WO2024046341A1 - 一种组合导航数据的完好性检测方法及系统 - Google Patents

一种组合导航数据的完好性检测方法及系统 Download PDF

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Publication number
WO2024046341A1
WO2024046341A1 PCT/CN2023/115678 CN2023115678W WO2024046341A1 WO 2024046341 A1 WO2024046341 A1 WO 2024046341A1 CN 2023115678 W CN2023115678 W CN 2023115678W WO 2024046341 A1 WO2024046341 A1 WO 2024046341A1
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Prior art keywords
data
detection
severity
integrity
generate
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PCT/CN2023/115678
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English (en)
French (fr)
Inventor
王杰德
庞靖
韩雷晋
司徒春辉
李荣熙
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广州导远电子科技有限公司
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Publication of WO2024046341A1 publication Critical patent/WO2024046341A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements

Definitions

  • the present application relates to the technical field of navigation and positioning, and specifically to a method and system for integrity detection of combined navigation data.
  • navigation and positioning have become an extremely important function. Deviations in positioning may cause vehicle crashes and fatalities. Therefore, navigation and positioning should be included in the functional safety system from a performance perspective or a reliability perspective.
  • the embodiment of the present application provides a method for integrity detection of combined navigation data, including;
  • Confidence of the positioning result is generated based on the integrity detection information.
  • An electronic device provided by an embodiment of the present application includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above is implemented. The steps of the integrated navigation data integrity detection method described above
  • Figure 1 is a schematic structural diagram of an integrated navigation system provided by an embodiment of the present application.
  • Figure 2 is a schematic flow chart of the integrity detection method of integrated navigation data provided by the embodiment of the present application.
  • Figure 3 is a schematic flowchart of obtaining integrity detection information provided by an embodiment of the present application.
  • Figure 4 is a schematic flowchart of generating first severity data provided by an embodiment of the present application.
  • Figure 5 is a schematic flow chart of original data integrity detection provided by the embodiment of the present application.
  • Figure 6 is a schematic flowchart of generating second severity data provided by an embodiment of the present application.
  • Figure 7 is a schematic flow chart of process integrity detection provided by the embodiment of the present application.
  • Figure 8 is a schematic flowchart of generating third severity data provided by an embodiment of the present application.
  • Figure 9 is a schematic flow chart of the result integrity detection provided by the embodiment of the present application.
  • Figure 10 is a schematic flow chart of the integrity detection method of integrated navigation data provided by the embodiment of the present application.
  • Figure 11 is a schematic flow chart of the integrity detection method of integrated navigation data provided by the embodiment of the present application.
  • Figure 12 is a schematic flow chart of the integrity detection method of integrated navigation data provided by the embodiment of the present application.
  • Figure 13 is a schematic flow chart of the integrity detection method of integrated navigation data provided by the embodiment of the present application.
  • Figure 14 is a schematic flow chart of the integrity detection method of integrated navigation data provided by the embodiment of the present application.
  • Figure 15 is a schematic flowchart of the integrity detection method of integrated navigation data provided by an embodiment of the present application.
  • Embodiments of the present application provide an integrity detection method, system, electronic device, and computer-readable storage medium for combined navigation data.
  • the integrity detection method for combined navigation data introduces original data integrity, process integrity, and result integrity.
  • a three-level integrity testing system is introduced, and a testing and evaluation system is introduced, that is, a severity index will be generated after each integrity testing, that is, the first severity data, the second severity data, and the third severity data.
  • Each The severity index can represent the confidence of the corresponding data, and the final integrity detection information can measure the accuracy and reliability of the positioning results; the original data integrity detection can detect the original navigation data to ensure the availability of the data from the source. This further improves the robustness of the combined navigation algorithm.
  • the process integrity detection further ensures the robustness of the combined navigation algorithm, and some defects from the algorithm modeling itself can also be covered. Therefore, this method can implement technology to improve the reliability and stability of the combined navigation. Effect.
  • integrated navigation refers to a navigation system that integrates various navigation devices and is controlled by a monitor and a computer. Most integrated navigation systems are based on inertial navigation systems. The main reason is that inertial navigation can provide a relatively large number of navigation parameters and can also provide full attitude information parameters, which is unmatched by other navigation systems. In the embodiment of this application, integrated navigation refers to a method of navigation using navigation parameters such as IMU information, GNSS information, and vehicle data.
  • the Inertial Measurement Unit is a sensor mainly used to detect and measure acceleration and rotational motion
  • the Global Navigation Satellite System uses the pseudoranges and satellites of a group of satellites. Observations such as almanac and satellite launch time must also be known, and the user clock offset must also be known.
  • the embodiment of the present application provides a method for integrity detection of combined navigation data, including:
  • Confidence of the positioning result is generated based on the integrity detection information.
  • the step of obtaining integrity detection information based on the first severity data, the second severity data, and the third severity data includes:
  • the comprehensive severity data is compared with the first preset value and the second preset value. If the comprehensive severity data is less than or equal to the first preset value, low-risk integrity detection information is generated; if the comprehensive severity data is less than or equal to the first preset value, low-risk integrity detection information is generated; If the comprehensive severity data is greater than the first preset value and less than or equal to the second preset value, medium risk integrity detection information is generated; if the comprehensive severity data is greater than the second preset value, then Generate high-risk integrity testing information.
  • the integrity detection information includes low-risk integrity detection information, medium-risk integrity detection information and high-risk integrity detection information.
  • the risk level is from low to high; the higher the risk level, the higher the data risk, indicating that The positioning results are less accurate.
  • the original navigation data includes one or more of IMU information, GNSS information, and vehicle data.
  • the original navigation data is inspected for original data integrity, and the first severity data is generated. steps, including:
  • the first severity data is generated according to the data integrity detection result.
  • the step of performing data integrity detection on one or more of the original navigation data and generating a data integrity detection result includes:
  • the original navigation data includes IMU information
  • the step of performing original data integrity detection on the original navigation data and generating first severity data includes:
  • the first severity data is generated based on the noise model integrity detection data.
  • the original navigation data includes at least two of IMU information, GNSS information, and vehicle data
  • the step of performing original data integrity detection on the original navigation data and generating first severity data include:
  • the first severity data is generated based on the consistency detection data.
  • the IMU information includes primary IMU information and redundant IMU information.
  • the step of performing consistency detection on at least two of the original navigation data and generating consistency detection data includes:
  • the IMU information includes primary IMU information and redundant IMU information.
  • the step of performing consistency detection on at least two of the original navigation data and generating consistency detection data includes:
  • the IMU information includes primary IMU information and redundant IMU information.
  • the step of performing consistency detection on at least two of the original navigation data and generating consistency detection data includes:
  • the consistency detection is performed between two/three/four of the GNSS information, the main IMU information, the redundant IMU information, and the vehicle data to generate the Describe the steps for consistency testing data, including:
  • the consistency detection data is generated based on the IMU information, the redundant IMU information, and the vehicle data; if not, the consistency detection data is generated based on the GNSS information, the main IMU information, and the redundant IMU information. Consistency testing data.
  • the consistency detection uses the least squares residual method, and the steps of performing consistency detection on at least two of the original navigation data and generating consistency detection data include:
  • the original navigation data includes at least two of IMU information, GNSS information, and vehicle data
  • the step of performing original data integrity detection on the original navigation data and generating first severity data include:
  • the first severity data is generated according to one or more of the data integrity detection results, the noise model integrity detection data, and the consistency detection data.
  • the step of performing process integrity detection on the running status of the preset combined navigation algorithm and generating second severity data includes:
  • the second severity data is generated according to the observation innovation test result.
  • the step of performing process integrity detection on the running status of the preset combined navigation algorithm and generating second severity data includes:
  • the second severity data is generated according to the variance matrix analysis result.
  • the step of performing process integrity detection on the running status of the preset combined navigation algorithm and generating second severity data includes:
  • the second severity data is generated according to the state vector residual test result.
  • the step of performing process integrity detection on the running status of the preset combined navigation algorithm and generating second severity data includes:
  • the second severity data is generated according to one or more of the observation innovation test results, the variance matrix analysis results, and the state vector residual test results.
  • the process integrity detects the operation of the integrated navigation algorithm.
  • the analysis methods of process integrity include three aspects: state vector residual test, variance matrix analysis, and observation innovation test.
  • the result integrity adopts parallel parameter integrity detection
  • the step of performing result integrity detection based on the positioning result and generating third severity data includes:
  • the redundant positioning result and the positioning result are checked for consistency to generate the third severity data.
  • the redundant navigation data is the parameters obtained by other methods of the redundant navigation unit (redundant IMU, etc.) in the integrated navigation system.
  • the redundant positioning results are obtained from the redundant navigation data.
  • the redundant positioning results and positioning The results can be compared, and the third severity data reflects the accuracy of the positioning results.
  • the redundant navigation data is data from a redundant IMU
  • the step of obtaining a redundant positioning result based on the redundant navigation data includes:
  • the data of the redundant IMU is processed through a preset strapdown solution algorithm to generate the redundant positioning result.
  • the step of checking the consistency of the redundant positioning result and the positioning result and generating the third severity data includes:
  • the corresponding parameters include one of a position parameter, a speed parameter, an attitude parameter, and a zero-bias parameter. or more.
  • the confidence level can directly reflect the accuracy of the positioning result. The higher the confidence level, the higher the accuracy of the positioning result.
  • Figure 1 is a schematic structural diagram of an integrated navigation system provided by an embodiment of the present application.
  • a basic integrated navigation system includes three parts: data collection.
  • the collected data includes IMU information, GNSS information, vehicle data, etc.; integrated navigation algorithm; and positioning result output.
  • the integrated navigation data integrity detection method provided by the embodiment of the present application introduces a three-level integrity detection system of original data integrity, process integrity and result integrity.
  • Figure 2 is a schematic flow chart of an integrity detection method for integrated navigation data provided by an embodiment of the present application.
  • the integrity detection method for integrated navigation data includes the following steps;
  • the original navigation data includes IMU information collected by the IMU and GNSS information received by the positioning chip.
  • the original navigation data may also include vehicle data.
  • IMU information includes acceleration, angular velocity, etc.
  • GNSS information includes positioning coordinates, etc.
  • vehicle data includes wheel speed, steering wheel, etc. It should be noted that the specific types of IMU information, GNSS information, and vehicle data can be determined as needed. The choice is not limited here.
  • S200 Perform original data integrity testing on the original navigation data and generate first severity data.
  • the original data integrity test is to analyze the original navigation data obtained from the S100.
  • the first severity data can measure the quality of the original navigation data.
  • S300 Process the original navigation data according to the preset combined navigation algorithm to obtain the positioning result.
  • S400 Perform process integrity detection on the running status of the preset integrated navigation algorithm and generate second severity data.
  • performing process integrity detection during the operation of the integrated navigation algorithm can detect the internal operation of the integrated navigation algorithm, and the second severity data can measure whether the integrated navigation algorithm is running normally.
  • S500 Perform result integrity detection based on the positioning result and generate third severity data.
  • the result integrity can be used to check the positioning result. If the verification fails, it indicates that there is a fault and the positioning result is distorted; the third severity data can measure the distortion of the positioning result.
  • S600 Obtain integrity detection information based on the first severity data, the second severity data, and the third severity data;
  • S700 Generate the confidence of the positioning result based on the integrity detection information.
  • the confidence level can directly reflect the accuracy of the positioning result. The higher the confidence level, the higher the accuracy of the positioning result.
  • the integrated navigation data integrity detection method introduces a three-level integrity detection system of original data integrity, process integrity and result integrity, and also introduces a detection evaluation system, that is, after each integrity detection, a A severity index, that is, first severity data, second severity data, and third severity data. Each severity index can represent the confidence of the corresponding data.
  • the final integrity detection information can measure the accuracy of the positioning result. Accuracy and reliability; original data integrity detection can detect original navigation data to ensure the availability of data from the source, thereby improving the robustness of the combined navigation algorithm. Process integrity detection further ensures the robustness of the combined navigation algorithm. Some of them come from the algorithm The defects of the modeling itself can also be covered; thus, this method can achieve the technical effect of improving the reliability and stability of combined navigation.
  • Figure 3 is a schematic flow chart of obtaining integrity detection information provided by an embodiment of the present application.
  • S600 the step of obtaining integrity detection information based on the first severity data, the second severity data, and the third severity data, including:
  • S610 Accumulate the first severity data, the second severity data, and the third severity data to obtain comprehensive severity data
  • S620 Compare the comprehensive severity data with the first preset value and the second preset value. If the comprehensive severity data is less than or equal to the first preset value, generate low-risk integrity detection information; if the comprehensive severity data is greater than If the first preset value is less than or equal to the second preset value, medium-risk integrity detection information is generated; if the comprehensive severity data is greater than the second preset value, high-risk integrity detection information is generated.
  • the integrity detection information includes low-risk integrity detection information, medium-risk integrity detection information and high-risk integrity detection information, with risk levels from low to high; the higher the risk level, the higher the data risk, indicating the positioning result. The less accurate.
  • the embodiment of the present application also provides partial severity data, which may be first severity data or first severity data + second severity data; similarly, partial severity data and comprehensive
  • partial severity data is used in the same way and will not be described again here.
  • Figure 4 is a schematic flowchart of generating first severity data provided by an embodiment of the present application.
  • the original navigation data includes IMU information and GNSS information.
  • the original navigation data may also include vehicle data.
  • S210 Perform data integrity testing on one or more types of original navigation data, and generate data integrity testing results
  • S220 Perform noise model integrity detection on the IMU information according to the preset noise model, and generate noise model integrity detection data
  • S230 Perform consistency detection on at least two types of original navigation data and generate consistency detection data
  • S240 Generate first severity data based on one or more of the data integrity detection results, noise model integrity detection data, and consistency detection data.
  • the data integrity test is first performed. If the test fails, the first severity data can be generated based on the data integrity test results without the need to perform steps S220 and S230. If the data integrity test passes, continue the noise model test on the IMU information. If the test fails, the first severity data can be generated based on the noise model integrity test data without performing step S230. If the noise model detection passes, continue to perform consistency detection on at least two of the original navigation data, generate consistency detection data, and generate first severity data based on the consistency detection data.
  • the consistency check on at least two of the original navigation data includes: checking the consistency of the IMU information and the GNSS information, and if it passes, then checking the IMU information, GNSS and vehicle data. Conduct consistency detection among them, and output the first severity data based on the consistency detection data of the three. If any link fails to pass the test, the first severity data can be output (the first severity data output at this time is E>6), and then the consistency test of subsequent steps can be continued or not.
  • S210 perform data integrity detection on one or more types of original navigation data and generate a data integrity detection result, including:
  • sequence detection check code detection, length detection, timeout detection, value range detection, and value jump detection can be performed in sequence in a preset order, and are not limited here.
  • the consistency detection uses the least squares residual method.
  • S230 Perform consistency detection on the original navigation data and generate consistency detection data, including:
  • the original navigation data is processed by the least squares residual method to generate consistency detection data.
  • the IMU information includes primary IMU information and redundant IMU information.
  • S230 Perform consistency detection on at least two types of original navigation data, and generate consistency detection data, including:
  • S230 Perform consistency detection on at least two types of original navigation data, and generate consistency detection data, including:
  • the consistency test of the main IMU information and the redundant IMU information is first performed. If the consistency of the two meets the requirements, the consistency test with the GNSS information is continued; if the consistency of the two does not meet the requirements, the consistency test of the main IMU information and the redundant IMU information is continued. Information and redundant IMU information generate consistency detection data;
  • Figure 5 is a schematic flow chart of original data integrity detection provided by an embodiment of the present application.
  • the integrated navigation system includes at least two IMUs, at least one of which serves as the main IMU and at least one of which serves as the redundant IMU.
  • data integrity means that all original data has been tested for data integrity, that is, analyzed at the data level.
  • Data integrity analysis methods include one or more of the following:
  • Sequence detection data must be sent sequentially to prevent loss or confusion; if it is found through sequence detection that the data is not sent sequentially, the data integrity is poor, indicating that the data integrity test fails, and a higher severity will be generated, that is, Generate higher first severity data.
  • the data length must be correct; if it is found through length detection that the length of the data is no longer within the preset range, the data integrity is poor, indicating that the data integrity test fails, and a higher severity will be generated, that is, a higher severity will be generated. High first severity data.
  • Timeout detection ensures that the data is the latest data and is timely; if it is found through timeout detection that the sending time of the data exceeds the threshold, the data integrity is poor, indicating that the data integrity test fails, and a higher severity will be generated at this time, that is, Generate higher first severity data.
  • the integrity of the noise model is tested based on the noise model.
  • the noise model parameters include zero bias, bias stability, and Allan variance; optionally, the noise model parameters can be set through empirical values. If they exceed the preset range, To increase the severity, generate higher first severity data.
  • Figure 6 is a schematic flowchart of generating second severity data provided by an embodiment of the present application.
  • S400 perform process integrity detection on the running status of the preset integrated navigation algorithm and generate second severity data, including:
  • S410 Perform observation innovation testing on the running status of the preset combined navigation algorithm, and generate observation innovation testing results
  • S420 Perform variance matrix analysis on the operating status of the preset combined navigation algorithm, and generate variance matrix analysis result data
  • S430 Perform a state vector residual test on the running status of the preset combined navigation algorithm, and generate a state vector residual test result;
  • S440 Generate second severity data based on one or more of the observation innovation test results, variance matrix analysis results, and state vector residual test results.
  • steps S410 to S430 are performed in sequence. Step S410 is performed first. If the observation innovation detection in step S410 passes, then step S420 is continued to perform variance matrix analysis. If the variance matrix analysis in step S420 is If passed, continue to step S430 to perform state vector residual detection. If one of the tests, analyzes or inspections fails, the second severity data can be generated based on the results of the previous test or analysis without continuing to subsequent steps.
  • S400 perform process integrity detection on the running status of the preset combined navigation algorithm and generate second severity data, including:
  • the second severity data is generated based on the observation innovation test results.
  • S400 perform process integrity detection on the running status of the preset combined navigation algorithm and generate second severity data, including:
  • Second severity data is generated based on the variance matrix analysis results.
  • S400 Perform process integrity detection on the running status of the preset integrated navigation algorithm, and generate a second strict Data-heavy steps include:
  • Second severity data is generated based on the state vector residual test results.
  • the second severity data may be generated based on the observation innovation test results, the second severity data may be generated based on the variance matrix analysis results, and the second severity data may be generated based on the state vector residual test results. Or the three are added together or added according to weights to generate the final second severity data.
  • FIG. 7 is a schematic flow chart of process integrity detection provided by an embodiment of the present application.
  • the process integrity detects the operation of the combined navigation algorithm, as shown in Figure 7.
  • the analysis methods of process integrity include: state vector residual test, variance matrix analysis, and observation innovation test.
  • the observation innovation is the difference between the actual observation vector and the observation vector estimated and calculated from the state before the observation is updated.
  • Peak detection and rationality testing of observation information can detect major anomalies.
  • standard deviation inspection is performed to detect small anomalies in a short period of time. If one or more of the above checks fail, it is considered that there may be a risk and the severity is increased, that is, a higher second severity data is generated.
  • variance matrix analysis after a long run, the continuous injection of noise or improper modeling leads to error covariance distortion; the main method of variance matrix analysis is error covariance matrix analysis.
  • error covariance matrix analysis For example, there is a variance matrix P with n rows and n columns. Compare each diagonal element Pii of the matrix P with the predefined values [Pmax, Pmin]. If Pii>Pmax or Pii ⁇ Pmin, it means that the variance matrix analysis fails. Then increase the severity, that is, generate higher second severity data, and multiply all elements in the i-th row and i-th column by the preset value ⁇ ii, where ⁇ ii is related to Pii, Pmax and Pmin.
  • the state vector residual is the difference between the true state vector and its Kalman filter estimate. If the residual is within the preset range, the algorithm is considered stable. If it exceeds the residual value, it is considered that the state vector residual test has failed, and risks may arise. The severity needs to be increased, that is, a higher second severity data is generated.
  • the second severity data may be generated based on one or more of the observation information test results, variance matrix analysis results, and state vector residual test results.
  • an observation innovation test is performed on the running status of the preset combined navigation algorithm, an observation innovation test result is generated, and second severity data is generated based on the observation innovation test result. If the second severity data Exceeding the preset threshold range indicates that the operating status of the integrated navigation algorithm is unstable. At this time, variance matrix analysis and state vector residual testing can no longer be performed. If the second severity data does not exceed the preset threshold range, it indicates that the integrated navigation algorithm The operating state is stable. At this time, the variance matrix analysis is continued, and the second severity data is generated based on the variance matrix analysis results.
  • the state vector residual test can no longer be performed. If the second severity data does not exceed the preset threshold range, it indicates that the operating state of the combined navigation algorithm is stable. At this time, the state vector residual test can be continued. According to the state vector residual The inspection results generate second severity data.
  • Figure 8 is a schematic flowchart of generating third severity data provided by an embodiment of the present application.
  • the result integrity uses parallel parameter integrity detection.
  • S500 Perform the result integrity detection based on the positioning result and generate the third severity data, including:
  • the redundant navigation data is parameters obtained by other methods of the redundant navigation unit (redundant IMU, etc.) in the integrated navigation system.
  • the redundant positioning result is obtained from the redundant navigation data.
  • the redundant positioning result and the positioning result can be In contrast, the third severity data reflects the accuracy of the positioning results.
  • a test method of making a difference between two values can be used. When the difference exceeds the allowable error range, the test is deemed to have failed and the positioning result is inaccurate. In this case, a higher value will be generated. of third severity data.
  • the redundant navigation data is the data of the redundant IMU.
  • S520 The step of obtaining redundant positioning results based on the redundant navigation data, including:
  • the data of the redundant IMU is processed through the preset strapdown solution algorithm to generate redundant positioning results.
  • S530 The step of checking the consistency of the redundant positioning results and the positioning results and generating the third severity data includes:
  • the corresponding parameters include one or more of position parameters, speed parameters, attitude parameters, and zero-bias parameters.
  • FIG. 9 is a schematic flow chart of result integrity detection provided by an embodiment of the present application.
  • the result integrity adopts a parallel parameter integrity detection scheme, that is, the parameters of each redundant navigation data are compared with the parameters of the combined navigation algorithm; if the check fails, it indicates that there is a fault, thereby determining the third severity data.
  • the parameters of the redundant navigation data correspond to the parameters of the combined navigation algorithm one-to-one; the navigation parameters use gyroscope, accelerometer, position, speed, attitude, bias, etc.
  • the embodiment of the present application also provides a method for integrity detection of combined navigation data; please refer to Figure 10.
  • the method includes the following steps:
  • the original navigation data includes IMU information and GNSS information. When applied to vehicle navigation, the original navigation data shown may also include vehicle data.
  • S102 Perform original data integrity testing on the original navigation data and generate first severity data.
  • raw data integrity detection is to analyze the raw navigation data obtained from S101.
  • the first severity data can measure the quality of the raw navigation data.
  • S103 Process the original navigation data according to the preset combined navigation algorithm to obtain the positioning result.
  • S104 Obtain integrity detection information based on the first severity data
  • S105 Generate the confidence of the positioning result based on the integrity detection information.
  • the original navigation data includes IMU information collected by the IMU and GNSS information received by the positioning chip.
  • the original navigation data may also include vehicle data.
  • Figure 10 shows a method for integrity detection using original data integrity detection provided by an embodiment of the present application.
  • Original data integrity detection can detect original navigation data, ensuring the availability of data from the source, thereby improving integrated navigation. Robustness of the algorithm; for specific implementation details of the original data integrity detection, please refer to the relevant parts of Embodiment 1, which will not be described again here.
  • the embodiment of the present application also provides a method for integrity detection of combined navigation data; please refer to Figure 11.
  • the method includes the following steps:
  • the original navigation data includes IMU information collected by the IMU, GNSS information received by the positioning chip, vehicle data, etc.
  • S202 Process the original navigation data according to the preset combined navigation algorithm to obtain the positioning result.
  • S203 Perform process integrity detection on the running status of the preset combined navigation algorithm, and generate second severity data.
  • performing process integrity detection during the operation of the integrated navigation algorithm can detect the internal operation of the integrated navigation algorithm, and the second severity data can measure whether the integrated navigation algorithm is running normally.
  • the original navigation data includes IMU information collected by the IMU and GNSS information received by the positioning chip.
  • the original navigation data may also include vehicle data.
  • Figure 11 shows a method for integrity detection using process integrity detection provided by the embodiment of the present application.
  • Process integrity detection can ensure the robustness of the combined navigation algorithm, and some defects from the algorithm modeling itself can also be covered;
  • process integrity detection can ensure the robustness of the combined navigation algorithm, and some defects from the algorithm modeling itself can also be covered;
  • process integrity detection please refer to the relevant parts of Embodiment 1, and will not be described again here.
  • the embodiment of the present application also provides a method for integrity detection of combined navigation data; please refer to Figure 12.
  • the method includes the following steps:
  • the original navigation data includes IMU information collected by the IMU, GNSS information received by the positioning chip, vehicle data, etc.
  • S302 Process the original navigation data according to the preset combined navigation algorithm to obtain the positioning result.
  • S303 Perform result integrity detection based on the positioning result and generate third severity data.
  • the result integrity can be used to check the positioning result. If the verification fails, it indicates that there is a fault and the positioning result is distorted; the third severity data can measure the distortion of the positioning result.
  • S304 Obtain integrity detection information based on the third severity data
  • the original navigation data includes IMU information collected by the IMU and GNSS information received by the positioning chip.
  • the original navigation data may also include vehicle data.
  • Figure 12 shows a method for performing integrity detection using result integrity detection provided by the embodiment of the present application.
  • the result integrity detection can ensure the accuracy of the positioning result; please refer to the embodiments for specific implementation details of the result integrity detection. The relevant parts of 1 will not be repeated here.
  • the embodiment of the present application also provides a method for integrity detection of combined navigation data; please refer to Figure 13.
  • the method includes the following steps: Steps:
  • the original navigation data includes IMU information collected by the IMU, GNSS information received by the positioning chip, vehicle data, etc.
  • S402 Perform original data integrity detection on the original navigation data and generate first severity data.
  • raw data integrity detection is to analyze the raw navigation data obtained from S101.
  • the first severity data can measure the quality of the raw navigation data.
  • S403 Process the original navigation data according to the preset combined navigation algorithm to obtain the positioning result.
  • S404 Perform process integrity detection on the running status of the preset integrated navigation algorithm, and generate second severity data.
  • performing process integrity detection during the operation of the integrated navigation algorithm can detect the internal operation of the integrated navigation algorithm, and the second severity data can measure whether the integrated navigation algorithm is running normally.
  • S405 Obtain integrity detection information based on the first severity data and the second severity data
  • S406 Generate the confidence of the positioning result based on the integrity detection information.
  • the original navigation data includes IMU information collected by the IMU and GNSS information received by the positioning chip.
  • the original navigation data may also include vehicle data.
  • Figure 13 shows a method for integrity detection using original data integrity detection and process integrity detection provided by the embodiment of the present application.
  • Original data integrity detection can detect original navigation data and ensure the availability of data from the source. , thereby improving the robustness of the combined navigation algorithm, and process integrity testing further ensures the robustness of the combined navigation algorithm, and some defects from the algorithm modeling itself can also be covered; for specific implementation details of original data integrity testing and process integrity testing, please Please refer to the relevant parts of Embodiment 1, which will not be described again here.
  • the embodiment of the present application also provides a method for integrity detection of combined navigation data; please refer to Figure 14.
  • the method includes the following steps:
  • the original navigation data includes IMU information collected by the IMU, GNSS information received by the positioning chip, vehicle data, etc.
  • S502 Perform original data integrity detection on the original navigation data and generate first severity data.
  • raw data integrity detection is to analyze the raw navigation data obtained from S101.
  • the first severity data can measure the quality of the raw navigation data.
  • S503 Process the original navigation data according to the preset combined navigation algorithm to obtain the positioning result.
  • S504 Perform result integrity detection based on the positioning result and generate third severity data.
  • the result integrity can be used to check the positioning result. If the verification fails, it indicates that there is a fault and the positioning result is distorted; the third severity data can measure the distortion of the positioning result.
  • S505 Obtain integrity detection information based on the first severity data and the third severity data
  • S506 Generate the confidence of the positioning result based on the integrity detection information.
  • the original navigation data includes IMU information collected by the IMU and GNSS information received by the positioning chip.
  • the original navigation data may also include vehicle data.
  • Figure 14 is a method for integrity detection using original data integrity detection and result integrity detection provided by the embodiment of the present application.
  • the original data integrity detection can detect the original navigation data and ensure the availability of the data from the source. , thereby improving the robustness of the combined navigation algorithm, and the result integrity detection can ensure the accuracy of the positioning results; for the specific implementation details of the original data integrity detection and result integrity detection, please refer to the relevant parts of Embodiment 1, which will not be discussed here. Repeat.
  • the embodiment of the present application also provides a method for integrity detection of combined navigation data; please refer to Figure 115.
  • the method includes the following steps:
  • the original navigation data includes IMU information collected by the IMU, GNSS information received by the positioning chip, vehicle data, etc.
  • S603 Process the original navigation data according to the preset combined navigation algorithm to obtain the positioning result.
  • S603 Perform process integrity detection on the running status of the preset combined navigation algorithm, and generate second severity data.
  • performing process integrity detection during the operation of the integrated navigation algorithm can detect the internal operation of the integrated navigation algorithm, and the second severity data can measure whether the integrated navigation algorithm is running normally.
  • S604 Perform result integrity detection based on the positioning result and generate third severity data.
  • the result integrity can be tested on the positioning result. If the verification fails, it indicates that there is a fault and the positioning result is invalid. True; third severity data measures distortion in location results.
  • S605 Obtain integrity detection information based on the second severity data and the third severity data
  • S606 Generate the confidence of the positioning result based on the integrity detection information.
  • the original navigation data includes IMU information collected by the IMU and GNSS information received by the positioning chip.
  • the original navigation data may also include vehicle data.
  • Figure 15 is a method for integrity detection using process integrity detection and result integrity detection provided by the embodiment of the present application.
  • Process integrity detection ensures the robustness of the combined navigation algorithm, and some defects from the algorithm modeling itself are also can be covered, and the result integrity detection can ensure the accuracy of the positioning result; for the specific implementation details of the process integrity detection and result integrity detection, please refer to the relevant parts of Embodiment 1, which will not be described again here.
  • the present application also provides an electronic device, which may include a processor, a communication interface, a memory, and at least one communication bus.
  • the communication bus is used to realize direct connection communication between these components.
  • the communication interface of the electronic device in the embodiment of the present application is used to communicate signaling or data with other node devices.
  • the processor can be an integrated circuit chip with signal processing capabilities.
  • the electronic device can perform various steps involved in the above embodiments.
  • Embodiments of the present application also provide a storage medium with instructions stored on the storage medium.
  • the storage medium includes but is not limited to: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other various media that can store program codes.
  • This application also provides a computer program product.
  • the computer program product When the computer program product is run on a computer, it causes the computer to execute the method described in the method embodiment.
  • a three-level integrity detection system of original data integrity, process integrity and result integrity is introduced.
  • a detection evaluation system is introduced, that is, each integrity detection Afterwards, a severity index will be generated, that is, the first severity data, the second severity data, and the third severity data.
  • Each severity index can represent the confidence of the corresponding data, and the final integrity detection information can be measured.
  • original data integrity detection can detect original navigation data to ensure the availability of data from the source, thereby improving the robustness of the integrated navigation algorithm.
  • Process integrity detection further ensures the robustness of the integrated navigation algorithm.

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Abstract

本申请实施例提供一种组合导航数据的完好性检测方法，涉及导航定位技术领域。该组合导航数据的完好性检测方法包括；获取原始导航数据；对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据；根据预设组合导航算法对所述原始导航数据进行处理，获得定位结果；对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据；根据所述定位结果进行结果完好性检测，生成第三严重度数据；根据所述第一严重度数据、所述第二严重度数据、所述第三严重度数据获得完好性检测信息；根据所述完好性检测信息生成所述定位结果的置信度。该组合导航数据的完好性检测方法可以实现提高组合导航的可靠性和稳定性的技术效果。

Description

一种组合导航数据的完好性检测方法及系统
相关申请的交叉引用
本申请要求于2022年08月30日提交中国专利局的申请号为202211049966.5、名称为“一种组合导航数据的完好性检测方法及系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及导航定位技术领域，具体而言，涉及一种组合导航数据的完好性检测方法及系统。
背景技术
目前，在新能源汽车的自动驾驶领域，导航定位成为极其重要的一种功能。定位的偏差可能会造成车毁人亡。所以导航定位无论是性能的角度还是可靠性的角度，都应该纳入功能安全的体系中。
现有技术中，很多参与导航定位的传感器很难满足功能安全要求；即使有部分传感器达到功能安全要求，但是在和其他要素协作和融合时，会引入不安全因数，使系统整体处在一个不安全的状态，导致定位结果不准确，可靠性和稳定性均不高。
发明内容
本申请实施例提供了一种组合导航数据的完好性检测方法，包括；
获取原始导航数据；
对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据；
根据预设组合导航算法对所述原始导航数据进行处理，获得定位结果；
对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据；
根据所述定位结果进行结果完好性检测，生成第三严重度数据；
根据所述第一严重度数据、所述第二严重度数据、所述第三严重度数据获得完好性检测信息；
根据所述完好性检测信息生成所述定位结果的置信度。
本申请实施例提供的一种电子设备，包括：存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上所述的组合导航数据的完好性检测方法的步骤
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对本申请实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本申请的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本申请实施例提供的组合导航系统的结构示意图；
图2为本申请实施例提供的组合导航数据的完好性检测方法的流程示意图；
图3为本申请实施例提供的获得完好性检测信息的流程示意图；
图4为本申请实施例提供的生成第一严重度数据的流程示意图；
图5为本申请实施例提供的原始数据完好性检测的流程示意图；
图6为本申请实施例提供的生成第二严重度数据的流程示意图；
图7为本申请实施例提供的过程完好性检测的流程示意图；
图8为本申请实施例提供的生成第三严重度数据的流程示意图；
图9为本申请实施例提供的结果完好性检测的流程示意图；
图10为本申请实施例提供的组合导航数据的完好性检测方法的流程示意图；
图11为本申请实施例提供的组合导航数据的完好性检测方法的流程示意图；
图12为本申请实施例提供的组合导航数据的完好性检测方法的流程示意图；
图13为本申请实施例提供的组合导航数据的完好性检测方法的流程示意图；
图14为本申请实施例提供的组合导航数据的完好性检测方法的流程示意图；
图15为本申请实施例提供的组合导航数据的完好性检测方法的流程示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行描述。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。同时，在本申请的描述中，术语“第一”、“第二”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
本申请实施例提供了一种组合导航数据的完好性检测方法、系统、电子设备及计算机可读存储介质，该组合导航数据的完好性检测方法引入了原始数据完好性、过程完好性和结果完好性的三级完好性检测体系，同时引入检测评估体系，即每次完好性检测后都会产生一个严重度指标，即第一严重度数据、第二严重度数据、第三严重度数据，每个严重度指标可表示代表对应数据的置信度，最终获得的完好性检测信息可衡量定位结果的准确性和可靠性；原始数据完好性检测可以对原始导航数据检测，从源头上保证数据的可用性，进而提高组合导航算法的稳健性，过程完好性检测进一步保证组合导航算法的稳健，一些来自算法建模本身的缺陷也能覆盖；从而，该方法可以实现提高组合导航的可靠性和稳定性的技术效果。
示例性地，组合导航是指综合各种导航设备，由监视器和计算机进行控制的导航系统。大多数组合导航系统以惯导系统为主，其原因主要是由于惯性导航能够提供比较多的导航参数，还能够提供全姿态信息参数，这是其他导航系统所不能比拟的。在本申请实施例中，组合导航指利用IMU信息、GNSS信息和车辆数据等导航参数进行导航的方法。
示例性地，惯性测量单元(IMU，Inertial Measurement Unit)是主要用来检测和测量加速度与旋转运动的传感器；全球导航卫星系统(GNSS，Global Navigation Satellite System)是利用一组卫星的伪距、星历、卫星发射时间等观测量，同时还必须知道用户钟差。
本申请实施例提供了一种组合导航数据的完好性检测方法，包括：
获取原始导航数据；
对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据；
根据预设组合导航算法对所述原始导航数据进行处理，获得定位结果；
对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据；
根据所述定位结果进行结果完好性检测，生成第三严重度数据；
根据所述第一严重度数据、所述第二严重度数据、所述第三严重度数据获得完好性检测信息；
根据所述完好性检测信息生成所述定位结果的置信度。
在一些实施方式中，所述根据所述第一严重度数据、所述第二严重度数据、所述第三严重度数据获得完好性检测信息的步骤，包括：
将所述第一严重度数据、所述第二严重度数据、所述第三严重度数据累计相加，获得综合严重度数据；
将所述综合严重度数据与第一预设值、第二预设值进行比较，若所述综合严重度数据小于等于所述第一预设值，则生成低风险完好性检测信息；若所述综合严重度数据大于所述第一预设值且小于等于所述第二预设值，则生成中风险完好性检测信息；若所述综合严重度数据大于所述第二预设值，则生成高风险完好性检测信息。
在上述实现过程中，完好性检测信息包括低风险完好性检测信息、中风险完好性检测信息和高风险完好性检测信息，风险等级由低到高；风险等级越高，数据风险越高，表明定位结果越不准确。
在一些实施方式中，所述原始导航数据包括IMU信息、GNSS信息、车辆数据中的一种或多种，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
对所述原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果；
根据所述数据完好性检测结果生成所述第一严重度数据。
在一些实施方式中，所述对所述原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果的步骤，包括：
对所述原始导航数据中的一种或多种进行序列检测、校验码检测、长度检测、超时检测、取值范围检测、数值跳变检测中的一种或多种检测，生成所述数据完好性检测结果。
在一些实施方式中，所述原始导航数据包括IMU信息，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
根据预设噪声模型对所述IMU信息进行噪声模型完好性检测，生成噪声模型完好性检测数据；
根据所述噪声模型完好性检测数据生成所述第一严重度数据。
在一些实施方式中，所述原始导航数据包括IMU信息、GNSS信息、车辆数据中的至少两种，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据；
根据所述一致性检测数据生成所述第一严重度数据。
在一些实施方式中，所述IMU信息包括主IMU信息和冗余IMU信息，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
对所述主IMU信息和冗余IMU信息之间进行一致性检测，生成所述一致性检测数据。
在一些实施方式中，所述IMU信息包括主IMU信息和冗余IMU信息，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
对所述GNSS信息、所述主IMU信息、所述冗余IMU信息中的两者/三者之间进行一致性检测，生成所述一致性检测数据。
在一些实施方式中，所述IMU信息包括主IMU信息和冗余IMU信息，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
对所述GNSS信息、所述主IMU信息、所述冗余IMU信息、所述车辆数据中的两者/三者/四者之间进行一致性检测，生成所述一致性检测数据。
在一些实施方式中，所述对所述GNSS信息、所述主IMU信息、所述冗余IMU信息、所述车辆数据中的两者/三者/四者之间进行一致性检测，生成所述一致性检测数据的步骤，包括：
进行所述主IMU信息、所述冗余IMU信息的一致性检测，若两者一致性符合要求，继续和所述GNSS信息进行一致性检测；若两者一致性不符合要求，根据所述主IMU信息、所述冗余IMU信息两者生成所述一致性检测数据；
判断所述主IMU信息、所述冗余IMU信息、所述GNSS信息三者的一致性是否符合要求，若是则继续和所述车辆数据进行一致性检测，并根据所述GNSS信息、所述主IMU信息、所述冗余IMU信息、所述车辆数据四者生成所述一致性检测数据；若否，根据所述GNSS信息、所述主IMU信息、所述冗余IMU信息三者生成所述一致性检测数据。
在一些实施方式中，所述一致性检测采用最小二乘残差法，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
对所述原始导航数据中的至少两种进行最小二乘残差法处理，生成所述一致性检测数据。
在一些实施方式中，所述原始导航数据包括IMU信息、GNSS信息、车辆数据中的至少两种，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
对所述原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果；
根据预设噪声模型对所述IMU信息进行噪声模型完好性检测，生成噪声模型完好性检测数据；
对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据；
根据所述数据完好性检测结果、所述噪声模型完好性检测数据、所述一致性检测数据中的一种或多种生成所述第一严重度数据。
在一些实施方式中，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
对所述预设组合导航算法的运行状态进行观测新息检验，生成观测新息检验结果；
根据所述观测新息检验结果生成所述第二严重度数据。
在一些实施方式中，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
对所述预设组合导航算法的运行状态进行方差矩阵分析，生成方差矩阵分析结果；
根据所述方差矩阵分析结果生成所述第二严重度数据。
在一些实施方式中，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
对所述预设组合导航算法的运行状态进行状态向量残差检验，生成状态向量残差检验结果；
根据所述状态向量残差检验结果生成所述第二严重度数据。
在一些实施方式中，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
对所述预设组合导航算法的运行状态进行观测新息检验，生成观测新息检验结果；
对所述预设组合导航算法的运行状态进行方差矩阵分析，生成方差矩阵分析结果；
对所述预设组合导航算法的运行状态进行状态向量残差检验，生成状态向量残差检验结果；
根据所述观测新息检验结果、所述方差矩阵分析结果、所述状态向量残差检验结果中的一种或多种生成所述第二严重度数据。
在上述实现过程中，过程完好性检测组合导航算法内的运行情况，过程完好性的分析方法包括状态向量残差检验、方差矩阵分析、观测新息检验三个方面。
在一些实施方式中，所述结果完好性采用并行参数完好性检测，所述根据所述定位结果进行结果完好性检测，生成第三严重度数据的步骤，包括：
获取冗余导航数据；
根据所述冗余导航数据获得冗余定位结果；
将所述冗余定位结果和所述定位结果进行一致性检验，生成所述第三严重度数据。
在上述实现过程中，冗余导航数据为组合导航系统中的冗余导航单元(冗余IMU等)通过其他方法获得的参数，由冗余导航数据获得冗余定位结果，冗余定位结果和定位结果可以形成对照，第三严重度数据反映定位结果的准确性。
在一些实施方式中，所述冗余导航数据为冗余IMU的数据，所述根据所述冗余导航数据获得冗余定位结果的步骤，包括：
通过预设捷联解算算法对所述冗余IMU的数据进行处理，生成所述冗余定位结果。
在一些实施方式中，所述将所述冗余定位结果和所述定位结果进行一致性检验，生成所述第三严重度数据的步骤，包括：
对所述冗余定位结果和所述定位结果的对应参数进行一致性检验，生成所述第三严重度数据，所述对应参数包括位置参数、速度参数、姿态参数、零偏参数中的一种或多种。
在上述实现过程中，置信度可以直接反映出定位结果的准确性，置信度越高，定位结果的准确性越高。
请参见图1，图1为本申请实施例提供的组合导航系统的结构示意图。
示例性地，基本的组合导航系统包括三部分：数据采集，采集的数据包括IMU信息、GNSS信息和车辆数据等；组合导航算法；定位结果输出。本申请实施例提供的组合导航数据的完好性检测方法在此基础上引入了原始数据完好性、过程完好性和结果完好性的三级完好性检测体系。
请参见图2，图2为本申请实施例提供的一种组合导航数据的完好性检测方法的流程示意图，该组合导航数据的完好性检测方法包括如下步骤；
S100：获取原始导航数据。
示例性地，原始导航数据包括IMU采集的IMU信息和定位芯片接收的GNSS信息。当应用于车辆导航时，所述原始导航数据还可以包括车辆数据。
可选地，IMU信息包括加速度、角速度等，GNSS信息包括定位坐标等，车辆数据包括轮速、方向盘等信息；需要注意的是，IMU信息、GNSS信息、车辆数据包括的具体类型可根据需要进行取舍，此处不作限定。
S200：对原始导航数据进行原始数据完好性检测，生成第一严重度数据。
示例性地，原始数据完好性检测就是从S100获得的原始导航数据进行分析，第一严重度数据可衡量原始导航数据的质量好坏。
S300：根据预设组合导航算法对原始导航数据进行处理，获得定位结果。
S400：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据。
示例性地，在组合导航算法的运行过程中进行过程完好性检测，可以检测组合导航算法内部的运行情况，第二严重度数据可衡量组合导航算法是否正常运行。
S500：根据定位结果进行结果完好性检测，生成第三严重度数据。
示例性地，结果完好性可以对定位结果进行检验，若校验失败，则表明存在故障，定位结果失真；第三严重度数据可衡量定位结果的失真情况。
S600：根据第一严重度数据、第二严重度数据、第三严重度数据获得完好性检测信息；
S700：根据完好性检测信息生成定位结果的置信度。
示例性地，置信度可以直接反映出定位结果的准确性，置信度越高，定位结果的准确性越高。
示例性地，该组合导航数据的完好性检测方法引入了原始数据完好性、过程完好性和结果完好性的三级完好性检测体系，同时引入检测评估体系，即每次完好性检测后都会产生一个严重度指标，即第一严重度数据、第二严重度数据、第三严重度数据，每个严重度指标可表示代表对应数据的置信度，最终获得的完好性检测信息可衡量定位结果的准确性和可靠性；原始数据完好性检测可以对原始导航数据检测，从源头上保证数据的可用性，进而提高组合导航算法的稳健性，过程完好性检测进一步保证组合导航算法的稳健，一些来自算法建模本身的缺陷也能覆盖；从而，该方法可以实现提高组合导航的可靠性和稳定性的技术效果。
请参见图3，图3为本申请实施例提供的获得完好性检测信息的流程示意图。
示例性地，S600：根据第一严重度数据、第二严重度数据、第三严重度数据获得完好性检测信息的步骤，包括：
S610：将第一严重度数据、第二严重度数据、第三严重度数据累计相加，获得综合严重度数据；
S620：将综合严重度数据与第一预设值、第二预设值进行比较，若综合严重度数据小于等于第一预设值，则生成低风险完好性检测信息；若综合严重度数据大于第一预设值且小于等于第二预设值，则生成中风险完好性检测信息；若综合严重度数据大于第二预设值，则生成高风险完好性检测信息。
示例性地，完好性检测信息包括低风险完好性检测信息、中风险完好性检测信息和高风险完好性检测信息，风险等级由低到高；风险等级越高，数据风险越高，表明定位结果越不准确。
在一些实施场景中，假设第一预设值为3，第二预设值为6；综合严重度数据为E，则：
E≤3，定位结果的数据可用，使用该定位结果的风险较低；
3<E≤6，不确定，故障未确认，使用定位结果可能有风险；
6<E，不可用，故障已确认，使用定位结果的风险较高。
可选地，本申请实施例还提供了部分严重度数据，该部分严重度数据可以是第一严重度数据或第一严重度数据+第二严重度数据；类似地，部分严重度数据与综合严重度数据的使用方法相同，此处不再赘述。
请参见图4，图4为本申请实施例提供的生成第一严重度数据的流程示意图。
示例性地，原始导航数据包括IMU信息和GNSS信息，在应用于车辆导航时，所述原始导航数据还可以包括车辆数据，S200：对原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
S210：对原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果；
S220：根据预设噪声模型对IMU信息进行噪声模型完好性检测，生成噪声模型完好性检测数据；
S230：对原始导航数据中的至少两种进行一致性检测，生成一致性检测数据；
S240：根据数据完好性检测结果、噪声模型完好性检测数据、一致性检测数据中的一种或多种生成第一严重度数据。
在一些实施方式中，首先进行数据完好性检测，若检测不通过，则可以根据数据完好性检测结果生成第一严重度数据，而无需再进行S220和S230的步骤。若数据完好性检测通过，则继续对IMU信息进行噪声模型检测，若检测不通过，则可以根据噪声模型完好性检测数据生成第一严重度数据，而无需再进行S230步骤。若噪声模型检测通过，则继续对原始导航数据中的至少两种进行一致性检测，生成一致性检测数据，并根据该一致性检测数据生成第一严重度数据。
在一些实施方式中，所述对原始导航数据中的至少两种进行一致性检测，包括：对IMU信息和GNSS信息进行一致性检测，若通过，则再对IMU信息、GNSS和车辆数据三者之间进行一致性检测，根据三者的一致性检测数据输出第一严重度数据。任何一个环节检测不通过，就可以输出第一严重度数据(此时输出的第一严重度数据E>6)，之后可以继续或不继续进行后续步骤的一致性检测。
示例性地，S210：对原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果的步骤，包括：
对原始导航数据中的一种或多种进行序列检测、校验码检测、长度检测、超时检测、取值范围检测、数值跳变检测中的一种或多种检测，生成数据完好性检测结果。
示例性地，序列检测、校验码检测、长度检测、超时检测、取值范围检测、数值跳变检测可以按照预设的顺序依次进行，此处不做限定。
示例性地，一致性检测采用最小二乘残差法，S230：对原始导航数据进行一致性检测，生成一致性检测数据的步骤，包括：
对原始导航数据进行最小二乘残差法处理，生成一致性检测数据。
示例性地，IMU信息包括主IMU信息和冗余IMU信息，S230：对原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
对主IMU信息和冗余IMU信息之间进行一致性检测，生成一致性检测数据。
可选地，S230：对原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
对GNSS信息、主IMU信息、冗余IMU信息中的两者/三者之间进行一致性检测，生成一致性检测数据。
可选地，S230：对原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
对GNSS信息、主IMU信息、冗余IMU信息、车辆数据中的两者/三者/四者之间进行一致性检测，生成一致性检测数据。
在一些实施方式中，首先进行主IMU信息、冗余IMU信息的一致性检测，若两者一致性符合要求，继续和GNSS信息进行一致性检测；若两者一致性不符合要求，根据主IMU信息、冗余IMU信息两者生成一致性检测数据；
然后，判断主IMU信息、冗余IMU信息、GNSS信息三者的一致性是否符合要求，若是则继续和车辆数据进行一致性检测，并根据GNSS信息、主IMU信息、冗余IMU信息、车辆数据四者生成一致性检测数据；若否，根据GNSS信息、主IMU信息、冗余IMU信息三者生成一致性检测数据。
请参见图5，图5为本申请实施例提供的原始数据完好性检测的流程示意图。
示例性地，组合导航系统中包括至少两个IMU，其中至少一个作为主IMU，至少一个作为冗余IMU。
示例性地，数据完好性指所有的原始数据经过数据完好性检测，即进行数据层面的分析，数据完好性分析方法包括如下的一种或多种：
1)序列检测，数据必须是顺序发送，防止丢失或错乱；若通过序列检测发现数据不是顺序发送的，则数据完好性差，表示数据完好性检测不通过，此时会生成较高严重度，即生成较高的第一严重度数据。
2)校验码检测，防止数据被删改或干扰；若通过校验码检测发现数据的校验码错误，则数据完好性差，表示数据完好性检测不通过，此时会生成较高严重度，即生成较高的第一严重度数据。
3)长度检测，数据长度必须正确；若通过长度检测发现数据的长度不再预设范围内，则数据完好性差，表示数据完好性检测不通过，此时会生成较高严重度，即生成较高的第一严重度数据。
4)超时检测，确保数据是最新数据，有时效性；若通过超时检测发现数据的发送时间超过阈值，则数据完好性差，表示数据完好性检测不通过，此时会生成较高严重度，即生成较高的第一严重度数据。
5)取值范围检测，数据必须在合理范围内；若通过取值范围检测发现数据偏差较大，则数据完好性差，表示数据完好性检测不通过，此时会生成较高严重度，即生成较高的第一严重度数据。
6)数值跳变检测，防止异常数据出现；若通过数值跳变检测发现数据出现跳变(相邻数值突然增大或减小)，则数据完好性差，表示数据完好性检测不通过，此时会生成较高严重度，即生成较高的第一严重度数据。
示例性地，噪声模型完好性即根据噪声模型进行检测，噪声模型参数包括零偏、零偏稳定性、Allan方差；可选地，噪声模型参数可以通过经验值进行设定，超过预设范围就要提升严重度，即生成较高的第一严重度数据。
请参见图6，图6为本申请实施例提供的生成第二严重度数据的流程示意图。
示例性地，S400：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
S410：对预设组合导航算法的运行状态进行观测新息检验，生成观测新息检验结果；
S420：对预设组合导航算法的运行状态进行方差矩阵分析，生成方差矩阵分析结果数据；
S430：对预设组合导航算法的运行状态进行状态向量残差检验，生成状态向量残差检验结果；
S440：根据所述观测新息检验结果、方差矩阵分析结果、状态向量残差检验结果中的一种或多种生成第二严重度数据。
在一些实施方式中，步骤S410至步骤S430是按顺序进行的，首先进行步骤S410，若步骤S410中的观测新息检测通过，则继续步骤S420进行方差矩阵分析，若步骤S420中的方差矩阵分析通过，则继续步骤S430进行状态向量残差检测。若其中一个检测、分析或检验不通过，则可以根据之前的检测或分析结果生成第二严重度数据，而不需要再继续进行之后步骤。
在一些实施方式中，S400：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
对预设组合导航算法的运行状态进行观测新息检验，生成观测新息检验结果；
根据观测新息检验结果生成第二严重度数据。
在一些实施方式中，S400：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
对预设组合导航算法的运行状态进行方差矩阵分析，生成方差矩阵分析结果；
根据方差矩阵分析结果生成第二严重度数据。
在一些实施方式中，S400：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严 重度数据的步骤，包括：
对预设组合导航算法的运行状态进行状态向量残差检验，生成状态向量残差检验结果；
根据状态向量残差检验结果生成第二严重度数据。
在一些实施例中，还可以将根据观测新息检验结果生成第二严重度数据、根据方差矩阵分析结果生成第二严重度数据、根据状态向量残差检验结果生成第二严重度数据中的两者或三者进行相加或按权重相加后生成最终的第二严重度数据。
请参见图7，图7为本申请实施例提供的过程完好性检测的流程示意图。
示例性地，过程完好性检测组合导航算法内的运行情况，如图7所示，过程完好性的分析方法包括：状态向量残差检验、方差矩阵分析、观测新息检验。
示例性地，在观测新息检验中，观测新息是实际观测向量与观测更新之前、由状态估计并计算得来的观测向量之间的差值。对观测新息进行峰值检测、合理性检验，可以检查出大异常。其次进行标准差检查，可以检查出短时间内小异常。以上的一项或多项检查不通过的，认为可能出现风险，提升严重度，即生成较高的第二严重度数据。
示例性地，在方差矩阵分析中，在长时间运行之后，噪声的不断注入或者建模不当，导致误差协方差失真；方差矩阵分析的主要方法是误差协方差矩阵分析。例如，现有n行n列的方差矩阵P，比较矩阵P的对角线各元素Pii与事先限定的值[Pmax,Pmin]，若Pii>Pmax或Pii<Pmin，表示方差矩阵分析不通过，则提升严重度，即生成较高的第二严重度数据，并将第i行和i列的所有元素乘以预设值λii，其中λii与Pii、Pmax和Pmin有关。
示例性地，在状态向量残差检验中，状态向量残差是真实状态向量与其卡尔曼滤波估计之间的差值。该残差在预设范围内认为算法稳定，超出则认为状态向量残差检验不通过，可能出现风险，要提升严重度，即生成较高的第二严重度数据。
本实施例中，可以根据观测信息检验结果、方差矩阵分析结果和状态向量残差检验结果中的一种或多种生成第二严重度数据。在一种实施方式中，首先对预设组合导航算法的运行状态进行观测新息检验，生成观测新息检验结果，根据观测新息检验结果生成第二严重度数据，若该第二严重度数据超出预设阈值范围，表明组合导航算法的运行状态不稳定，此时可以不再进行方差矩阵分析和状态向量残差检验，若该第二严重度数据未超出预设阈值范围，表明组合导航算法的运行状态稳定，此时继续进行方差矩阵分析，再根据方差矩阵分析结果生成第二严重度数据，若该第二严重度数据超出预设阈值范围，则表明组合导航算法的运行状态不稳定，此时可以不再进行状态向量残差检验，若该第二严重度数据未超出预设阈值范围，表明组合导航算法的运行状态稳定，此时继续进行状态向量残差检验，根据状态向量残差检验结果生成第二严重度数据。
请参见图8，图8为本申请实施例提供的生成第三严重度数据的流程示意图。
示例性地，结果完好性采用并行参数完好性检测，S500：根据定位结果进行结果完好性检测，生成第三严重度数据的步骤，包括：
S510：获取冗余导航数据；
S520：将冗余定位结果和定位结果进行一致性检验，生成第三严重度数据；
S530：根据冗余导航数据获得冗余定位结果。
示例性地，冗余导航数据为组合导航系统中的冗余导航单元(冗余IMU等)通过其他方法获得的参数，由冗余导航数据获得冗余定位结果，冗余定位结果和定位结果可以形成对照，第三严重度数据反映定位结果的准确性。
在一些实施方式中，冗余定位结果和定位结果比较时可以采用两个数值作差的检验方法，当差值超过误差允许范围，则认为检验失败，定位结果不准确，此时将生成较高的第三严重度数据。
示例性地，冗余导航数据为冗余IMU的数据，S520：根据冗余导航数据获得冗余定位结果的步骤，包括：
通过预设捷联解算算法对冗余IMU的数据进行处理，生成冗余定位结果。
示例性地，S530：将冗余定位结果和定位结果进行一致性检验，生成第三严重度数据的步骤，包括：
对冗余定位结果和定位结果的对应参数进行一致性检验，生成第三严重度数据，对应参数包括位置参数、速度参数、姿态参数、零偏参数中的一种或多种。
请参见图9，图9为本申请实施例提供的结果完好性检测的流程示意图。
示例性地，结果完好性采用并行参数完好性检测方案，即每个冗余导航数据的参数都与组合导航算法的参数进行比较；如果检验失败，则表明存在故障，由此确定第三严重度数据。
示例性地，冗余导航数据的参数都与组合导航算法的参数一一对应；导航参数使用陀螺、加速度计、位置、速度、姿态、零偏等。
本申请实施例还提供了一种组合导航数据的完好性检测方法；请参见图10，该方法包括如下步骤：
S101：获取原始导航数据。
原始导航数据包括IMU信息和GNSS信息，在应用于车辆导航时，所示原始导航数据还可以包括车辆数据。
S102：对原始导航数据进行原始数据完好性检测，生成第一严重度数据。
示例性地，原始数据完好性检测就是从S101获得的原始导航数据进行分析，第一严重度数据可衡量原始导航数据的质量好坏。
S103：根据预设组合导航算法对原始导航数据进行处理，获得定位结果。
S104：根据第一严重度数据获得完好性检测信息；
S105：根据完好性检测信息生成定位结果的置信度。
示例性地，原始导航数据包括IMU采集的IMU信息和定位芯片接收的GNSS信息。当应用于车辆导航时，所述原始导航数据还可以包括车辆数据。
示例性地，图10为本申请实施例提供的使用原始数据完好性检测进行完好性检测的方法，原始数据完好性检测可以对原始导航数据检测，从源头上保证数据的可用性，进而提高组合导航算法的稳健性；关于原始数据完好性检测的具体实施细节请参见实施例一的相关部分，此处不再赘述。
本申请实施例还提供了一种组合导航数据的完好性检测方法；请参见图11，该方法包括如下步骤：
S201：获取原始导航数据。
示例性地，原始导航数据包括IMU采集的IMU信息、定位芯片接收的GNSS信息以及车辆数据等。
S202：根据预设组合导航算法对原始导航数据进行处理，获得定位结果。
S203：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据。
示例性地，在组合导航算法的运行过程中进行过程完好性检测，可以检测组合导航算法内部的运行情况，第二严重度数据可衡量组合导航算法是否正常运行。
S204：根据第二严重度数据获得完好性检测信息；
S205：根据完好性检测信息生成定位结果的置信度。
示例性地，原始导航数据包括IMU采集的IMU信息和定位芯片接收的GNSS信息。当应用于车辆导航时，所述原始导航数据还可以包括车辆数据。
示例性地，图11为本申请实施例提供的使用过程完好性检测进行完好性检测的方法，过程完好性检测可保证组合导航算法的稳健性，一些来自算法建模本身的缺陷也能覆盖；关于过程完好性检测的具体实施细节请参见实施例一的相关部分，此处不再赘述。
本申请实施例还提供了一种组合导航数据的完好性检测方法；请参见图12，该方法包括如下步骤：
S301：获取原始导航数据。
示例性地，原始导航数据包括IMU采集的IMU信息、定位芯片接收的GNSS信息以及车辆数据等。
S302：根据预设组合导航算法对原始导航数据进行处理，获得定位结果。
S303：根据定位结果进行结果完好性检测，生成第三严重度数据。
示例性地，结果完好性可以对定位结果进行检验，若校验失败，则表明存在故障，定位结果失真；第三严重度数据可衡量定位结果的失真情况。
S304：根据第三严重度数据获得完好性检测信息；
S305：根据完好性检测信息生成定位结果的置信度。
示例性地，原始导航数据包括IMU采集的IMU信息和定位芯片接收的GNSS信息。当应用于车辆导航时，所述原始导航数据还可以包括车辆数据。
示例性地，图12为本申请实施例提供的使用结果完好性检测进行完好性检测的方法，结果完好性检测可保证定位结果的准确性；关于结果完好性检测的具体实施细节请参见实施例一的相关部分，此处不再赘述。
本申请实施例还提供了一种组合导航数据的完好性检测方法；请参见图13，该方法包括如下步 骤：
S401：获取原始导航数据。
示例性地，原始导航数据包括IMU采集的IMU信息、定位芯片接收的GNSS信息以及车辆数据等。
S402：对原始导航数据进行原始数据完好性检测，生成第一严重度数据。
示例性地，原始数据完好性检测就是从S101获得的原始导航数据进行分析，第一严重度数据可衡量原始导航数据的质量好坏。
S403：根据预设组合导航算法对原始导航数据进行处理，获得定位结果。
S404：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据。
示例性地，在组合导航算法的运行过程中进行过程完好性检测，可以检测组合导航算法内部的运行情况，第二严重度数据可衡量组合导航算法是否正常运行。
S405：根据第一严重度数据和第二严重度数据获得完好性检测信息；
S406：根据完好性检测信息生成定位结果的置信度。
示例性地，原始导航数据包括IMU采集的IMU信息和定位芯片接收的GNSS信息。当应用于车辆导航时，所述原始导航数据还可以包括车辆数据。
示例性地，图13为本申请实施例提供的使用原始数据完好性检测和过程完好性检测进行完好性检测的方法，原始数据完好性检测可以对原始导航数据检测，从源头上保证数据的可用性，进而提高组合导航算法的稳健性，过程完好性检测进一步保证组合导航算法的稳健，一些来自算法建模本身的缺陷也能覆盖；关于原始数据完好性检测、过程完好性检测的具体实施细节请参见实施例一的相关部分，此处不再赘述。
本申请实施例还提供了一种组合导航数据的完好性检测方法；请参见图14，该方法包括如下步骤：
S501：获取原始导航数据。
示例性地，原始导航数据包括IMU采集的IMU信息、定位芯片接收的GNSS信息以及车辆数据等。
S502：对原始导航数据进行原始数据完好性检测，生成第一严重度数据。
示例性地，原始数据完好性检测就是从S101获得的原始导航数据进行分析，第一严重度数据可衡量原始导航数据的质量好坏。
S503：根据预设组合导航算法对原始导航数据进行处理，获得定位结果。
S504：根据定位结果进行结果完好性检测，生成第三严重度数据。
示例性地，结果完好性可以对定位结果进行检验，若校验失败，则表明存在故障，定位结果失真；第三严重度数据可衡量定位结果的失真情况。
S505：根据第一严重度数据和第三严重度数据获得完好性检测信息；
S506：根据完好性检测信息生成定位结果的置信度。
示例性地，原始导航数据包括IMU采集的IMU信息和定位芯片接收的GNSS信息。当应用于车辆导航时，所述原始导航数据还可以包括车辆数据。
示例性地，图14为本申请实施例提供的使用原始数据完好性检测和结果完好性检测进行完好性检测的方法，原始数据完好性检测可以对原始导航数据检测，从源头上保证数据的可用性，进而提高组合导航算法的稳健性，结果完好性检测可保证定位结果的准确性；关于原始数据完好性检测、结果完好性检测的具体实施细节请参见实施例一的相关部分，此处不再赘述。
本申请实施例还提供了一种组合导航数据的完好性检测方法；请参见图115，该方法包括如下步骤：
S601：获取原始导航数据。
示例性地，原始导航数据包括IMU采集的IMU信息、定位芯片接收的GNSS信息以及车辆数据等。
S603：根据预设组合导航算法对原始导航数据进行处理，获得定位结果。
S603：对预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据。
示例性地，在组合导航算法的运行过程中进行过程完好性检测，可以检测组合导航算法内部的运行情况，第二严重度数据可衡量组合导航算法是否正常运行。
S604：根据定位结果进行结果完好性检测，生成第三严重度数据。
示例性地，结果完好性可以对定位结果进行检验，若校验失败，则表明存在故障，定位结果失 真；第三严重度数据可衡量定位结果的失真情况。
S605：根据第二严重度数据和第三严重度数据获得完好性检测信息；
S606：根据完好性检测信息生成定位结果的置信度。
示例性地，原始导航数据包括IMU采集的IMU信息和定位芯片接收的GNSS信息。当应用于车辆导航时，所述原始导航数据还可以包括车辆数据。
示例性地，图15为本申请实施例提供的使用过程完好性检测和结果完好性检测进行完好性检测的方法，过程完好性检测保证组合导航算法的稳健，一些来自算法建模本身的缺陷也能覆盖，结果完好性检测可保证定位结果的准确性；关于过程完好性检测、结果完好性检测的具体实施细节请参见实施例一的相关部分，此处不再赘述。
本申请还提供一种电子设备，该电子设备可以包括处理器、通信接口、存储器和至少一个通信总线。其中，通信总线用于实现这些组件直接的连接通信。其中，本申请实施例中电子设备的通信接口用于与其他节点设备进行信令或数据的通信。处理器可以是一种集成电路芯片，具有信号的处理能力。电子设备可以执行上述实施例涉及的各个步骤。
本申请实施例还提供一种存储介质，所述存储介质上存储有指令，当所述指令在计算机上运行时，所述计算机程序被处理器执行时实现方法实施例所述的方法，为避免重复，此处不再赘述。所述存储介质包括但不限于：U盘、移动硬盘、只读存储器(ROM)、随机存取存储器(RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
本申请还提供一种计算机程序产品，所述计算机程序产品在计算机上运行时，使得计算机执行方法实施例所述的方法。
工业实用性
根据本申请所述的组合导航数据的完好性检测方法及系统引入了原始数据完好性、过程完好性和结果完好性的三级完好性检测体系，同时引入检测评估体系，即每次完好性检测后都会产生一个严重度指标，即第一严重度数据、第二严重度数据、第三严重度数据，每个严重度指标可表示代表对应数据的置信度，最终获得的完好性检测信息可衡量定位结果的准确性和可靠性；原始数据完好性检测可以对原始导航数据检测，从源头上保证数据的可用性，进而提高组合导航算法的稳健性，过程完好性检测进一步保证组合导航算法的稳健，一些来自算法建模本身的缺陷也能覆盖；从而，该方法可以实现提高组合导航的可靠性和稳定性的技术效果，并被应用在例如自动驾驶等工业应用中。

Claims (20)

  1. 一种组合导航数据的完好性检测方法，其特征在于，包括；
    获取原始导航数据；
    对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据；
    根据预设组合导航算法对所述原始导航数据进行处理，获得定位结果；
    对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据；
    根据所述定位结果进行结果完好性检测，生成第三严重度数据；
    根据所述第一严重度数据、所述第二严重度数据、所述第三严重度数据获得完好性检测信息；
    根据所述完好性检测信息生成所述定位结果的置信度。
  2. 根据权利要求1所述的组合导航数据的完好性检测方法，其特征在于，所述根据所述第一严重度数据、所述第二严重度数据、所述第三严重度数据获得完好性检测信息的步骤，包括：
    将所述第一严重度数据、所述第二严重度数据、所述第三严重度数据累计相加，获得综合严重度数据；
    将所述综合严重度数据与第一预设值、第二预设值进行比较，若所述综合严重度数据小于等于所述第一预设值，则生成低风险完好性检测信息；若所述综合严重度数据大于所述第一预设值且小于等于所述第二预设值，则生成中风险完好性检测信息；若所述综合严重度数据大于所述第二预设值，则生成高风险完好性检测信息。
  3. 根据权利要求1或2所述的组合导航数据的完好性检测方法，其特征在于，所述原始导航数据包括IMU信息、GNSS信息、车辆数据中的一种或多种，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
    对所述原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果；
    根据所述数据完好性检测结果生成所述第一严重度数据。
  4. 根据权利要求3所述的组合导航数据的完好性检测方法，其特征在于，所述对所述原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果的步骤，包括：
    对所述原始导航数据中的一种或多种进行序列检测、校验码检测、长度检测、超时检测、取值范围检测、数值跳变检测中的一种或多种检测，生成所述数据完好性检测结果。
  5. 根据权利要求1或2所述的组合导航数据的完好性检测方法，其特征在于，所述原始导航数据包括IMU信息，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
    根据预设噪声模型对所述IMU信息进行噪声模型完好性检测，生成噪声模型完好性检测数据；
    根据所述噪声模型完好性检测数据生成所述第一严重度数据。
  6. 根据权利要求1或2所述的组合导航数据的完好性检测方法，其特征在于，所述原始导航数据包括IMU信息、GNSS信息、车辆数据中的至少两种，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
    对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据；
    根据所述一致性检测数据生成所述第一严重度数据。
  7. 根据权利要求6所述的组合导航数据的完好性检测方法，其特征在于，所述IMU信息包括主IMU信息和冗余IMU信息，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
    对所述主IMU信息和冗余IMU信息之间进行一致性检测，生成所述一致性检测数据。
  8. 根据权利要求6所述的组合导航数据的完好性检测方法，其特征在于，所述IMU信息包括主IMU信息和冗余IMU信息，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
    对所述GNSS信息、所述主IMU信息、所述冗余IMU信息中的两者/三者之间进行一致性检测，生成所述一致性检测数据。
  9. 根据权利要求6所述的组合导航数据的完好性检测方法，其特征在于，所述IMU信息包括主IMU信息和冗余IMU信息，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
    对所述GNSS信息、所述主IMU信息、所述冗余IMU信息、所述车辆数据中的两者/三者/四者之间进行一致性检测，生成所述一致性检测数据。
  10. 根据权利要求9所述的组合导航数据的完好性检测方法，其特征在于，所述对所述GNSS信息、所述主IMU信息、所述冗余IMU信息、所述车辆数据中的两者/三者/四者之间进行一致性检测，生成所述一致性检测数据的步骤，包括：
    进行所述主IMU信息、所述冗余IMU信息的一致性检测，若两者一致性符合要求，继续和所述GNSS信息进行一致性检测；若两者一致性不符合要求，根据所述主IMU信息、所述冗余IMU信息两者生成所述一致性检测数据；
    判断所述主IMU信息、所述冗余IMU信息、所述GNSS信息三者的一致性是否符合要求，若是则继续和所述车辆数据进行一致性检测，并根据所述GNSS信息、所述主IMU信息、所述冗余IMU信息、所述车辆数据四者生成所述一致性检测数据；若否，根据所述GNSS信息、所述主IMU信息、所述冗余IMU信息三者生成所述一致性检测数据。
  11. 根据权利要求1或2所述的组合导航数据的完好性检测方法，其特征在于，所述原始导航数据包括IMU信息、GNSS信息、车辆数据中的至少两种，所述对所述原始导航数据进行原始数据完好性检测，生成第一严重度数据的步骤，包括：
    对所述原始导航数据中的一种或多种进行数据完好性检测，生成数据完好性检测结果；
    根据预设噪声模型对所述IMU信息进行噪声模型完好性检测，生成噪声模型完好性检测数据；
    对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据；
    根据所述数据完好性检测结果、所述噪声模型完好性检测数据、所述一致性检测数据中的一种或多种生成所述第一严重度数据。
  12. 根据权利要求6-10中任一项所述的组合导航数据的完好性检测方法，其特征在于，所述一致性检测采用最小二乘残差法，所述对所述原始导航数据中的至少两种进行一致性检测，生成一致性检测数据的步骤，包括：
    对所述原始导航数据中的至少两种进行最小二乘残差法处理，生成所述一致性检测数据。
  13. 根据权利要求1-12中任一项所述的组合导航数据的完好性检测方法，其特征在于，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
    对所述预设组合导航算法的运行状态进行观测新息检验，生成观测新息检验结果；
    根据所述观测新息检验结果生成所述第二严重度数据。
  14. 根据权利要求1-12中任一项所述的组合导航数据的完好性检测方法，其特征在于，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
    对所述预设组合导航算法的运行状态进行方差矩阵分析，生成方差矩阵分析结果；
    根据所述方差矩阵分析结果生成所述第二严重度数据。
  15. 根据权利要求1-12中任一项所述的组合导航数据的完好性检测方法，其特征在于，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
    对所述预设组合导航算法的运行状态进行状态向量残差检验，生成状态向量残差检验结果；
    根据所述状态向量残差检验结果生成所述第二严重度数据。
  16. 根据权利要求1-12中任一项所述的组合导航数据的完好性检测方法，其特征在于，所述对所述预设组合导航算法的运行状态进行过程完好性检测，生成第二严重度数据的步骤，包括：
    对所述预设组合导航算法的运行状态进行观测新息检验，生成观测新息检验结果；
    对所述预设组合导航算法的运行状态进行方差矩阵分析，生成方差矩阵分析结果；
    对所述预设组合导航算法的运行状态进行状态向量残差检验，生成状态向量残差检验结果；
    根据所述观测新息检验结果、所述方差矩阵分析结果、所述状态向量残差检验结果中的一种或多种生成所述第二严重度数据。
  17. 根据权利要求1-16中任一项所述的组合导航数据的完好性检测方法，其特征在于，所述根据所述定位结果进行结果完好性检测，生成第三严重度数据的步骤，包括：
    获取冗余导航数据；
    根据所述冗余导航数据获得冗余定位结果；
    将所述冗余定位结果和所述定位结果进行一致性检验，生成所述第三严重度数据。
  18. 根据权利要求17所述的组合导航数据的完好性检测方法，其特征在于，所述冗余导航数据为冗余IMU的数据，所述根据所述冗余导航数据获得冗余定位结果的步骤，包括：
    通过预设捷联解算算法对所述冗余IMU的数据进行处理，生成所述冗余定位结果。
  19. 根据权利要求17所述的组合导航数据的完好性检测方法，其特征在于，所述将所述冗余定位结果和所述定位结果进行一致性检验，生成所述第三严重度数据的步骤，包括：
    对所述冗余定位结果和所述定位结果的对应参数进行一致性检验，生成所述第三严重度数据，所述对应参数包括位置参数、速度参数、姿态参数、零偏参数中的一种或多种。
  20. 一种电子设备，其特征在于，包括：存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1至19任一项所述的组合导航数据的完好性检测方法的步骤。
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