value, represents the carrier phase, represents the code measurement pseudorange, Indicates frequency, subscript is the station number, with superscript is the satellite number, Represents GNSS systems, such as GPS, BDS-3, and other Code Division Multiple Access (CDMA) systems.

[0052] The following formula (26) can be used to perform multi-epoch smoothing on the second HMW combined carrier phase observation value to reduce the influence of observation noise and multipath effect: Formula (26); in, represents the epoch number, Represents the smoothed second HMW combined carrier phase observation value.

[0053] In the above embodiment, HMW combined observations are constructed by combining wide-lane carrier phase and narrow-lane pseudorange observations, and multi-epoch recursive averaging and smoothing are used to effectively reduce observation noise, improve the accuracy of the first GNSS wide-lane real ambiguity, and provide reliable input for subsequent wide-lane ambiguity fixation.

[0054] In some embodiments, step S120 "the terminal determines the user coordinate floating point solution, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, and the GNSS receiver clock error based on the precise single point positioning algorithm integrated with the low-orbit satellite LEO and the GNSS" includes the following steps: Step S1201: The terminal determines ionospheric-free combined pseudorange observation values ​​of the user receiver for the LEO satellite and the GNSS satellite based on the geometric distance from the user receiver to the LEO satellite and the GNSS satellite, the speed of light, the receiver clock error, the tropospheric delay, and the pseudorange observation error term. Step S1202: The terminal determines a first LEO ionospheric-free carrier-phase combination observation value and a first LEO ionospheric-free pseudorange combination observation value of the user receiver of the LEO satellite, and a second GNSS ionospheric-free carrier-phase combination observation value and a second GNSS ionospheric-free pseudorange combination observation value of the GNSS satellite based on the geometric distance from the user receiver to the LEO satellite and the GNSS satellite, the speed of light, the receiver clock error, the tropospheric delay, the wavelength corresponding to the ionospheric-free combination, the ionospheric-free combination real ambiguity, and the carrier phase observation error value. In step S1203, the terminal constructs a function model based on the first LEO ionospheric-free carrier phase combination observation value, the first LEO ionospheric-free pseudorange combination observation value, the second GNSS ionospheric-free carrier phase combination observation value, and the second GNSS ionospheric-free pseudorange combination observation value, and solves the function model using a Kalman filter parameter estimation method to obtain a user coordinate floating-point solution, an initial LEO ionospheric-free combination real ambiguity, an initial GNSS ionospheric-free combination real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error.

[0055] Among them, based on the function model of LEO / GNSS PPP shown in the following formula (27), the Kalman filter parameter estimation method can be used to solve the user coordinate floating point solution and the LEO and GNSS ionospheric-free combined real ambiguity (that is, the initial LEO ionospheric-free combined real ambiguity and the initial GNSS ionospheric elimination combined real ambiguity ) and other parameters.

[0056] Formula (27); Among them, the superscript K represents the GNSS system, represents the LEO system, and Indicates the geometric distance between the station (receiver, i.e. observation station) and the star, represents the second GNSS ionospheric-free pseudorange combined observation value, represents the second GNSS ionospheric-free carrier-phase combined observation value, represents the first LEO ionospheric-free pseudorange combined observation value, represents the first LEO deionospheric carrier phase combined observation value; is the speed of light in vacuum, and is the re-parameterized receiver clock error that incorporates the receiver hardware delay. and is the tropospheric delay in the direction of signal propagation, is the wavelength corresponding to the deionospheric combination, and is the real number ambiguity of the ionospheric elimination combination, is the ionospheric-free combined code measurement pseudorange (i.e., pseudorange observation error term), is the observation noise error of the ionosphere-free combined carrier phase (i.e., the carrier phase observation error value).

[0057] In the above embodiment, a precise single-point positioning algorithm based on the fusion of LEO and GNSS is constructed, a function model containing multiple types of observation values ​​is constructed, and a floating-point solution of user coordinates and multiple error parameters are obtained through Kalman filtering. The advantage of the rapid geometric changes of LEO satellites is fully utilized to accelerate positioning convergence and improve parameter estimation accuracy.

[0058] In some embodiments, in step S140, "the terminal determines the first GNSS narrowlane real ambiguity based on the GNSS ionospheric-free combined real ambiguity after inter-satellite single-difference processing and the first GNSS widelane integer ambiguity" includes the following steps: In step S1401, the terminal performs a weighted operation on the GNSS ionospheric-free combined real ambiguity after inter-satellite single-difference processing and the first GNSS wide-lane integer ambiguity based on the frequency of the GNSS dual-frequency carrier to obtain a first GNSS narrow-lane real ambiguity.

[0059] The GNSS ionospheric-free combined real ambiguity is single-differenced between satellites and combined with the fixed first GNSS wide-lane integer ambiguity to calculate the first GNSS narrow-lane real ambiguity, as shown in the following formula (28): Formula (28); in, represents the first GNSS narrow lane real ambiguity, represents the first GNSS wide-lane integer ambiguity, It represents the GNSS ionospheric-free combined real ambiguity after inter-satellite single difference. represents the reference star, Formula (29); As shown in formula (29), represents the first GNSS narrow lane integer ambiguity, For the satellite end narrow-lane FCB (i.e. narrow-lane phase fractional offset product), the above formula uses the narrow-lane FCB product and adopts the LAMBDA algorithm partial ambiguity strategy to fix the narrow-lane ambiguity, to obtain the first GNSS narrow-lane integer ambiguity. The main idea is as follows: the elevation angles of the sequence of satellites whose narrow-lane ambiguities need to be fixed are arranged, and the elevation angle sorting method is: the ambiguity fixing strategy based on the elevation angle considers that the lower the elevation angle, the worse the ambiguity accuracy. The ambiguities of the first three satellites with higher elevation angles are selected for fixing first. If the ambiguity fixing is successful, the fourth satellite with the highest elevation angle is added for ambiguity fixing. If the ambiguity fixing is successful, the fifth satellite is added for ambiguity fixing, and so on. If the ambiguity fixing fails after adding a satellite, the ambiguity fixing is stopped, and the last added satellite is removed. The sequence of satellites after the last successful ambiguity fixing is used. Through this multi-cycle traversal partial ambiguity strategy based on the elevation angle, the ambiguity fixing success rate can be improved, and the fixing time can be saved.

[0060] In the above embodiment, the GNSS ionosphere-free combined real ambiguity after the inter-satellite single difference and the wide-lane integer ambiguity are weighted and operated to accurately deduce the first GNSS narrow-lane real ambiguity, to provide an accurate initial value for narrow-lane ambiguity fixing, and to further improve the positioning accuracy.

[0061] In some embodiments, the first GNSS combined observation value includes the frequency of the GNSS double-frequency carrier, and the step S150 "the terminal calculates the fixed solution of the user coordinates based on the first GNSS combined observation value, the first GNSS narrow-lane integer ambiguity, the first GNSS narrow-lane real ambiguity, the first GNSS wide-lane integer ambiguity, the initial LEO ionosphere-free combined real ambiguity, the initial GNSS ionosphere-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating point solution" includes the following steps: Step S1501, the terminal remolds the initial GNSS ionosphere-free combined real ambiguity based on the frequency of the GNSS double-frequency carrier, the first GNSS narrow-lane integer ambiguity, the narrow-lane phase fractional offset product, and the first GNSS wide-lane integer ambiguity, to obtain the remolded GNSS ionosphere-free combined real ambiguity; wherein the initial GNSS ionosphere-free combined real ambiguity is remolded by inspection, as shown in the following formula (30): ( - ) + Formula (30); wherein, represents the real ambiguity of the GNSS ionospheric elimination combination after reshaping, represents the first GNSS narrow lane integer ambiguity, Indicates the narrow-lane phase fractional deviation product (i.e., satellite-side narrow-lane FCB), Represents the first GNSS widelane integer ambiguity.

[0062] In step S1502, the terminal determines a user coordinate fixed solution based on the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the reshaped GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

[0063] Among them, the user coordinate fixed solution can be calculated based on the conditional adjustment, as shown in the following formula (31): Formula (31); Where, represents the initial GNSS ionospheric elimination combination real number ambiguity parameter solved by the parameter estimation method, Represents other parameters, including user coordinate floating point solution, receiver species difference, zenith direction tropospheric wet delay and initial LEO ionospheric elimination combination real ambiguity parameter, represents the variance-covariance matrix of the initial GNSS ionospheric-free combination real ambiguity parameters obtained by the parameter estimation method, represents the variance-covariance matrix of the initial GNSS ionospheric elimination combination real ambiguity parameters and other parameters obtained by the parameter estimation method, is the reshaped GNSS ionospheric-free combined ambiguity, Fix the solution for other parameters.

[0064] In the above embodiment, the GNSS ionospheric-free combined real ambiguity is reshaped based on the dual-frequency carrier frequency and the fixed wide-lane and narrow-lane integer ambiguities, and multiple types of parameters are integrated to calculate the user coordinate fixed solution, making full use of the fixed ambiguity information, thereby significantly improving the accuracy and stability of the positioning results.

[0065] The following are four experiments using the GPS system as an example to control the number of low-orbit satellites for augmentation. Using two LEO satellites as an example, the method for controlling the number of LEO satellites is explained: when there are more than two LEO satellites, the first two satellites are retained based on their elevation angles. If a retained LEO satellite is degraded (i.e., its elevation angle falls below a cutoff elevation angle), a satellite with a higher elevation angle is selected as a replacement.

[0066] Figure 5The TTFF (time to first fix) of 1, 2, 3, 4 and all LEO satellite enhanced single system PPP AR (Precise Point Positioning with Ambiguity Resolution) in different latitude regions is shown. 0 represents the case without low-orbit satellite enhancement, 1, 2, 3 and 4 represent 1, 2, 3 and 4 LEO satellite enhanced PPP AR respectively, - represents no control on the number of low-orbit satellites (no limit on the number of LEO satellites, all available satellites participate in enhancement), that is, all low-orbit satellites observed. 1 LEO satellite can hardly accelerate the first fixing of ambiguity, and 2, 3, 4 and all LEO satellites can accelerate the first fixing time (TTFF) of single system PPP from about 15 min to 9.0, 7.5, 6.8 and 6.4 min.

[0067] In view of the fact that a long observation time is required for reliable ambiguity fixing, which limits the real-time application of PPP, the embodiments of the present application provide a precise point positioning method and device based on low-orbit satellite auxiliary enhancement ambiguity fixing. A long short-term memory (LSTM) is used to perform multi-epoch smoothing processing on the HMW combined observation values, so as to reduce the influence of observation noise and multipath effect, and improve the accuracy of the smoothed observation values. In order to ensure simple program implementation and maximum data compression, increase the stability of data transmission, the Chinese PPP-B2b data format, the Japanese QZSS centimeter-level augmentation service (CLAS) information format and the IGS state domain real-time precise correction product format are referred to, a binary format with a bit as the minimum storage unit is used, and a binary phase decimal bias message data format based on RTCM3 is designed and implemented.

[0068] In addition, after solving the parameters based on the LEO / GNSS PPP function model, the GNSS ambiguity is partially fixed. Since the LEO ambiguity observation arc is short, it is not conducive to ambiguity fixing, and partial ambiguity fixing of GNSS ambiguity can improve the ambiguity fixing success rate, reduce the algorithm complexity and solving time. In addition, this method has low requirements for the number of low-orbit satellites, and fast PPP AR can be realized through 3-4 LEO satellites.

[0069] Based on the foregoing embodiments, the embodiments of the present application further provide a precise positioning acceleration device based on a low-orbit satellite / LSTM neural network. The device includes the modules included therein and the units included in each module, which can be implemented by a processor in an electronic device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0070] Figure 6 A schematic diagram of the composition structure of a precision positioning acceleration device based on a low-orbit satellite / LSTM neural network provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the device 600 includes: a server module 61 and a terminal module 62, wherein: The server module 61 includes a generation and encoding module 611, and the terminal module 62 includes an acquisition module 621, a determination module 622, a first fixing module 623, a second fixing module 624 and a calculation module 625; The generation and encoding module 611 is used to generate wide-lane fractional phase deviation products and narrow-lane fractional phase deviation products based on the LSTM method, and encode them using a specific method and then send them to the terminal; The acquisition module 621 is configured to acquire the encoded widelane fractional phase deviation product, the narrowlane fractional phase deviation product, and the first GNSS combined observation value; and determine the first GNSS widelane real ambiguity based on the first GNSS combined observation value; The determination module 622 is configured to determine a user coordinate floating-point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error based on a precise point positioning algorithm that integrates low-orbit satellites (LEO) and GNSS. The first fixing module 623 is configured to perform inter-satellite single-difference processing on the first GNSS wide-lane real ambiguity, and fix the GNSS wide-lane real ambiguity after the inter-satellite single-difference processing using the wide-lane phase fractional deviation product to obtain a first GNSS wide-lane integer ambiguity; The second fixing module 624 is configured to perform inter-satellite single-difference processing on the initial GNSS ionospheric-free combined real ambiguity, determine a first GNSS narrowlane real ambiguity based on the inter-satellite single-differenced GNSS ionospheric-free combined real ambiguity and the first GNSS widelane integer ambiguity, and fix the first GNSS narrowlane real ambiguity using a partial ambiguity fixing method using the narrowlane phase fractional deviation product to obtain a first GNSS narrowlane integer ambiguity. The calculation module 625 is configured to calculate a user coordinate fixed solution based on the first GNSS combined observation value, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, the first GNSS widelane integer ambiguity, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

[0071] In some possible embodiments, the generation and encoding module 611 includes: an acquisition submodule, configured to acquire GNSS raw observation values, wherein the GNSS raw observation values ​​include the frequency, dual-frequency carrier phase, dual-frequency pseudorange, wide-lane carrier phase observation value, and narrow-lane pseudorange observation value of the GNSS dual-frequency carrier; a first operation submodule, configured to perform a weighted operation on the dual-frequency carrier phase based on the frequency of the GNSS dual-frequency carrier to obtain a first GNSS ionospheric-free carrier phase combined observation value; a parameterization submodule, configured to perform a weighted operation on the dual-frequency pseudorange based on the frequency of the GNSS dual-frequency carrier to obtain a first GNSS ionospheric-free pseudorange combined observation value; and the first GNSS ionospheric-free pseudorange combined observation value, by analyzing the coupling relationship between the ionospheric-free combined real ambiguity and the hardware delay, the ionospheric-free combined real ambiguity and the hardware delay are re-parameterized to obtain the GNSS ionospheric-free combined real ambiguity containing the hardware delay bias; a first generation submodule is used to determine the second GNSS widelane real ambiguity based on the widelane carrier phase observation value and the narrowlane pseudorange observation value; based on the linear relationship between the fractional part of the second GNSS widelane real ambiguity and the phase fractional deviation, generate the widelane phase fractional deviation product; based on the widelane phase fractional deviation product, fix the second GNSS widelane real ambiguity to obtain the second GNSS widelane integer ambiguity; The second generating submodule is configured to determine a second GNSS narrowlane real ambiguity based on the GNSS ionospheric-free combined real ambiguity including the hardware delay bias and the second GNSS widelane integer ambiguity; and generate the narrowlane fractional phase deviation product based on a linear relationship between a fractional part of the second GNSS narrowlane real ambiguity and the fractional phase deviation.

[0072] In some possible embodiments, the first generating submodule comprises: a first constructing unit configured to construct a first virtual observation equation based on a linear relationship between a fractional part of the second GNSS wide-lane real ambiguity and a phase fractional offset; and a first resolving unit configured to set a phase fractional offset of a certain receiver or satellite as a reference, and resolve the wide-lane phase fractional offset product based on the reference and the first virtual observation equation. The second generating submodule comprises: a second constructing unit configured to construct a second virtual observation equation based on a linear relationship between a fractional part of the second GNSS narrow-lane real ambiguity and a phase fractional offset; and a second resolving unit configured to set a phase fractional offset of a certain receiver or satellite as a reference, and resolve the narrow-lane phase fractional offset product based on the reference and the second virtual observation equation.

[0073] In some possible embodiments, the generating and encoding module 611 further comprises: a compression submodule configured to compress the wide-lane phase fractional offset product and the narrow-lane phase fractional offset product in a binary format; a structured packaging processing submodule configured to perform structured packaging processing on the compressed wide-lane phase fractional offset product and the compressed narrow-lane phase fractional offset product based on a preset message basic framework, the message basic framework comprising a synchronization header, a reserved flag, a message length, and a data message, and the data message comprising an information type, an information reference time, a data synchronization flag, an information body, and data check information; and a transmission rule determining submodule configured to determine a transmission rule based on a preset encoding protocol, and transmit the packaged wide-lane phase fractional offset product and the packaged narrow-lane phase fractional offset product based on the transmission rule. The transmission rule comprises splitting and broadcasting according to satellite systems and satellites, and distinguishing transmission according to frequency points and signal types. The wide-lane phase fractional offset product and the narrow-lane phase fractional offset product are subtracted by a preset integer part of a week, and the wide-lane phase fractional offset product and the narrow-lane phase fractional offset product of different systems are assigned with unique message numbers.

[0074] In some possible embodiments, the first generating submodule further comprises: a third constructing unit configured to construct a first HMW combined carrier phase observation value based on a difference between the wide-lane carrier phase observation value and the narrow-lane pseudorange observation value; and a smoothing processing unit configured to perform smoothing processing on the first HMW combined carrier phase observation value by using a multi-epoch LSTM auxiliary neural network method, and determine the second GNSS wide-lane real ambiguity based on the smoothed first HMW combined carrier phase observation value and a wide-lane combined wavelength.

[0075] In some possible embodiments, the first GNSS combined observation value includes a widelane carrier phase combined observation value and a narrowlane pseudorange combined observation value, and the acquisition module 621 includes: a first construction submodule, used to construct a second HMW combined carrier phase observation value by differencing the widelane carrier phase combined observation value and the narrowlane pseudorange combined observation value; a smoothing submodule, used to smooth the second HMW combined carrier phase observation value using a multi-epoch recursive averaging method, and determine the first GNSS widelane real ambiguity based on the smoothed second HMW combined carrier phase observation value and the widelane combined wavelength.

[0076] In some possible embodiments, the determination module 622 includes: a first determination submodule, configured to determine the ionospheric-free combined pseudorange observation values ​​of the user receiver for the LEO satellite and the GNSS satellite based on the geometric distance, light speed, receiver clock error, tropospheric delay, and pseudorange observation error term from the user receiver to the LEO satellite and the GNSS satellite; and a second determination submodule, configured to determine the first LEO ionospheric-free carrier phase combination observation value and the first LEO ionospheric-free carrier phase combination observation value of the user receiver for the LEO satellite based on the geometric distance, light speed, receiver clock error, tropospheric delay, the wavelength corresponding to the ionospheric-free combination, the ionospheric-free combination real ambiguity, and the carrier phase observation error term from the user receiver to the LEO satellite and the GNSS satellite. The LEO satellite is provided with a first GNSS ionospheric-free carrier phase combined observation value, a second GNSS ionospheric-free pseudorange combined observation value, and a second GNSS ionospheric-free pseudorange combined observation value of the GNSS satellite; and a solution submodule is configured to construct a function model based on the first LEO ionospheric-free carrier phase combined observation value, the first LEO ionospheric-free pseudorange combined observation value, the second GNSS ionospheric-free carrier phase combined observation value, and the first GNSS ionospheric-free pseudorange combined observation value, and solve the function model using a Kalman filter parameter estimation method to obtain a user coordinate floating-point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error.

[0077] In some possible embodiments, the second fixing module 624 includes: a second operation submodule, configured to perform a weighted operation on the GNSS ionospheric-free combined real ambiguity after inter-satellite single-difference processing and the first GNSS wide-lane integer ambiguity based on the frequency of the GNSS dual-frequency carrier to obtain the first GNSS narrow-lane real ambiguity.

[0078] In some possible embodiments, the first GNSS combined observation value includes the frequency of the GNSS dual-frequency carrier, and the calculation module 625 includes: a reshaping submodule, configured to reshape the initial GNSS ionospheric-free combined real ambiguity based on the frequency of the GNSS dual-frequency carrier, the first GNSS narrow-lane integer ambiguity, the narrow-lane phase fractional deviation product, and the first GNSS wide-lane integer ambiguity to obtain a reshaped GNSS ionospheric-free combined real ambiguity; and a third determination submodule, configured to determine a user coordinate fixed solution based on the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the reshaped GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

[0079] It should be noted that the description of the above device embodiment is similar to the description of the above method embodiment and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of this application, please refer to the description of the method embodiment of this application for understanding.

[0080] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0081] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0083] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. In addition, the functional units in the various embodiments of the present application may all be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0084] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling the automatic test line of the device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0085] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments. The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0086] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A precision positioning acceleration method based on low-orbit satellite / LSTM neural network, characterized in that: include: The server generates wide-lane fractional phase deviation products and narrow-lane fractional phase deviation products based on the LSTM method, encodes them using a specific method, and sends them to the terminal. The terminal obtains the encoded widelane phase fractional deviation product, the narrowlane phase fractional deviation product, and the first GNSS combined observation value; and determines the first GNSS widelane real ambiguity based on the first GNSS combined observation value; The terminal determines a user coordinate floating-point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error based on a precise point positioning algorithm that integrates low-orbit satellites (LEO) and GNSS. The terminal performs inter-satellite single-difference processing on the first GNSS wide-lane real ambiguity, and fixes the GNSS wide-lane real ambiguity after the inter-satellite single-difference processing using the wide-lane phase fractional deviation product to obtain a first GNSS wide-lane integer ambiguity; The terminal performs inter-satellite single-difference processing on the initial GNSS ionospheric-free combined real ambiguity, determines a first GNSS narrowlane real ambiguity based on the inter-satellite single-differenced GNSS ionospheric-free combined real ambiguity and the first GNSS widelane integer ambiguity, and fixes the first GNSS narrowlane real ambiguity using a partial ambiguity fixing method using the narrowlane phase fractional deviation product to obtain a first GNSS narrowlane integer ambiguity; The terminal calculates a user coordinate fixed solution based on the first GNSS combined observation value, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, the first GNSS widelane integer ambiguity, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

2. The method according to claim 1, characterized in that The method further comprises: The server obtains GNSS raw observation values, wherein the GNSS raw observation values ​​include the frequency of the GNSS dual-frequency carrier, the dual-frequency carrier phase, the dual-frequency pseudorange, the wide-lane carrier phase observation value, and the narrow-lane pseudorange observation value; The server performs a weighted operation on the dual-frequency carrier phase based on the frequency of the GNSS dual-frequency carrier to obtain a first GNSS ionospheric-free carrier phase combined observation value; The server performs a weighted operation on the dual-frequency pseudorange based on the frequency of the GNSS dual-frequency carrier to obtain a first GNSS ionospheric-free pseudorange combined observation value; The server re-parameterizes the ionospheric-free combined real ambiguity and the hardware delay based on the first GNSS ionospheric-free carrier phase combined observation value and the first GNSS ionospheric-free pseudorange combined observation value, thereby obtaining the GNSS ionospheric-free combined real ambiguity containing the hardware delay bias; The server determines a second GNSS widelane real ambiguity based on the widelane carrier phase observation value and the narrowlane pseudorange observation value; The server generates the widelane fractional phase deviation product based on a linear relationship between a fractional part of the second GNSS widelane real ambiguity and the fractional phase deviation; and fixes the second GNSS widelane real ambiguity based on the widelane fractional phase deviation product to obtain a second GNSS widelane integer ambiguity. The server determines a second GNSS narrowlane real ambiguity based on the GNSS ionospheric-free combined real ambiguity including the hardware delay bias and the second GNSS widelane integer ambiguity; The server generates the narrowlane phase fractional deviation product based on a linear relationship between a fractional part of the second GNSS narrowlane real ambiguity and the phase fractional deviation.

3. The method according to claim 2, characterized in that The server generates the widelane phase fractional deviation product based on a linear relationship between a fractional part of the second GNSS widelane real ambiguity and the phase fractional deviation, including: The server constructs a first virtual observation equation based on a linear relationship between a fractional part of the second GNSS wide lane real ambiguity and a fractional phase deviation; The server sets the fractional phase deviation of a certain receiver or satellite to 0 as a reference, and solves the widelane fractional phase deviation product based on the reference and the first virtual observation equation; The server generates the narrowlane phase fractional deviation product based on a linear relationship between a fractional part of the second GNSS narrowlane real ambiguity and the phase fractional deviation, including: The server constructs a second virtual observation equation based on a linear relationship between a fractional part of the second GNSS narrow lane real ambiguity and a fractional phase deviation; The server sets the phase fractional deviation of a certain receiver or satellite to 0 as a benchmark, and solves the narrowlane phase fractional deviation product based on the benchmark and the second virtual observation equation.

4. The method according to claim 2, characterized in that The method further comprises: The server compresses the widelane fractional phase deviation product and the narrowlane fractional phase deviation product in a binary format; and performs structured packaging on the compressed widelane fractional phase deviation product and the compressed narrowlane fractional phase deviation product based on a preset basic message framework. The basic message framework includes a synchronization header, a reserved flag bit, a message length, and a data message. The data message includes an information type, an information reference time, a data synchronization flag, an information body, and data verification information. The server determines a transmission rule based on a preset coding protocol, and transmits the encapsulated widelane fractional phase deviation product and the encapsulated narrowlane fractional phase deviation product based on the transmission rule. The transmission rule includes splitting broadcasting by satellite system and satellite, differentiating transmission by frequency and signal type, deducting an integer part greater than a preset number of weeks from the widelane fractional phase deviation product and the narrowlane fractional phase deviation product, and assigning unique telegram numbers to the widelane fractional phase deviation products and the narrowlane fractional phase deviation products of different systems.

5. The method according to claim 2, characterized in that The server determines, based on the widelane carrier phase observation value and the narrowlane pseudorange observation value, a second GNSS widelane real ambiguity, including: The server constructs a first HMW combined carrier phase observation value based on the difference between the widelane carrier phase observation value and the narrowlane pseudorange observation value; The server uses a multi-epoch LSTM-assisted neural network method to smooth the first HMW combined carrier phase observation value, and determines the second GNSS wide lane real ambiguity based on the smoothed first HMW combined carrier phase observation value and the wide lane combined wavelength.

6. The method according to claim 1, characterized in that The first GNSS combined observation value includes a widelane carrier phase combined observation value and a narrowlane pseudorange combined observation value, and determining a first GNSS widelane real ambiguity based on the first GNSS combined observation value includes: The terminal constructs a second HMW combined carrier phase observation value by taking a difference between the widelane carrier phase combined observation value and the narrowlane pseudorange combined observation value; The terminal smoothes the second HMW combined carrier phase observation value using a multi-epoch recursive averaging method, and determines the first GNSS wide lane real ambiguity based on the smoothed second HMW combined carrier phase observation value and the wide lane combined wavelength.

7. The method according to claim 1, characterized in that The terminal determines a user coordinate floating point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error based on a precise point positioning algorithm that integrates a low-orbit satellite (LEO) and a GNSS, including: The terminal determines the ionospheric-free combined pseudorange observation values ​​of the user receiver to the LEO satellite and the GNSS satellite based on the geometric distance from the user receiver to the LEO satellite and the GNSS satellite, the speed of light, the receiver clock error, the tropospheric delay and the pseudorange observation error term; The terminal determines, based on the geometric distance from the user receiver to the LEO satellite and the GNSS satellite, the speed of light, the receiver clock error, the tropospheric delay, the wavelength corresponding to the ionospheric elimination combination, the real ambiguity of the ionospheric elimination combination, and the carrier phase observation error value, a first LEO ionospheric elimination carrier phase combination observation value and a first LEO ionospheric elimination pseudorange combination observation value of the user receiver of the LEO satellite, and a second GNSS ionospheric elimination carrier phase combination observation value and a second GNSS ionospheric elimination pseudorange combination observation value of the GNSS satellite; The terminal constructs a function model based on the first LEO ionospheric-free carrier phase combination observation value, the first LEO ionospheric-free pseudorange combination observation value, the second GNSS ionospheric-free carrier phase combination observation value, and the second GNSS ionospheric-free pseudorange combination observation value, and solves the function model using a Kalman filter parameter estimation method to obtain a user coordinate floating-point solution, an initial LEO ionospheric-free combination real ambiguity, an initial GNSS ionospheric-free combination real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error.

8. The method according to claim 1, characterized in that The determining of the first GNSS narrowlane real ambiguity based on the GNSS ionospheric-free combined real ambiguity after inter-satellite single-difference processing and the first GNSS widelane integer ambiguity includes: The terminal performs a weighted operation on the GNSS ionospheric-free combined real ambiguity after inter-satellite single-difference processing and the first GNSS wide-lane integer ambiguity based on the frequency of the GNSS dual-frequency carrier to obtain the first GNSS narrow-lane real ambiguity.

9. The method according to claim 1, characterized in that The first GNSS combined observation value includes the frequency of the GNSS dual-frequency carrier, and the terminal calculates a user coordinate fixed solution based on the first GNSS combined observation value, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, the first GNSS widelane integer ambiguity, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution, including: The terminal reshapes the initial GNSS ionospheric-free combined real ambiguity based on the frequency of the GNSS dual-frequency carrier, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, and the first GNSS widelane integer ambiguity to obtain a reshaped GNSS ionospheric-free combined real ambiguity; The terminal determines a user coordinate fixed solution based on the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the reshaped GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

10. A precision positioning acceleration device based on low-orbit satellite / LSTM neural network, characterized in that: include: Server module and terminal module, including: The server module includes a generation and encoding module, and the terminal module includes an acquisition module, a determination module, a first fixing module, a second fixing module and a calculation module; The generation and encoding module is used to generate wide-lane fractional phase deviation products and narrow-lane fractional phase deviation products based on the LSTM method, and encode them using a specific method and then send them to the terminal; The acquisition module is configured to acquire the encoded widelane phase fractional deviation product, the narrowlane phase fractional deviation product, and the first GNSS combined observation value; and determine the first GNSS widelane real ambiguity based on the first GNSS combined observation value; The determination module is configured to determine a user coordinate floating-point solution, an initial LEO ionospheric-free combined real ambiguity, an initial GNSS ionospheric-free combined real ambiguity, a tropospheric delay, a LEO receiver clock error, and a GNSS receiver clock error based on a precise point positioning algorithm that integrates low-orbit satellites (LEO) and GNSS. The first fixing module is configured to perform inter-satellite single-difference processing on the first GNSS wide-lane real ambiguity, and fix the GNSS wide-lane real ambiguity after the inter-satellite single-difference processing using the wide-lane phase fractional deviation product to obtain a first GNSS wide-lane integer ambiguity; The second fixing module is configured to perform inter-satellite single-difference processing on the initial GNSS ionospheric-free combined real ambiguity, determine a first GNSS narrowlane real ambiguity based on the inter-satellite single-differenced GNSS ionospheric-free combined real ambiguity and the first GNSS widelane integer ambiguity, and fix the first GNSS narrowlane real ambiguity using a partial ambiguity fixing method using the narrowlane phase fractional deviation product to obtain a first GNSS narrowlane integer ambiguity. The calculation module is configured to calculate a user coordinate fixed solution based on the first GNSS combined observation value, the first GNSS narrowlane integer ambiguity, the narrowlane phase fractional deviation product, the first GNSS widelane integer ambiguity, the initial LEO ionospheric-free combined real ambiguity, the initial GNSS ionospheric-free combined real ambiguity, the tropospheric delay, the LEO receiver clock error, the GNSS receiver clock error, and the user coordinate floating-point solution.

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