Beidou navigation signal optimization and error correction system based on big data

Abstract

The present invention relates to the field of Beidou navigation technology, and in particular to a Beidou navigation signal optimization and error correction system based on big data, comprising a multi-source heterogeneous data acquisition module, a data preprocessing module, a multi-dimensional error analysis module, an error collaborative prediction module, a real-time correction parameter generation module and an optimized signal reconstruction module, wherein: the multi-source heterogeneous data acquisition module acquires multi-source heterogeneous data in real time; the data preprocessing module performs time-space alignment; the multi-dimensional error analysis module builds a dynamic multi-path feature library; the error collaborative prediction module generates a three-dimensional space error distribution matrix; the real-time correction parameter generation module generates a frequency-domain-time domain joint correction parameter; the optimized signal reconstruction module outputs an optimized Beidou navigation signal; the present invention continuously provides high-precision and high-stability Beidou navigation services, and provides an accurate and efficient solution for the application of the Beidou system.

Description

Technical Field

[0001] The present invention relates to the field of Beidou navigation technology, and in particular to a Beidou navigation signal optimization and error correction system based on big data. Background Art

[0002] With the widespread application of global satellite navigation systems, especially the construction and development of the Beidou satellite navigation system, positioning accuracy has been significantly improved in many fields. As a global satellite navigation system independently developed by my country, the Beidou system is widely used in transportation, aviation, ocean and other fields, providing users with high-precision and real-time positioning services. However, due to the influence of the atmospheric environment, ionospheric disturbances, multipath effects, satellite clock errors and other factors, the accuracy and stability of Beidou navigation signals in complex environments still face certain challenges. In particular, in the process of signal propagation, the existence of error sources such as ionospheric disturbances, satellite clock errors and frequency drift often leads to a decrease in positioning accuracy, affecting the reliability of the navigation system.

[0003] At present, many Beidou navigation signal optimization technologies are mainly focused on correction methods for single error sources. For example, some technologies attempt to improve signal quality through ionospheric models or compensation methods based on satellite clock errors. However, these methods are mostly local error corrections and fail to fully consider the interaction of multiple error sources. In addition, existing technologies also have certain limitations in processing the fusion and real-time adjustment of multi-source heterogeneous data. It is difficult to achieve coordinated prediction and compensation of multiple errors such as ionospheric disturbances, satellite clock errors, and frequency drift. Due to the lack of accurate error models and efficient data processing mechanisms, existing technologies cannot provide sufficiently stable and high-precision navigation signals in complex environments.

[0004] The purpose of the present invention is to provide a Beidou navigation signal optimization and error correction system based on big data, which can not only effectively reduce the errors caused by factors such as ionospheric disturbances and satellite clock errors, but also adapt to complex changes in dynamic environments, ensuring that the navigation system can provide high-precision and stable positioning services in different application scenarios. Summary of the invention

[0005] The present invention provides a Beidou navigation signal optimization and error correction system based on big data.

[0006] The Beidou navigation signal optimization and error correction system based on big data includes a multi-source heterogeneous data acquisition module, a data preprocessing module, a multi-dimensional error analysis module, an error collaborative prediction module, a real-time correction parameter generation module, and an optimized signal reconstruction module, among which;

[0007] The multi-source heterogeneous data acquisition module acquires multi-source heterogeneous data of Beidou satellites in real time, including original observation data, differential data of ground-based augmentation stations, ionospheric disturbance characteristic maps, and aging parameter curves of onboard atomic clocks;

[0008] The data preprocessing module performs spatial and temporal alignment of the original observation data and the differential data of the ground-based augmentation station, and extracts the sunspot cycle correlation parameters in the ionospheric disturbance characteristic spectrum and the frequency drift gradient in the aging parameter curve of the onboard atomic clock;

[0009] The multi-dimensional error analysis module establishes an ionospheric disturbance prediction model based on the sunspot cycle correlation parameters, generates satellite clock error compensation coefficients in combination with the frequency drift gradient, and simultaneously constructs a dynamic multi-path feature library;

[0010] The error collaborative prediction module fuses the output of the ionospheric disturbance prediction model, the satellite clock error compensation coefficient and the dynamic multipath feature library to generate a three-dimensional spatial error distribution matrix;

[0011] The real-time correction parameter generation module dynamically adjusts the signal transmission power allocation scheme according to the three-dimensional spatial error distribution matrix to generate frequency domain-time domain joint correction parameters;

[0012] The optimized signal reconstruction module performs phase compensation and power spectrum reshaping on the original Beidou navigation signal based on the generated frequency domain-time domain joint correction parameters, and outputs an optimized Beidou navigation signal.

[0013] Optionally, the multi-source heterogeneous data acquisition module includes:

[0014] Acquisition of original observation data: Beidou navigation signals are received in real time through Beidou satellite receivers, and the signals are preliminarily demodulated to extract original observation data, including pseudorange, carrier phase, and signal strength;

[0015] Ground-based augmentation station differential data acquisition: by cooperating with ground differential stations, real-time ground-based augmentation station data is obtained to provide differential correction data for satellite positioning accuracy;

[0016] Acquisition of ionospheric disturbance characteristic maps: Acquisition of ionospheric disturbance characteristic maps in real time through ionospheric detection instruments or space weather service platforms;

[0017] Acquisition of aging parameter curve of onboard atomic clock: Acquisition and real-time monitoring of aging parameter curve of onboard atomic clock.

[0018] Optionally, the data preprocessing module includes:

[0019] Space-time alignment: The original observation data and the differential data of the ground-based augmentation station are aligned in space and time through timestamps and satellite orbit information;

[0020] Extraction of parameters associated with the sunspot cycle: Use historical data of the sunspot cycle to fit and analyze the ionospheric disturbance map and extract parameters related to the solar activity cycle (sunspot number);

[0021] Frequency drift gradient extraction: Extract the frequency drift gradient from the aging parameter curve of the onboard atomic clock to compensate for the frequency error of the onboard atomic clock.

[0022] Optionally, the multi-dimensional error analysis module includes:

[0023] Establish an ionospheric disturbance prediction model: Through regression analysis algorithm, according to the number of sunspots and the amplitude of ionospheric disturbance The relationship between them is used to establish an ionospheric disturbance prediction model to predict the ionospheric disturbance in the future time period;

[0024] Generate satellite clock compensation coefficients: Generate satellite clock compensation coefficients by combining the frequency drift gradient extracted from the aging parameter curve of the onboard atomic clock , compensate for the frequency drift error of the atomic clock;

[0025] Synchronously build a dynamic multipath feature library: Based on ionospheric disturbances and satellite clock error compensation coefficients, build a dynamic multipath feature library to optimize the multipath effect impact.

[0026] Optionally, the error collaborative prediction module includes:

[0027] Multi-dimensional error data fusion: The output of the ionospheric disturbance prediction model, satellite clock error compensation coefficients and dynamic multipath feature library are fused using the spatiotemporal graph convolutional neural network (ST-GCN);

[0028] Generate a three-dimensional spatial error distribution matrix: Based on the results of multi-dimensional error data fusion, generate a three-dimensional spatial error distribution matrix and display the distribution of errors in space and time.

[0029] Optionally, the multi-dimensional error data fusion includes:

[0030] Constructing a spatiotemporal graph convolutional neural network: Construct a spatiotemporal graph convolutional neural network (ST-GCN) to process the output of the ionospheric disturbance prediction model, the satellite clock error compensation coefficient, and the data of the dynamic multipath feature library. The graph convolution operation is used to capture the spatial dependency between the data, and the temporal convolution layer is used to capture the dynamic changes in time. The ionospheric disturbance, satellite clock error, and multipath features are used as the input data of the nodes and edges to form a spatiotemporal graph structure, which can be expressed as:

[0031] ;

[0032] in, For the The node feature matrix of the layer, For the The node feature matrix of the layer, is the degree matrix of the node, is the adjacency matrix, For the The weight matrix of the layer, is the ReLU activation function;

[0033] ;

[0034] in, is the output of the temporal convolutional layer, For the moment The node characteristics of is the temporal convolution kernel, * represents the convolution operation, is the length of the time series;

[0035] Fusion of multi-dimensional error data and output of fusion results: Based on the constructed spatiotemporal graph convolutional neural network, the ionospheric disturbance prediction model output, satellite clock error compensation coefficients and dynamic multipath feature library are integrated to output comprehensive error prediction results. .

[0036] Optionally, generating a three-dimensional space error distribution matrix includes:

[0037] Construct a three-dimensional spatial error distribution matrix: Based on the results of multi-dimensional error data fusion, it will be mapped to a three-dimensional spatial coordinate system, where the spatial dimension represents different geographical locations, the time dimension represents different moments, and the error value is used as the element of the matrix to generate a three-dimensional spatial error distribution matrix ;

[0038] Display the distribution of errors in space and time: Use visualization tools to display the three-dimensional spatial error distribution matrix and show the changes in errors in space and time.

[0039] Optionally, the real-time correction parameter generation module includes:

[0040] Adjust the signal transmission power allocation scheme: According to the three-dimensional spatial error distribution matrix The error information in the signal is used to dynamically adjust the signal transmission power allocation scheme;

[0041] Generate frequency domain-time domain joint correction parameters: According to the power allocation scheme of the adjusted signal, generate frequency domain-time domain joint correction parameters by performing joint correction in the frequency domain and time domain on the adjusted signal .

[0042] Optionally, generating the frequency domain-time domain joint correction parameter includes:

[0043] Frequency domain correction parameter calculation: Based on the adjusted signal power allocation scheme, the frequency domain correction parameters are calculated by compensating for the frequency offset caused by ionospheric disturbances and satellite clock errors. ;

[0044] Time domain correction parameter calculation: Based on the adjusted signal power allocation scheme, the time domain correction parameters are calculated by compensating for the phase offset caused by ionospheric disturbances and satellite clocks. ;

[0045] Frequency domain-time domain joint correction parameter calculation: Combined with the frequency domain correction parameters and time domain correction parameters , generate frequency domain-time domain joint correction parameters .

[0046] Optionally, the optimized signal reconstruction module includes:

[0047] Phase compensation and frequency correction: Based on the generated frequency domain-time domain joint correction parameters , perform phase compensation and frequency correction on the original Beidou navigation signal;

[0048] Power spectrum reshaping: Adjust the power spectrum of the signal by jointly correcting the parameters in the frequency domain and time domain;

[0049] Output optimized Beidou navigation signal: Convert the jointly corrected signal from frequency domain back to time domain and output the final optimized Beidou navigation signal .

[0050] Beneficial effects of the present invention:

[0051] The present invention comprehensively improves the accuracy and reliability of Beidou navigation signals through real-time collection and preprocessing of multi-source heterogeneous data. The system integrates multiple data sources from Beidou satellite original observation data, ground-based augmentation station differential data, ionospheric disturbance characteristic maps and satellite-borne atomic clock aging parameter curves. After time-space alignment and feature extraction, it can accurately capture and analyze influencing factors such as ionospheric disturbances and satellite clock errors, ensure the consistency and accuracy of signals in time and space dimensions, effectively reduce error sources, and improve the overall stability and robustness of the system.

[0052] The present invention, through multi-dimensional error analysis and collaborative prediction modules, can accurately analyze and predict different types of errors in real time, such as ionospheric disturbances, satellite clock errors and multipath effects, and fuse error data through a spatiotemporal graph convolutional neural network to generate a three-dimensional spatial error distribution matrix, thereby effectively displaying the changes in errors in space and time, optimizing the error prediction accuracy, and dynamically adjusting the signal transmission power allocation scheme to generate frequency domain-time domain joint correction parameters, providing all-round compensation for the signal's phase, frequency and power spectrum, thereby improving the signal's accuracy and stability and adapting to dynamic changes in complex environments.

[0053] The present invention improves the compensation effect of the signal in the frequency domain by reshaping the power spectrum, which can not only effectively reduce the errors caused by factors such as ionospheric interference and satellite clock error, but also enhance the anti-interference ability of the signal, ensuring the reliability and high-precision positioning of the signal in complex environments. Through this comprehensive signal optimization process, the system can continuously provide high-precision and high-stability Beidou navigation services in a dynamically changing environment, providing a more accurate and efficient solution for the application of the Beidou system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of a multi-dimensional error analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0058] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0059] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figure 1-Figure 2 As shown, the Beidou navigation signal optimization and error correction system based on big data includes a multi-source heterogeneous data acquisition module, a data preprocessing module, a multi-dimensional error analysis module, an error collaborative prediction module, a real-time correction parameter generation module and an optimized signal reconstruction module, wherein;

[0061] The multi-source heterogeneous data acquisition module acquires multi-source heterogeneous data of Beidou satellites in real time, including original observation data, differential data of ground-based augmentation stations, ionospheric disturbance characteristic maps, and aging parameter curves of onboard atomic clocks;

[0062] The data preprocessing module aligns the original observation data with the differential data of the ground-based augmentation station in time and space, and extracts the sunspot cycle correlation parameters in the ionospheric disturbance characteristic map and the frequency drift gradient in the aging parameter curve of the satellite-borne atomic clock;

[0063] The multi-dimensional error analysis module establishes an ionospheric disturbance prediction model based on the sunspot cycle correlation parameters, generates satellite clock error compensation coefficients based on the frequency drift gradient, and simultaneously builds a dynamic multipath feature library;

[0064] The error collaborative prediction module fuses the output of the ionospheric disturbance prediction model, the satellite clock error compensation coefficient and the dynamic multipath feature library to generate a three-dimensional spatial error distribution matrix;

[0065] The real-time correction parameter generation module dynamically adjusts the signal transmission power allocation scheme according to the three-dimensional spatial error distribution matrix and generates frequency-domain-time-domain joint correction parameters;

[0066] The optimized signal reconstruction module performs phase compensation and power spectrum reshaping on the original Beidou navigation signal based on the generated frequency domain-time domain joint correction parameters, and outputs the optimized Beidou navigation signal.

[0067] The multi-source heterogeneous data acquisition module includes:

[0068] Acquisition of original observation data: Beidou navigation signals are received in real time through Beidou satellite receivers, and the signals are preliminarily demodulated to extract original observation data, including pseudorange, carrier phase, and signal strength;

[0069] Ground-based augmentation station differential data acquisition: By cooperating with ground differential stations, real-time ground-based augmentation station data is obtained to provide differential correction data for satellite positioning accuracy, including:

[0070] (1) Calculation of differential correction value: ;

[0071] in, is the differential correction value, which represents the positioning error correction between the mobile receiver and the ground-based augmentation station. is the observed value (pseudorange) measured by the mobile receiver, The observations obtained from the ground-based augmentation station;

[0072] (2) Calculation of corrected positioning coordinates: Assume that the position error of the mobile receiver is , then the corrected mobile receiver coordinates It is expressed as:

[0073] ;

[0074] in, is the original positioning coordinates of the mobile receiver, is the position correction obtained by the differential correction value;

[0075] (3) Calculation of differentially corrected pseudorange (pseudorange): Pseudorange after differential correction It is expressed as:

[0076] ;

[0077] in, is the original pseudorange measured by the mobile receiver, is the differential correction value obtained from the ground-based enhancement station;

[0078] Acquisition of ionospheric disturbance characteristic maps: Acquisition of ionospheric disturbance characteristic maps in real time through ionospheric detection instruments or space weather service platforms;

[0079] Acquisition of aging parameter curve of onboard atomic clock: Acquisition and real-time monitoring of aging parameter curve of onboard atomic clock;

[0080] Through the above content, the integration of multiple data sources including Beidou satellite original observation data, ground-based augmentation station differential data, ionospheric disturbance characteristic maps and satellite-borne atomic clock aging parameter curves can achieve comprehensive and accurate real-time data acquisition and processing, which can effectively improve positioning accuracy and signal stability. Through comprehensive analysis and correction of data from different sources, the errors caused by ionospheric disturbances, satellite clock errors and multipath effects can be significantly reduced. At the same time, considering the spatiotemporal synchronization between different data sources, the reliability and accuracy of the system are further enhanced, enabling the system to cope with complex environmental changes and ensuring that Beidou navigation signals can provide high-precision and stable positioning services in different application scenarios.

[0081] The data preprocessing module includes:

[0082] Time and space alignment: The original observation data and the ground-based augmentation station differential data are time and space aligned through the timestamp and satellite orbit information. Suppose the timestamp of the original observation data is The timestamp of the differential data from the ground-based augmentation station is , the goal of spatiotemporal alignment is to synchronize time through linear interpolation method, which is expressed as:

[0083] ;

[0084] in, is the time after alignment, is the timestamp of the original observation data, is the timestamp of the differential data of the ground-based augmentation station, is the interpolation step size;

[0085] Extraction of parameters associated with the sunspot cycle: Use historical data of the sunspot cycle to fit and analyze the ionospheric disturbance spectrum, extract parameters related to the solar activity cycle (sunspot number), and perform regression analysis on the correlation of ionospheric disturbances through sunspot cycle data. and sunspot number The relationship between them is expressed as:

[0086] ;

[0087] in, is the ionospheric disturbance amplitude, is the sunspot number, and is the regression coefficient;

[0088] Frequency drift gradient extraction: The frequency drift gradient in the aging parameter curve of the onboard atomic clock is extracted to compensate for the frequency error of the onboard atomic clock and ensure the accuracy of the navigation signal. It is expressed as:

[0089] ;

[0090] in, is the frequency drift gradient, is the frequency value at the current moment, is the frequency value at the previous moment, is the time interval between two moments;

[0091] Through the above content, the original observation data and the differential data of the ground-based augmentation station are aligned in time and space, and the sunspot cycle correlation parameters in the ionospheric disturbance characteristic map and the frequency drift gradient in the satellite-borne atomic clock aging parameter curve are extracted, which significantly improves the overall accuracy and reliability of the system. Through time and space alignment, the module ensures that data from different sources are processed synchronously in the same time and space coordinate system, eliminating the errors caused by time and space differences. The extracted correlation parameters provide an accurate basis for subsequent error analysis and compensation, especially in the compensation of ionospheric disturbances and atomic clock frequency drift, which can effectively reduce the errors in the signal optimization process.

[0092] The multi-dimensional error analysis module includes:

[0093] Establish an ionospheric disturbance prediction model: Through regression analysis algorithm, according to the number of sunspots and the amplitude of ionospheric disturbance The relationship between them is used to establish an ionospheric disturbance prediction model to predict the ionospheric disturbance in the future time period;

[0094] Generate satellite clock compensation coefficients: Generate satellite clock compensation coefficients by combining the frequency drift gradient extracted from the aging parameter curve of the onboard atomic clock , to compensate the frequency drift error of the atomic clock, expressed as:

[0095] ;

[0096] in, and is the weight coefficient to be optimized, is the ionospheric disturbance amplitude, is the frequency drift gradient;

[0097] Synchronously build a dynamic multipath feature library: Based on ionospheric disturbances and satellite clock error compensation coefficients, build a dynamic multipath feature library to optimize the multipath effect The influence of enhancing positioning accuracy is expressed as:

[0098] ;

[0099] in, is the estimated value of the multipath effect, and is the weight coefficient associated with different satellites and environments, is the ionospheric disturbance amplitude, is the satellite clock error compensation coefficient, is the number of paths involved in the calculation;

[0100] Through the above content, various errors in the Beidou navigation signal can be accurately analyzed and compensated. Through the ionospheric disturbance prediction model established based on the sunspot cycle correlation parameters, combined with the satellite clock error compensation coefficient generated by the frequency drift gradient, and the synchronously constructed dynamic multipath feature library, the module can comprehensively and real-timely perform error analysis and correction, so that the system can maintain high-precision positioning services in complex environmental changes. At the same time, it can effectively reduce the negative impact of ionospheric interference, satellite clock error and multipath effects on signal accuracy, thereby greatly improving the reliability and robustness of the navigation system.

[0101] The error collaborative prediction module includes:

[0102] Multi-dimensional error data fusion: The output of the ionospheric disturbance prediction model, satellite clock error compensation coefficients and dynamic multipath feature library are fused using the spatiotemporal graph convolutional neural network (ST-GCN);

[0103] Generate a three-dimensional spatial error distribution matrix: Based on the results of multi-dimensional error data fusion, generate a three-dimensional spatial error distribution matrix and display the distribution of errors in space and time;

[0104] Through the above content, it is possible to comprehensively integrate multi-source error data, accurately capture the spatiotemporal dependencies between different error sources, and more finely analyze and predict errors in navigation signals, especially in complex environments. This greatly improves the accuracy and robustness of error prediction. The generated three-dimensional spatial error distribution matrix intuitively shows the changes in errors in different spatial positions and time periods.

[0105] Multi-dimensional error data fusion includes:

[0106] Constructing a spatiotemporal graph convolutional neural network: Construct a spatiotemporal graph convolutional neural network (ST-GCN) to process the output of the ionospheric disturbance prediction model, the satellite clock error compensation coefficient, and the data of the dynamic multipath feature library. The graph convolution operation is used to capture the spatial dependency between the data, and the temporal convolution layer is used to capture the dynamic changes in time. The ionospheric disturbance, satellite clock error, and multipath features are used as the input data of the nodes and edges to form a spatiotemporal graph structure, which can be expressed as:

[0107] ;

[0108] in, For the The node feature matrix of the layer, For the The node feature matrix of the layer, is the degree matrix of the node, is the adjacency matrix, For the The weight matrix of the layer, is the ReLU activation function;

[0109] ;

[0110] in, is the output of the temporal convolutional layer, For the moment The node characteristics of is the temporal convolution kernel, * represents the convolution operation, is the length of the time series;

[0111] Fusion of multi-dimensional error data and output of fusion results: Based on the constructed spatiotemporal graph convolutional neural network, the ionospheric disturbance prediction model output, satellite clock error compensation coefficients and dynamic multipath feature library are integrated to output comprehensive error prediction results. , expressed as:

[0112] ;

[0113] in, is the ionospheric disturbance amplitude output by the ionospheric disturbance prediction model, is the satellite clock error compensation coefficient, is a dynamic multi-path feature library. Represents the spatiotemporal graph convolutional neural network fusion operation;

[0114] Through the above content, the data of multiple error sources such as ionospheric disturbances, satellite clock compensation coefficients and dynamic multipath feature libraries are effectively integrated, which can comprehensively capture the complex dependencies of various errors in the time and space dimensions, not only improving the accuracy of error analysis, but also improving the adaptability to different environmental changes, so that the navigation system can predict and correct errors in real time and accurately. By jointly processing spatial structure and time dynamics, the fused results can provide more refined data support for subsequent error compensation, significantly improving positioning accuracy and system robustness, especially maintaining high stability in complex environments.

[0115] Generating a three-dimensional spatial error distribution matrix includes:

[0116] Construct a three-dimensional spatial error distribution matrix: Based on the results of multi-dimensional error data fusion, it will be mapped to a three-dimensional spatial coordinate system, where the spatial dimension represents different geographical locations, the time dimension represents different moments, and the error value is used as the element of the matrix to generate a three-dimensional spatial error distribution matrix , expressed as:

[0117] ;

[0118] in, is the error value in the three-dimensional space error distribution matrix, expressed in spatial coordinates and time The error in time, Ionospheric disturbances in space and time The predicted value of is the satellite clock error compensation coefficient in space and time The value of The multipath effect in space and time The predicted value of Represents the operation of mapping multi-dimensional error data into three-dimensional space;

[0119] Display the distribution of errors in space and time: Use visualization tools to display the three-dimensional spatial error distribution matrix and show the changes in errors in space and time;

[0120] Through the above content, the changes in errors in space and time dimensions can be intuitively displayed, providing a more comprehensive error analysis perspective for the positioning system. By integrating multi-dimensional error data such as ionospheric disturbances, satellite clock errors and multipath effects into a three-dimensional matrix, the distribution characteristics of errors at different positions and times can be clearly seen. This visualization method not only helps to understand the spatiotemporal characteristics of errors, but also provides accurate data support for subsequent error correction and signal optimization, thereby significantly improving the robustness and positioning accuracy of the navigation system in complex environments.

[0121] The real-time correction parameter generation module includes:

[0122] Adjust the signal transmission power allocation scheme: According to the three-dimensional spatial error distribution matrix The error information in the signal is used to dynamically adjust the signal transmission power allocation scheme to optimize the signal quality and positioning accuracy, which is expressed as:

[0123] ;

[0124] in, is the adjusted signal power, indicating that and time The power distribution at each moment, is the basic power allocation, indicating the initial signal power allocation scheme, is the error value in the three-dimensional space error distribution matrix, is the maximum value of the error value in the three-dimensional space error distribution matrix;

[0125] Generate frequency domain-time domain joint correction parameters: According to the power allocation scheme of the adjusted signal, generate frequency domain-time domain joint correction parameters by performing joint correction in the frequency domain and time domain on the adjusted signal , optimize the phase, frequency and power spectrum of the signal to adapt it to the changes of different error sources;

[0126] Through the above content, accurate correction of Beidou navigation signals can be achieved, which can comprehensively consider various error sources such as ionospheric disturbances, satellite clock errors, frequency drift, etc., and optimize the signal propagation characteristics in the frequency domain and time domain by dynamically adjusting the signal power allocation scheme in real time. This not only improves the accuracy of the signal, but also effectively reduces the errors caused by environmental changes (such as ionospheric changes, antenna multipath effects, etc.), ensuring the stability and high precision of the signal.

[0127] Generating frequency domain-time domain joint correction parameters includes:

[0128] Frequency domain correction parameter calculation: Based on the adjusted signal power allocation scheme, the frequency domain correction parameters are calculated by compensating for the frequency offset caused by ionospheric disturbances and satellite clock errors. , expressed as:

[0129] ;

[0130] in, is the frequency domain correction parameter, indicating the frequency The correction value under is the frequency shift caused by ionospheric disturbance, is the frequency offset caused by the satellite clock error, is the frequency compensation factor, which indicates the sensitivity to frequency error, Based on the adjusted signal power allocation scheme at frequency Power allocation under

[0131] Time domain correction parameter calculation: Based on the adjusted signal power allocation scheme, the time domain correction parameters are calculated by compensating for the phase offset caused by ionospheric disturbances and satellite clocks. , expressed as:

[0132] ;

[0133] in, is the time domain correction parameter, indicating the time The correction value under is the phase error caused by ionospheric disturbance, is the phase error caused by the satellite clock error, is the phase compensation factor, which indicates the sensitivity to phase error;

[0134] Frequency domain-time domain joint correction parameter calculation: Combined with the frequency domain correction parameters and time domain correction parameters , generate frequency domain-time domain joint correction parameters , expressed as:

[0135] ;

[0136] Through the above content, the signal errors in frequency and time domains can be considered at the same time, so as to achieve more comprehensive signal correction. By combining the frequency domain and time domain correction parameters, the system can perform comprehensive compensation for error sources such as ionospheric disturbances and satellite clock errors, ensuring that the signal will not be affected by a single error source during transmission. This joint correction method can improve the accuracy and stability of the signal, especially in a dynamic environment, and can effectively deal with complex error sources and optimize signal quality.

[0137] The optimized signal reconstruction module includes:

[0138] Phase compensation and frequency correction: Based on the generated frequency domain-time domain joint correction parameters , the original Beidou navigation signal is phase compensated and frequency corrected, and the joint correction parameters are used to simultaneously correct the frequency and delay of the signal, thereby achieving comprehensive signal optimization, which is expressed as:

[0139] ;

[0140] in, is the signal after phase compensation, is the original Beidou navigation signal, is the frequency domain-time domain joint correction parameter, is an imaginary unit;

[0141] Power spectrum reshaping: By jointly correcting the parameters in the frequency domain and time domain, the power spectrum of the signal is adjusted to ensure that the compensation effect at different frequencies is more accurate, which can be expressed as:

[0142] ;

[0143] in, is the signal after power spectrum reshaping, the optimized frequency domain signal, is the signal after phase compensation;

[0144] Output optimized Beidou navigation signal: Convert the jointly corrected signal from frequency domain back to time domain and output the final optimized Beidou navigation signal , expressed as:

[0145] ;

[0146] in, For the final optimized Beidou navigation signal, is the inverse Fourier transform operation, which converts the frequency domain signal back to the time domain signal. is the optimized frequency domain signal;

[0147] Through the above content, by combining the joint correction parameters of the frequency domain and time domain, the frequency and phase errors in the Beidou navigation signal can be comprehensively corrected, which not only improves the accuracy of the signal, but also optimizes the stability and anti-interference ability of the signal. Through phase compensation and power spectrum reshaping, it can effectively compensate for the errors caused by factors such as ionospheric disturbances and satellite clock errors, ensuring the reliability and accuracy of the signal in complex environments.

[0148] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0149] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Beidou navigation signal optimization and error correction system based on big data, characterized in that: It includes a multi-source heterogeneous data acquisition module, a data preprocessing module, a multi-dimensional error analysis module, an error collaborative prediction module, a real-time correction parameter generation module and an optimized signal reconstruction module, among which; The multi-source heterogeneous data acquisition module acquires multi-source heterogeneous data of Beidou satellites in real time, including original observation data, differential data of ground-based augmentation stations, ionospheric disturbance characteristic maps, and aging parameter curves of onboard atomic clocks; The data preprocessing module performs spatial and temporal alignment of the original observation data and the differential data of the ground-based augmentation station, and extracts the sunspot cycle correlation parameters in the ionospheric disturbance characteristic spectrum and the frequency drift gradient in the aging parameter curve of the onboard atomic clock; The multi-dimensional error analysis module establishes an ionospheric disturbance prediction model based on the sunspot cycle correlation parameters, generates satellite clock error compensation coefficients in combination with the frequency drift gradient, and simultaneously constructs a dynamic multi-path feature library; The error collaborative prediction module fuses the output of the ionospheric disturbance prediction model, the satellite clock error compensation coefficient and the dynamic multipath feature library to generate a three-dimensional spatial error distribution matrix; The real-time correction parameter generation module dynamically adjusts the signal transmission power allocation scheme according to the three-dimensional spatial error distribution matrix to generate frequency domain-time domain joint correction parameters; The optimized signal reconstruction module performs phase compensation and power spectrum reshaping on the original Beidou navigation signal based on the generated frequency domain-time domain joint correction parameters, and outputs an optimized Beidou navigation signal.

2. The Beidou navigation signal optimization and error correction system based on big data according to claim 1, characterized in that: The multi-source heterogeneous data acquisition module includes: Acquisition of original observation data: Beidou navigation signals are received in real time through Beidou satellite receivers, and the signals are preliminarily demodulated to extract original observation data, including pseudorange, carrier phase, and signal strength; Ground-based augmentation station differential data acquisition: by cooperating with ground differential stations, real-time ground-based augmentation station data is obtained to provide differential correction data for satellite positioning accuracy; Acquisition of ionospheric disturbance characteristic maps: Acquisition of ionospheric disturbance characteristic maps in real time through ionospheric detection instruments or space weather service platforms; Acquisition of aging parameter curve of onboard atomic clock: Acquisition and real-time monitoring of aging parameter curve of onboard atomic clock.

3. The Beidou navigation signal optimization and error correction system based on big data according to claim 1, characterized in that: The data preprocessing module comprises: Space-time alignment: The original observation data and the differential data of the ground-based augmentation station are aligned in space and time through timestamps and satellite orbit information; Extraction of parameters associated with the sunspot cycle: Use historical data of the sunspot cycle to fit and analyze the ionospheric disturbance spectrum and extract parameters related to the solar activity cycle; Frequency drift gradient extraction: Extract the frequency drift gradient from the aging parameter curve of the onboard atomic clock to compensate for the frequency error of the onboard atomic clock.

4. The Beidou navigation signal optimization and error correction system based on big data according to claim 3 is characterized in that: The multi-dimensional error analysis module includes: Establish an ionospheric disturbance prediction model: Through regression analysis algorithm, according to the number of sunspots and the amplitude of ionospheric disturbance The relationship between them is used to establish an ionospheric disturbance prediction model to predict the ionospheric disturbance in the future time period; Generate satellite clock compensation coefficients: Generate satellite clock compensation coefficients by combining the frequency drift gradient extracted from the aging parameter curve of the onboard atomic clock , compensate for the frequency drift error of the atomic clock; Synchronously build a dynamic multipath feature library: Based on ionospheric disturbances and satellite clock error compensation coefficients, build a dynamic multipath feature library to optimize the multipath effect impact.

5. The Beidou navigation signal optimization and error correction system based on big data according to claim 1, characterized in that: The error collaborative prediction module includes: Multi-dimensional error data fusion: Use the spatiotemporal graph convolutional neural network to fuse the ionospheric disturbance prediction model output, satellite clock error compensation coefficients and dynamic multipath feature library; Generate a three-dimensional spatial error distribution matrix: Based on the results of multi-dimensional error data fusion, generate a three-dimensional spatial error distribution matrix and display the distribution of errors in space and time.

6. The Beidou navigation signal optimization and error correction system based on big data according to claim 5 is characterized in that: The multi-dimensional error data fusion includes: Constructing a spatiotemporal graph convolutional neural network: Constructing a spatiotemporal graph convolutional neural network to process the output of the ionospheric disturbance prediction model, the satellite clock error compensation coefficient, and the data of the dynamic multipath feature library. The spatial dependency between the data is captured through the graph convolution operation, and the temporal convolution layer is used to capture the dynamic changes in time. The ionospheric disturbance, satellite clock error, and multipath features are used as the input data of the nodes and edges to form a spatiotemporal graph structure, which can be expressed as: ； in, For the The node feature matrix of the layer, For the The node feature matrix of the layer, is the degree matrix of the node, is the adjacency matrix, For the The weight matrix of the layer, is the ReLU activation function; ； in, is the output of the temporal convolutional layer, For the moment The node characteristics of is the temporal convolution kernel, * represents the convolution operation, is the length of the time series; Fusion of multi-dimensional error data and output of fusion results: Based on the constructed spatiotemporal graph convolutional neural network, the ionospheric disturbance prediction model output, satellite clock error compensation coefficients and dynamic multipath feature library are integrated to output comprehensive error prediction results. .

7. The Beidou navigation signal optimization and error correction system based on big data according to claim 6 is characterized in that: Generating a three-dimensional space error distribution matrix comprises: Construct a three-dimensional spatial error distribution matrix: Based on the results of multi-dimensional error data fusion, it will be mapped to a three-dimensional spatial coordinate system, where the spatial dimension represents different geographical locations, the time dimension represents different moments, and the error value is used as the element of the matrix to generate a three-dimensional spatial error distribution matrix ; Display the distribution of errors in space and time: Use visualization tools to display the three-dimensional spatial error distribution matrix and show the changes in errors in space and time.

8. The Beidou navigation signal optimization and error correction system based on big data according to claim 7 is characterized in that: The real-time correction parameter generation module comprises: Adjust the signal transmission power allocation scheme: According to the three-dimensional spatial error distribution matrix The error information in the signal is used to dynamically adjust the signal transmission power allocation scheme; Generate frequency domain-time domain joint correction parameters: According to the power allocation scheme of the adjusted signal, generate frequency domain-time domain joint correction parameters by performing joint correction in the frequency domain and time domain on the adjusted signal .

9. The Beidou navigation signal optimization and error correction system based on big data according to claim 8, characterized in that: The generating of the frequency domain-time domain joint correction parameters comprises: Frequency domain correction parameter calculation: Based on the adjusted signal power allocation scheme, the frequency domain correction parameters are calculated by compensating for the frequency offset caused by ionospheric disturbances and satellite clock errors. ; Time domain correction parameter calculation: Based on the adjusted signal power allocation scheme, the time domain correction parameters are calculated by compensating for the phase offset caused by ionospheric disturbances and satellite clocks. ; Frequency domain-time domain joint correction parameter calculation: Combined with the frequency domain correction parameters and time domain correction parameters , generate frequency domain-time domain joint correction parameters .

10. The Beidou navigation signal optimization and error correction system based on big data according to claim 9, characterized in that: The optimized signal reconstruction module comprises: Phase compensation and frequency correction: Based on the generated frequency domain-time domain joint correction parameters , perform phase compensation and frequency correction on the original Beidou navigation signal; Power spectrum reshaping: Adjust the power spectrum of the signal by jointly correcting the parameters in the frequency domain and time domain; Output optimized Beidou navigation signal: Convert the jointly corrected signal from frequency domain back to time domain and output the final optimized Beidou navigation signal .

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