Inertial auxiliary-based dynamic compensation system for compass high-precision space-time reference

Abstract

The application relates to the technical field of inertial assistance, in particular to a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance, which comprises the following steps: using original measurement data of an inertial measurement unit module, wherein the original measurement data comprises carrier phase and Doppler observation value; using a processing unit module to fuse the inertial measurement unit, to perform real-time environment sensing and dynamic modeling, to classify the operation environment and to predict error characteristics; performing an adaptive robust fusion filtering algorithm, the algorithm dynamically adjusts filter parameters and switches filter models to estimate the position and posture of a carrier; combining radio wave precise point positioning enhanced service to realize ultra-tight combined signal tracking and integrity monitoring, feeding back the estimated inertial assistance system state to a tracking loop of the multi-frequency Beidou receiving module, and performing real-time fault detection on the original measurement data.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of inertial assistance, in particular to a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance. BACKGROUND

[0002] In the field of Beidou high-precision space-time reference dynamic compensation assisted by inertial assistance, there are many technologies dedicated to solving the problems of navigation accuracy and robustness. These patents usually focus on the integration of GNSS and INS, error compensation methods and performance improvement in complex environments. Existing patents also include methods for compensating and correcting GPS / INS integrated navigation systems using neural network algorithms, which can output accurate navigation data through inertial assistance even in GPS signal loss state. In addition, effective fusion of the angular velocity, linear velocity and position information of the visual odometry with the inertial assistance can also improve the accuracy and reliability of the integrated navigation system. At present, error compensation for multipath and sensors still needs to achieve deeper intelligent fusion under the premise of ensuring real-time performance.

[0003] The application proposes a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance, which constructs a highly intelligent multi-source heterogeneous sensor fusion framework. The innovative fusion algorithm can solve the problems of data rate, delay and calibration difference between different sensors, and realize more accurate time synchronization and sensor self-calibration. SUMMARY

[0004] The application is a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance, which can more comprehensively and accurately reflect the motion state and environmental information of the carrier.

[0005] The Beidou high-precision space-time reference dynamic compensation system based on inertial assistance comprises:

[0006] The processing unit module: fuses the original measurement data set, constructs a fusion data function, classifies the operating environment and predicts error characteristics, and generates a dynamic error model; the original measurement data set includes pseudorange, carrier phase and Doppler observation value;

[0007] The execution algorithm module: executes an adaptive robust fusion filtering algorithm based on the dynamic error model and real-time radio sensor data quality, dynamically adjusts the filter parameters and switches the filter model to estimate the position and attitude of the carrier;

[0008] The monitoring and tracking module: constructs a residual model using the position and attitude data of the estimated carrier collected by the filter parameter set model, realizes super-tight combined signal tracking and integrity monitoring, and performs real-time fault detection on the original measurement data.

[0009] Preferably, the fusion raw measurement data, constructing a fusion data function, classifying the operating environment and predicting error characteristics, generating a dynamic error model implementation process includes:

[0010] The auxiliary sensor is used to obtain environmental data, including temperature, air pressure, humidity, light intensity, object shape, distance, electromagnetic field strength and change, and a convolutional neural network containing 3 convolutional layers and 2 fully connected layers is used, the input is an N*M matrix, N is the type of auxiliary sensor, including temperature, air pressure and light, M is the data of the past M sampling points, and the output is a probability distribution vector of terrain classification. Cross-entropy loss function is used for training to classify multiple terrains of the operating environment, including mountainous area, plain, urban canyon, water area, desert and different environmental conditions, including sunny, rainy, snowy and foggy weather conditions. Through the obtained environmental classification information, a database is established in advance and the system operation state is monitored in real time. Each data is weighted and summed according to the weight to construct a fusion data function, to classify and predict the error of the operating environment, analyze the error characteristics, and generate a dynamic error model of Beidou and the processing unit. The dynamic error model can predict the various error impacts on Beidou signal and processing unit in a specific environment in real time.

[0011] Preferably, the implementation process of the adaptive robust fusion filtering algorithm includes:

[0012] The filter variants include robust adaptive Kalman filter, unscented Kalman filter and particle filter; and the measurement noise covariance matrix and the process noise covariance matrix are dynamically adjusted according to the signal quality index; the signal-to-noise ratio, pseudorange measurement residual, carrier phase multipath effect index, accelerometer and gyroscope noise level and error fluctuation data quality index are extracted from the various sensor data output by the error model and the execution algorithm module, combined with the current operating environment classification and error model obtained by real-time environmental perception, the system can finely and dynamically adjust the current measurement noise covariance matrix and process noise covariance matrix. The adjustment is based on rigorous mathematical models and statistical analysis methods, and the covariance matrix parameter values matched with the actual noise characteristics and error distribution are calculated in real time.

[0013] Preferably, the implementation process of the adaptive robust fusion filtering algorithm includes:

[0014] The processing unit feeds back the speed and attitude information to the numerical control oscillator of the monitoring tracking module to narrow the tracking loop bandwidth; and before measurement update, real-time fault detection and exclusion is performed on the original multi-frequency Beidou measurement data using statistical tests and residual analysis, the residual model is constructed by collecting position, speed and attitude data, a multi-frequency and multi-epoch residual model is constructed, potential error sources and fault characteristics in the observation data are deeply mined, after detecting fault and abnormal data, the system will immediately start the exclusion mechanism, and the original measurement data is purified by data rejection, error correction and redundant measurement replacement.

[0015] Preferably, the dynamic antenna tilt compensation module comprises:

[0016] The dynamic antenna tilt compensation module determines and compensates the antenna tilt based on the execution algorithm module data and the satellite elevation angle, and a set of antenna tilt error models are constructed inside the module, which comprehensively considers the geometric shape, electrical characteristics and electromagnetic wave propagation law factors of the antenna, and quantifies the errors caused by the antenna tilt to the Beidou signal reception and measurement.

[0017] Preferably, the multi-path effect adaptive suppression module comprises:

[0018] The multi-path effect adaptive suppression module uses multi-epoch residual analysis and an improved band-pass filtering technology for Beidou constellation characteristics, the improved band-pass filtering technology is a multi-scale analysis method based on wavelet transform, which identifies and rejects the periodic oscillation signals caused by multi-path effect by threshold processing of wavelet coefficients at a certain scale, and can better preserve the non-stationary characteristics of the signal and identify and alleviate the multi-path error compared with the traditional FIR band-pass filter, the module effectively identifies and alleviates the influence of multi-path error on positioning accuracy, and uses multi-epoch residual analysis technology to deeply analyze multi-frequency Beidou measurement data at different times and frequencies, which can capture the small phase and code observation changes caused by multi-path signals and provide basis for subsequent multi-path identification.

[0019] Preferably, the atmospheric delay real-time correction module comprises:

[0020] The atmospheric delay real-time correction module uses external models and precise point positioning enhancement services, and applies real-time ionospheric and tropospheric correction in ionospheric delay correction, the module integrates external models and precise point positioning enhancement services, the external models can provide real-time ionospheric electron density distribution, time and space variation characteristics and other key information based on a large amount of ionospheric observation data and physical models, which provides basic data support for ionospheric delay estimation, combined with the high-precision satellite orbit and clock error information provided by the precise point positioning enhancement service, the system can determine the propagation path and delay of satellite signals in the ionosphere.

[0021] Compared with the prior art, the application has the following beneficial effects:

[0022] 1. The application combines real-time environment perception with a dynamic error model generated by a dynamic modeling module, which can more comprehensively and accurately reflect the motion state of the carrier and environmental information. By fusing the pseudo-range, carrier phase and Doppler observation values of the processing unit module, as well as the angular rate and acceleration data of the algorithm module, the error and uncertainty of a single data source are effectively reduced, thereby significantly improving the positioning accuracy.

[0023] 2. The adaptive robust fusion filtering algorithm proposed in the application can quickly switch to a suitable filtering model and adjust the parameters. According to real-time environment perception and signal quality indicators, the filter parameters are dynamically adjusted and the filter model is switched, ensuring that the filter can adaptively optimize the estimation results under different environments and signal conditions, effectively suppressing errors caused by multipath effects, further improving positioning accuracy, and enabling the system to flexibly cope with signal characteristics and error changes in different environments without manual intervention to automatically achieve optimal filtering results, thereby improving the adaptability of the system to complex and variable environments.

[0024] 3. The ultra-tight combined signal tracking and integrity monitoring proposed in the application feeds the speed and attitude information derived by the processing unit back to the monitoring and tracking module, narrows the tracking loop bandwidth, and improves the stability and anti-interference ability of signal tracking. Furthermore, the constructed residual model is used for real-time fault detection and exclusion of the original measurement data, which can quickly and accurately identify and exclude abnormal data, prevent the influence of incorrect data on the positioning results, and enhance the reliability and safety of the system. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The application discloses a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance.

[0026] Figure 2 The application discloses a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance.

[0027] Figure 3 The application discloses a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance. DETAILED DESCRIPTION

[0028] Embodiment one

[0029] The application discloses a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance, which comprises a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance. Figure 1The satellite signal is easily blocked and reflected in the urban canyon environment, resulting in obvious multipath effect, poor signal quality, and difficulty for the Beidou receiver to obtain accurate positioning results. Moreover, complex traffic conditions and frequent acceleration, deceleration, and turning operations increase the demand for high-precision positioning and navigation. The Beidou high-precision space-time reference dynamic compensation system assisted by inertia includes a processing unit module: fusing raw measurement data sets, constructing a fusion data function, classifying the operating environment and predicting error characteristics, and generating a dynamic error model; the raw measurement data sets include pseudorange, carrier phase and Doppler observation values, carrier angular velocity and acceleration; an execution algorithm module: executing an adaptive robust fusion filtering algorithm based on the dynamic error model and real-time sensor data quality, dynamically adjusting filter parameters and switching filter models to estimate the position, velocity and attitude of the carrier; a monitoring and tracking module: using the estimated position, velocity and attitude data of the carrier to construct a residual model, realizing ultra-tight combined signal tracking and integrity monitoring, and performing real-time fault detection on the raw measurement data.

[0030] Further, the process of fusing raw measurement data, constructing a fusion data function, classifying the operating environment and predicting error characteristics, and generating a dynamic error model includes:

[0031] The system is equipped with auxiliary sensors including but not limited to thermometers, barometers, hygrometers, optical cameras, laser radars, electromagnetic sensors, etc. These sensors can measure and provide various key environmental parameters such as temperature, air pressure, humidity, light intensity, shape and distance of surrounding objects, strength of electromagnetic field and its changes in real time. Through comprehensive analysis of these environmental parameters, the system can more comprehensively and accurately understand the current operating environment, providing a solid data foundation for subsequent environmental classification and error prediction. After normalizing the carrier phase and Doppler observation values, weighted summation is performed according to the real-time signal-to-noise ratio to construct a fusion data function. Through convolutional neural network model training with a large amount of data under different terrain and environmental conditions, the current terrain type can be accurately identified, including complex and diverse terrain and landforms such as mountains, plains, urban canyons, water areas, deserts, and accurate judgment of the current environmental state, covering various weather conditions such as sunny, rainy, snowy, and foggy. In the classification process, the machine learning model automatically learns the data distribution rules under different environmental conditions by analyzing the features and patterns of environmental data.

[0032] In generating the dynamic error model of the Beidou and processing unit, the flowchart is as follows Figure 2As shown, the system dynamically constructs an error model that matches the current environment according to the environment classification information obtained by real-time environment perception. The dynamic error model is a three-layer feedforward neural network, the input layer nodes correspond to the environment classification and signal quality indicators, and the output layer nodes are the error standard deviations of the predicted pseudo-range and carrier phase. The error model can predict the various errors that the Beidou signal and processing unit may be subjected to in a specific environment in real time, including signal blocking errors in mountainous environments, multipath effect errors in urban canyons, and weather errors in adverse weather conditions. By accurately predicting and modeling these errors, the system can take appropriate compensation measures in advance.

[0033] The embodiment generates a dynamic error model by fusing measurement data and speed parameters. Through comprehensive analysis of these environmental parameters, the system can more comprehensively and accurately understand the current operating environment, effectively reduce the impact of errors on positioning accuracy, and ensure stable operation and high-precision positioning performance of the system in various complex environments, providing a solid data foundation for subsequent environment classification and error prediction.

[0034] Further, the implementation process of the adaptive robust fusion filtering algorithm includes:

[0035] The system intelligently selects the filter type that best suits the current working condition from these filter variants based on the current operating environment classification obtained by real-time environment perception and the error model, as well as the real-time monitoring of sensor data quality indicators. Figure 3 As shown, the filter includes a robust adaptive Kalman filter, an unscented Kalman filter, and a particle filter. To achieve dynamic adjustment of filter parameters, when the standardized residual error of the i-th satellite exceeds threshold 3, the measurement noise variance corresponding to it is increased by 10 times. The rule for switching filter models is: when the covariance determinant value of the Kalman filter innovation is below the threshold for 5 consecutive epochs, it indicates that the model is diverging, and the system switches to the particle filter model. The system monitors various sensor data output by the processing unit module and the algorithm execution module in real time, and extracts a series of key data quality indicators. These indicators include signal signal-to-noise ratio, which is used to evaluate the strength and reliability of the signal; pseudo-range measurement residual error, which reflects the difference between the pseudo-range observation value and the theoretical value; carrier phase multipath effect indicator, which quantifies the degree of influence of multipath effect on carrier phase measurement; and these data are used to represent the measurement accuracy and stability of the inertial measurement unit.

[0036] In terms of dynamically adjusting the measurement noise covariance matrix and the process noise covariance matrix, the system, according to a rigorous mathematical model and statistical analysis method, calculates the covariance matrix parameter value matched with the actual noise characteristics and error distribution in real time. The system analyzes the statistical characteristics of the data quality indicators, including mean, variance, and covariance, and combines the prior knowledge in the error model to finely adjust each element in the measurement noise covariance matrix and the process noise covariance matrix. When the signal-to-noise ratio of the detected signal is low or the pseudorange measurement residual is large, the system correspondingly increases the corresponding elements in the measurement noise covariance matrix to reflect the increase in the uncertainty of the measurement data. When the sensor data quality is good, the system appropriately reduces the element value in the covariance matrix to increase the reliance of the filter on the measurement data.

[0037] The embodiment can dynamically adjust the filtering strategy according to the complex real-time environmental conditions and sensor data quality by performing the adaptive robust fusion filtering algorithm, ensure that the filter can adaptively optimize the estimation result under different environments and signal conditions, fully exert the advantages of various filters, improve the positioning accuracy and reliability of the system, and provide high-precision position, velocity, and attitude estimation information for the carrier to meet the high-precision navigation and positioning requirements in complex and variable environments.

[0038] Further, the residual model is constructed using the estimated position, velocity, and attitude data of the carrier to realize ultra-tight combined signal tracking and integrity monitoring, and real-time fault detection is performed on the original measurement data. The specific implementation process includes:

[0039] The system realizes ultra-tight combined signal tracking by feeding back the high-precision velocity and attitude information derived by the processing unit to the numerical control oscillator of the monitoring and tracking module in real time. This feedback mechanism can effectively narrow the bandwidth of the tracking loop, making the tracking loop more sensitive to signal changes, thereby improving the accuracy and stability of signal tracking. Especially in complex environments, the narrowed tracking loop can better resist the influence of noise and interference, maintain stable tracking of satellite signals, and ensure that the system can continuously and accurately receive satellite signals to provide reliable positioning information for the carrier. Before measurement update, the system uses a rigorous statistical test and residual analysis method to comprehensively detect and exclude real-time faults in the original multi-frequency Beidou measurement data. The system first constructs a multi-frequency, multi-epoch residual model. The residual model compares the actually received satellite measurement data with the predicted measurement data based on the estimated position, velocity, and attitude of the carrier to calculate the residual value. Through in-depth analysis of the residual value, the system can identify potential error sources and fault characteristics in the observation data.

[0040] Once a fault or abnormal data is detected, the system quickly initiates a complete exclusion mechanism, including data rejection, error correction and redundant measurement replacement; for obvious errors or unreliable data, the system will directly reject it to avoid its negative impact on subsequent positioning calculation; for data with small errors, the system will correct it through error correction algorithm to restore the accuracy of the data as much as possible; and when the proportion of fault data exceeding the preset threshold of 3 meters reaches 15%, the system will also use redundant measurement data to replace the fault data, immediately selecting 5 qualified data from the redundant data for replacement, and the residual error of the replaced data and the original normal data is controlled within 0.5 meters, to ensure the integrity and reliability of the measurement data, effectively improve the quality of the data, and thus improve the overall performance and positioning accuracy of the system.

[0041] Through real-time monitoring and analysis of the residual error model, the system can also dynamically compensate and optimize various errors in the signal tracking process. According to the residual error analysis results, the gain and filter parameters of the tracking loop can be adjusted in real time to adapt to changes in signal conditions, further improving the stability and accuracy of signal tracking. At the same time, the system will also update the dynamic error model in time according to the results of fault detection and exclusion, providing more accurate error information for subsequent adaptive robust fusion filtering algorithm, forming a closed-loop optimization process, and continuously improving the performance of the system.

[0042] This embodiment realizes super-tight combined signal tracking and integrity monitoring by constructing a residual error model. It realizes high-precision tracking of satellite signals and strict quality control of measurement data, effectively improves the reliability and positioning accuracy of the system in complex environments, provides a strong guarantee for high-precision navigation and positioning applications, and significantly improves the stability of signal tracking and the reliability of data.

[0043] Further, the system further comprises a dynamic antenna tilt compensation module:

[0044] The module is closely connected with the execution algorithm module, and can receive high-precision angular rate and acceleration data provided by the inertial measurement unit in real time. Through in-depth analysis and processing of these data, the module can accurately calculate the real-time attitude information of the antenna in three-dimensional space, including the pitch angle, roll angle and heading angle, and at the same time, the module can also obtain the elevation angle information of the current satellite. In the dynamic antenna tilt compensation module, an accurate antenna tilt error model is constructed, which comprehensively considers various factors such as the geometric shape, electrical characteristics and electromagnetic wave propagation law of the antenna. Through accurate modeling of the antenna geometric shape, the system can analyze the signal reception conditions of each part of the antenna under different attitudes; the consideration of electrical characteristics can reflect the gain change and phase characteristics of the antenna in different directions; combined with the electromagnetic wave propagation law, the model can predict the influence of antenna tilt on signal propagation path and phase, so as to comprehensively quantify the errors caused by antenna tilt, including code observation error, phase observation error and multipath effect enhancement.

[0045] Based on the above error model, the module adopts various advanced compensation algorithms to compensate for the errors caused by antenna tilt in real time. For code observation error, the system can perform geometric correction on pseudorange measurement value, adjust the pseudorange calculation model according to the attitude information of the antenna, and in urban canyon environment, the code observation error can be reduced from an average of 0.1 meters to within 0.02 meters, with an error reduction rate of more than 80%, thereby reducing the influence of antenna tilt on code observation; for phase observation error, the system can dynamically correct the carrier phase by introducing a phase compensation term, and the standard deviation of phase observation error can be reduced from 0.1 degrees to 0.03 degrees, so as to eliminate the phase change caused by antenna attitude change; and for the problem of multipath effect enhancement, the module reduces the sensitivity of the antenna in a certain direction by optimizing the beam pattern of the antenna, and the positioning accuracy is improved from an average of 5 meters to within 1.5 meters, thereby reducing the interference of multipath signals and increasing the positioning accuracy.

[0046] Through the real-time compensation mechanism of the dynamic antenna tilt compensation module, the embodiment can effectively reduce the influence of antenna tilt on Beidou signal reception and measurement, and improve the quality and reliability of the signal. Whether in the working condition of large maneuvering flight, rapid maneuvering driving or driving in complex terrain environment, etc. causing the antenna attitude to change dramatically, the dynamic antenna tilt compensation module can ensure that the system stably receives satellite signals and provides high-precision positioning information, significantly improves the usability and reliability of the system, and widens the application scenarios of the system.

[0047] Further, the system further comprises a multipath effect adaptive suppression module:

[0048] The module first uses multi-epoch residual analysis technology to deeply analyze multi-frequency Beidou measurement data at different times and different frequencies. By constructing a residual sequence, the module can capture the small phase and code observation changes caused by multipath signals. On the basis of multi-epoch residual analysis, the past 10 epochs of pseudorange and carrier phase residual data are selected to form a multi-dimensional data set. Through statistical analysis and signal processing methods, the potential laws and characteristics in the residual data are mined. Combined with the band-pass filtering technology improved for the characteristics of the Beidou constellation, the suppression ability of the multipath error is further enhanced, and the positioning accuracy of the system is significantly improved. The improved band-pass filter can effectively filter out the interference frequency components caused by multipath signals, while maximizing the retention of useful information of direct signals. Through dynamic adjustment of the filter parameters, the system can adapt to different environmental conditions and signal characteristics, improving the filtering effect. At the same time, the module also has adaptive learning ability, which can automatically optimize the parameter settings of multi-epoch residual analysis and band-pass filtering according to the real-time monitoring of signal characteristics and environmental information. This adaptive feature enables the system to quickly adapt to environmental changes, continuously maintain effective suppression of multipath errors, and ensure stable and reliable operation of the system in various complex and variable environments.

[0049] The multi-path effect adaptive suppression module effectively identifies and mitigates multipath errors, and the system can significantly improve the accuracy and reliability of the positioning result. In urban environments, multipath signals can cause pseudorange measurement values to be larger, resulting in positioning errors. The multi-path effect adaptive suppression module of the present application can identify these multipath-induced errors in a timely manner and effectively suppress and correct them through filtering and data processing techniques, significantly improving the positioning accuracy of the system.

[0050] Further, the system also includes an atmospheric delay real-time correction module:

[0051] The module integrates a high-precision external model and a precise point positioning enhancement service. The external model is constructed based on a large amount of ionospheric observation data and physical models. The total electron content (TEC) and the zenith tropospheric delay (ZTD) obtained from the precise point positioning enhancement service are mapped onto the satellite signal path through the corresponding projection function. The original pseudorange and carrier phase observations are corrected before entering the fusion filter, which can provide real-time information on ionospheric electron density distribution, temporal variation characteristics, and spatial variation characteristics. Through accurate modeling of ionospheric electron density, the system can estimate the size and trend of ionospheric delay, providing basic data support for ionospheric delay correction. At the same time, the external model can also update the ionospheric electron density distribution in real time according to changes in solar activity, Earth's magnetic field, etc., ensuring the accuracy and timeliness of the model.

[0052] The precise point positioning augmentation service provides high-precision satellite orbit and clock error information for the system. These information is an important basis for implementing precise ionospheric delay correction. By combining the satellite orbit and clock correction parameters provided by the precise point positioning augmentation service, the system can more accurately determine the propagation path and delay of the satellite signal in the ionosphere, calculate the specific path of the signal when passing through the ionosphere according to the accurate orbit position and transmission time of the satellite, and combine the electron density distribution information provided by the external model to accurately estimate the value of the ionospheric delay. Thus, the original pseudo-range and carrier phase observation values are corrected in real time to eliminate the errors caused by ionospheric delay. Through analysis and modeling of meteorological data, the system can roughly correct the tropospheric delay, and by considering the influence of the tropospheric delay, the system can further improve the positioning accuracy.

[0053] The atmospheric delay real-time correction module of the embodiment can effectively correct the delay error. Especially in periods and regions with strong ionospheric activity, such as low-latitude regions, sunrise and sunset periods, etc., the influence of ionospheric delay is more significant. Through accurate ionospheric and tropospheric delay correction, the system can significantly improve the positioning accuracy and reduce the errors caused by atmospheric delay. For example, in the case of severe ionospheric delay changes, a positioning system without atmospheric delay correction may produce positioning errors of several meters or even tens of meters, while the system of the present application can control the positioning error within the centimeter level through the atmospheric delay real-time correction module, meeting the needs of high-precision navigation and positioning.

[0054] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, alternatives, and variations can be made thereto without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

[0055] Embodiment two

[0056] The embodiment of the present application discloses a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance. In mountainous environments, the terrain is complex and changeable, the mountain is seriously blocked, the satellite signal is easily blocked and reflected, and the signal strength is weak. The weather conditions in the mountainous area are complex and changeable, such as frequent occurrence of weather phenomena such as cloud, rain, etc., which further increases the difficulty of positioning and monitoring.

[0057] The Beidou high-precision space-time reference dynamic compensation system based on inertial assistance comprises a processing unit module: after the original measurement data set, the carrier phase and the Doppler observation value are normalized, the weighted sum is calculated according to the real-time signal-to-noise ratio, the fusion data function is constructed, the operation environment is classified and the error characteristics are predicted, the input layer nodes correspond to the environment classification and the signal quality index, the output layer nodes are the error standard deviation of the predicted pseudo-range and carrier phase, and the dynamic error model is generated; the original measurement data set comprises pseudo-range, carrier phase and Doppler observation value, carrier angular velocity and acceleration; the execution algorithm module: the adaptive robust fusion filtering algorithm is executed, the adaptive robust fusion filtering algorithm is based on the dynamic error model and the real-time sensor data quality, the filter parameters and the filter model are dynamically adjusted, and when the standardized residual error of the i-th satellite exceeds the threshold value 2, the measurement noise variance corresponding to the i-th satellite is increased by 5 times. The rule for switching the filter model is: when the Kalman filter innovation covariance determinant value of three consecutive epochs is lower than the threshold value, it indicates that the model diverges, and the system switches to the particle filter model to estimate the position, velocity and attitude of the carrier; the monitoring and tracking module: the residual error model is constructed by using the estimated position, velocity and attitude data of the carrier, the super-tight combined signal tracking and integrity monitoring are realized, and real-time fault detection is performed on the original measurement data. Through the cooperative action of the processing unit module, the execution algorithm module and the monitoring and tracking module of the Beidou high-precision space-time reference dynamic compensation system based on inertial assistance, the functional advantages of each module are fully utilized, the whole process optimization processing from data fusion, error prediction, adaptive filtering to signal tracking and fault detection is realized, the precision and reliability of the carrier position, velocity and attitude estimation are effectively improved, and the adaptability and anti-interference ability of the system in complex environment are significantly enhanced.

[0058] Further, the environment data obtained by the auxiliary sensor, including temperature, air pressure, humidity, light intensity, object shape, distance, electromagnetic field strength and change, etc., is classified by a machine learning model, and it is identified that the current environment is in a mountainous area. And according to the historical data and real-time monitoring information, the dynamic error model of Beidou and the processing unit is generated, which can predict the influence of various factors on the positioning accuracy in the mountainous environment in real time, such as the error caused by mountain shielding and the error caused by weather condition change.

[0059] Further, the processing unit module selects the most suitable filter model, such as the unscented Kalman filter, from multiple filter variants according to the mountainous environment classification and error model obtained from real-time environment perception. Meanwhile, the processing unit module and the execution algorithm module output various types of sensor data, which are monitored in real time to extract signal-to-noise ratio, pseudorange measurement residual, carrier phase multipath effect indicator, accelerometer and gyroscope noise level and error fluctuation, and other data quality indicators. Based on these indicators and error models, the system makes fine and dynamic adjustments to the measurement noise covariance matrix and the process noise covariance matrix to ensure the optimal performance of the filter in mountainous environments.

[0060] The velocity and attitude information derived by the processing unit is fed back to the numerically controlled oscillator of the processing unit module, narrowing the tracking loop bandwidth and improving the stability and anti-interference ability of signal tracking. Before measurement update, real-time fault detection and exclusion are performed on the original multi-frequency Beidou measurement data using statistical tests and residual analysis. By constructing a multi-frequency, multi-epoch residual model, potential error sources and fault characteristics in the observation data are thoroughly explored. Upon detecting faulty or abnormal data, the system immediately activates the exclusion mechanism, using data rejection, error correction, redundant measurement replacement, and other means to purify the original measurement data, ensuring that the data used for positioning calculation has high reliability and high precision.

[0061] The dynamic antenna tilt compensation module determines and compensates for antenna tilt errors in real time based on the original measurement data and satellite elevation angle information. The antenna tilt error model built in this module takes into account factors such as the geometry of the antenna, electrical characteristics, and electromagnetic wave propagation laws, quantifying the various errors caused by antenna tilt in Beidou signal reception and measurement, including code observation error, phase observation error, and enhanced multipath effect. After determining the error, a compensation algorithm is used to compensate for the error caused by antenna tilt in real time, effectively reducing the impact of attitude changes during the flight of the unmanned aerial vehicle on positioning accuracy.

[0062] The multipath effect adaptive suppression module uses multi-epoch residual analysis and bandpass filtering technology improved for Beidou constellation characteristics to identify and mitigate multipath errors. By analyzing multi-frequency Beidou measurement data at different times and frequencies in depth, constructing residual sequences, and extracting multipath feature information, the module can capture small phase and code observation changes caused by multipath signals. Based on this feature information, the module can effectively identify and mitigate the impact of multipath errors on positioning accuracy, improving the reliability of the positioning result.

[0063] The atmospheric delay real-time correction module uses external models and precise point positioning augmentation service to apply real-time ionospheric and tropospheric corrections. The external model can provide real-time ionospheric electron density distribution, time and space variation characteristics and other key information based on a large number of ionospheric observation data and physical models. Combined with the high-precision satellite orbit and clock error information provided by the precise point positioning augmentation service, the system accurately determines the propagation path and delay of satellite signals in the ionosphere and corrects the atmospheric delay error in real time, further improving the positioning accuracy.

[0064] In mountainous environments, the unmanned aerial vehicle can obtain stable and high-precision positioning information and accurate flight attitude control through the inertial auxiliary Beidou high-precision space-time reference dynamic compensation system of the application. Compared with traditional positioning and navigation systems, the positioning error caused by factors such as mountain obstruction and weather condition changes can be effectively reduced, providing reliable technical support for the flight safety, geographic mapping and environmental monitoring of unmanned aerial vehicles in mountainous areas, and improving the task execution efficiency and data acquisition quality of unmanned aerial vehicles.

[0065] Although embodiments of the application have been shown and described, it is to be understood that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. The BeiDou high-precision time-space reference dynamic compensation system based on inertial assistance is characterized by: include: Processing unit module: This module integrates the original measurement data set containing pseudorange, carrier phase and Doppler observations, the carrier angular rate and acceleration provided by the inertial measurement unit, and the signal quality indicators of the Beidou satellite signal. It uses auxiliary sensors to obtain real-time environmental data and classifies the terrain and weather conditions of the carrier based on a machine learning model to obtain environmental classification information. Based on the environmental classification information and signal quality indicators, combined with a pre-established error model library, a dynamic error model is constructed through a neural network. The error of the Beidou satellite signal is predicted based on the environmental classification information, and the standard deviation of the predicted error of the pseudorange and carrier phase of the Beidou satellite signal is output. Execution algorithm module: executes an adaptive robust fusion filtering algorithm that fuses the carrier angular rate and acceleration provided by the inertial measurement unit. The adaptive robust fusion filtering algorithm dynamically adjusts filter parameters and switches filter models based on a dynamic error model and real-time radio sensor data quality to estimate the carrier's position and attitude. Dynamic adjustment of filter parameters includes increasing the measurement noise variance corresponding to a satellite when the standardized residual of a satellite exceeds a preset threshold. Switching the filter model includes switching the filter model to a particle filter model when the Kalman filter innovation covariance determinant value for multiple consecutive epochs is lower than a preset threshold. Monitoring and tracking module: This module uses the estimated carrier position and attitude data output by the execution algorithm module to build a residual model, achieve ultra-tight combined signal tracking and integrity monitoring, and perform real-time fault detection on the original measurement data; Dynamic antenna tilt compensation module: The module determines and compensates for antenna tilt based on the execution algorithm module data and satellite elevation angle. An antenna tilt error model is built within the module. The model comprehensively considers the antenna's geometry, electrical characteristics, and electromagnetic wave propagation laws to quantify the various errors caused by antenna tilt on Beidou signal reception and measurement.

2. The inertial-assisted BeiDou high-precision space-time reference dynamic compensation system according to claim 1 is characterized in that: The construction process of the dynamic error model includes: The carrier phase and Doppler observation values ​​in the original measurement data set are weighted and summed according to the real-time signal-to-noise ratio in the original measurement data set to construct a fusion data function; environmental data is acquired using auxiliary sensors, and a machine learning model is adopted to classify the terrain and weather conditions in which the carrier is located, in combination with the carrier angular rate and acceleration in the original measurement data set, to obtain environmental classification information; based on the environmental classification information and the signal quality index, in combination with a pre-established error model library, the dynamic error model is constructed through a neural network, and the standard deviation of the prediction error of the pseudorange and carrier phase of the Beidou satellite signal is output.

3. The inertial-assisted BeiDou high-precision space-time reference dynamic compensation system according to claim 1 is characterized in that: The process of implementing the adaptive robust fusion filtering algorithm includes: Select from multiple filter variants, including robust adaptive Kalman filtering, unscented Kalman filtering, and particle filtering; based on the analysis of sensor data quality indicators including signal-to-noise ratio, pseudorange measurement residuals, and noise levels of accelerometers and gyroscopes, combined with the environmental classification information and dynamic error model, dynamically adjust the measurement noise covariance matrix and the process noise covariance matrix; when the standardized residual of a satellite exceeds a preset threshold, increase the corresponding measurement noise variance; when the Kalman filter innovation covariance determinant value of multiple consecutive epochs is lower than a preset threshold, switch the filter model.

4. The inertial-assisted BeiDou high-precision time-space reference dynamic compensation system according to claim 1 is characterized in that: The process of constructing a residual model using the estimated carrier position, velocity, and attitude data to achieve ultra-tight combined signal tracking and integrity monitoring, and performing real-time fault detection on the original measurement data includes: The speed and attitude information exported by the processing unit is fed back to the numerically controlled oscillator of the monitoring and tracking module to narrow the tracking loop bandwidth; and before the measurement update, statistical tests and residual analysis are used to perform real-time fault detection and troubleshooting on the original multi-frequency Beidou measurement data. The residual model constructs a multi-frequency, multi-epoch residual model based on the collected position, speed and attitude data, and deeply explores the potential error sources and fault characteristics in the observation data. After detecting faults and abnormal data, the system will immediately activate the elimination mechanism and use data elimination, error correction, and redundant measurement replacement to purify the original measurement data.

5. The inertial-assisted BeiDou high-precision time-space reference dynamic compensation system according to claim 1 is characterized in that: Also includes: The multipath effect adaptive suppression module uses multi-epoch residual analysis and bandpass filtering technology improved for the characteristics of the Beidou constellation to identify and mitigate multipath errors. The module uses multi-epoch residual analysis technology to conduct in-depth analysis of multi-frequency Beidou measurement data at different times and frequencies. Multi-epoch residual analysis can capture tiny phase and code observation changes caused by multipath signals.

6. The inertial-assisted BeiDou high-precision space-time reference dynamic compensation system according to claim 1, characterized in that: Also includes: The atmospheric delay real-time correction module uses external models and precise point positioning augmentation services to apply real-time ionospheric and tropospheric corrections. In terms of ionospheric delay correction, the module integrates external models and precise point positioning augmentation services. The external model is based on a large amount of ionospheric observation data and physical models, and can provide real-time information on the ionospheric electron density distribution, time and space variation characteristics, providing basic data support for the estimation of ionospheric delay. Combined with the high-precision satellite orbit and clock error information provided by the precise point positioning augmentation service, the system can determine the propagation path and delay of satellite signals in the ionosphere.

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