Airborne Beidou positioning method and device suitable for extra-high voltage converter station, and medium

Abstract

The invention discloses an airborne Beidou positioning method and device suitable for an extra-high voltage converter station. The method comprises the following steps: acquiring original observation data of a base station and a moving station in an extra-high voltage converter station scene, and preprocessing the original observation data to obtain preprocessed observation values; performing cycle slip detection on a carrier phase in the observation value according to a preset criterion to obtain cycle slip information; constructing a factor graph optimization model according to the preprocessed observation value and the cycle slip information; performing floating point solution on the factor graph optimization model to obtain an ambiguity floating point solution; and carrying out ambiguity fixation on the ambiguity floating point solution to obtain a high-precision ambiguity fixed solution.

Description

Technical Field

[0001] This invention relates to the field of power transmission and transformation equipment operation and maintenance technology, and more specifically, to an airborne Beidou positioning method and device medium applicable to ultra-high voltage converter stations. Background Technology

[0002] In the complex electromagnetic environment of ultra-high voltage converter stations, due to the high voltage level, large rated current, and high geometric height of the equipment, the noise of the equipment itself is greater and the noise impact is more significant. The noise component of the Beidou navigation and positioning signal increases significantly, resulting in a decrease in signal quality.

[0003] With the rapid development and large-scale construction of ultra-high voltage direct current (UHVDC) transmission projects in my country, the safe, stable, and efficient operation and maintenance of UHV converter stations, as core hubs, is of paramount importance. Power line inspection drones, with their advantages of flexibility, efficiency, and accessibility to high-risk areas, have become an indispensable technical means for equipment condition monitoring, defect identification, and emergency response in UHV converter stations.

[0004] High-precision and high-reliability positioning information is crucial for UAVs to achieve autonomous flight, precise hovering, target identification, and data acquisition in complex electromagnetic environments. However, due to the high voltage levels, large rated currents, and high geometric heights of UHV converter station equipment, the internal and surrounding environments of UHV converter stations exhibit extremely harsh electromagnetic characteristics, leading to a significant increase in noise components in BeiDou navigation and positioning signals and a severe decline in signal quality. Simultaneously, the complex environment increases the frequency of carrier phase observation interruptions, accompanied by multipath propagation, gross errors, and cycle slips, all of which affect the positioning accuracy of the equipment. Therefore, developing a BeiDou correction algorithm capable of achieving high-precision and robust positioning using only the domestically produced BeiDou satellite system as a single positioning source in the complex electromagnetic environment of UHV converter stations, along with a rigorous and scientific testing and verification methodology, is urgently needed and of great significance. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an airborne BeiDou positioning method and device medium suitable for ultra-high voltage converter stations.

[0006] According to one aspect of the present invention, an airborne BeiDou positioning method suitable for ultra-high voltage converter stations is provided, comprising:

[0007] In the scenario of an ultra-high voltage converter station, the raw observation data of the base station and the rover station are acquired and preprocessed to obtain the preprocessed observation values.

[0008] Cycle slip detection is performed on the carrier phase in the observations based on preset criteria to obtain cycle slip information;

[0009] A factor graph optimization model is constructed based on preprocessed observations and cycle slip information;

[0010] Floating-point solutions are obtained by performing floating-point calculations on the factor graph optimization model;

[0011] By fixing the fuzziness of the floating-point fuzzy solution, a high-precision fixed fuzzy solution is obtained.

[0012] Optionally, the raw observation data includes pseudorange, carrier phase, and Doppler, and the preprocessing operations include checking the cutoff elevation angle, cutoff signal-to-noise ratio, and observation consistency.

[0013] Optionally, the preset criterion is:

[0014]

[0015] In the formula, gf k-1 The GF combination observations for the previous epoch k-1; gf k Δgf is the GF combination observation value at the current epoch k; Δgf is the difference between the GF combination observation value at the previous epoch k-1 and the GF combination observation value at the current epoch k.

[0016] Optionally, the factor graph optimization model includes inter-station single-difference observation equations, an elevation angle stochastic model, and a signal-to-noise ratio stochastic model, and

[0017] Floating-point solutions are obtained by performing floating-point calculations on the factor graph optimization model, including:

[0018] The variance-covariance matrix of the single-difference observations is obtained by using the error propagation law based on the elevation angle stochastic model and the signal-to-noise ratio stochastic model.

[0019] The variance-covariance matrix and the inter-station single-difference observation equations are solved to obtain an iterative solution;

[0020] The IGGIII weighting function is used to perform post-validation on the results of each iterative solution to obtain the ambiguity floating-point solution that satisfies the preset criteria.

[0021] Optionally, the expression for the inter-station single-difference observation equation is:

[0022]

[0023] In the formula, This is the optimal estimate of the state vector to be estimated. This represents the inter-station single-difference residual of the carrier phase observation at epoch k, satellite i, and frequency f. ΔL represents the inter-station single-difference residual of the pseudorange observations at epoch k, satellite i, and frequency f, ΔP represents the carrier phase single-difference observation, and ΔP represents the inter-station single-difference pseudorange observation. Vel Let be the covariance matrix of the velocity error. e is the velocity residual of epoch k.Mar As a marginalized prior factor, This is the weighted matrix of the marginalized prior factors.

[0024] Optionally, the expression for the IGGIII weight function is:

[0025]

[0026] In the formula, γ i，k The weight coefficients of IGGⅢ are used to assign weights to the i-th observation at the k-th epoch, and the weights are between [0,1]. The standardized residuals of this observation are τ0, which is the normal residual threshold, and τ1, which is the gross error judgment threshold.

[0027] Optionally, the fuzziness of the floating-point fuzzy solution is fixed to obtain a high-precision fixed fuzzy solution, including:

[0028] The ambiguity of the floating-point ambiguity solution is fixed by using integer least squares estimation and optimal integer equivariant estimation methods, resulting in a high-precision ambiguity fixed solution.

[0029] Optionally, the ambiguity of the floating-point ambiguity solution is fixed using integer least squares estimation and optimal integer equivariant estimation methods to obtain a high-precision fixed ambiguity solution, including:

[0030] The fuzzy floating-point solution is subjected to the optimal integer aperture test. If the test passes, the fuzziness of the fuzzy floating-point solution is fixed by the integer least squares method. Otherwise, the fuzziness of the fuzzy floating-point solution is fixed by the optimal integer isovariant estimation method, thus obtaining a high-precision fuzzy fixed solution.

[0031] Optionally, the optimal integer equivariant estimation method uses a new criterion to adaptively determine the candidate number:

[0032]

[0033] In the formula, ε p To determine the threshold, Let z be the floating-point ambiguity vector. j Let j be the j-th integer candidate vector. It is a weighted quadratic form with covariance. Let be the covariance matrix of the floating-point solution of ambiguity;

[0034] The optimal weight function optimization expression for the optimal integer isovariant estimation method is:

[0035]

[0036] In the formula, For candidate solution z j The corresponding baseline solution, For floating-point baseline solutions; The normalized weights assigned to the j-th integer candidate vector. The covariance matrix of the floating-point solution of ambiguity, The covariance matrix corresponding to the baseline floating-point solution, where z represents the total number of ambiguity candidate values, Z n This represents an n-dimensional integer space, to which all ambiguity candidate values ​​z belong.

[0037] According to another aspect of the present invention, an airborne BeiDou positioning device suitable for ultra-high voltage converter stations is provided, comprising:

[0038] The preprocessing module is used to acquire the raw observation data of the base station and the rover in the UHV converter station scenario and preprocess them to obtain the preprocessed observation values.

[0039] The detection module is used to perform cycle slip detection on the carrier phase in the observation value according to the preset criteria to obtain cycle slip information;

[0040] The building module is used to construct a factor graph optimization model based on preprocessed observations and cycle slip information;

[0041] The solution module is used to perform floating-point solutions on the factor graph optimization model to obtain the fuzzy floating-point solution;

[0042] The fixed module is used to fix the fuzziness of the fuzzy floating-point solution to obtain a high-precision fuzzy fixed solution.

[0043] Optionally, the raw observation data includes pseudorange, carrier phase, and Doppler, and the preprocessing operations include checking the cutoff elevation angle, cutoff signal-to-noise ratio, and observation consistency.

[0044] Optionally, the preset criterion is:

[0045]

[0046] In the formula, gf k-1 The GF combination observations for the previous epoch k-1; gf k Δgf is the GF combination observation value at the current epoch k; Δgf is the difference between the GF combination observation value at the previous epoch k-1 and the GF combination observation value at the current epoch k.

[0047] Optionally, the factor graph optimization model includes inter-station single-difference observation equations, an elevation angle stochastic model, and a signal-to-noise ratio stochastic model, and

[0048] The solution module includes:

[0049] The submodule is obtained to obtain the variance-covariance matrix of the single difference observations based on the altitude angle random model and the signal-to-noise ratio random model according to the error propagation law.

[0050] The solution submodule is used to solve the variance-covariance matrix and the inter-station single-difference observation equations to obtain iterative solutions.

[0051] The detection submodule is used to perform post-testing on the results of each iterative solution using the IGGIII weight function to obtain the ambiguity floating-point solution that meets the preset criteria.

[0052] Optionally, the expression for the inter-station single-difference observation equation is:

[0053]

[0054] In the formula, where This is the optimal estimate of the state vector to be estimated. This represents the inter-station single-difference residual of the carrier phase observation at epoch k, satellite i, and frequency f. Let ΔP be the inter-station single-difference residual of the pseudorange observations at epoch k, satellite i, and frequency point f, and let ΔP be the inter-station single-difference pseudorange observation. e is the velocity observation value at epoch k. Mar This is a marginalized prior factor.

[0055] Optionally, the expression for the IGGIII weight function is:

[0056]

[0057] In the formula, γ i，k The weight coefficients of IGGⅢ are used to assign weights to the i-th observation at the k-th epoch, and the weights are between [0,1]. The standardized residuals of this observation are τ0, which is the normal residual threshold, and τ1, which is the gross error judgment threshold.

[0058] Optionally, the fixed module includes:

[0059] The fixed submodule is used to fix the fuzziness of the floating-point fuzzy solution by employing integer least squares estimation and optimal integer equivariant estimation methods, thereby obtaining a high-precision fuzzy fixed solution.

[0060] Optionally, a fixed submodule includes:

[0061] The testing unit is used to perform optimal integer aperture testing on the fuzzy floating-point solution. If the test passes, the fuzziness of the fuzzy floating-point solution is fixed using the integer least squares method; otherwise, the fuzziness of the fuzzy floating-point solution is fixed using the optimal integer isovariant estimation method, thus obtaining a high-precision fuzzy fixed solution.

[0062] Optionally, the optimal integer equivariant estimation method uses a new criterion to adaptively determine the candidate number:

[0063]

[0064] In the formula, ε p To determine the threshold, Let z be the floating-point ambiguity vector. j Let j be the j-th integer candidate vector. It is a weighted quadratic form with covariance. Let be the covariance matrix of the floating-point solution of ambiguity;

[0065] The optimal weight function optimization expression for the optimal integer isovariant estimation method is:

[0066]

[0067] In the formula, For candidate solution z j The corresponding baseline solution, For floating-point baseline solutions; The normalized weights assigned to the j-th integer candidate vector. The covariance matrix of the floating-point solution of ambiguity, The covariance matrix corresponding to the baseline floating-point solution, where z represents the total number of ambiguity candidate values, Z n This represents an n-dimensional integer space, to which all ambiguity candidate values ​​z belong.

[0068] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0069] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0070] Therefore, this invention effectively eliminates abnormal environmental data by changing the threshold of gross error detection, employs a step-by-step ambiguity resolution algorithm combining ILS-BIE, and uses graph factor-based sliding windowing to correct detection bias, suppressing the influence of abnormal data, improving resolution accuracy, and achieving centimeter-level navigation and positioning accuracy. This enables intelligent inspection equipment to accurately and in real-time locate its own position, solving the problem of interference from the complex electromagnetic environment of converter stations on BeiDou communication and positioning. Furthermore, a testing and verification technique for the airborne BeiDou navigation and positioning correction algorithm is proposed, using a combination of static simulation and dynamic resolution, to verify the positioning accuracy of the BeiDou positioning correction algorithm in an UHV converter station. Attached Figure Description

[0071] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0072] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention for an airborne BeiDou positioning method applicable to ultra-high voltage converter stations;

[0073] Figure 2 This is another schematic flowchart of an exemplary embodiment of the present invention for an airborne BeiDou positioning method applicable to ultra-high voltage converter stations;

[0074] Figure 3 This is a flowchart illustrating a data quality control method based on window data provided in an exemplary embodiment of the present invention;

[0075] Figure 4 This is a satellite count chart provided in an exemplary embodiment of the present invention;

[0076] Figure 5 This is a schematic diagram of the UAV dynamic experiment * BeiDou RTK positioning error sequence provided by an exemplary embodiment of the present invention;

[0077] Figure 6 This is a test trajectory diagram of a converter station provided in an exemplary embodiment of the present invention;

[0078] Figure 7 This is a schematic diagram of the structure of an airborne Beidou positioning device suitable for ultra-high voltage converter stations provided in an exemplary embodiment of the present invention;

[0079] Figure 8 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0080] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0081] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0082] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0083] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0084] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0085] Furthermore, the term "and / or" in this invention 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, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0086] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0087] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0088] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0089] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0090] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0091] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0092] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0093] Exemplary methods

[0094] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention for an airborne BeiDou positioning method applicable to ultra-high voltage converter stations. This embodiment can be applied to electronic devices, such as… Figure 1 As shown, the airborne BeiDou positioning method 100 applicable to UHV converter stations includes the following steps:

[0095] Step 101: Obtain the original observation data of the base station and the rover in the UHV converter station scenario and preprocess them to obtain the preprocessed observation values;

[0096] Step 102: Perform cycle slip detection on the carrier phase in the observation value according to the preset criteria to obtain cycle slip information;

[0097] Step 103: Construct a factor graph optimization model based on preprocessed observations and cycle slip information;

[0098] Step 104: Perform floating-point calculations on the factor graph optimization model to obtain the ambiguity floating-point solution;

[0099] Step 105: Fix the fuzziness of the floating-point fuzzy solution to obtain a high-precision fixed fuzzy solution.

[0100] Specifically, this invention aims to address the problems of low positioning accuracy, poor reliability, and lack of verification for unmanned aerial vehicles (UAVs) in the strong electromagnetic interference environment of UHV converter stations. It proposes an airborne positioning correction algorithm and testing and verification method based on the BeiDou Navigation Satellite System. At the algorithm level, a positioning correction algorithm robust to strong electromagnetic interference is constructed to achieve centimeter-level real-time positioning accuracy. At the verification level, a quantifiable and reproducible testing and verification system is proposed. Thus, the correction algorithm and verification method are integrated into a closed-loop system, forming an integrated "algorithm-verification" solution. (Reference) Figure 2 As shown, the specific implementation steps are as follows:

[0101] Step 1. The receiver continuously receives BeiDou satellite signals, collects observation data such as carrier phase and pseudorange, and records satellite orbit information (ephemeris), time information, etc.

[0102] Step 2. Preprocess the received raw observation data, including limiting the cutoff elevation angle / signal-to-noise ratio and checking the consistency of observations, to remove obvious outliers or gross errors. Then, use the TurboEdit method to detect phase cycle slips, preparing for subsequent ambiguity arc segmentation within the window.

[0103] Step 3. Use the preprocessed pseudorange and phase observations to perform inter-station differencing, constructing the Between-receiver Single-Difference (BRSD) observation equation. Additionally, the inter-epoch velocity obtained from Doppler or TDCP is also added as a constraint to this observation equation, resulting in the final sliding window factor map-optimized observation model. The variance-covariance matrix of the observations (stochastic model) is obtained through the elevation angle / signal-to-noise ratio stochastic model and the error propagation law.

[0104] Step 4. Quality Control. The factor graph optimization process involves multiple iterative calculations. The state estimate obtained after each iteration is used to calculate the posterior residual and the standardized residual. If the maximum standardized residual exceeds a set threshold, it indicates that gross errors or anomalous observations still exist within the window. For pseudorange anomalous observations, they are typically removed directly from the observation equation or subjected to weight reduction. For phase anomalous observations, weight reduction is usually performed, or they are treated as cycle slips to initialize ambiguity. If treated as cycle slips, the ambiguity arcs for that satellite need to be redefined. Iterative calculations continue until no anomalous observations are detected, thus obtaining a reliable floating-point solution (position and ambiguity, etc.).

[0105] Step 5. Input the floating-point ambiguity and its variance-covariance matrix into the ambiguity fixing function, and use the combined ILS and BIE partial ambiguity fixing method to fix the ambiguity, thereby obtaining the final ambiguity fixing solution.

[0106] Further, step 2 data preprocessing

[0107] Before performing RTK positioning calculations, the raw observations (pseudorange, carrier phase, and Doppler) need to be preprocessed to remove obvious outliers. The specific processing steps include:

[0108] (1) Cutoff elevation angle. A cutoff elevation angle (e.g., 15 degrees) is preset. If the elevation angle of a satellite is less than this value, the satellite observation value is directly discarded.

[0109] (2) Cutoff signal-to-noise ratio. A cutoff signal-to-noise ratio is preset (e.g., 25 dB). If the signal-to-noise ratio of a satellite observation is less than this value, the satellite observation is directly discarded.

[0110] (3) Consistency test of observations. The consistency between pseudorange / phase observations and Doppler observations is determined to confirm whether there are any abnormal observations.

[0111] Then, the TurboEdit method is used to detect carrier phase cycle slips, in preparation for ambiguity arc segmentation in the subsequent observation equation.

[0112] The TurboEdit method is a commonly used cycle slip detection method. It utilizes a combination of MW and GF combinations to detect cycle slips. First, it uses MW combination observations to detect wide-lane cycle slips, with the following specific steps:

[0113] By constructing a MW combination using pseudorange and phase, the formula for calculating wide-lane ambiguity is:

[0114]

[0115] In equation (1-1), Φ1 and Φ2 are phase observations at different frequencies (unit: cycles), P1 and P2 are pseudorange observations at different frequencies (unit: meters), and L WL For the wide lane combination (unit: meters), λ WL The wavelength is the wide-lane wavelength.

[0116] To mitigate the impact of noise, the following recursive algorithm is used to calculate the wide-lane ambiguity and its variance:

[0117]

[0118] In equation (1-2), σ represents the average value of the wide-lane ambiguity, k and k-1 represent the current epoch and the previous epoch, respectively. 2 This represents the variance of the wide-lane ambiguity. When a cycle slip occurs, the wide-lane ambiguity will change abruptly, which can be used to determine whether a cycle slip has occurred. The cycle slip judgment formula is established as follows:

[0119]

[0120] When N WL If (k) satisfies the above formula, then it is considered that a cycle jump has occurred in epoch k.

[0121] The observations for the GF combination are as follows:

[0122] For the non-differenced phase observations φ at frequencies i and j i and φ j Its GF combination is:

[0123] gf = λ i φ i -λ j φ j (1-4)

[0124] The corresponding inter-station single-difference phase observation value dφ i and dφ j Its GF combination is:

[0125] gf = λ i dφ i -λ j dφ j (1-5)

[0126] The difference between the GF values ​​of the previous epoch and the current epoch is used to determine whether a cycle slip has occurred, based on whether the absolute value of the difference exceeds a threshold.

[0127]

[0128] The criterion for cycle slip is that the difference between GF combination observations between epochs is greater than 5cm, that is, the threshold is generally set to 5cm.

[0129] Step 3: Factor graph optimization model

[0130] 1. Functional Model (Single Difference Model)

[0131] Real-time dynamic positioning (RTK) technology achieves centimeter-level positioning accuracy by fusing observation data from a base station and a rover. However, traditional RTK suffers from linearization errors when handling nonlinear problems and struggles to effectively utilize the structural correlations of historical observations. This paper employs a factor graph-based sliding window optimization gross error detection technique to optimize and control data quality. Factor graph optimization (FGO), as a graphical model optimization method, transforms the positioning problem into a probabilistic graphical model, naturally fusing multi-source observation constraints and balancing accuracy and efficiency using a sliding window mechanism, providing a more robust framework for BeiDou solutions.

[0132] Parameter description:

[0133] The receiver's three-dimensional position is p = [x, y, z]. T (Geocentric Earth-Fixed Coordinate System, ECEF), where x, y, and z represent the coordinate values ​​of the three coordinate axes in the ECEF coordinate system, which are key parameters describing the receiver's position in Earth's space.

[0134] Carrier phase integer ambiguity N = [N1, N2, ..., N m ] T Where m is the number of observed satellites, N i (i = 1, 2, ..., m) represents the integer ambiguity of the carrier phase of the i-th satellite, which is an unknown parameter introduced in carrier phase observation because the initial integer number cannot be directly measured.

[0135] Receiver three-dimensional velocity v = [vx ,v y ,v z ] T (Used for dynamic scene constraints), v x v y v z These represent the velocity components along the three coordinate axes in the ECEF coordinate system. In dynamic scenes, velocity information can be added to the factor graph as a constraint to improve the accuracy and stability of positioning.

[0136] Observational Model and Factor Construction:

[0137] Based on the shared-view satellites of the base station and the rover, an inter-station single-difference observation equation can be constructed:

[0138]

[0139] In the above formula, i is the satellite prn number, and Δ(g) is the BRSD operator. With Δt k These represent BRSD ambiguity and receiver clock bias, respectively. Unlike the double-difference model, the receiver clock bias Δt... k Since it cannot be completely eliminated, its residual terms need to be estimated. The estimation uses a single-difference ambiguity, which naturally solves the problem of frequent reference star switching in complex scenarios. Simultaneously, velocity constraints are also considered in the framework to strengthen the constraint of motion information on the state.

[0140] v k =(x k -x k-1 ) / Δt (2-2)

[0141] Structural relevance is one of the key advantages of FGO; without it, FGO is indistinguishable from single-epoch least squares. Ambiguity retains its inherent invariance in the absence of cycle slips and interruptions. Since BRSD ambiguity is unaffected by reference satellite conversion, the continuity of BRSD ambiguity arcs is extended. To fully utilize this, ambiguities of the same constellation and frequency are collectively estimated as a single arc solution. Once cycle slips and interruptions occur, the arc is segmented. However, due to the arc structure of ambiguity, incorrect segmentation can lead to significant biases. Therefore, outlier detection auxiliary methods, including arc segmentation, will be introduced later. Furthermore, velocity constraints are used to enhance structural relevance; the formula is [formula missing]. Based on the above model, the GNSS factor map positioning model shown in the figure above is implemented. The model's state space X [context missing]. * Including receiver position, clock bias, and ambiguity:

[0142]

[0143] X k =[xk Δt k ] T k = 1, 2, 3, ..., n (2-3)

[0144] Based on equation (2-5), the factor form for encoding the single-difference pseudorange and the single-difference carrier phase can be:

[0145]

[0146] Therefore, the final overall cost function for BeiDou floating-point positioning is as follows, which includes pseudorange, carrier phase, velocity constraints, and marginalization prior factors:

[0147]

[0148] 2. Stochastic Model

[0149] (1) Stochastic model of elevation angle

[0150] Satellite elevation angle-weighted stochastic models are, to some extent, approximations of ideal stochastic models. Elevation angle-weighted models utilize the relationship between the observation quality of the carrier phase signal and the elevation angle to obtain the variance of the observations, and are widely used in the establishment of BDS stochastic models. The elevation angle-based stochastic model expresses the observation noise σ as a function of the satellite elevation angle E, i.e.:

[0151] σ 2 =f(E) (2-6)

[0152] Based on the differences in the elevation angle function f, various stochastic models based on satellite elevation angles have been derived, among which the exponential function model and the sine and cosine function model are currently the most widely used stochastic elevation angle models.

[0153] The exponential function model is as follows:

[0154]

[0155] In equation (2-7), σ0 is the standard deviation of the observed value in the near-zenith direction, E0 is the reference elevation angle, and a is the magnification factor.

[0156] The cosine function model used in Bernese, a well-known international geodetic data processing software, is as follows:

[0157] σ 2 =a 2 +b 2 cos 2 E ' (2-8)

[0158] In equation (2-8), the satellite elevation angle E ' The unit is radians.

[0159] The GAMIT software uses a sine function model:

[0160] σ 2 =a 2 +b 2 / sin 2 E' (2-9)

[0161] In equation (2-9), a and b are undetermined coefficients, which are generally given based on experience or determined by fitting methods.

[0162] In this invention, the elevation angle random model adopts the sine function model in (2-9).

[0163] (2) Signal-to-noise ratio stochastic model

[0164] The receiver signal-to-noise ratio (SNR) is related to factors such as atmospheric delay error, multipath effects, antenna gain, and receiver internal circuitry. It reflects the data quality of the observations to a certain extent and can be used to measure the noise level of the observations. The carrier phase SIMGA-ε stochastic model based on SNR observations can be expressed as:

[0165]

[0166] In equation (2-10), S is the measured signal-to-noise ratio, and B... i For the phase tracking loop bandwidth (Hz), λ i C1 is the carrier phase wavelength (m). In practical calculations, C1 is usually taken as 0.00224m. 2 HZ, C2 = 0.00077m 2 HZ. Based on this, we can draw on the exponential function method in the stochastic model of altitude angle to establish a simplified stochastic model, namely:

[0167]

[0168] In equation (2-11), S0 is the reference signal-to-noise ratio. This simplified method achieves formal unification between the signal-to-noise ratio stochastic model and the elevation angle stochastic model.

[0169] It's important to note that SNR is not a mandatory option in RINEX's output format, and in many cases, users cannot obtain the measured SNR value. Starting with RINEX version 2.0, the output phase observation value includes a signal strength index (I) in the last two digits. The corresponding SNR value can be calculated using the following formula based on the signal strength index:

[0170]

[0171] Accordingly, its stochastic model also changes to:

[0172]

[0173] After determining the variance of each original observation using the satellite elevation angle method or signal-to-noise ratio method, various stochastic models of combined and non-combined observations can be obtained according to the variance-covariance propagation law. The so-called stochastic model of observations is the variance of the observations, characterizing the reliability of the observations. It is determined by the elevation angle and signal-to-noise ratio in the original observations, which is the elevation angle and signal-to-noise ratio stochastic model mentioned in the document.

[0174] Step 4: Quality Control Based on Sliding Window

[0175] In complex electromagnetic environments, frequent cycle slips and gross errors can significantly reduce the accuracy and reliability of GNSS positioning. Especially when missed cycle slips occur, incorrect ambiguity segment segmentation can lead to fixed positioning deviations across multiple epochs. To address this issue, this invention proposes a factor graph optimization (FGO) data quality control method based on sliding window data. This method employs a dual mechanism of prior and posterior detection to ensure the robustness of the optimization process (implementation process see...). Figure 2 ).

[0176] To address the common multipath effects and non-line-of-sight signal interference problems in complex scenarios, this method innovatively introduces a sliding window-based data quality control mechanism for the state estimate obtained in each iteration during the iterative calculation of equation (2-5) to obtain the optimal floating-point solution.

[0177] This quality control method has the following advantages:

[0178] (1) Full access to historical data: All historical measurement data within the sliding window is stored and can be accessed retrospectively;

[0179] (2) Dynamic linearization correction: The linearization point is dynamically updated during the iteration process, so even if there are missed gross errors, the influence of the deviation is still controlled;

[0180] (3) Multi-round detection and correction: By utilizing structural correlation and subsequent observation data, it is possible to correct the deviation linearization point and repeatedly detect the gross errors missed within the window.

[0181] (4) Redundant data utilization: Combining the iterative linearization characteristics of FGO, the historical gross errors within the sliding window are re-detected and the results are corrected.

[0182] like Figure 3 As shown, the window data quality control algorithm flow is as follows:

[0183] 1) In the prior detection section, some simple and effective metrics are first applied, including cutoff elevation angle / signal-to-noise ratio and consistency checks, to eliminate obvious outliers. Subsequently, the TurboEdit method is also used as the prior cycle slip detection method (this point has been described in detail in 5.1.1);

[0184] 2) In the post-hoc detection section, traditional methods assume that only one measurement (i.e., the measurement with the largest standardized residual) is contaminated in each iteration. This is inconsistent with the large number of measurements within the window, leading to excessive iterations. Therefore, to adapt to the window data and meet real-time requirements, the assumptions of the quality control algorithm were modified, assuming that there is at most one outlier in each epoch of the sliding window. After selecting the maximum residual for each epoch using a modified greedy search, post-hoc detection is performed using the IGGⅢ weight function as the standard, where τ0 is the normal residual threshold and τ1 is the gross error judgment threshold.

[0185]

[0186] 3) If all residuals are less than τ0, the measurement results are considered to have no outliers. Otherwise, the corresponding information matrix (the inverse of the covariance matrix) will be multiplied by γ. i,k The weights are adjusted accordingly. Specifically, when the residual is greater than τ1, the pseudorange factor will be directly removed from the graph, while the carrier phase factor will be treated as periodic slip, and the arc division will be updated to make full use of the carrier phase measurement. The factor graph will be reconstructed and re-optimized until no outliers are detected. Then, the final floating-point solution will be output as the input for subsequent ambiguity fixing.

[0187] Step 5: Ambiguity Fixation

[0188] Accurate and reliable carrier phase ambiguity resolution is a crucial prerequisite for navigation and positioning. Currently, Integer Least Squares (ILS) estimation is the most widely used ambiguity resolution method, which mainly includes four steps: floating-point ambiguity and baseline vector estimation, floating-point ambiguity fixing, ambiguity fixing solution verification, and ambiguity fixing solution baseline vector update. Among these, ambiguity fixing solution verification is particularly important in dynamic positioning ambiguity resolution. Currently, there are three main types of ambiguity reliability verification methods: statistical significance testing based on the optimal and suboptimal candidate groups of ambiguity, testing based on the ambiguity fixing success rate / failure rate index, and a combination of the two. However, these methods, which rely on threshold settings for judgment, still struggle to balance false alarms and missed detections. False alarms refer to mistaking an incorrect ambiguity solution for a correct one, while missed detections refer to failing to identify a correct ambiguity solution. These problems lead to RTK positioning results based on Integer Least Squares (ILS) fixing failures or errors, resulting in low accuracy of the output floating-point solution or severe errors in the fixed solution.

[0189] Besides the ILS method, there is another ambiguity resolution method based on Optimal Integer Equivariant (BIE) estimation: by weighted fusion of all potential ambiguity integral solutions, the ambiguity estimate under the minimum mean square error criterion is obtained. This method is generally superior to the ILS estimation method, but the ILS estimation method has advantages in computational efficiency and accuracy. Therefore, an integrated method combining ILS and BIE estimation is proposed, forming a step-by-step ambiguity resolution strategy. This method compensates for the accuracy stratification problem between fixed and floating-point solutions caused by single ILS estimation, while also considering computational efficiency. It mainly includes the following steps:

[0190] 1) Optimal Integer Aperture (OIA) Test: The model strength is determined by calculating the posterior probability ratio of candidate solutions. The formula is:

[0191]

[0192] in, For the candidate solution of the minimum residual quadratic form, c p This is the threshold (usually 0.99).

[0193] If the OlA test passes (OlA ≥ c) p The model is considered to have high strength, and ILS is used for fixed ambiguity resolution.

[0194] If the OlA test fails (OlA) <c p The BIE method is used for fixed ambiguity resolution.

[0195] 2) ILS Fixed Solution Process

[0196] (1) Decorrelation transformation: The covariance matrix C is transformed by Z-transform. N Convert to an approximate diagonal form to reduce ambiguity correlation:

[0197]

[0198] (2) Ellipsoidal constraint search: using floating-point solutions Centered on the ellipsoid Internally enumerate integer candidate solutions;

[0199] (3) Optimal solution selection: through residual sum of squares Minimize the determination of the optimal integer solution N LLS .

[0200] Fixed solution verification and baseline update:

[0201] The R-ratio test was used to verify N. ILS The reliability of the solution is verified; if the verification passes, the baseline solution is updated. in, This is the fixed ambiguity vector.

[0202] BIE weighted solution process

[0203] Traditional BIE requires enumerating a large number of candidate solutions, resulting in low computational efficiency. Here, a new criterion is used to adaptively determine the number of candidates:

[0204]

[0205] Where, ε p To determine the threshold, the candidate solution residuals are accumulated in ascending order, and the enumeration stops when the condition is met. Compared with the traditional chi-square method, the number of candidates is more stable.

[0206] Weight function optimization:

[0207] Adding a quadratic form of the baseline residual to the traditional BE weights improves the ability to distinguish erroneous candidates.

[0208]

[0209] in, For candidate solution z j The corresponding baseline solution, This is the floating-point baseline solution. This weighting function reduces the number of invalid candidates and accelerates convergence.

[0210] B1E weighted average solution:

[0211] The ambiguity estimate is obtained by taking a weighted average of the selected candidate solutions.

[0212] in, For the space of candidate solutions after filtering, w z The optimized weights.

[0213] In summary, when the OIA test passes, the ILS fixed solution is used directly, resulting in high positioning accuracy; when the OIA test fails, the BIE weighted solution is used to avoid jumps caused by incorrect fixation and maintain positioning stability.

[0214] Furthermore, the airborne BeiDou correction algorithm testing and verification technology

[0215] *The BeiDou positioning accuracy test first involves static simulation and dynamic calculation tests (static observation and dynamic calculation), followed by real-time dynamic calculation tests.

[0216] 1) RTK positioning accuracy test based on static observation

[0217] Set up a full-system multi-frequency receiver at a location with unobstructed surroundings and good observation conditions. The baseline length between the receiver and the *BeiDou test board should be less than 10km. After configuring the receiver, conduct RTK positioning tests and record the *BeiDou RTK positioning results and raw observation data. Maintain synchronized static observation between the *BeiDou test board and the receiver for at least 1 hour. Perform full-system multi-frequency bidirectional filtering on the raw data and then perform post-processing. Use the obtained multi-system static baseline solution as the reference true value to evaluate the *BeiDou RTK positioning accuracy.

[0218] 2) Real-time dynamic solution test

[0219] The BeiDou positioning module with the best positioning accuracy and most reliable performance was selected through static testing. Then, the selected BeiDou positioning module was mounted on a drone and subjected to on-site dynamic testing at the converter station.

[0220] The testing procedure is as follows: An open area near the converter station equipment area is selected as the operating area. The *BeiDou positioning module is installed on the UAV within this area. The UAV remains stationary. After the data link is established and RTK initialization is complete, the UAV moves within a certain range at a normal speed while maintaining dynamic RTK observations. During the test, the base station data uses Qianxun Network's RTK service. The baseline length must be less than 10km during UAV flight. The *BeiDou RTK positioning results and raw observation data are recorded, with a synchronous observation time of no less than 20 minutes.

[0221] After the UAV lands, the post-processing results of the multi-frequency bidirectional filtering of the entire system are used as the reference true value. The original data file is processed by the *BeiDou correction algorithm to obtain the measured value. The measured value is compared with the true value to obtain the *BeiDou navigation and positioning deviation.

[0222] Using the airborne BeiDou correction algorithm proposed in this patent, a high-performance BeiDou board is selected from the current mainstream BeiDou test boards. The BeiDou correction algorithm is then deployed and integrated into the high-performance BeiDou board to form a BeiDou module. The BeiDou module is then installed and deployed on a UAV to test and verify the navigation and positioning accuracy of the UAV airborne BeiDou correction algorithm.

[0223] (1) Performance testing and screening of Beidou boards

[0224] Three test boards were selected for static testing in both open and semi-obstructed environments. The mainstream boards used were the GNSS-EVK-K8, GNSS-EVK, and the UCD9810 *BeiDou full-frequency RF baseband and high-precision algorithm integrated BDS SoC chip. Through testing, the board model with the best positioning accuracy and stability was selected.

[0225] (2) On-site testing and verification of UHV converter station

[0226] Based on the static test results, a Beidou module was fabricated using a core board with better positioning performance and the *Beidou correction algorithm proposed in this invention. The Beidou module was then tested at an ultra-high voltage converter station using a DJI RTK350 drone.

[0227] Figure 4 This comparison shows the number of satellites participating in floating-point calculations under the test environment and the partially obscured environment. Both sets of data were tested around noon to eliminate the influence of time on the satellite count. It can be seen that the number of satellites under the test environment is slightly lower than the number under the partially obscured environment.

[0228] Data processing was performed using the *BeiDou real-time RTK algorithm developed in this patent, and the multi-system positioning results obtained after post-processing were used as the reference true value to evaluate the *BeiDou RTK positioning performance. The *BeiDou RTK positioning error sequence is as follows: Figure 5 As shown.

[0229] It is evident that positioning accuracy decreases in the UHV converter station environment compared to a partially obscured environment. This is likely due to the lower quality of observation data in the converter station environment, primarily manifested in increased carrier phase observation interruption frequency and reduced signal-to-noise ratio. In the converter station environment test, the 95% positioning errors in the E and N directions were controlled at 6.5cm and 6.9cm respectively, both within 10cm, while the 95% positioning error in the U direction was 20cm. This is partly because satellite signals are less distributed vertically but more densely distributed horizontally (E and U), resulting in relatively higher positioning accuracy on the horizontal plane. The horizontal positioning error well meets the requirements for real-time (centimeter-level) navigation and positioning using the BeiDou system.

[0230] Figure 6 The trajectory diagram from the on-site test shows that the device's trajectory has no obvious deviation in the horizontal direction. With the help of the map, the movement trajectory and landmarks along the way can be accurately determined.

[0231] Therefore, this invention proposes an airborne *BeiDou navigation and positioning correction algorithm and testing and verification method for UAVs in the complex and strong electromagnetic environment of UHV converter stations. By changing the threshold for gross error detection to effectively eliminate abnormal environmental data, a step-by-step ambiguity resolution algorithm combining ILS-BIE is employed, along with a sliding windowing method based on graph factors to correct detection biases, suppressing the influence of abnormal data and improving the resolution accuracy. This achieves centimeter-level navigation and positioning accuracy, enabling intelligent inspection equipment to accurately and in real-time locate its own position, thus solving the interference problem of the complex electromagnetic environment of converter stations on BeiDou communication and positioning. Furthermore, a testing and verification technique for the airborne *BeiDou navigation and positioning correction algorithm is proposed, employing a combination of static simulation and dynamic solution and real-time dynamic solution. The positioning accuracy of the *BeiDou positioning correction algorithm was verified on-site at an UHV converter station.

[0232] Exemplary device

[0233] Figure 7 This is a schematic diagram of the structure of an airborne BeiDou positioning device suitable for ultra-high voltage converter stations, provided by an exemplary embodiment of the present invention. Figure 7 As shown, the device 700 includes:

[0234] The preprocessing module 710 is used to acquire the raw observation data of the base station and rover in the converter station scenario and preprocess them to obtain the preprocessed observation values.

[0235] The detection module 720 is used to perform cycle slip detection on the carrier phase in the observation value according to the preset criteria to obtain cycle slip information;

[0236] Module 730 is used to construct a factor graph optimization model based on preprocessed observations and cycle slip information;

[0237] The solution module 740 is used to perform floating-point solution on the factor graph optimization model to obtain the fuzzy floating-point solution.

[0238] The fixed module 750 is used to fix the fuzziness of the fuzzy floating-point solution to obtain a high-precision fuzzy fixed solution.

[0239] Optionally, the raw observation data includes pseudorange, carrier phase, and Doppler, and the preprocessing operations include checking the cutoff elevation angle, cutoff signal-to-noise ratio, and observation consistency.

[0240] Optionally, the preset criterion is:

[0241]

[0242] In the formula, gf k-1 The GF combination observations for the previous epoch k-1; gf kΔgf is the GF combination observation value at the current epoch k; Δgf is the difference between the GF combination observation value at the previous epoch k-1 and the GF combination observation value at the current epoch k.

[0243] Optionally, the factor graph optimization model includes inter-station single-difference observation equations, an elevation angle stochastic model, and a signal-to-noise ratio stochastic model, and

[0244] Solution module 740 includes:

[0245] The submodule is obtained to obtain the variance-covariance matrix of the single difference observations based on the altitude angle random model and the signal-to-noise ratio random model according to the error propagation law.

[0246] The solution submodule is used to solve the variance-covariance matrix and the inter-station single-difference observation equations to obtain iterative solutions.

[0247] The detection submodule is used to perform post-testing on the results of each iterative solution using the IGGIII weight function to obtain the ambiguity floating-point solution that meets the preset criteria.

[0248] Optionally, the expression for the inter-station single-difference observation equation is:

[0249]

[0250] In the formula, This is the optimal estimate of the state vector to be estimated. This represents the inter-station single-difference residual of the carrier phase observation at epoch k, satellite i, and frequency f. ΔL represents the inter-station single-difference residual of the pseudorange observations at epoch k, satellite i, and frequency f, ΔP represents the carrier phase single-difference observation, and ΔP represents the inter-station single-difference pseudorange observation. Vel Let be the covariance matrix of the velocity error. e is the velocity residual of epoch k. Mar As a marginalized prior factor, This is the weighted matrix of the marginalized prior factors.

[0251] Optionally, the expression for the IGGIII weight function is:

[0252]

[0253] In the formula, γ i，k The weight coefficients of IGGⅢ are used to assign weights to the i-th observation at the k-th epoch, and the weights are between [0,1]. The standardized residuals of this observation are τ0, which is the normal residual threshold, and τ1, which is the gross error judgment threshold.

[0254] Optionally, the fixed module 750 includes:

[0255] The fixed submodule is used to fix the fuzziness of the floating-point fuzzy solution by employing integer least squares estimation and optimal integer equivariant estimation methods, thereby obtaining a high-precision fuzzy fixed solution.

[0256] Optionally, a fixed submodule includes:

[0257] The testing unit is used to perform optimal integer aperture testing on the fuzzy floating-point solution. If the test passes, the fuzziness of the fuzzy floating-point solution is fixed using the integer least squares method; otherwise, the fuzziness of the fuzzy floating-point solution is fixed using the optimal integer isovariant estimation method, thus obtaining a high-precision fuzzy fixed solution.

[0258] Optionally, the optimal integer equivariant estimation method uses a new criterion to adaptively determine the candidate number:

[0259]

[0260] In the formula, ε p To determine the threshold, Let z be the floating-point ambiguity vector. j Let j be the j-th integer candidate vector. It is a weighted quadratic form with covariance. Let be the covariance matrix of the floating-point solution of ambiguity;

[0261] The optimal weight function optimization expression for the optimal integer isovariant estimation method is:

[0262]

[0263] In the formula, For candidate solution z j The corresponding baseline solution, For floating-point baseline solutions; The normalized weights assigned to the j-th integer candidate vector. The covariance matrix of the floating-point solution of ambiguity, The covariance matrix corresponding to the baseline floating-point solution, where z represents the total number of ambiguity candidate values, Z n This represents an n-dimensional integer space, to which all ambiguity candidate values ​​z belong.

[0264] Exemplary electronic devices

[0265] Figure 8 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 8 As shown, the electronic device 80 includes one or more processors 81 and memory 82.

[0266] The processor 81 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0267] The memory 82 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 81 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 83 and an output device 84, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0268] In addition, the input device 83 may also include, for example, a keyboard, a mouse, etc.

[0269] The output device 84 can output various information to the outside. The output device 84 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0270] Of course, for the sake of simplicity, Figure 8 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0271] Exemplary computer program products and computer-readable storage media

[0272] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0273] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0274] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0275] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable 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.

[0276] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0277] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0278] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0279] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0280] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0281] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for airborne Beidou positioning suitable for an extra-high voltage converter station, characterized in that, The method comprises the following steps: Obtaining and preprocessing original observation data of reference stations and mobile stations under the scene of an ultra-high voltage converter station to obtain preprocessed observation values; Detecting cycle slip of carrier phase in the observation values according to a preset criterion to obtain cycle slip information; Constructing a factor graph optimization model according to the preprocessed observation values and the cycle slip information; Floating point solution of the factor graph optimization model is performed to obtain a floating point ambiguity solution; The floating point ambiguity solution is fixed to obtain a high-precision ambiguity fixed solution.

2. The method of claim 1, wherein, The original observation data includes pseudo-range, carrier phase and Doppler, and the preprocessing operation includes cutoff elevation angle, cutoff signal-to-noise ratio and observation value consistency test.

3. The method of claim 1, wherein, The preset criterion is: wherein gf k-1 is the GF combined observation value of the previous epoch k-1; gf k is the GF combined observation value of the current epoch k; and Δgf is the difference between the GF combined observation value of the previous epoch k-1 and the GF combined observation value of the current epoch k.

4. The method of claim 1, wherein, The factor graph optimization model includes inter-station single-difference observation equations, elevation angle random models and signal-to-noise ratio random models, and The floating point solution of the factor graph optimization model includes: Obtaining the variance-covariance matrix of single-difference observation values according to the elevation angle random models and the signal-to-noise ratio random models by the error propagation law; Solving the variance-covariance matrix and the inter-station single-difference observation equations to obtain an iterative solution; Posterior detection of the result of each step of iterative solution is performed by using an IGGIII weight function to obtain a floating point ambiguity solution that meets the preset criterion.

5. The method according to claim 1 or 4, characterized in that, The expression of the inter-station single-difference observation equation is: wherein, is the optimal estimation of the state vector to be estimated, is the inter-station single-difference residual of the carrier phase observation at epoch k, satellite i, and frequency f, is the inter-station single-difference residual of the pseudo-range observation at epoch k, satellite i, and frequency f, AL is the single-difference observation of the carrier phase, AP is the inter-station single-difference observation of the pseudo-range, and Vel is the covariance matrix of the velocity error, is the velocity residual at epoch k, e Mar is the marginalized prior factor, is the weighting matrix of the marginalized prior factor.

6. The method of claim 4, wherein, The expression of the IGGIII weight function is: where γ i，k is the weight coefficient of IGG III, which is used to assign weight to the kth epoch and the ith observation, and the weight is between [0, 1]; is the standardized residual of the observation, τ0is the normal residual threshold, and τ1is the gross error judgment threshold.

7. The method of claim 1, wherein, The floating point ambiguity solution is fixed to obtain a high-precision ambiguity fixed solution, including: The floating point ambiguity solution is fixed by using integer least square estimation and optimal integer equivalent estimation method to obtain a high-precision ambiguity fixed solution.

8. The method of claim 7, wherein, The floating point ambiguity solution is fixed by using integer least square estimation and optimal integer equivalent estimation method to obtain a high-precision ambiguity fixed solution, including: Optimal integer aperture test is performed on the floating point ambiguity solution, if the test is passed, the floating point ambiguity solution is fixed by using integer least square method, otherwise, the floating point ambiguity solution is fixed by using optimal integer equivalent estimation method to obtain a high-precision ambiguity fixed solution.

9. The method of claim 8, wherein, The optimal integer equivalent estimation method adaptively determines the candidate number by using a new criterion: where ε p is a discrimination threshold, is a float ambiguity vector, z j is the jth integer candidate vector, is a weighted quadratic form with covariance, is the covariance matrix of the ambiguity float solution; The weight function optimization expression of the optimal integer equivalent estimation method is: where is the candidate solution z j is the corresponding baseline solution, is the float baseline solution; is the normalized weight assigned to the jth integer candidate vector, is the covariance matrix of the ambiguity float solution, is the covariance matrix of the corresponding baseline float solution, z represents the ambiguity candidate value as a whole, Z n represents the n-dimensional integer space, and all ambiguity candidates z belong to this space.

10. A Beidou positioning device for use in an ultra-high voltage converter station, characterized in that, The method comprises the following steps: A preprocessing module is configured to obtain and preprocess original observation data of reference stations and mobile stations under the scene of an ultra-high voltage converter station to obtain preprocessed observation values; A detection module is configured to detect cycle slip of carrier phase in the observation values according to a preset criterion to obtain cycle slip information; A construction module is configured to construct a factor graph optimization model according to the preprocessed observation values and the cycle slip information; A solution module is configured to perform floating point solution of the factor graph optimization model to obtain a floating point ambiguity solution; A fixing module is configured to fix the floating point ambiguity solution to obtain a high-precision ambiguity fixed solution.

11. The apparatus of claim 10, wherein, The original observation data includes pseudo-range, carrier phase and Doppler, and the preprocessing operation includes cutoff elevation angle, cutoff signal-to-noise ratio and observation value consistency test.

12. The apparatus of claim 10, wherein, The preset criterion is: wherein gf k-1 is the GF combined observation value of the previous epoch k-1; gf k is the GF combined observation value of the current epoch k; and Δgf is the difference between the GF combined observation value of the previous epoch k-1 and the GF combined observation value of the current epoch k.

13. The apparatus of claim 10, wherein, The factor graph optimization model comprises an inter-station single-difference observation equation, a height angle stochastic model and a signal-to-noise ratio stochastic model, and The solving module comprises: The obtaining submodule is configured to obtain a variance-covariance matrix of the single-difference observation value according to the height angle stochastic model and the signal-to-noise ratio stochastic model by an error propagation law; The solving submodule is configured to solve the variance-covariance matrix and the inter-station single-difference observation equation to obtain an iterative solution; The detection submodule is configured to perform a posteriori detection on a result of each step of the iterative solution by using an IGGIII weight function to obtain a fuzzy float solution satisfying a preset criterion.

14. The apparatus of claim 10 or 13, wherein, An expression of the inter-station single-difference observation equation is as follows: wherein, is the optimal estimation of the state vector to be estimated, is the inter-station single-difference residual of the carrier phase observation at epoch k, satellite i and frequency f, is the inter-station single-difference residual of the pseudo-range observation at epoch k, satellite i and frequency f, ΔL is the single-difference observation of the carrier phase, ΔP is the inter-station single-difference observation of the pseudo-range, Σ Vel is the covariance matrix of the velocity error, is the velocity residual at epoch k, e Mar is the marginalized prior factor, is the weighting matrix of the marginalized prior factor.

15. The apparatus of claim 13, wherein, An expression of the IGGIII weight function is as follows: where γ i，k is the weight coefficient of IGG III, which is used to assign weight to the kth epoch and the ith observation, and the weight is between [0, 1]; is the standardized residual of the observation, τ0is the normal residual threshold, and τ1is the gross error judgment threshold.

16. The apparatus of claim 10, wherein, The fixing module comprises: The fixing submodule is configured to perform fuzzy fixing on the fuzzy float solution by using an integer least square estimation and an optimal integer equivalent estimation method to obtain a high-precision fuzzy fixed solution.

17. The apparatus of claim 16, wherein, The fixing submodule comprises: The test unit is configured to perform optimal integer aperture testing on the fuzzy float solution, and if the testing is passed, perform fuzzy fixing on the fuzzy float solution by using an integer least square method, otherwise, perform fuzzy fixing on the fuzzy float solution by using an optimal integer equivalent estimation method to obtain a high-precision fuzzy fixed solution.

18. The apparatus of claim 17, wherein, The optimal integer equivalent estimation method adaptively determines a candidate number by using a new criterion: where ε p is a decision threshold, is a float ambiguity vector, z j is the jth integer candidate vector, is a weighted quadratic form with covariance, is the covariance matrix of the ambiguity float solution; An expression of a weight function of the optimal integer equivalent estimation method is as follows: wherein is the candidate solution z j is the corresponding baseline solution, is the float baseline solution; is the normalized weight assigned to the jth integer candidate vector, is the covariance matrix of the ambiguity float solution, is the covariance matrix of the corresponding baseline float solution, z represents the ambiguity candidate value as a whole, Z n represents the n-dimensional integer space, all ambiguity candidate values z belong to this space.

19. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is configured to execute the method of any one of claims 1-9.

20. An electronic device, comprising: The electronic device comprises: A processor; A memory for storing executable instructions of the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-9.

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