A precise point positioning method for leo-gnss based on independent estimation of system biases

Abstract

The present application relates to a LEO-GNSS precise point positioning method based on system bias independent estimation. The method can include: obtaining satellite observation data and product data of global navigation satellite system (GNSS) satellites and low earth orbit (LEO) satellites, the GNSS satellites including GPS satellites and BDS satellites; estimating rough state parameters based on the obtained satellite observation data and product data using pseudorange point positioning (SPP), the rough state parameters including inter-system bias (ISB) parameters of each of the LEO satellites and the GNSS satellites; constructing a PPP measurement model based at least in part on the rough state parameters, including constructing measurement equations for the GPS satellites, the BDS satellites and the LEO satellites respectively, and linearizing the measurement equations to obtain an observation coefficient matrix expression; and estimating a float solution of the PPP measurement model to determine the positioning of the receiver. In this regard, the positioning abnormality caused by the difference in signal processing delay of different LEO satellites is avoided, and the convergence efficiency of LEO enhanced PPP is improved.

Description

Technical Field

[0001] This application relates to satellite navigation technology, and more particularly to navigation technology involving low-Earth orbit satellites. Background Technology

[0002] Precise Point Positioning (PPP) technology enables high-precision absolute positioning based on a single receiver, offering significant advantages in positioning flexibility. However, limitations such as numerous model state parameters, strong correlation of ambiguity parameters, and vulnerability of GNSS systems in complex scenarios result in long initialization times and low reliability, hindering its large-scale application. Combining PPP with Low Earth Orbit (LEO) satellite navigation signals can compensate for the slow convergence and weak signal power of existing high-orbit navigation satellite signals in GNSS. By leveraging the rapid geometric changes of LEO satellites, inter-epoch measurement model correlation can be reduced, state parameter observability can be improved, and the slow convergence of PPP can be resolved, enabling fast and high-precision precise point positioning.

[0003] Even so, PPP performance still relies on accurate error modeling. During the modeling process, the inter-system bias (ISB) caused by differences in the time delays of different satellite system equipment is one of the important parameters in PPP. The ISB parameter is related to clock bias; without error compensation, not only will pseudorange observations have systematic biases, but excessive clock bias will also lead to errors in calculating satellite signal transmission times, resulting in orbit calculation errors and severely affecting the accuracy of position calculation. Summary of the Invention

[0004] The following provides a brief overview of one or more aspects to offer a basic understanding of them. This overview is not an exhaustive summary of all conceived aspects, nor is it intended to identify key or decisive elements of all aspects, nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form to prepare for the more detailed descriptions that follow.

[0005] To overcome the aforementioned problems in the existing technology, this application proposes a method and corresponding apparatus for performing precise point positioning (PPP) by individually modeling and estimating the ISB between each LEO satellite and GNSS satellite, thereby improving the positioning accuracy and precision of PPP.

[0006] The first aspect of this application provides a method for performing Precise Point Positioning (PPP), which may include: acquiring satellite observation data and product data from Global Navigation Satellite System (GNSS) satellites and Low Earth Orbit (LEO) satellites, the GNSS satellites including Global Positioning System (GPS) satellites and Beidou System (BDS) satellites, the satellite observation data including multi-frequency pseudorange observations and carrier phase observations; estimating approximate state parameters using pseudorange point positioning (SPP) based on the acquired satellite observation data and product data, the approximate state parameters including the inter-system bias (ISB) parameter between the GNSS and each LEO satellite; constructing a PPP measurement model based at least in part on the approximate state parameters, the PPP measurement model being based on a dual-frequency ionospheric-free model, and the construction of the PPP measurement model including constructing measurement equations for GPS satellites, BDS satellites and LEO satellites respectively, and linearizing the simultaneous measurement equations to obtain an observation coefficient matrix expression; and estimating the floating-point solution of the PPP measurement model to determine the receiver's positioning.

[0007] In some examples, estimating the floating-point solution of the PPP measurement model to determine the receiver's location may further include: solving the expression for the observation coefficient matrix to obtain a floating-point solution for the state vector of the parameters to be estimated; and determining the receiver's location based on the floating-point solution.

[0008] In some examples, estimating the floating-point solution of the PPP measurement model to determine the receiver's location may further include: analyzing the a posteriori residual of the floating-point solution of the state vector of the parameter to be estimated to determine whether there is an out-of-tolerance measurement equation; in response to determining that there is an out-of-tolerance measurement equation, eliminating the out-of-tolerance measurement equation and updating the state of the parameter to be estimated; and in response to determining that there is no out-of-tolerance measurement equation, outputting the floating-point solution of the state vector of the parameter to be estimated to determine the receiver's location.

[0009] In some examples, the method may further include: receiving correction information before constructing the PPP measurement model; and performing error correction based on the processing strategy and the correction information.

[0010] A second aspect of this application provides an apparatus for performing Precise Point Positioning (PPP), the apparatus including: a memory; and a processor communicatively coupled to the memory, the memory and the processor being configured to perform the methods described in the first aspect of this application.

[0011] A third aspect of this application provides a system for performing Precise Point Positioning (PPP), the system comprising: an acquisition module configured to acquire satellite observation data and product data from Global Navigation Satellite System (GNSS) satellites and Low Earth Orbit (LEO) satellites, the GNSS satellites including Global Positioning System (GPS) satellites and BeiDou Navigation Satellite System (BDS) satellites. The system includes: a BDS satellite observation data, which may include multi-frequency pseudorange observations and carrier phase observations; a probabilistic state parameter determination module, which can be configured to estimate probabilistic state parameters using pseudorange single-point positioning (SPP) based on the acquired satellite observation data and product data, the probabilistic state parameters including the inter-system bias (ISB) parameter between GNSS and each LEO satellite; a PPP measurement model construction module, which can be configured to construct a PPP measurement model based at least in part on the probabilistic state parameters, the PPP measurement model being based on a dual-frequency ionospheric-free model, and can be further configured to construct measurement equations for GPS satellites, BDS satellites and LEO satellites respectively, and to linearize the simultaneous measurement equations to obtain an observation coefficient matrix expression; and a positioning module, which can be configured to estimate the floating-point solution of the PPP measurement model to determine the receiver's positioning.

[0012] The fourth aspect of this application provides a non-transient storage medium having processor-executable instructions stored thereon, which, when executed by a processor, cause the processor to perform a method for performing Precision Point Positioning (PPP) as described in the first aspect of this application. Attached Figure Description

[0013] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0014] Figure 1 Schematic diagrams illustrating application scenarios suitable for implementing various aspects of this application are provided.

[0015] Figure 2 A flowchart illustrating a method for performing precise single-point positioning PPP according to various aspects of this application is shown.

[0016] Figure 3 A flowchart illustrating a method for performing precise single-point positioning (PPP) according to various aspects of this application is shown;

[0017] Figure 4Functional block diagrams of a system for performing Precise Point Positioning (PPP) according to various aspects of this application are illustrated; and

[0018] Figure 5 A block diagram illustrating an exemplary computer system suitable for implementing various aspects of this application is provided. Detailed Implementation

[0019] The following specific embodiments illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Although the description of this application is presented in conjunction with preferred embodiments, this does not mean that the features of this invention are limited to these embodiments. On the contrary, the purpose of describing the invention in conjunction with embodiments is to cover other options or modifications that may be derived based on the claims of this application. To provide a thorough understanding of this application, many specific details will be included in the following description. This application may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of this application, some specific details will be omitted in the description.

[0020] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0022] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0025] To facilitate understanding of the technical solution of this application, the relevant concepts involved in this application will be introduced first.

[0026] A Global Navigation Satellite System (GNSS) is a radio navigation and positioning system that provides global or regional coverage via satellite signals. It offers users on the Earth's surface or in near-Earth space three-dimensional coordinates (longitude, latitude, and elevation), velocity, and time information (Position Velocity Time, PVT). GNSS encompasses various types of satellite navigation systems, including global, regional, and augmented satellite navigation systems, such as the US Global Positioning System (GPS), China's BeiDou System (BDS), Russia's GLONASS, and Europe's Galileo.

[0027] Precise Point Positioning (PPP) is a technology that uses a single GNSS receiver in conjunction with precise satellite orbit (precise ephemeris) and precise satellite clock bias data provided by GNSS to achieve static or dynamic positioning at the millimeter to centimeter level.

[0028] Pseudo-range Single Point Positioning (SPP) is a positioning method that uses a ranging code signal transmitted by a satellite to calculate the pseudorange (the distance including errors such as clock bias and atmospheric delay) by measuring the signal propagation time, and then uses the satellite orbit and clock bias parameters provided by the broadcast ephemeris to solve the receiver's three-dimensional coordinates.

[0029] Ephemeris data is a dataset of orbital parameters describing a satellite's spatial position and velocity at a specific point in time. It can include broadcast ephemeris and precise ephemeris. Broadcast ephemeris is broadcast by the satellite in real time, predicting the satellite's orbit and clock bias for the next two weeks, with relatively low accuracy (orbital error of about 5-7 meters, clock bias error of about 20 ns). Precise ephemeris is generated post-processing by ground monitoring stations, with orbital accuracy down to the centimeter level (e.g., the post-processing precise ephemeris of IGS (International GNSS Service) has an orbital error of 2-3 cm).

[0030] Clock bias data refers to a set of parameters describing the deviation between a satellite or receiver clock and a standard time system. It is generally used in GNSS systems to correct time synchronization errors. Clock bias data can include satellite clock bias, receiver clock bias, and precision clock bias. Satellite clock bias refers to the deviation between the atomic clock (such as a cesium clock or rubidium clock) onboard the satellite and the GNSS system time, including clock face offset, frequency drift, and random noise. Receiver clock bias refers to the deviation between the receiver's built-in clock and the system time, typically representing the low-precision error of a quartz clock. Precision clock bias refers to high-precision correction parameters generated by ground monitoring stations through multi-station joint observations and post-processing. For example, precise satellite clock bias can be calculated by subtracting the payload delay parameters from the clock bias calculated from the high-orbit navigation signals observed by the onboard receiver.

[0031] As mentioned earlier, PPP performance relies on accurate error modeling. For example, the Inter-System Bias (ISB) parameter, caused by differences in signal delays between different GNSS systems processed by the receiver, is one of the important parameters in PPP. Typically, there is only one ISB parameter between two GNSS systems; that is, the ISB is system-dependent and independent of specific satellites. Therefore, when constructing PPP for multiple systems, the joint establishment of inter-system measurement models only constructs one ISB parameter, i.e., a comprehensive estimation of the ISB is performed.

[0032] However, due to the low density of current low-Earth orbit (LEO) satellite ground monitoring stations and the lack of onboard payload delay calibration capabilities, satellite clock bias acquisition mostly relies on autonomous calculation, utilizing medium- and high-Earth orbit (MEO) satellite navigation signals for clock bias calculation. This results in differences in onboard relay delays among different LEO satellites. Furthermore, the equipment delays of ground monitoring terminals for MEO and different LEO satellites also vary. These two factors combined lead to inconsistent and unstable ISB (Independent Time Between Bicycles) between GNSS signals and different LEO satellites. Specifically, current methods for overall ISB estimation ignore the inconsistency in equipment delay characteristics (ISB) between different LEO and GNSS systems. This leads to anomalies in the estimation of ISB state parameters in the LEO satellite measurement model, and different systematic clock bias anomalies in the pseudorange observations received by the receiver from different LEO satellites. In these observations, ISB parameters are related to clock bias. Without error compensation, not only will there be systematic biases in the pseudorange observations, but excessive clock bias deviations will also occur, leading to errors in satellite signal transmission time calculations, and consequently, orbit calculation errors, severely impacting position calculation accuracy. To this end, this application proposes a method and corresponding apparatus for performing precise point positioning (PPP) by individually modeling and estimating the ISB between each LEO satellite and GNSS satellite, thereby improving the positioning accuracy and precision of PPP. Specifically, this application treats different LEO satellites as independent satellite systems, and individually models and estimates the ISB between each LEO satellite and GNSS satellite. By taking into account the equipment delay characteristics between different LEO and GNSS systems when performing PPP, the impact of system measurement bias induced by differences in ISB consistency and stability is reduced, thereby further improving the positioning accuracy of PPP using LEO satellites.

[0033] The following is for reference. Figures 1 to 5 This paper explains the method and corresponding apparatus for performing precise single-point positioning (PPP) according to various aspects of this application. First, refer to... Figure 1 The diagram illustrates application scenarios suitable for implementing various aspects of this application. For example... Figure 1 As shown, satellites (such as satellites S1 to S7) and ground receivers measure the distance between them by generating the same ranging code (a signal for measuring satellite-to-ground distance). The ground receiver can then use this distance for positioning. In real-world scenarios, the distance between the satellite and the receiver measured by the receiver is not the "true distance" due to clock errors, atmospheric refraction, etc. Therefore, positioning based on this non-true distance is generally called pseudorange point positioning (SPP). Positioning based on a distance measured after eliminating errors caused by clock errors, atmospheric refraction, etc., is generally called precise point positioning (PPP). In some aspects of this application, the ground receiver, in addition to receiving data from high-orbit navigation satellites in the Global Navigation Satellite System (GNSS) (such as...),... Figure 1In addition to receiving signals from satellites S1, S2, S3, S5, and S7, signals can also be received from low-Earth orbit (LEO) navigation satellites (such as... Figure 1 The satellites S4 and S6 shown in the diagram receive signals. In some aspects of this application, combining LEO satellite navigation signals to perform PPP can compensate for the shortcomings of existing GNSS high-orbit navigation satellite signals, such as slow convergence and weak signal power. By leveraging the advantage of rapid changes in the geometric configuration of LEO satellites, the correlation of measurement models between epochs is reduced, the observability of state parameters is improved, the problem of slow PPP convergence is solved, and rapid, high-precision, precise point positioning is achieved.

[0034] According to one aspect of this application, a method for performing precise single-point positioning (PPP) is provided. Reference is now made to... Figure 2 This illustrates a process flowchart of process 200 for performing Precise Point Positioning (PPP) according to various aspects of this application. In some examples, process 200 can be performed by a ground receiver, such as those described in this application. Figure 1 , Figure 4 and Figure 5 The receivers and computing devices shown are examples.

[0035] In some aspects, process 200 may include data acquisition 205. In some examples, data acquisition 205 may include acquiring satellite observation data 2051 and product data 2052. In some examples, satellite observation data 2051 may include data from low-Earth orbit (LEO) satellites (such as...) Figure 1 Satellite observation data (such as satellites S4 and S6 as illustrated in the text) and satellite observation data from GNSS satellites (such as...) Figure 1The examples illustrate satellites S1, S2, S3, S5, and S7. In some examples, GNSS satellites may include GPS satellites and BDS satellites. In some examples, satellite observation data may include multi-frequency pseudorange observations and carrier phase observations. In some examples, product data 2052 may include precise orbit and precise clock bias data of LEO and GNSS satellites (medium-to-high orbit navigation satellites). In some examples, data acquisition 205 may optionally include receiving other correction information 2053, including but not limited to, satellite antenna phase center corrections (Phase Center Variation (PCV) and Phase Center Offset (PCO)), fractional cycle bias (FCB), tidal corrections, Observable-Specific Bias (OSB), Earth Rotation Parameters (ERP), etc.

[0036] In some aspects, process 200 may include preprocessing the acquired data 210. In some examples, data preprocessing 210 may include removing problematic data from the acquired data 2101, constructing a pseudorange point positioning (SPP) error model 2102 based on this, and estimating approximate state parameters 2103 based on the acquired data. In some examples, constructing the SPP error model 2102 may include augmenting the ISB parameters between each LEO satellite and the GNSS based on the number of LEO satellites. In some examples, such as combining... Figure 1 The example shown illustrates this, where the receiver receives signals from two LEO satellites (such as...). Figure 1 In the case of satellites S4 and S6 (as illustrated), which receive observation data, the estimated ISB parameters between the two LEO satellites and the GNSS can be augmented. In some examples, estimating the approximate state parameter 2103 may include estimating the approximate state parameter based on the acquired satellite observation data and product data, using the constructed SPP error model and least squares iterative calculations. In some examples, the approximate state parameter may include the approximate receiver position, clock bias, GNSS system-to-system (IGSB) deviation parameters, and the ISB parameters between the GNSS and each LEO satellite, etc.

[0037] In some aspects, data preprocessing 210 may optionally include model correction 2104 for systematic error sources. In some examples, systematic error sources may include modeled satellite antenna and receiver antenna phase center offsets, relativistic effects, phase winding errors, Earth rotation, solid tides, ocean tides, polar tides, etc. In some examples, correcting model errors 2104 may include performing error correction based on processing strategies and acquired correction information 2053, including correcting satellite coordinates, receiver coordinates, or observations. In some examples, Table 1 lists processing strategies that may be used for error parameters of various error types according to various aspects of this application.

[0038]

[0039] Table 1

[0040] In some aspects, process 200 may include constructing a PPP measurement model 215. In some examples, constructing the PPP measurement model 215 may include constructing the PPP measurement model based at least in part on approximate state parameters. In some examples, the PPP measurement model may be based on a dual-frequency ionospheric-free model to eliminate the effects of ionospheric delay. In some examples, in the context of GNSS using US GPS and Chinese BDS as examples, constructing the PPP measurement model may include: constructing measurement equations 2151 separately for GPS satellites, BDS satellites, and LEO satellites; and linearizing the simultaneous measurement equations to obtain the observation coefficient matrix expression 2152.

[0041] In some examples, the measurement equations constructed for GPS satellites may include:

[0042] Equation (1)

[0043] Subscript and This indicates a receiver combined with an ionosphere-free system. and These are pseudorange and phase observations, respectively; Geometric distance; superscript i,G Indicates GPS satellite number i; and These are receiver clock bias and satellite clock bias, respectively. The flow layer delay along the signal propagation path; The signal wavelength after ionosphere-free processing; For dual-frequency ionosphere-free combined integer ambiguity; and These are pseudorange measurement noise and phase measurement noise, respectively. It is the speed of light.

[0044] In some examples, the measurement equations constructed for BDS satellites may include:

[0045] Equation (2)

[0046] Among them, superscript j,B Instruction for BDS satellite j, Let ISB be the BDS parameter relative to GPS. As can be seen from equation (2), the BDS measurement equation amplifies the inter-system bias. This parameter is primarily used to account for delay differences in receiver processing signals from different systems. In the context of BDS satellites, the delay for a receiver processing signals from different satellites within the same system is typically the same, and the same ISB parameter can be used when simultaneously solving measurement equations for two different satellite systems.

[0047] In some examples, the measurement equations constructed for LEO satellites may include:

[0048] Equation (3)

[0049] Among them, superscript k,L Indicates LEO satellite number k, For the first The ISB parameters of LEO satellites relative to GPS. As can be seen from Equation (3), unlike BDS satellites, in the context of LEO satellites, to account for the problem of inconsistent ISB parameters in LEO satellite observations, each LEO satellite is treated as an independent system, and its ISB parameters relative to GPS are modeled separately. In some examples, the dynamic model of the ISB parameters of LEO satellites relative to GPS can be modeled as a random walk, where the process noise parameter can be set to 0.001 m / s. It should be noted that the process noise parameter can be dynamically configured according to the actual scenario and is not limited to this. Thus, as shown in Equation (3), by independently modeling and estimating the ISB, the accuracy of the deviation calculation between different LEO satellites and the GNSS system is improved, avoiding the problems of low signal quality, incorrect signal transmission time, and incorrect satellite orbit calculation caused by different time delay differences.

[0050] In some examples, combining equations (1), (2), and (3) and performing linearization yields a matrix form expression:

[0051] Equation (4)

[0052] in and are the observation vector and measurement noise vector, respectively; Let the state vector of the parameters to be estimated be represented as:

[0053] Equation (5)

[0054] in To correct for positional errors, Let be the moist delay component of the zenith troposphere. Let be observed at a certain epoch. GPS satellites BDS satellites and For each LEO satellite, the observation coefficient matrix It can be expressed as:

[0055] Equation (6)

[0056] in, It is the identity matrix. This is the tropospheric projection matrix. This refers to the direction cosine matrix, which is linearized based on the position information in the approximate state parameters of GPS. This refers to the direction cosine matrix after linearization of position information in the approximate state parameters for BDS. This is the direction cosine matrix for LEO, which is linearized based on the position information in the approximate state parameters.

[0057] In some aspects, process 200 may further include obtaining a floating-point solution of the PPP measurement model constructed at 215 to determine the receiver's location 225. In some examples, 225 may include solving the observation coefficient matrix expression to obtain a floating-point solution of the state vector of the parameters to be estimated; and determining the receiver's location based on the floating-point solution. In some examples, solving the observation coefficient matrix expression to obtain a floating-point solution of the state vector of the parameters to be estimated may include using an extended Kalman filter to estimate a floating-point solution of the state vector of the parameters to be estimated, including floating-point solutions for receiver position correction, receiver clock error correction, ISB, zenith tropospheric wet delay, and carrier phase ambiguity. In some examples, 225 may further include determining the receiver's location based on the floating-point solution.

[0058] In some aspects, process 200 may optionally include performing quality control 220 prior to determining the receiver's location. In some examples, quality control 220 may include analyzing the a posteriori residuals of the floating-point solutions of the state vector of the parameter to be estimated to determine whether an out-of-tolerance measurement equation exists; in response to determining the existence of an out-of-tolerance measurement equation, eliminating the out-of-tolerance measurement equation and updating the state of the parameter to be estimated; and in response to determining that no out-of-tolerance measurement equation exists, outputting the floating-point solutions of the state vector of the parameter to be estimated to determine the receiver's location.

[0059] Those skilled in the art will understand that aspects not elaborated in the description of process 200 (e.g., SPP measurement equation construction, simultaneous PPP measurement equations and linearization, estimation of floating-point solutions using extended Kalman filtering, etc.) can be performed in ways well known to those skilled in the art, and therefore will not be described further herein. Those skilled in the art should also understand that the parameter settings and calculation method selections provided in the description of process 200 are for illustrative purposes only and not as limitations, and any other parameter settings (such as the dynamic model of ISB parameters, process noise parameters, etc.) and calculation method selections also fall within the scope of this application.

[0060] Next, refer to Figure 3 The flowchart illustrates a method 300 for performing Precise Point Positioning (PPP) according to various aspects of this application. In some examples, method 300 may be performed by a ground receiver, such as those described in this application. Figure 1 , Figure 4 and Figure 5 The receivers and computing devices shown are examples.

[0061] In some aspects, method 300 may include step 305, acquiring satellite observation data and product data from Global Navigation Satellite System (GNSS) satellites and Low Earth Orbit (LEO) satellites. In some examples, the GNSS satellites may include Global Positioning System (GPS) satellites and BeiDou Navigation Satellite System (BDS) satellites. In some examples, the satellite observation data may include multi-frequency pseudorange observations and carrier phase observations. In some examples, the product data may include precise orbit and precise clock bias data for LEO and GNSS satellites.

[0062] In some aspects, method 300 may include step 310, estimating approximate state parameters using pseudorange single-point positioning (SPP) based on the acquired satellite observation data and product data. In some examples, the approximate state parameters may include the inter-system bias (ISB) parameter between the GNSS and each of the LEO satellites. In some examples, the approximate state parameters may further include the approximate receiver position, clock bias, GNSS inter-system bias (ISB) parameter, etc.

[0063] In some aspects, method 300 may include step 315, constructing a PPP measurement model based at least in part on approximate state parameters. In some examples, the PPP measurement model may be based on a dual-frequency ionosphere-free model. In some examples, constructing the PPP measurement model may include: constructing measurement equations separately for GPS satellites, BDS satellites, and LEO satellites; and linearizing the simultaneous measurement equations to obtain an expression for the observation coefficient matrix. In some examples, in the context of LEO satellites, each LEO satellite is treated as an independent system, and its ISB parameters relative to GPS are modeled separately. In some examples, the dynamics of the LEO satellite's ISB parameters relative to GPS may be modeled as a random walk, where the process noise parameter may be set to 0.001 m / s.

[0064] In some aspects, method 300 may include step 320, estimating a floating-point solution of the PPP measurement model to determine the receiver's location. In some examples, step 320 may further include solving the observation coefficient matrix expression to obtain a floating-point solution of the state vector of the parameters to be estimated; and determining the receiver's location based on the floating-point solution. In some examples, solving the observation coefficient matrix expression to obtain a floating-point solution of the state vector of the parameters to be estimated may include using an extended Kalman filter to estimate a floating-point solution of the state vector of the parameters to be estimated, including floating-point solutions for receiver position correction, receiver clock error correction, ISB, zenith tropospheric wet delay, and carrier phase ambiguity.

[0065] In some respects, method 300 may optionally include step 325 (not included in...). Figure 3 As shown in the diagram, before constructing the PPP measurement model: receiving correction information; and performing error correction based on the processing strategy and the correction information. In some examples, step 325 may include modeling corrections for systematic error sources. In some examples, systematic error sources may include modeling satellite antenna and receiver antenna phase center offsets, relativistic effects, phase winding errors, Earth's rotation, solid tides, ocean tides, polar tides, etc. In some examples, step 325 may include performing error correction based on the processing strategy and the acquired correction information, including correcting satellite coordinates, receiver coordinates, or observations, as listed in Table 1 above regarding the processing strategies that can be used for error parameters of various error types.

[0066] In some respects, method 300 may optionally include step 330 (not included in...). Figure 3As shown in the figure, before estimating the floating-point solution of the model to determine the receiver's location, the a posteriori residual of the floating-point solution of the state vector of the parameter to be estimated can be analyzed to determine whether there is an out-of-tolerance measurement equation. In some examples, step 330 may include, in response to determining the existence of an out-of-tolerance measurement equation, eliminating the out-of-tolerance measurement equation and updating the state of the parameter to be estimated; and, in response to determining that there is no out-of-tolerance measurement equation, outputting the floating-point solution of the state vector of the parameter to be estimated to determine the receiver's location.

[0067] According to one aspect of this application, a system for performing precise single-point positioning (PPP) is provided. Reference is now made to... Figure 4 This illustrates a functional block diagram of a system 400 for performing Precise Point Positioning (PPP) according to various aspects of this application. In some examples, system 400 may include an acquisition module 405, a general state parameter determination module 410, a PPP measurement model construction module 415, and a positioning module 420, and may optionally include a correction module 425 and a quality control module 430. In some examples, any of the above modules are interconnected and communicatively coupled via a bus.

[0068] The acquisition module 405 can be configured to acquire satellite observation data and product data from GNSS satellites and LEO satellites. In some examples, the acquisition module 405 can be further configured to acquire satellite observation data from GPS satellites and BDS satellites. In some examples, the acquisition module 405 can be further configured to acquire multi-frequency pseudorange observations and carrier phase observations. In some examples, the acquisition module 405 can be further configured to acquire precise orbit and precise clock bias data of LEO and GNSS satellites.

[0069] The approximate state parameter determination module 410 can be configured to estimate approximate state parameters using pseudorange single-point positioning (SPP) based on acquired satellite observation data and product data. In some examples, the approximate state parameter determination module 410 can be further configured to estimate the inter-system bias (ISB) parameter between the GNSS and each of the LEO satellites. In some examples, the approximate state parameter determination module 410 can be further configured to estimate the approximate receiver position, clock bias, GNSS inter-system bias (ISB) parameter, etc.

[0070] The PPP measurement model building module 415 can be configured to build the PPP measurement model at least in part based on approximate state parameters. In some examples, the PPP measurement model can be based on a dual-frequency ionosphere-free model. In some examples, the PPP measurement model building module 415 can be further configured to build measurement equations separately for GPS satellites, BDS satellites, and LEO satellites. In some examples, the PPP measurement model building module 415 can be further configured to linearize the simultaneous measurement equations to obtain an expression for the observation coefficient matrix. In some examples, the PPP measurement model building module 415 can be further configured to treat each LEO satellite as an independent system in the context of LEO satellites and model its ISB parameters relative to GPS separately. In some examples, the PPP measurement model building module 415 can be further configured to model the dynamics of the ISB parameters of LEO satellites relative to GPS as a random walk, where the process noise parameter can be set to 0.001 m / s.

[0071] The positioning module 420 can be configured to estimate a floating-point solution of the PPP measurement model to determine the receiver's positioning. In some examples, the positioning module 420 can be further configured to solve the observation coefficient matrix expression to obtain a floating-point solution of the state vector of the parameters to be estimated. In some examples, the positioning module 420 can be further configured to determine the receiver's positioning based on the floating-point solution. In some examples, the positioning module 420 can be further configured to utilize an extended Kalman filter to estimate a floating-point solution of the state vector of the parameters to be estimated, including floating-point solutions for receiver position correction, receiver clock error correction, ISB, zenith tropospheric wet delay, and carrier phase ambiguity.

[0072] The correction module 425 can be configured to: receive correction information before constructing the PPP measurement model; and perform error correction based on the processing strategy and the correction information. In some examples, the correction module 425 can be further configured to perform modeling corrections for systematic error sources. In some examples, systematic error sources may include modeling satellite antenna and receiver antenna phase center offsets, relativistic effects, phase winding errors, Earth rotation, solid tides, ocean tides, polar tides, etc. In some examples, the correction module 425 can be further configured to perform error correction based on the processing strategy and the acquired correction information, including correcting satellite coordinates, receiver coordinates, or observations, as listed in Table 1 above regarding the processing strategies that can be used for error parameters of various error types.

[0073] The quality control module 430 can be configured to analyze the a posteriori residuals of the floating-point solutions of the state vector of the parameters to be estimated to determine whether out-of-tolerance measurement equations exist before estimating the floating-point solutions of the PPP measurement model to determine the receiver's location. In some examples, the quality control module 430 can be further configured to, in response to determining the existence of out-of-tolerance measurement equations, eliminate out-of-tolerance measurement equations and update the state of the parameters to be estimated; and in response to determining that no out-of-tolerance measurement equations exist, output the floating-point solutions of the state vector of the parameters to be estimated to determine the receiver's location.

[0074] According to one aspect of this application, an apparatus for performing precise single-point positioning (PPP) is provided. Reference is now made to... Figure 5 The diagram illustrates a block diagram of an exemplary computer system suitable for implementing various aspects of this application. Figure 5 The computer system 512 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0075] like Figure 5 As shown, the computer system 512 is represented in the form of a general-purpose computing device. The components of the computer system 512 may include, but are not limited to: one or more processors or processing units 516, system memory 528, and bus 518 connecting different system components (including system memory 528 and processing unit 516).

[0076] Bus 518 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0077] Computer system 512 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer system 512, including volatile and non-volatile media, removable and non-removable media.

[0078] System memory 528 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 530 and / or cache memory 532. Computer system 512 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 534 may be used to read and write non-removable, non-volatile magnetic media (… Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 518 via one or more data media interfaces. Memory 528 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0079] A program / utility 540 having a set (at least one) of program modules 542 may be stored, for example, in memory 528. Such program modules 542 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 542 typically perform the functions and / or methods described in the embodiments of this application.

[0080] The computer system 512 can also communicate with one or more external devices 514 (e.g., keyboard, pointing device, display 524, etc.). In this application, the computer system 512 communicates with external radar equipment, and can also communicate with one or more devices that enable a user to interact with the computer system 512, and / or with any device that enables the computer system 512 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through the input / output (I / O) interface 522. Furthermore, the computer system 512 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through the network adapter 520. As shown, the network adapter 520 communicates with other modules of the computer system 512 through the bus 518. It should be understood that, although... Figure 5 As not shown in the diagram, other hardware and / or software modules may be used in conjunction with computer system 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0081] The processing unit 516 executes various functional applications and data processing by running programs stored in the system memory 528, such as implementing the method flow provided in the embodiments of this application.

[0082] Continue to refer to Figure 5According to one aspect of this application, a non-transient storage medium having processor-executable instructions stored thereon is provided, which, when executed by a processor, cause the processor to perform any aspect of the method for performing precise single-point positioning (PPP) as described above.

[0083] The aforementioned computer program can be stored in a computer storage medium, meaning the computer storage medium is encoded with a computer program. When executed by one or more computers, this program causes the one or more computers to perform the method flows and / or apparatus operations shown in the above embodiments of this application. For example, the method flows provided in the embodiments of this application can be executed by one or more processors.

[0084] With the advancement of time and technology, the meaning of "medium" has become increasingly broad. The dissemination of computer programs is no longer limited to tangible media; they can also be downloaded directly from the internet. Any combination of one or more computer-readable media can be used.

[0085] A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of a computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0086] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0087] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0088] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0089] In the technical solution of this application embodiment, the index of the input image sequence for each focused pixel of the target object is learned, and the part corresponding to the clearest part in the input image sequence is extracted to perform pixel-level fusion to fuse image sequences with different focused areas in the same scene into a single image of the target object that is fully clear. This achieves a fully clear fused image with pixel-level precision while retaining the detailed information of the target object, effectively improving the information utilization rate of the image.

[0090] Those skilled in the art will understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this hardware-software interchangeability, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this application.

[0091] The prior description of this application is provided to enable any person skilled in the art to make or use this application. Various modifications to this application will be apparent to those skilled in the art, and the general principles defined herein can be applied to other variations without departing from the spirit or scope of this application. Therefore, this application is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for performing precise single-point positioning (PPP), comprising: Acquire satellite observation data and product data from GNSS satellites and LEO satellites of the Global Navigation Satellite System. The GNSS satellites include GPS satellites and BDS satellites of the BeiDou Navigation Satellite System. The satellite observation data includes multi-frequency pseudorange observations and carrier phase observations. Based on the acquired satellite observation data and product data, pseudorange single-point positioning (SPP) is used to estimate approximate state parameters, which include the inter-system bias (ISB) parameter between the GNSS and each of the LEO satellites. A PPP measurement model is constructed, at least in part, based on the aforementioned approximate state parameters. This PPP measurement model is based on a dual-frequency, ionosphere-free model, and the construction of the PPP measurement model includes: Measurement equations were constructed separately for GPS satellites, BDS satellites, and LEO satellites. The simultaneous measurement equations are linearized to obtain the expression for the observation coefficient matrix; as well as The floating-point solution of the PPP measurement model is estimated to determine the receiver's location.

2. The method as described in claim 1, characterized in that, Estimating the floating-point solution of the PPP measurement model to determine the receiver's location includes: Solve the expression for the observation coefficient matrix to obtain a floating-point solution for the state vector of the parameters to be estimated; The location of the receiver is determined based on the floating-point solution.

3. The method as described in claim 1, characterized in that, The measurement equation includes: Measurement equations for GPS satellites include: ， Subscript and This indicates a receiver combined with an ionosphere-free system. and These are pseudorange and phase observations, respectively; Geometric distance; superscript i,G Indicates GPS satellite number i; and These are receiver clock bias and satellite clock bias, respectively. The flow layer delay along the signal propagation path; The signal wavelength after ionosphere-free processing; For dual-frequency ionosphere-free combined integer ambiguity; and These are pseudorange measurement noise and phase measurement noise, respectively. The speed of light; The measurement equations for the BDS satellite include: Among them, superscript j,B Instruction for BDS satellite j, The ISB parameters of BDS relative to GPS; The measurement equations for LEO satellites include: Among them, superscript k,L Indicates LEO satellite number k, For the first ISB parameters of the LEO satellite relative to GPS.

4. The method as described in claim 1, characterized in that, The expression for the observation coefficient matrix, after linearization, is obtained according to the following formula: in and These are the observation vector and the measurement noise vector, respectively; Let the state vector of the parameters to be estimated be represented as: in To correct for positional errors, subscript Indicates receiver, subscript Indicates an assembly without an ionosphere. For receiver clock bias, These are the ISB parameters of the BDS relative to GPS. For LEO relative to GPS, This is the wet delay component of the zenith troposphere. For dual-frequency ionosphere-free combined integer ambiguity; Observation coefficient matrix It is expressed as: in, , , These indicate the number of GPS satellites, BDS satellites, and LEO satellites observed at one epoch. It is the identity matrix. This is the tropospheric projection matrix. This refers to the direction cosine matrix, which is linearized based on the position information in the approximate state parameters of GPS. This refers to the direction cosine matrix, which is linearized based on the position information in the approximate state parameters, for BDS. This is the direction cosine matrix for LEO, which is linearized based on the position information in the approximate state parameters.

5. The method as described in claim 2, characterized in that, Obtaining the floating-point solution of the PPP measurement model to determine the receiver's location further includes: Analyze the apostolic residuals of the floating-point solutions of the state vector of the parameter to be estimated to determine whether there is an out-of-tolerance measurement equation; In response to the determination of the existence of an out-of-tolerance measurement equation, the out-of-tolerance measurement equation is removed and the state of the parameter to be estimated is updated; and In response to determining that there is no out-of-tolerance measurement equation, a floating-point solution of the state vector of the parameter to be estimated is output to determine the location of the receiver.

6. The method as described in claim 1, characterized in that, The method further includes, before constructing the PPP measurement model: Receive correction information; and Error correction is performed based on the processing strategy and the correction information.

7. An apparatus for performing Precision Point Positioning (PPP), comprising: Memory; as well as A processor, which is communicatively coupled to the memory, and the memory and the processor are configured to perform the method as described in any one of claims 1-6.

8. A system for performing precise single-point positioning (PPP), comprising: The acquisition module is configured to acquire satellite observation data and product data from GNSS satellites and LEO satellites, wherein the GNSS satellites include GPS satellites and BDS satellites, and the satellite observation data includes multi-frequency pseudorange observations and carrier phase observations. A probabilistic state parameter determination module is configured to estimate probabilistic state parameters based on acquired satellite observation data and product data using pseudorange single-point positioning (SPP). The probabilistic state parameters include the inter-system bias (ISB) parameter between the GNSS and each of the LEO satellites. A PPP measurement model building module, configured to build a PPP measurement model at least in part based on the probabilistic state parameters, the PPP measurement model being based on a dual-frequency ionosphere-free model, and further configured to: Measurement equations were constructed separately for GPS satellites, BDS satellites, and LEO satellites. The simultaneous measurement equations are linearized to obtain the expression for the observation coefficient matrix; as well as A positioning module is configured to estimate the floating-point solution of the PPP measurement model to determine the positioning of the receiver.

9. A non-transient storage medium having processor-executable instructions stored thereon, the processor-executable instructions causing the processor, when executed by a processor, to perform a method for performing Precise Point Positioning (PPP) as described in any one of claims 1-6.

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