A Beidou navigation antenna calibration system based on multi-knowledge body

Abstract

The invention provides a Beidou navigation antenna calibration system based on multiple knowledge bodies, belonging to the field of satellite navigation and positioning technology, in order to solve the problem that when the position and attitude of the antenna change in a dynamic environment, the existing calibration system is difficult to adapt to the antenna change in real time and accurately, thereby affecting the calibration accuracy; the system consists of three units: a data acquisition unit, a fusion processing unit and an execution feedback unit, the data acquisition unit acquires satellite navigation, antenna signals and real-time environmental information; the fusion processing unit performs fusion processing and analysis on these data, including modules such as data processing, historical data management, environmental model construction, knowledge fusion algorithm and calibration parameter calculation; the execution feedback unit performs calibration compensation and monitoring on the antenna according to the analysis result; the system improves the calibration accuracy and reliability through the multiple knowledge body fusion algorithm, and improves the performance of the Beidou satellite navigation system.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a Beidou navigation antenna calibration system based on multiple knowledge bodies. Background Art

[0002] As a key device for receiving Beidou satellite signals, the main function of the Beidou navigation antenna is to convert weak electromagnetic signals from Beidou satellites into electrical signals that can be processed by navigation equipment. It plays a vital role in the Beidou satellite navigation system and directly affects the accuracy and reliability of navigation positioning. In actual applications, due to various factors, the Beidou navigation antenna may experience performance degradation, reduced accuracy and other problems, so it needs to be calibrated when used.

[0003] For example, the patent with the announcement number CN101180551B discloses an antenna array calibration system and method. Each antenna has a calibration coupler for providing a monitoring signal representing a signal passing through a transceiver associated therewith, and a processing unit, the processing unit including at least one signal splitter and a combiner array, the signal splitter splits at least one monitoring signal, and the combiner array includes one or more combiners for combining at least two split monitoring signals from a first antenna and a second antenna, thereby generating a first combined signal representing an in-phase superposition and a second combined signal representing an orthogonal superposition. The power detector communicates with the processing unit and is configured to estimate the power level of the signal passing through each of the first antenna and the second antenna and the in-phase power and the orthogonal power of the in-phase superposition and the orthogonal superposition, thereby determining the phase difference of the signal on the antenna. However, in a dynamic environment, the position and posture of the antenna are constantly changing, and it is difficult for the existing calibration system to accurately adapt to these changes in real time, resulting in a decrease in calibration accuracy.

[0004] Therefore, a Beidou navigation antenna calibration system based on multiple knowledge bodies is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a Beidou navigation antenna calibration system based on multiple knowledge bodies, aiming to solve the problem in the above-mentioned background technology that when the position and attitude of the antenna change in a dynamic environment, the existing calibration system is difficult to adapt to the antenna changes in real time and accurately, thus affecting the calibration accuracy.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a Beidou navigation antenna calibration system based on multiple knowledge bodies, including a data acquisition unit for collecting satellite navigation information, antenna signal information and antenna real-time environment information through sensors, a fusion processing unit for fusing and calculating and analyzing the collected antenna calibration raw data of different aspects, and an execution feedback unit for calibrating, compensating and monitoring the antenna according to the analysis results, wherein the fusion processing unit specifically includes: a data processing module for filtering and removing impurities from the antenna sensor data collected by the sensor in real time, and smoothing the Beidou signal strength data; a historical data management module for storing historical calibration data of the antenna at different positions and times; The calibration data includes calibration parameters and signal quality information, and it mines feature information from historical data to automatically analyze the relationship between antenna performance and position and environmental factors; the environmental model construction module is used to establish a mathematical model of environmental parameters including temperature and humidity and antenna performance, and predict and evaluate the impact of environmental parameters on antenna performance; the knowledge fusion algorithm module is used to use a multi-knowledge fusion algorithm to fuse sensor data, historical data and environmental data to give a comprehensive fusion result, providing a basis for antenna calibration decisions; the calibration parameter calculation module is used to calculate the calibration parameters suitable for the current antenna position and environment based on the comprehensive fusion results, and optimize the calibration parameters based on the antenna type and signal frequency factors.

[0007] Furthermore, the data acquisition unit includes a sensor module, which is used to detect the acceleration and motion state of the antenna through an acceleration sensor, provide real-time information on the antenna position change, and measure the angular velocity of the antenna based on the gyroscope to assist in determining the antenna's attitude change; a signal transceiver module, which is used to receive Beidou satellite signals in real time, provide original data for the calibration of the navigation antenna, and use a signal strength monitor to monitor the Beidou signal strength in real time to reflect the quality of the antenna receiving signal; an environmental monitoring module, which is used to measure the ambient temperature and humidity of the navigation antenna in real time, and provide a basis for interference analysis of environmental impact during antenna calibration.

[0008] Furthermore, the execution feedback unit includes a calibration execution module, which is used to send calibration parameters to the antenna calibration device so that it performs calibration operations on the navigation antenna according to the calibration parameters and monitors the calibration process of the navigation antenna in real time; a monitoring feedback module, which is used to monitor the antenna performance of the navigation antenna after calibration, including signal strength and positioning accuracy, in real time, and send feedback information back to the main control computer; and a system status monitoring module, which is used to monitor the operating status of the entire calibration system, including the hardware equipment status and software operation status, in real time after the navigation antenna is calibrated, so as to promptly detect and handle system faults.

[0009] Furthermore, the steps of the knowledge fusion algorithm module using a multi-knowledge body fusion algorithm to fuse sensor data, historical data and environmental data to give a comprehensive fusion result are as follows: let the sensor knowledge body be S, the historical data knowledge body be H, the environmental knowledge body be E, the antenna signal quality index be Q, and the antenna rotation angle be θ, determine the comprehensive knowledge body variable K, K=ω1S+ω2H+ω3E, where ω1, ω2 and ω3 are weight coefficients of the sensor knowledge body, the historical data knowledge body and the environmental knowledge body, respectively, and satisfy ω1+ω2+ω3=1; calculate the antenna signal quality index Q: Q=f(K), where function f is a regression model based on machine learning, which is obtained by training the comprehensive knowledge body data and the corresponding signal quality data; determine the objective function for optimizing the antenna rotation angle θ: objective function G(θ)=Q(θ)+αR(θ), where R(θ) represents the indicator function of system stability and reliability, and α is the weight coefficient and is a constant; optimize the antenna rotation angle θ: solve the maximum or minimum antenna rotation angle of the objective function G(θ) through the gradient descent optimization algorithm ,Right now or ; Calculate the final fusion result and further adjust the antenna rotation angle based on the fusion result.

[0010] Furthermore, the specific formula of the function f is: ,in, represents the signal strength corresponding to the comprehensive knowledge body variable K, represents the signal-to-noise ratio corresponding to the comprehensive knowledge body variable K, It represents the bit error rate corresponding to the comprehensive knowledge body variable K. a, b and c are positive weight coefficients, all of which are constants.

[0011] Furthermore, the calculation formula of the indicator function R(θ) is: when the antenna rotation angle is θ, the measured value of the received signal strength within a period of time , then the mean signal strength is , the variance of the signal intensity is expressed as , R(θ) is a function inversely proportional to the signal strength variance, that is, , where m is a constant.

[0012] Furthermore, the fusion result is calculated as follows: Let the antenna state variable be X t , the state transfer matrix is ​​A, the system noise covariance matrix is ​​P, the observation matrix is ​​T, and the observation noise covariance matrix is ​​Z; calculate the initial estimate of the weighted average Medium S t is the value of the sensor data at time t, H t is the value of historical data at time t, E tis the value of environmental data at time t; prediction steps: ,in, is the optimal estimate at the previous moment, ,in is the estimated error covariance matrix of the previous moment; update steps: ,in The optimal estimate that finally integrates sensor data, historical data and environmental data is O t is the observed value of the comprehensive knowledge body variable at time t, I represents the unit matrix, that is, the elements on the main diagonal are all 1, and the rest of the elements are all 0.

[0013] Furthermore, the state transfer matrix A is constructed based on the actual change characteristics of the antenna position and signal strength state parameters. The system state includes the antenna position (x, y, z) and the signal strength s. The state transfer matrix is ​​in the form of Among them, Δt x , Δt y and Δt z Respectively represent the rate of change of position over time in the x, y and z directions; the system noise is independent of each other in position and signal strength, and has a standard deviation σ x、 σ y , σ z and σ s , then the system noise covariance matrix is ​​in the form of: Among them, σ x、 σ y , σ z and σ s Determined by statistical analysis of system noise or by estimation based on system hardware characteristics.

[0014] Furthermore, the observation matrix T is The observation noise of sensor data, historical data, environmental data and weighted average intermediate results are independent of each other and have a standard deviation σ S、 σ H , σ E and σ WA , then the observation noise covariance matrix is ​​in the form of: , where σ S、 σ H , σ E and σ WA Determined by statistical analysis of the noise in the observed data or by estimation based on sensor accuracy factors.

[0015] Furthermore, the calibration process of the execution feedback unit for the navigation antenna is as follows: Step 1, data collection: collecting sensor data, historical data and environmental data to provide a comprehensive information basis for subsequent calibration;

[0016] Step 2: Fusion calculation: By defining comprehensive knowledge body variables, calculating signal quality indicators, determining objective functions, solving the optimal rotation angle and optimizing and updating the rotation angle, various information is integrated to determine the best calibration parameters; Step 3: Calibration execution: Adjust the antenna rotation angle, verify the calibration results, ensure that the calibration effect meets expectations, and record the calibration process to form a report.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a Beidou navigation antenna calibration system based on multiple knowledge bodies, the data acquisition unit comprehensively collects satellite navigation information, antenna signal information and real-time environmental information, the various modules in the fusion processing unit can effectively process and analyze the data, the historical data management module mines the characteristics of historical data, the environmental model construction module establishes a relationship model between the environment and antenna performance, the knowledge fusion algorithm module fuses various data to provide a reliable basis for calibration decisions, the calibration parameter calculation module calculates and optimizes the calibration parameters according to the fusion results, the execution feedback unit ensures that the calibration parameters are accurately sent to the antenna calibration equipment, monitors the calibration process in real time, monitors the antenna performance and feeds back information, and monitors the operating status of the entire system at the same time, and promptly discovers and handles faults. Through the synergistic effect of these functions, the system can improve the calibration accuracy and reliability of the Beidou navigation antenna, so that it can accurately receive satellite signals in different environments, provide accurate data support for navigation positioning, and bring better navigation experience to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the overall architecture diagram of the present invention; Figure 2 It is a diagram of the execution feedback unit architecture of the present invention; Figure 3 A navigation antenna calibration topology diagram of the present invention; Figure 4 This is a navigation antenna calibration flow chart of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In order to solve the problem that the existing calibration system is difficult to accurately adapt to the antenna changes in real time when the position and attitude of the antenna change in a dynamic environment, which affects the calibration accuracy, please refer to Figure 1-Figure 4 , provide the following preferred technical solutions:

[0021] A Beidou navigation antenna calibration system based on multiple knowledge bodies is composed of a data acquisition unit for obtaining satellite navigation information, antenna signal information and antenna real-time environmental information with the help of sensors, a fusion processing unit for fusing and calculating and analyzing the collected antenna calibration raw data of different aspects, and an execution feedback unit for calibrating, compensating and monitoring the antenna according to the analysis results. Among them, the fusion processing unit specifically includes: a data processing module: this module mainly performs filtering and removing clutter operations on the antenna sensor data collected by the sensor in real time, and smoothes the Beidou signal strength data. A historical data management module: this module is used to store the historical calibration data of the antenna at different positions and times, and these calibration data cover calibration parameters and signal quality information. At the same time, it can also mine feature information from historical data and automatically analyze the relationship between antenna performance and position and environmental factors. An environmental model construction module: this module is dedicated to establishing a mathematical model of environmental parameters including temperature and humidity and antenna performance, and predicting and evaluating the degree of influence of environmental parameters on antenna performance. A knowledge fusion algorithm module: this module uses a multi-knowledge body fusion algorithm to fuse sensor data, historical data and environmental data, so as to give a comprehensive fusion result and provide a basis for antenna calibration decision-making.

[0022] The knowledge fusion algorithm module uses a multi-knowledge body fusion algorithm to fuse sensor data, historical data and environmental data to give a comprehensive fusion result. The steps are as follows: let the sensor knowledge body be S, the historical data knowledge body be H, the environmental knowledge body be E, the antenna signal quality index be Q, and the antenna rotation angle be θ. Determine the comprehensive knowledge body variable K, K=ω1S+ω2H+ω3E, where ω1, ω2 and ω3 are the weight coefficients of the sensor knowledge body, the historical data knowledge body and the environmental knowledge body, respectively, and satisfy ω1+ω2+ω3=1; calculate the antenna signal quality index Q: Q=f(K), where function f is a regression model based on machine learning, which is obtained by training the comprehensive knowledge body data and the corresponding signal quality data; determine the objective function for optimizing the antenna rotation angle θ: objective function G(θ)=Q(θ)+αR(θ), where R(θ) represents the indicator function of system stability and reliability, and α is the weight coefficient and a constant; optimize the antenna rotation angle θ: solve the maximum or minimum antenna rotation angle of the objective function G(θ) through the gradient descent optimization algorithm. ,Right now or ; Calculate the final fusion result, and further adjust the antenna rotation angle based on the fusion result. The specific formula of function f is: ,in, represents the signal strength corresponding to the comprehensive knowledge body variable K, represents the signal-to-noise ratio corresponding to the comprehensive knowledge body variable K, It represents the bit error rate corresponding to the comprehensive knowledge body variable K. a, b and c are positive weight coefficients, all of which are constants.

[0023] For the calculation of signal strength, the direct signal strength measurement in the sensor data and the correction of signal strength by historical data and environmental data can be considered, such as: ,in, is the signal strength part based on the sensor knowledge body S, It is the signal strength correction part based on the historical data knowledge body H. It is the signal strength correction part based on the environmental knowledge body E, and ω4 and ω5 are the corresponding weight coefficients. Similarly, the signal-to-noise ratio and bit error rate can also be calculated similarly based on different knowledge bodies.

[0024] The calculation formula of the indicator function R (θ) is: when the antenna rotates at an angle of θ, the measured value of the received signal strength over a period of time , then the mean signal strength is , the variance of the signal intensity is expressed as , R(θ) is a function inversely proportional to the signal strength variance, that is, , where m is a constant.

[0025] The fusion result is calculated as follows:

[0026] Let the antenna state variable be X t , the state transfer matrix is ​​A, the system noise covariance matrix is ​​P, the observation matrix is ​​T, and the observation noise covariance matrix is ​​Z; calculate the initial estimate of the weighted average , where S t is the value of the sensor data at time t, H t is the value of historical data at time t, E t is the value of environmental data at time t; prediction steps: in is the optimal estimate at the previous moment, ,in The estimated error covariance matrix of the previous moment; update steps: in The optimal estimate that finally integrates sensor data, historical data and environmental data is O t is the observed value of the comprehensive knowledge body variable at time t, I represents the unit matrix, that is, the elements on the main diagonal are all 1, and the rest of the elements are all 0.

[0027] The state transfer matrix A is constructed based on the actual change characteristics of the antenna position and signal strength state parameters. The system state includes the antenna position (x, y, z) and the signal strength s. The state transfer matrix is ​​in the form of , where Δtx , Δt y and Δt z Respectively represent the rate of change of position over time in the x, y and z directions; the system noise is independent of each other in position and signal strength, and has a standard deviation σ x、 σ y , σ z and σ s , then the system noise covariance matrix is ​​in the form of Among them, σ x、 σ y , σ z and σ s Determined by statistical analysis of system noise or by estimation based on system hardware characteristics.

[0028] The observation matrix is ​​T. , while the observation noise of sensor data, historical data, environmental data and weighted average intermediate results are independent of each other and have a standard deviation σ S、 σ H , σ E and σ WA , then the observation noise covariance matrix is ​​in the form of: Among them, σ S、 σ H , σ E and σ WA It is determined by statistical analysis of the noise of the observed data or by estimation based on sensor accuracy factors. Calibration parameter calculation module: This module calculates the calibration parameters that are suitable for the current antenna position and environment based on the comprehensive fusion results, and optimizes the calibration parameters based on factors such as antenna type and signal frequency.

[0029] Specifically, the data acquisition unit includes: sensor module: This module detects the acceleration and motion state of the antenna through an accelerometer, provides real-time information on the change in the antenna position, and measures the angular velocity of the antenna with the help of a gyroscope to assist in determining the change in the antenna's attitude. Signal transceiver module: This module is used to receive Beidou satellite signals in real time, provide raw data for the calibration of the navigation antenna, and use a signal strength monitor to monitor the Beidou signal strength in real time to reflect the quality of the antenna receiving signal. Environmental monitoring module: This module is used to measure the temperature and humidity of the surrounding environment of the navigation antenna in real time, providing a basis for interference analysis of environmental impacts during antenna calibration.

[0030] In addition, the execution feedback unit includes: calibration execution module: this module is responsible for sending the calibration parameters to the antenna calibration equipment, so that it can calibrate the navigation antenna according to the calibration parameters, and monitor the calibration process of the navigation antenna in real time. Monitoring feedback module: this module is used to monitor the antenna performance of the navigation antenna after calibration in real time, including signal strength and positioning accuracy, and send feedback information back to the main control computer. System status monitoring module: after the navigation antenna is calibrated, this module monitors the operating status of the entire calibration system in real time, including the hardware device status and software operation status, and promptly discovers and handles system faults. The calibration process of the execution feedback unit for the navigation antenna is as follows: S1, data acquisition: collect sensor data, historical data and environmental data to provide a comprehensive information basis for subsequent calibration; S2, fusion calculation: by defining comprehensive knowledge body variables, calculating signal quality indicators, determining objective functions, solving the optimal rotation angle and optimizing and updating the rotation angle, comprehensive information from multiple aspects is used to determine the best calibration parameters; S3, calibration execution: adjust the antenna rotation angle, verify the calibration results, ensure that the calibration effect meets expectations, and record the calibration process to form a report.

[0031] Specifically, the data acquisition unit collects satellite navigation information, antenna signal information and real-time environmental information through the sensor module, signal transceiver module and environmental monitoring module. The fusion processing unit includes a data processing module, a historical data management module, an environmental model construction module, a knowledge fusion algorithm module and a calibration parameter calculation module. The data processing module filters and removes impurities from the antenna sensor data and smoothes the Beidou signal strength data; the historical data management module stores historical calibration data and mines feature information; the environmental model construction module establishes a mathematical model of environmental parameters and antenna performance and performs predictive evaluation; the knowledge fusion algorithm module uses a multi-knowledge body fusion algorithm to fuse various types of data to give a comprehensive fusion result, providing a basis for calibration decisions; the calibration parameter calculation module According to the fusion results, the calibration parameters suitable for the current situation are calculated and optimized. The execution feedback unit includes a calibration execution module, a monitoring feedback module and a system status monitoring module. It is responsible for sending the calibration parameters to the antenna calibration equipment, monitoring the performance of the antenna after calibration in real time and sending feedback information back to the main control computer. At the same time, it monitors the operating status of the entire calibration system in real time. During the working process, the knowledge fusion algorithm module determines the comprehensive knowledge body variables, calculates the antenna signal quality index, determines the objective function, optimizes the antenna rotation angle and other steps, and finally calculates the fusion result and adjusts the antenna rotation angle. The whole system realizes the calibration of the Beidou navigation antenna through processes such as data acquisition, fusion calculation and calibration execution, ensuring that it can work accurately and reliably in a dynamic environment.

[0032] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0033] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A Beidou navigation antenna calibration system based on multiple knowledge bodies, characterized in that: The invention comprises a data acquisition unit for collecting satellite navigation information, antenna signal information and antenna real-time environment information through sensors, a fusion processing unit for fusing and calculating and analyzing the collected antenna calibration raw data of different aspects, and an execution feedback unit for calibrating, compensating and monitoring the antenna according to the analysis results. The fusion processing unit specifically comprises: a data processing module for filtering and removing impurities from the antenna sensor data collected by the sensor in real time, and smoothing the Beidou signal strength data; a historical data management module for storing the historical calibration data of the antenna at different positions and times, the calibration data including calibration parameters and signal quality information, mining feature information from the historical data, and automatically analyzing the relationship between the antenna performance and the position and environmental factors; an environmental model construction module for establishing a mathematical model of environmental parameters including temperature and humidity and antenna performance, and predicting and evaluating the influence of environmental parameters on antenna performance; a knowledge fusion algorithm module for fusing sensor data, historical data and environmental data using a multi-knowledge body fusion algorithm to give a comprehensive fusion result, so as to provide a basis for antenna calibration decision; a calibration parameter calculation module for calculating calibration parameters suitable for the current antenna position and environment according to the comprehensive fusion results, and calculating and optimizing the calibration parameters according to the antenna type and signal frequency factors; The steps of the knowledge fusion algorithm module using a multi-knowledge body fusion algorithm to fuse sensor data, historical data and environmental data to give a comprehensive fusion result are as follows: let the sensor knowledge body be S, the historical data knowledge body be H, the environmental knowledge body be E, the antenna signal quality index be Q, and the antenna rotation angle be θ, determine the comprehensive knowledge body variable K, K=ω1S+ω2H+ω3E, where ω1, ω2 and ω3 are the weight coefficients of the sensor knowledge body, the historical data knowledge body and the environmental knowledge body, respectively, and satisfy ω1+ω2+ω3=1; calculate the antenna signal quality index Q: Q=f(K), where function f is a regression model based on machine learning, which is obtained by training the comprehensive knowledge body data and the corresponding signal quality data; determine the objective function for optimizing the antenna rotation angle θ: objective function G(θ)=Q(θ)+αR(θ), where R(θ) represents the indicator function of system stability and reliability, and α is the weight coefficient and is a constant; optimize the antenna rotation angle θ: solve the maximum or minimum antenna rotation angle of the objective function G(θ) through the gradient descent optimization algorithm ,Right now or ; Calculate the final fusion result, and further adjust the antenna rotation angle according to the fusion result; the calculation method of the fusion result is as follows: Let the antenna state variable be X t , the state transfer matrix is ​​A, the system noise covariance matrix is ​​P, the observation matrix is ​​T, and the observation noise covariance matrix is ​​Z; calculate the initial estimate of the weighted average , where S t is the value of the sensor data at time t, H t is the value of historical data at time t, E t is the value of environmental data at time t; prediction steps: ,in is the optimal estimate at the previous moment, ,in is the estimated error covariance matrix of the previous moment; update steps: in The optimal estimate that finally integrates sensor data, historical data and environmental data is O t is the observed value of the comprehensive knowledge body variable at time t, I represents the unit matrix, that is, the elements on the main diagonal are all 1, and the rest of the elements are all 0.

2. A Beidou navigation antenna calibration system based on multiple knowledge bodies as claimed in claim 1, characterized in that: The data acquisition unit includes a sensor module, which is used to detect the acceleration and motion state of the antenna through an acceleration sensor, provide real-time information of the antenna position change, and measure the angular velocity of the antenna according to the gyroscope to assist in determining the attitude change of the antenna; The signal transceiver module is used to receive Beidou satellite signals in real time, provide raw data for the calibration of the navigation antenna, and use the signal strength monitor to monitor the Beidou signal strength in real time to reflect the quality of the antenna receiving signal; The environmental monitoring module is used to measure the temperature and humidity of the surrounding environment of the navigation antenna in real time, providing a basis for interference analysis of environmental impact during antenna calibration.

3. A Beidou navigation antenna calibration system based on multiple knowledge bodies as claimed in claim 1, characterized in that: The execution feedback unit includes a calibration execution module, which is used to send calibration parameters to the antenna calibration device so that it performs a calibration operation on the navigation antenna according to the calibration parameters and monitors the calibration process of the navigation antenna in real time; The monitoring feedback module is used to monitor the antenna performance of the navigation antenna after calibration, including signal strength and positioning accuracy, in real time, and send feedback information back to the main control computer; the system status monitoring module is used to monitor the operating status of the entire calibration system, including the hardware equipment status and software operation status, in real time after the navigation antenna is calibrated, so as to detect and handle system faults in time.

4. A Beidou navigation antenna calibration system based on multiple knowledge bodies as claimed in claim 1, characterized in that: The specific formula of the function f is: , represents the signal strength corresponding to the comprehensive knowledge body variable K, represents the signal-to-noise ratio corresponding to the comprehensive knowledge body variable K, It represents the bit error rate corresponding to the comprehensive knowledge body variable K. a, b and c are positive weight coefficients, all of which are constants.

5. The Beidou navigation antenna calibration system based on multiple knowledge bodies as claimed in claim 1, characterized in that: The calculation formula of the indicator function R(θ) is: When the antenna rotation angle is θ, the measured value of the received signal strength over a period of time , then the mean signal strength is , the variance of the signal strength is expressed as , R(θ) is a function inversely proportional to the signal strength variance, that is, , where m is a constant.

6. A Beidou navigation antenna calibration system based on multiple knowledge bodies as claimed in claim 1, characterized in that: The state transfer matrix A is constructed based on the actual change characteristics of the antenna position and signal strength state parameters. The system state includes the antenna position (x, y, z) and the signal strength s. The state transfer matrix is ​​in the form of , where Δt x , Δt y and Δt z Respectively represent the rate of change of position over time in the x, y and z directions; the system noise is independent of each other in position and signal strength, and has a standard deviation σ x、 σ y , σ z and σ s , then the system noise covariance matrix is ​​in the form of: , where σ x、 σ y , σ z and σ s Determined by statistical analysis of system noise or by estimation based on system hardware characteristics.

7. The Beidou navigation antenna calibration system based on multiple knowledge bodies as claimed in claim 1, characterized in that: The measurement matrix T is The observation noise of sensor data, historical data, environmental data and weighted average intermediate results are independent of each other and have a standard deviation σ S、 σ H , σ E and σ WA , then the observation noise covariance matrix is ​​in the form of: , where σ S、 σ H , σ E and σ WA Determined by statistical analysis of the noise in the observed data or by estimation based on sensor accuracy factors.

8. The Beidou navigation antenna calibration system based on multiple knowledge bodies as claimed in claim 1, characterized in that: The execution feedback unit performs the following calibration process for the navigation antenna: Step 1, data acquisition: collect sensor data, historical data and environmental data to provide a comprehensive information basis for subsequent calibration; Step 2, fusion calculation: by defining comprehensive knowledge body variables, calculating signal quality indicators, determining the objective function, solving the optimal rotation angle and optimizing the rotation angle, various information is integrated to determine the best calibration parameters; Step 3, calibration execution: adjust the antenna rotation angle, verify the calibration results, ensure that the calibration effect meets expectations, and record the calibration process to form a report.

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