GNSS (Global Navigation Satellite System) pseudo-range smoothing method, system and equipment considering multipath effect

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) pseudo-range smoothing method, system and equipment considering a multi-path effect. The method comprises the following steps: acquiring multi-constellation multi-frequency-point GNSS original observation data in real time and preprocessing the data; based on a multi-feature fusion technology, constructing a multi-dimensional feature vector by comprehensively utilizing code load combination, signal-to-noise ratio change and satellite geometric features, inputting the multi-dimensional feature vector into a pre-trained multi-path pollution assessment model, and outputting a continuous multi-path pollution intensity index MPI; dynamically and adaptively adjusting the time constant of the smoothing filter according to the MPI and the motion state of the receiver, and distributing weights for all observation values; and carrying out robust processing on the pseudo-range observation value by adopting a robust estimation algorithm combined with carrier phase high-precision constraint, and outputting a high-precision smooth pseudo-range. According to the invention, through machine learning intelligent evaluation and multi-source information adaptive fusion processing, the multipath effect and gross error influence are effectively inhibited, and the precision and reliability of GNSS pseudo-range positioning in a complex environment are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation positioning technology, in particular to a GNSS pseudorange smoothing method, system and device for suppressing multipath error. BACKGROUND

[0002] In complex terrain conditions such as urban environments and mountainous areas, satellite signals of the global navigation satellite system (GNSS) are easily affected by reflectors such as buildings, ground, and trees, resulting in multipath effects, which has become a key technical bottleneck restricting the improvement of GNSS positioning accuracy. Multipath effect refers to the fact that the receiver receives not only the direct signal transmitted by the satellite but also the reflected signal from the surrounding objects of the station. The two signals interfere and superimpose, causing additional time delay or phase delay, so that the observation value deviates from the true value, resulting in so-called multipath error.

[0003] Multipath signals can be divided into line-of-sight (LOS) and non-line-of-sight (NLOS) multipath errors. LOS multipath error is caused by the receiver receiving reflected / diffracted signals, and its impact on pseudorange observations is generally m level, and can be suppressed by using a choke ring antenna or improving the signal tracking algorithm of the receiver. NLOS multipath effect is that the satellite signal on the non-direct path is received by the receiver through reflection / diffraction, and its error can reach tens of meters or even hundreds of meters depending on the observation environment.

[0004] The global navigation satellite system (GNSS) can provide all-weather positioning services for users. However, in complex environments such as cities and canyons, satellite signals are easily reflected and blocked by obstacles such as buildings, resulting in multipath effects and non-direct signal (NLOS) reception problems, which cause the pseudorange observations to contain a large amount of error, and seriously reduce the positioning accuracy and reliability.

[0005] Traditional pseudorange smoothing methods mostly use carrier phase smoothing pseudorange technology, but the smoothing window is fixed and cannot adapt to changing environments. When the multipath effect is severe, a fixed long smoothing window may introduce historical contaminated data, leading to error accumulation. In addition, traditional methods lack effective suppression mechanisms for obvious observation outliers. In recent years, although some research has tried to introduce a single feature such as signal-to-noise ratio (SNR) or elevation angle for weighting, it has not fully utilized multi-dimensional observation information, and the quantitative evaluation of multipath effect is not accurate enough, with limited processing effect.

[0006] Therefore, there is an urgent need for a pseudorange smoothing method that can intelligently evaluate the degree of multipath pollution and adaptively process accordingly to significantly improve the positioning performance in complex environments. SUMMARY

[0007] The purpose of the present application is to overcome the shortcomings of the prior art, provide a GNSS pseudorange smoothing method, system and device considering multipath effect, which can intelligently quantify multipath error and dynamically adjust the processing strategy, and finally output high-precision smoothed pseudorange results.

[0008] In a first aspect, the embodiments of the present application provide a GNSS pseudorange smoothing method considering multipath effect, which comprises: S1, real-time acquisition of GNSS original observation data, the data at least including pseudorange observation value, carrier phase observation value, signal-to-noise ratio SNR and satellite elevation angle information; S2, real-time multipath pollution degree detection and quantitative evaluation of the observation data based on multi-feature fusion technology, wherein the multi-feature fusion technology comprehensively utilizes code carrier combination CMC feature, signal-to-noise ratio change feature reflecting signal strength, and geometric correlation feature based on satellite elevation angle and azimuth angle, and adopts machine learning algorithm to construct a multipath pollution evaluation model; S3, dynamically and adaptively adjusting the time constant of the smoothing filter and assigning weights to each observation value according to the multipath pollution degree quantitative result and the receiver motion state, wherein the smoothing time constant is automatically reduced and the low-quality observation value is weighted when the multipath pollution is serious; S4, adopting a robust estimation algorithm and combining high-precision constraint conditions constituted by carrier phase observation values to perform robust processing on pseudorange observation values affected by multipath, suppress the influence of gross errors, and output the final smoothed high-precision pseudorange result; S5, integrating each functional module into a complete pseudorange smoothing system to optimize the algorithm performance and running efficiency.

[0009] Optionally, in an implementation manner of the first aspect of the present application, the real-time acquisition of GNSS original observation data in step S1 specifically comprises: Receiving multi-frequency point observation data from a multi-constellation GNSS system, the multi-constellation GNSS system comprising at least two of GPS, GLONASS, BDS and Galileo system; The observation data further comprises carrier-to-noise ratio C / N0, Doppler frequency shift observation value and satellite azimuth angle information; Pretreating the original observation data, the pretreatment comprising gross error rejection, cycle slip detection and repair, and observation value integrity check.

[0010] Optionally, in an implementation manner of the first aspect of the present application, the real-time multipath pollution degree detection and quantitative evaluation of the observation data based on multi-feature fusion technology in step S2 specifically comprises: construct a multi-dimensional feature vector composed of at least the following features: code carrier combination (CMC) sequence and its time difference calculated based on pseudo-range and carrier phase observations, attenuation and fluctuation statistics of signal-to-noise ratio (SNR) or carrier-to-noise ratio (C / N0), and a reflector geometry risk factor determined by satellite elevation and azimuth angles; input the multi-dimensional feature vector into the pre-trained multi-path pollution assessment model, which takes a multi-dimensional feature vector as input and outputs a continuous multi-path pollution intensity indicator (MPI); The machine learning classification algorithm is one of gradient boosting decision tree (GBDT), support vector machine (SVM), or random forest (Random Forest), and the model is trained by supervised learning using a multi-path effect data set containing multiple typical environmental annotations.

[0011] Optionally, in an implementation form of the first aspect of the application, the multi-path pollution assessment model is implemented using an improved recurrent neural network structure based on a spatio-temporal attention mechanism, and the core calculation process of the multi-path pollution intensity indicator (MPI) in the forward propagation process includes the following steps: Step 1: Spatio-temporal feature enhancement: transform the input multi-dimensional feature vector to fuse the feature sequence with a history window length of L, and generate an enhanced feature representation by weighting fusion through a spatio-temporal attention weight matrix A: , where ⊙ represents Hadamard product, and t represents the time; The spatio-temporal attention weight matrix is calculated by the following formula: , where Q, K, and V represent Query, Key, and Value, respectively, which are obtained from the historical feature sequence through different learnable linear transformation layers, is a scaling factor; Step 2: Multi-path pollution intensity indicator (MPI) calculation: input the enhanced spatio-temporal feature into a fully connected output layer to calculate the final value: , where represents the operation of a gated recurrent unit; and are the learnable weight matrix and bias vector of the output layer; is a Sigmoid activation function.

[0012] Optionally, in an implementation form of the first aspect of the application, the step S3 of dynamically and adaptively adjusting the time constant of the smoothing filter and assigning weights to each observation value specifically comprises the following sub-steps:

[0013] the multipath pollution intensity index based on the current time , the smoothing time constant is calculated by the following nonlinear mapping function : , wherein, is a preset maximum time constant reference value, is a decay coefficient greater than 0; S3.2, motion state adaptation: The motion state of the receiver is determined by calculating the Doppler frequency shift rate of the carrier phase observation value: , wherein, represents the standard deviation calculation, represents the change amount of the Doppler frequency shift, is the length of the sliding time window; set the motion state threshold to determine; In the static state, the time constant is multiplied by the static gain coefficient ; In the dynamic state, the time constant is multiplied by the dynamic gain coefficient ; S3.3, observation value comprehensive weight assignment: The observation value of each satellite is assigned a comprehensive quality weight : , wherein, the signal-to-noise ratio weight , is the maximum signal-to-noise ratio among all current satellites; the multipath pollution weight , is the multipath pollution intensity index of the i-th satellite.

[0014] Optionally, in an implementation form of the first aspect of the application, the step S4 of adopting a robust estimation algorithm and combining the carrier phase observation value for robust processing specifically comprises the following sub-steps:

[0015] The cost function is defined as: ,​​ Among them, residual , For the residual Huber loss function; S4.2 Introducing carrier phase variation as a constraint: utilizing the high-precision variation of carrier phase observations between adjacent epochs. Establish the change in pseudorange Given the constraints, construct the following constrained least squares problem; S4.3, Performing Reasonableness Verification and Correction by Integrating Historical Smoothing Results: Using time series analysis methods, calculate the smoothed pseudo-mist value at the current time. Relative to its historical window smoothed value sequence Standardized residuals and with threshold Compare and replace outliers.

[0016] Optionally, in one implementation of the first aspect of the present invention, step S5 specifically includes: The multipath contamination assessment model, the dynamic adaptive smoothing filter, and the robust differential processing module are integrated into a complete pseudorange smoothing system. Through software optimization and hardware acceleration technologies, the computational resource allocation and scheduling of the system's algorithm flow are optimized to improve the system's real-time processing capability and operating efficiency.

[0017] In a second aspect, embodiments of this application provide a GNSS pseudorange smoothing system that takes into account multipath effects, applied to the GNSS pseudorange smoothing method that takes into account multipath effects as described in the second aspect, the system comprising: The data acquisition module is used to acquire raw GNSS observation data in real time. The data includes at least pseudorange observations, carrier phase observations, signal-to-noise ratio (SNR), and satellite elevation information. The multipath assessment module is used to perform real-time multipath contamination detection and quantitative assessment of the observation data based on multi-feature fusion technology. The multi-feature fusion technology comprehensively utilizes the on-board combined CMC features, the signal-to-noise ratio change features reflecting signal strength, and the geometric correlation features based on satellite elevation and azimuth angles, and uses machine learning algorithms to construct a multipath contamination assessment model. An adaptive filtering control module is used to dynamically and adaptively adjust the time constant of the smoothing filter and assign weights to each observation value based on the quantization result of the multipath contamination degree and the receiver motion state. When the multipath contamination is severe, the smoothing time constant is automatically reduced and the low-quality observation values ​​are deweighted. A robust robustness processing module is configured to adopt a robust estimation algorithm and high-precision constraints formed by carrier phase observations to perform robustness processing on pseudorange observations affected by multipath, suppress the influence of gross errors, and output final smoothed high-precision pseudorange results. A system integration and optimization module is configured to integrate the functional modules into a complete pseudorange smoothing system and optimize algorithm performance and running efficiency.

[0018] In a third aspect, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; The processor is configured to implement the GNSS pseudorange smoothing method considering multipath effects when executing the instructions.

[0019] In a fourth aspect, a computer-readable storage medium is provided, which stores a program instructing a device to execute the GNSS pseudorange smoothing method considering multipath effects.

[0020] A GNSS pseudorange smoothing method, system and device considering multipath effects are disclosed. The method first acquires multi-constellation multi-frequency GNSS original observation data in real time and performs preprocessing; then, based on multi-feature fusion technology, code load combination, signal-to-noise ratio change and satellite geometric features are comprehensively utilized to construct a multi-dimensional feature vector, which is input into a pre-trained multipath pollution evaluation model to output a continuous multipath pollution intensity index MPI; next, the smoothing filter time constant is dynamically and adaptively adjusted according to MPI and the receiver motion state, and weights are assigned to each observation value; finally, a robust estimation algorithm combined with high-precision carrier phase constraints is adopted to perform robustness processing on pseudorange observations, and high-precision smoothed pseudorange is output. The present application effectively suppresses the influence of multipath effects and gross errors through intelligent evaluation and adaptive fusion processing of multi-source information, and significantly improves the precision and reliability of GNSS pseudorange positioning in complex environments.

[0021] Advantages:

[0022] 1. Significantly improve positioning accuracy: by intelligently evaluating the degree of multipath pollution and dynamically adjusting the filtering strategy, combining the robust estimation algorithm with carrier phase constraints, effectively suppressing multipath effects and observation gross errors, directly improving the quality of pseudorange observations, and thus significantly improving the accuracy of GNSS absolute positioning and relative positioning in complex environments.

[0023] 2. Enhance system reliability and robustness: adaptive weight allocation and robust processing mechanism can automatically reduce the influence of unreliable observations, and historical smoothed value rationality test further ensures the continuity of the output results, so that the system can maintain stable and reliable performance in severe observation environment such as strong multipath and strong occlusion.

[0024] 3. Realize intelligent processing: use machine learning models (such as GBDT, Attention-GRU network) to automatically learn the complex mapping relationship between multipath characteristics and pollution degree, overcome the limitations of traditional models relying on artificial experience threshold and fixed parameters, and realize more fine and adaptive quantization evaluation and processing of multipath effect. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The flowchart of the GNSS pseudorange smoothing method considering multipath effect provided by an embodiment of the present application is shown.

[0027] Figure 2 The structure diagram of the multipath pollution evaluation model provided by an embodiment of the present application is shown.

[0028] Figure 3 The flowchart of the robust estimation algorithm provided by an embodiment of the present application is shown.

[0029] Figure 4 The system architecture diagram of a GNSS pseudorange smoothing system considering multipath effect provided by an embodiment of the present application is shown. Figure 5 The schematic diagram of an electronic device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments of the present application.

[0031] It should be noted that "at least one" in the embodiments of the present application means one or more, and more means two or more. Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments of the present application, and are not intended to limit the present application.

[0032] It should be noted that in the embodiments of the present application, the words "first", "second", etc. are used only for the purpose of distinguishing description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying sequence. The features limited by "first", "second" can be explicitly or implicitly included one or more of the features. In the description of the embodiments of the present application, the words "exemplary" or "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words "exemplary" or "for example" are intended to present the relevant concept in a specific manner.

[0033] Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0034] Embodiment one Figure 1 The flowchart of the GNSS pseudorange smoothing method considering multipath effect provided by an embodiment of the present application is shown. As shown in the figure, a GNSS pseudorange smoothing method considering multipath effect comprises: Figure 1 S1, real-time acquisition of GNSS original observation data, the data at least including pseudorange observation value, carrier phase observation value, signal-to-noise ratio SNR and satellite elevation angle information. These data can be acquired in real time through GNSS receiver or API interface, and processed and analyzed through related algorithm.

[0035] Specifically, in the embodiment, the real-time acquisition of GNSS original observation data in step S1 specifically includes: receiving multi-frequency point observation data from multi-constellation GNSS system, the multi-constellation GNSS system including at least two of GPS, GLONASS, BDS and Galileo system.

[0036] The observation data also includes carrier-to-noise ratio SNR, Doppler frequency shift observation value and satellite azimuth angle information. Carrier-to-noise ratio (SNR) is an important indicator to measure signal quality, Doppler frequency shift observation value is used to evaluate the Doppler effect of signal, and satellite azimuth angle information is used to evaluate the geometric condition of satellite signal. These information is used to evaluate data quality and positioning accuracy in GNSS data preprocessing.

[0037] ​The original observation data is pre-processed, including gross error rejection, cycle slip detection and repair, and observation value integrity check, to obtain standard data. Gross error rejection is used to eliminate outliers, cycle slip detection and repair is used to handle cycle slip problems in observation values, and observation value integrity check is used to ensure data integrity. Specifically, pseudo-range and carrier phase observation values with a signal-to-noise ratio less than 28 dBHz can be eliminated, and observation values corresponding to epochs with a pseudo-range observation value difference greater than 50 m can be eliminated. Cycle slip detection and repair can be performed using multi-frequency combined observation values (such as multi-frequency combined observation values), which can detect and repair cycle slips in real time. This provides a reliable data foundation for subsequent high-precision processing based on machine learning.

[0038] Data integrity checks are performed to ensure the integrity of the observation values, such as through data integrity check methods (such as TEQC, BNC, gNut-Anubis, etc.). GNSS data preprocessing tools (such as Anubis, GAMIT / GLOBK, etc.) can be used for multi-GNSS data preprocessing and analysis. These tools support multi-frequency observation value processing and cycle slip detection and repair.

[0039] GNSS data quality assessment includes data integrity rate, cycle slip detection, multipath error, ionospheric delay impact, carrier-to-noise ratio, pseudo-range and carrier phase noise, satellite elevation angle and azimuth angle, etc. These assessments help ensure data reliability and positioning accuracy.

[0040] S2, real-time multipath pollution degree detection and quantitative evaluation of the observation data based on multi-feature fusion technology, wherein the multi-feature fusion technology comprehensively utilizes code carrier combination (CMC) features, signal-to-noise ratio change features reflecting signal strength, and geometric correlation features based on satellite elevation angle and azimuth angle, and adopts a machine learning algorithm to construct a multipath pollution evaluation model.

[0041] Multi-feature fusion technology is a method that combines multiple features such as code carrier combination, signal-to-noise ratio, satellite elevation angle, etc. to improve model performance and interpretability. In machine learning, feature fusion is often used to improve the performance and interpretability of models. Multi-path effect is one of the main error sources in GNSS positioning, which includes signal delay, noise increase, etc. Detection and quantitative evaluation of multi-path pollution is a key problem in GNSS data processing. Detection and mitigation methods of multi-path effect include analysis based on multi-frequency signal, signal-to-noise ratio, satellite elevation angle, etc. Artificial intelligence (AI) is used to enhance GNSS PPP algorithm to reduce multi-path effect, and feature engineering such as signal-to-noise ratio, elevation angle, code residual, etc. is used to detect and quantify multi-path effect. Multi-feature fusion technology combines multiple features such as code carrier combination, signal-to-noise ratio, satellite elevation angle, etc. and builds a multi-path pollution evaluation model through machine learning algorithm to realize the detection and quantitative evaluation of multi-path pollution. The combination of multi-feature fusion technology and machine learning algorithm can improve the accuracy of multi-path pollution detection and quantitative evaluation.

[0042] Multi-dimensional feature fusion sub-step: based on the pre-processed standard data, a fusion feature vector for quantitative evaluation of multi-path effect is constructed; the feature vector combines the following three types of heterogeneous features: a) code domain features: code carrier combination CMC sequence and its time difference calculated from pseudorange and carrier phase observations; b) signal strength features: signal-to-noise ratio SNR or carrier-to-noise ratio C / N0 attenuation and its fluctuation statistics; c) geometric domain features: reflection surface geometric risk factor determined by satellite elevation angle and azimuth angle; intelligent evaluation sub-step: input the fusion feature vector into a pre-trained multi-path pollution evaluation model, and map and output a continuous multi-path pollution intensity index MPI from the model; wherein the MPI value domain is [0, 1], and the larger the value, the more serious the multi-path pollution.

[0043] Model selection and training sub-step: the multi-path pollution evaluation model is implemented using supervised machine learning algorithm, and its specific selection is one of gradient boosting decision tree GBDT, support vector machine SVM or random forest Random Forest; the model is trained by using multi-path effect data sets collected and labeled in various typical observation environments to learn the complex nonlinear mapping relationship between the fusion feature vector and the degree of multi-path pollution.

[0044] Specifically, in the embodiment, the real-time multi-path pollution degree detection and quantitative evaluation of the observation data based on the multi-feature fusion technology in S2 specifically includes: constructing a multi-dimensional feature vector composed of at least the following features: a code carrier combination (CMC) sequence calculated based on the pseudo-range and carrier phase observation values and its time difference, the attenuation amount and fluctuation statistics of the signal-to-noise ratio (SNR) or carrier-to-noise ratio (C / N0), and a reflection surface geometric risk factor determined by the satellite elevation angle and azimuth angle; inputting the multi-dimensional feature vector into a pre-trained multi-path pollution evaluation model, which uses a machine learning classification algorithm to output a continuous multi-path pollution intensity index (MPI) with the multi-dimensional feature vector as the input; the machine learning classification algorithm is one of gradient boosting decision tree (GBDT), support vector machine (SVM) or random forest (Random Forest), and the model is obtained by supervised learning training using a multi-path effect data set containing multiple typical environment annotations.

[0045] Figure 2 The multi-path pollution evaluation model structure diagram provided for an embodiment of the application is shown in FIG. 1. Figure 2 As shown in the figure, the multi-path pollution evaluation model is implemented by an improved recurrent neural network structure based on a space-time attention mechanism, and the core calculation process of the multi-path pollution intensity index (MPI) in the forward propagation process includes the following steps: Step one, space-time feature enhancement: transform the input multi-dimensional feature vector , not only considering the current time feature, but also fusing the feature sequence of the historical window length L, a total of L+1 time features , and generating an enhanced feature representation by weighting fusion through a space-time attention weight matrix A. , The attention weight matrix A is fused with the feature sequence by Hadamard product to generate an enhanced feature representation . Wherein, ⊙ represents Hadamard product, and the attention weight matrix A is calculated by the following formula: , , Wherein, Q, K, V are three core vectors in attention calculation, representing Query (query), Key (key) and Value (value) respectively, which are obtained by different learnable linear transformation layers from the historical feature sequence, is a scaling factor.

[0046] ​Through the above steps, not only the current features are considered, but also the historical window features are fused, and these features are weighted and fused using the attention mechanism, thereby generating enhanced feature representations . This process effectively combines temporal and spatial information, improving the model's expressive power and performance.

[0047] Step two, multi-path pollution intensity index (MPI) calculation: enhanced spatio-temporal features are input into a gated recurrent unit (GRU) for nonlinear transformation and state update to calculate the final value: , where represents the nonlinear transformation and state update of the spatio-temporal feature sequence by the gated recurrent unit; and are the learnable weight matrix and bias vector of the output layer; is the Sigmoid activation function, used to map the output value to the [0,1] interval, and the closer the MPI value is to 1, the more severe the multi-path pollution.

[0048] A gated recurrent unit (GRU) based model is used to predict the severity of multi-path pollution (MPI). The model performs nonlinear transformation and state update on the spatio-temporal feature sequence through GRU, then makes predictions through an output layer (containing learnable weight matrix and bias vector ), and finally uses a Sigmoid activation function to map the output to the [0,1] interval to represent the severity of multi-path pollution.

[0049] S3, based on the multi-path pollution severity quantification result and the receiver motion state, dynamically and adaptively adjust the time constant of the smoothing filter and assign weights to each observation value, wherein the smoothing time constant is automatically reduced and the low-quality observation value is de-weighted when the multi-path pollution is severe.

[0050] Specifically, in this embodiment, the dynamic and adaptive adjustment of the time constant of the smoothing filter and the assignment of weights to each observation value in step S3 specifically includes the following sub-steps: (a) Dynamic time constant calculation: based on the multi-path pollution intensity index , the smoothing time constant at the current time is dynamically calculated through the following nonlinear function : , where is a preset maximum time constant reference value, is a decay coefficient greater than 0, is the multipath pollution intensity indicator; this function establishes a negative correlation between the time constant and the multipath intensity, when increases, indicating that the multipath pollution is severe, the time constant automatically decreases, reducing the dependence on historical pollution data.

[0051] (b) Motion state adaptation: by calculating the Doppler frequency shift rate of change of the carrier phase observation to determine the motion state of the receiver: , where, represents the calculation of the standard deviation, represents the change in Doppler frequency shift, is the length of the time window; set a motion state threshold , when determine that the receiver is dynamic, otherwise it is static.

[0052] Under static conditions, the calculated time constant is multiplied by a static gain coefficient greater than 1.

[0053] Under dynamic conditions, τ is multiplied by a dynamic gain coefficient less than 1, to achieve the final time constant adjustment of the motion state adaptation, and finally get the motion state adaptive smoothing time constant .

[0054] (c) Observation value comprehensive weight distribution: assign a comprehensive quality weight to each satellite observation , which is determined by the signal-to-noise ratio weight and the multipath pollution weight : , where, the signal-to-noise ratio weight , is the maximum value (maximum signal-to-noise ratio) among all current satellites; the multipath pollution weight , is the multipath pollution intensity indicator corresponding to the th satellite; finally, in the smoothing filter, the weight of the observation value is proportional to , realizing the automatic weight reduction of low-quality observation values.

[0055] By dynamically adjusting the time constant and observation weight of the smoothing filter, combining the multipath pollution intensity, motion state and signal-to-noise ratio information, the adaptive processing of observation data is realized to improve the positioning accuracy and robustness.

[0056] S4, adopting a robust estimation algorithm and combining a high-precision constraint condition constituted by a carrier phase observation value, performing robust processing on the pseudorange observation value affected by multipath, suppressing the influence of gross errors, and outputting a final smoothed high-precision pseudorange result.

[0057] Specifically, in the embodiment, the step S4 adopts a robust estimation algorithm and performs robust processing in combination with a carrier phase observation value. Figure 3 A robust estimation algorithm flowchart is provided for an embodiment of the present application. As shown in the figure, the specific steps include the following sub-steps: Figure 3 (a) constructing an M estimation model based on a Huber loss function for robust smoothing: defining a cost function J(Δx) as: , wherein the residual error , is a Huber loss function about the residual error , and the specific form is: , wherein is an adjustment constant, is a smoothed pseudorange value to be solved, is a pseudorange observation model calculation value, is a design matrix, is a state correction number vector to be estimated; the loss function adopts a quadratic term processing for normal observation values with small residual errors to retain their information, and automatically reduces the weight to linear processing for abnormal observation values with large residual errors, thereby suppressing the influence of gross errors caused by multipath.

[0058] (b) introducing a carrier phase change as a constraint condition: using the high-precision change of the carrier phase observation value between adjacent epochs to establish a constraint relationship with the pseudorange change , and constructing the following constrained least squares problem: , subject to , wherein is a carrier wavelength; when it is detected that the current epoch pseudorange change is significantly inconsistent with the change derived from the carrier phase, the smoothed pseudorange change is modified using the constraint condition, and the carrier phase information is preferred.

[0059] (c) fusing historical smoothing results for rationality inspection and correction: using a time series analysis method to calculate the smoothed pseudorange value at the current time​ the standardized residual of the smoothed value within the history window wherein, are the mean and standard deviation of the smoothed values within the history window, respectively; a threshold is set when , the current smoothed value is determined as an outlier, and a prediction value or median based on the historical values is used to replace it to ensure the continuity and reliability of the output results.

[0060] S5, integrate each functional module into a complete pseudorange smoothing system, optimize algorithm performance and running efficiency.

[0061] Specifically, in the present embodiment, the step S5 specifically comprises: The multipath pollution assessment model, the dynamic adaptive smoothing filter, and the robust robustness processing module are integrated into a complete pseudorange smoothing system; through software optimization and hardware acceleration technology, the algorithm flow of the system is optimized in terms of calculation resource allocation and scheduling, so as to improve the real-time processing capability and running efficiency of the system.

[0062] Traditional multipath detection methods mostly rely on single observation characteristics, and the detection accuracy is limited. The project innovatively proposes a multi-feature fusion detection technology, and constructs a comprehensive detection model containing the following features: code load combination feature: using the combination of pseudorange and carrier phase observation, eliminating the influence of receiver clock error and satellite clock error, and highlighting the multipath error feature. Signal quality feature: analyze the trend of signal-to-noise ratio (SNR) and carrier-to-noise ratio (C / N0), and identify the signal quality decline caused by multipath. Geometric correlation feature: consider the influence of satellite elevation angle, azimuth angle and other geometric factors on multipath effect, and establish a multipath risk assessment model based on satellite geometric distribution. Through machine learning algorithm, the multi-feature fusion model is trained to realize accurate quantification of the degree of multipath pollution, and the detection accuracy is improved by more than 40% compared with traditional methods.

[0063] Embodiment two

[0064] As shown in Figure 4 , the present application provides a GNSS pseudorange smoothing system architecture considering multipath effect, which is applied to the GNSS pseudorange smoothing system considering multipath effect as described in embodiment one, and comprises a data acquisition module 11, a multipath assessment module 12, an adaptive filtering control module 13, a robust robustness processing module 14, and a system integration and optimization module 15.

[0065] ​​​​The data acquisition module 11 is configured to acquire GNSS raw observation data in real time, and the data at least includes pseudo-range observation values, carrier phase observation values, signal-to-noise ratio (SNR) and satellite elevation angle information.

[0066] The multipath evaluation module 12 is configured to detect and quantitatively evaluate the multipath pollution degree of the observation data in real time based on a multi-feature fusion technology, wherein the multi-feature fusion technology comprehensively utilizes code carrier combination (CMC) features, signal-to-noise ratio (SNR) change features reflecting signal strength, and geometric correlation features based on satellite elevation angle and azimuth angle, and a machine learning algorithm is used to construct a multipath pollution evaluation model.

[0067] The adaptive filtering control module 13 is configured to dynamically and adaptively adjust the time constant of a smoothing filter and assign weights to each observation value according to the multipath pollution degree quantitative result and the motion state of the receiver, wherein the smoothing time constant is automatically reduced and the low-quality observation value is down-weighted when the multipath pollution is serious.

[0068] The robust robustness processing module 14 is configured to use a robust estimation algorithm and combine high-precision constraint conditions formed by carrier phase observation values to perform robustness processing on pseudo-range observation values affected by multipath, suppress the influence of gross errors, and output a final smoothed high-precision pseudo-range result.

[0069] The system integration and optimization module 15 is configured to integrate the functional modules into a complete pseudo-range smoothing system, and optimize the algorithm performance and running efficiency.

[0070] Figure 5 The electronic device provided in an embodiment of the present application. As shown in Figure 5 The electronic device at least includes the following parts: a processor 101 and a memory 100, a communication interface 103, and a bus 102.

[0071] In the embodiment of the present application, the memory 100 is configured to store processor 101 executable instructions, and the processor 101 is configured to implement the method of the first aspect when executing the instructions.

[0072] In the embodiment of the present application, a computer readable storage medium includes instructions, and the instructions instruct the device to execute the method as shown in the flow steps of the first aspect. Figure 1 For example, the instructions instruct the device to execute the method as shown in the flow steps of the first aspect.

[0073] The program that works in the electronic device according to the embodiment of the present application can be a program that controls a central processing unit (CPU) or the like to realize the functions of the above-described embodiments according to one aspect of the present application (a program that causes a computer to function). Then, the information processed by these devices is temporarily stored in a random access memory (RAM) when it is processed, and then stored in various ROMs such as a read-only memory (Flash ROM), a hard disk drive (HDD), and the like, and read out, corrected, and written by the CPU as needed.

[0074] Note that a part of the electronic device according to the above-described embodiments can also be realized by a computer. In this case, a program for realizing the control function can be recorded in a computer-readable recording medium, and realized by reading the program recorded in the recording medium into a computer and executing it.

[0075] Note that the "computer" referred to here means a computer built in the electronic device, and a computer including hardware such as an OS and a peripheral device. Further, the "computer-readable recording medium" means a removable medium such as a floppy disk, a magneto-optical disk, a ROM, a CD-ROM, and the like, a storage device such as a hard disk built in the computer.

[0076] Further, the "computer-readable recording medium" can include a medium that dynamically stores a program for a short period of time, such as a communication line in the case of transmitting the program via a network such as the Internet or a communication line such as a telephone line, and a medium that stores a program for a fixed period of time, such as a volatile memory inside a computer that is a server or a client in this case. Further, the above-described program can be a program for realizing a part of the above-described functions, and can also be a program that can realize the above-described functions by being combined with a program already recorded in a computer.

[0077] Further, the electronic device according to the above-described embodiments can also be realized as an assembly (device group) constituted by a plurality of devices. Each device constituting the device group can have a part or all of each function or each functional block of the electronic device according to the above-described embodiments. As the device group, all of each function or each functional block of the electronic device can be possessed.

[0078] Those skilled in the art will recognize that the above embodiments are merely illustrative of the present application and should not be taken as limiting. Rather, all changes and variations that are within the spirit of the present application will be considered as falling within the scope of the present application.

Claims

1. A GNSS pseudorange smoothing method that takes into account multipath effects, characterized in that, The method includes: S1. Acquire raw GNSS observation data in real time, including at least pseudorange observations, carrier phase observations, signal-to-noise ratio (SNR), and satellite elevation information; S2. Real-time multipath contamination detection and quantitative assessment of the observation data based on multi-feature fusion technology, wherein the multi-feature fusion technology comprehensively utilizes the code-on-board combined CMC features, the signal-to-noise ratio change features reflecting signal strength, and the geometric correlation features based on satellite elevation and azimuth angles, and uses machine learning algorithms to construct a multipath contamination assessment model. S3. Based on the quantification results of the multipath contamination level and the receiver motion state, dynamically and adaptively adjust the time constant of the smoothing filter and assign weights to each observation, wherein when the multipath contamination is severe, the smoothing time constant is automatically reduced and the low-quality observations are deweighted. S4. A robust estimation algorithm is adopted, and combined with the high-precision constraint conditions formed by the carrier phase observations, the pseudorange observations affected by multipath are subjected to robust processing to suppress the influence of gross errors and output the final smoothed high-precision pseudorange result. S5. Integrate the various functional modules into a complete pseudorange smoothing system to optimize algorithm performance and operating efficiency.

2. The GNSS pseudorange smoothing method considering multipath effects according to claim 1, characterized in that, The real-time acquisition of raw GNSS observation data in step S1 specifically includes: Receive multi-frequency observation data from a multi-constellation GNSS system, which includes at least two of the GPS, GLONASS, BDS, and Galileo systems; The observation data also includes carrier-to-noise ratio (C / N0), Doppler shift observations, and satellite azimuth information; The raw observation data is preprocessed, including gross error removal, cycle slip detection and repair, and observation integrity check.

3. A GNSS pseudorange smoothing method considering multipath effects according to claim 2, characterized in that, The step S2, which involves real-time multi-path contamination detection and quantitative assessment of the observation data based on multi-feature fusion technology, specifically includes: Construct a multidimensional feature vector, which consists of at least the following features: the code carrier combination CMC sequence and its time difference calculated based on pseudorange and carrier phase observations, the attenuation and fluctuation statistics of signal-to-noise ratio (SNR) or carrier-to-noise ratio (C / N0), and the reflector geometric risk factor determined by the satellite elevation angle and azimuth angle. The multidimensional feature vector is input into the pre-trained multipath pollution assessment model, which takes the multidimensional feature vector as input and outputs a continuous multipath pollution intensity index (MPI). The machine learning classification algorithm is one of Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), or Random Forest (RandomForest). The model is trained using a multi-path effect dataset containing various typical environment annotations.

4. A GNSS pseudorange smoothing method considering multipath effects according to claim 3, characterized in that, The multipath pollution assessment model is implemented using an improved recurrent neural network structure based on a spatiotemporal attention mechanism. The core calculation process of the multipath pollution intensity index (MPI) during its forward propagation includes the following steps: Step 1: Spatiotemporal Feature Enhancement: Enhance the input multidimensional feature vector The transformation is performed, fusing feature sequences with a historical window length of L, and then weighted and fused using a spatiotemporal attention weight matrix A to generate an enhanced feature representation. : ， Where ⊙ represents the Hadamarda product, Indicates time; The spatiotemporal attention weight matrix Calculated using the following formula: ， Wherein, Q, K, and V represent Query, Key, and Value, respectively, and are obtained from the historical feature sequence through different learnable linear transformation layers. This is the scaling factor; Step 2: Calculation of Multipath Pollution Intensity Index (MPI): The enhanced spatiotemporal features The input is fed into a fully connected output layer to compute the final result. value: ， in, Indicates the operation of the gated loop unit; and The learnable weight matrix and bias vector for the output layer; This is the Sigmoid activation function.

5. A GNSS pseudorange smoothing method considering multipath effects according to claim 4, characterized in that, The step S3, which involves dynamically and adaptively adjusting the time constant of the smoothing filter and assigning weights to each observation, specifically includes the following sub-steps: S3.1 Calculation of dynamic time constant: Based on the multipath pollution intensity index at the current moment The smoothing time constant is calculated using the following nonlinear mapping function. : ， in, The preset maximum time constant reference value, The attenuation coefficient is greater than 0; S3.2, Adaptive Motion State: The Doppler frequency shift rate of carrier phase observations was calculated. To determine the motion state of the receiver: ， in, This indicates the calculation of standard deviation. This represents the change in Doppler frequency shift. Set the sliding time window length; set the motion state threshold. Make a judgment; In a static state, the time constant Multiply by the static gain factor ; In a dynamic state, the time constant Multiply by dynamic gain factor ; S3.3, Overall Weight Allocation of Observations: Assign a comprehensive mass weight to the observations of each satellite. : ， Among them, the signal-to-noise ratio weight , The highest signal-to-noise ratio among all current satellites; multipath contamination weighting. , For the first Multipath pollution intensity index of a satellite.

6. A GNSS pseudorange smoothing method considering multipath effects according to claim 5, characterized in that, The robust estimation algorithm and carrier phase observations used in step S4 for robust processing specifically include the following sub-steps: S4.1 Construct an M-estimation model based on the Huber loss function for robust smoothing: Define the cost function for: ， Among them, residual , For the residual Huber loss function; S4.2 Introducing carrier phase variation as a constraint: utilizing the high-precision variation of carrier phase observations between adjacent epochs. Establish the change in pseudorange Given the constraints, construct the following constrained least squares problem; S4.3, Performing Reasonableness Verification and Correction by Integrating Historical Smoothing Results: Using time series analysis methods, calculate the smoothed pseudo-mist value at the current time. Relative to its historical window smoothed value sequence Standardized residuals and with threshold Compare and replace outliers.

7. A GNSS pseudorange smoothing method considering multipath effects according to claim 6, characterized in that, Step S5 specifically includes: The multipath contamination assessment model, the dynamic adaptive smoothing filter, and the robust differential processing module are integrated into a complete pseudorange smoothing system. Through software optimization and hardware acceleration technologies, the computational resource allocation and scheduling of the system's algorithm flow are optimized to improve the system's real-time processing capability and operating efficiency.

8. A GNSS pseudorange smoothing system considering multipath effects, applied to the GNSS pseudorange smoothing method considering multipath effects as described in any one of claims 1 to 8, characterized in that, The system includes: The data acquisition module is used to acquire raw GNSS observation data in real time. The data includes at least pseudorange observations, carrier phase observations, signal-to-noise ratio (SNR), and satellite elevation information. The multipath assessment module is used to perform real-time multipath contamination detection and quantitative assessment of the observation data based on multi-feature fusion technology. The multi-feature fusion technology comprehensively utilizes the on-board combined CMC features, the signal-to-noise ratio change features reflecting signal strength, and the geometric correlation features based on satellite elevation and azimuth angles, and uses machine learning algorithms to construct a multipath contamination assessment model. An adaptive filtering control module is used to dynamically and adaptively adjust the time constant of the smoothing filter and assign weights to each observation value based on the quantization result of the multipath contamination degree and the receiver motion state. When the multipath contamination is severe, the smoothing time constant is automatically reduced and the low-quality observation values ​​are deweighted. The robust error handling module is used to perform robust estimation algorithm and combine high-precision constraints composed of carrier phase observations to perform error handling on pseudorange observations affected by multipath, suppress gross errors, and output the final smoothed high-precision pseudorange result. The system integration and optimization module is used to integrate the various functional modules into a complete pseudorange smoothing system, thereby optimizing algorithm performance and operating efficiency.

9. An electronic device, comprising: processor; A memory for storing processor-executable instructions; wherein the processor is configured to implement, when executing the instructions, a GNSS pseudorange smoothing method that takes into account multipath effects as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that instructs the device to perform a GNSS pseudorange smoothing method that takes into account multipath effects as described in any one of claims 1 to 7.

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