An inertial-aided RAIM availability monitoring method based on a tight coupling system

Abstract

A method for inertial-assisted RAIM availability monitoring based on tightly coupled systems includes: constructing a tightly coupled GNSS / IRS Kalman filter; setting the null hypothesis as satellite fault-free and the alternative hypothesis as satellite faulty; and, under the null hypothesis, determining the fault detection statistic of the Kalman filter. s The system follows a chi-square distribution; based on the false detection rate of the RAIM system, the detection threshold for false detections under the null hypothesis is calculated; according to the integrity risk of the RAIM system, the inflation factor is calculated based on the inverse function of the Gaussian distribution. K Based on the false negative rate of the RAIM system and the false positive detection threshold under the null hypothesis, the non-central chi-square parameter when the system experiences a false negative under the alternative hypothesis is calculated; a unit slowly varying fault is introduced into the measurement information of the Kalman filter to calculate the slope factor; and the expansion factor is combined with the... K Calculate the horizontal protection level (HPL) using non-central chi-square parameters and slope factors; determine the usability of the inertial-assisted RAIM algorithm based on the calculated HPL value.

Description

Technical Field

[0001] This invention relates to an inertial-assisted RAIM availability monitoring method based on a tightly coupled system, belonging to the field of navigation information processing. Background Technology

[0002] When satellites are obstructed or have poor satellite geometry, a single GNSS (Global Navigation Satellite System) cannot perform Receiver Autonomous Integrity Monitoring (RAIM) and may even experience RAIM black holes. Therefore, other auxiliary information (such as inertial navigation system IRS, atmospheric altitude, etc.) is required. Previous research has explored two integrity monitoring methods for tightly coupled GNSS / IRS systems: the Multi-Period Extrapolation Method (AIME) proposed by Litton and the Multiple Solution Separation Method (MSS) proposed by Honeywell. The Multi-Period Extrapolation Method detects slowly accumulating errors by observing GNSS measurement data over a long period (e.g., 30 minutes), thus achieving high detection efficiency. Through the Multi-Period Extrapolation Method, the system can detect and eliminate satellite faults. Simultaneously, the system outputs the Horizontal Protection Level (HPL) to determine GNSS availability, thereby enabling inertial-assisted RAIM availability monitoring. Therefore, the analysis and calculation of the HPL needs to be addressed.

[0003] HPL describes the statistical boundary value of the maximum possible positioning error in the horizontal direction given a false negative rate and a false positive rate. A circle is drawn on the horizontal plane (the local tangent plane of the WGS-84 ellipsoid) with the actual position as the center and HPL as the radius. This circle can be considered to contain the aircraft's actual position with extremely high confidence. When the aircraft's position error exceeds HPL, the RAIM algorithm should detect and eliminate satellite malfunctions within a specified time. When HPL exceeds the Horizontal Alarm Threshold (HAL) required by the flight path, the maximum positioning error assessed by the system is considered to have exceeded the safety threshold, meaning the RAIM algorithm has failed and the aircraft can no longer rely on GNSS for navigation. Therefore, HPL can be used for RAIM availability monitoring.

[0004] Existing research only proposes RAIM availability monitoring methods for a single GNSS, without adjusting and optimizing the HPL calculation method in the inertial navigation-assisted RAIM algorithm, and without considering the impact of horizontal position error introduced by satellite gradual failures on HPL. Summary of the Invention

[0005] The technical problem solved by the present invention is to overcome the shortcomings of the prior art and provide an inertial-assisted RAIM availability monitoring method based on a tightly coupled system, so as to output a horizontal protection level (HPL) suitable for tightly coupled flight systems to monitor the availability of inertial-assisted RAIM.

[0006] The technical solution of this invention is: Firstly, an inertial-assisted RAIM availability monitoring method based on a tightly coupled system is provided, including: Construct a tightly coupled GNSS / IRS Kalman filter; The assumption that the satellite is fault-free is taken as the original assumption. Satellite malfunction was set as the alternative hypothesis. Under the original hypothesis Under the premise of [condition], the fault detection statistic of the constructed Kalman filter s It satisfies a chi-square distribution; Based on the false detection rate of the RAIM system, the calculation of the system under the null hypothesis is performed. Detection threshold for false positives under certain conditions ; Based on the integrity risk of the RAIM system, the inflation factor is calculated using the inverse function of the Gaussian distribution. K ; Based on the false negative rate of the RAIM system and the system's performance under the null hypothesis Detection threshold for false positives under the premise of The computing system in the alternative hypothesis Non-central chi-square parameter when a missed detection occurs ; A unit slowly varying fault is introduced into the measurement information of the Kalman filter, and the slope factor is calculated. ; Combined expansion factor K Non-central chi-square parameters Slope factor Calculate the Horizontal Protection Level (HPL); The availability of the inertial-assisted RAIM algorithm is determined based on the calculated values ​​of HPL.

[0007] Preferably, the fault detection statistic of the Kalman filter s Specifically:

[0008] in, For Kalman filter information, Let be the new information variance matrix.

[0009] Preferably, based on the false detection rate of the RAIM system, the calculation system under the null hypothesis... Detection threshold for false positives under certain conditions The solution equation is:

[0010] , The false detection rate of the RAIM system; nThis represents the number of satellites used for tight coupling.

[0011] Preferably, based on the integrity risk of the RAIM system, the inflation factor is calculated using the inverse function relationship of the Gaussian distribution. K Specifically:

[0012] in, It is the inverse function of the Gaussian distribution. This refers to the probability of integrity risk.

[0013] Preferably, based on the false negative rate of the RAIM system, and The computing system in the alternative hypothesis Non-central chi-square parameter when a missed detection occurs The solution equation is:

[0014] This represents the false negative rate of the RAIM system.

[0015] Preferably, a unit slowly varying fault is introduced into the measurement information of the Kalman filter to calculate the slope factor. hour: For each satellite, Kalman filter estimation is performed using both the original and new measurement information. The difference in horizontal position error between the two estimations is... ; Based on the new interest rate spread corresponding to the two estimates The difference between the corresponding test statistics is calculated to be ;in i Indicates the satellite's index value; The slope factor for each satellite is: ; Final slope factor for: .

[0016] Preferably, the horizontal protection level (HPL) is as follows:

[0017] in:

[0018] is the standard deviation of the largest eigenvalue of the horizontal position covariance matrix of the Kalman filter.

[0019] Preferably, when determining whether the inertial-assisted RAIM algorithm is available based on the calculated value of HPL: Compare the HPL with the horizontal warning threshold HAL required by the flight path. If the HPL If HAL is enabled, the inertial-assisted RAIM algorithm is available; otherwise, it is not.

[0020] Secondly, a terminal device is provided, comprising: Memory, used to store at least one instruction executed by a processor; A processor is used to execute instructions stored in memory to implement the methods described above.

[0021] Thirdly, a computer-readable storage medium is provided that stores computer instructions that, when executed on a computer, cause the computer to perform the method described above.

[0022] Compared with the prior art, the present invention has the following advantages: A High Position Scale (HPL) calculation method suitable for multi-cycle extrapolation fault detection is proposed to monitor the availability of inertial-assisted RAIM. The calculation comprehensively considers both system error sources and horizontal position errors introduced by gradually changing satellite faults. HPL1 is related to the system position error and forms a high-confidence protection boundary through an expansion factor; HPL2 is related to gradually changing satellite faults. Since the detection of gradually changing faults requires a certain amount of time, the horizontal position error will continuously increase if the gradually changing fault is not detected and eliminated. Therefore, HPL2 is needed to protect against this error. Combining HPL1 and HPL2 provides a more accurate basis for monitoring the availability of inertial-assisted RAIM. When satellite signals experience sudden changes and gradually changing faults, the inertial-assisted RAIM availability monitoring basis provided by this invention not only ensures a low system miss rate but also... P md ≤10 -3 / h, false positive rate P fd ≤10 -5 / h, integrity risk ≤10 -7 It also ensures the continuity and stability of the flight process. Attached Figure Description

[0023] Figure 1 This is a flowchart of the inertial-assisted RAIM algorithm based on a tightly coupled GNSS / IRS system, as described in this invention. Figure 2 This is a flowchart of the calculation process for the Horizontal Protection Level (HPL) of this invention. Figure 3 The simulation verification results are shown in the figure provided for the embodiments of the present invention. Detailed Implementation

[0024] The technical problem this invention aims to solve is: for inertial-assisted RAIM availability monitoring, a High Position Scale (HPL) calculation method suitable for multi-period extrapolation fault detection is proposed, which protects against horizontal position errors introduced by system error sources and gradually changing satellite faults. When using autonomous fault detection, for a selected set of satellites, the horizontal region indicated by this HPL satisfies the required false negative rate. P md ≤10 -3 / h and false positive rate P fd ≤10 -5 / h, integrity risk controlled at 10 -7 The level.

[0025] The technical solution of this invention is: to achieve autonomous integrity monitoring of the inertial-assisted receiver based on a tightly coupled GNSS / IRS system, thereby realizing the detection and elimination of satellite faults, such as... Figure 1 As shown. Based on this, the Level of Protection (HPL) that satisfies the system's missed detection rate, false detection rate, and integrity risk is output to monitor the availability of inertial-assisted RAIM, such as... Figure 2 As shown, the specific steps are as follows: (1) Construct a fusion filtering architecture and design a tightly coupled GNSS / IRS Kalman filter. The filter state variables include IRS error states (attitude, velocity, position errors, gyroscope zero bias, accelerometer zero bias) and GNSS related states (receiver clock bias, clock drift). The main filter and sub-filters are allocated according to the number of satellites. Each sub-filter has the same IRS core but has a different subset of satellite observations.

[0026] (2) Combining the residual chi-square detection method and the multi-period extrapolation method, the test statistics of the main filter and sub-filters at each period are calculated. These statistics are calculated based on the innovation of the Kalman filter and the covariance of the innovation, and follow a chi-square distribution. Based on the inverse cumulative distribution function of the chi-square distribution, and according to the false detection rate of the RAIM system... P fd And the number of detected satellites, and the calculation of satellite fault detection thresholds. T D Satellite fault detection and troubleshooting are accomplished by comparing test statistics and detection thresholds.

[0027] (3) Calculate the expansion factor K Based on the integrity risk of the RAIM system, and using the inverse function relationship of the Gaussian distribution, the corresponding inflation factor is obtained. K .

[0028] (4) Calculate the non-central chi-square parameters. Based on the non-central chi-square distribution relationship, and according to the satellite fault detection threshold... T D False negative rateP md And the number of detected satellites, and the calculation of non-central chi-square parameters. .

[0029] (5) Calculate the slope factor Slope A unit slowly varying fault is simulated on each satellite, and the changes in horizontal position error and test statistic caused by the fault are calculated. The ratio of the two is the slope factor.

[0030] (6) Calculate the horizontal protection level (HPL). HPL consists of two parts, HPL1 and HPL2, which are used to cover horizontal position errors introduced by different error sources. Among them, HPL1 is the expansion factor in step (3). K The product of the standard deviation of the largest eigenvalue of the horizontal position covariance matrix of the Kalman filter is mainly used to cover the horizontal position error introduced by the inherent error sources of the system (such as ionospheric error, tropospheric error, multipath effect, etc.). HPL2 is the product of the square root of the non-central chi-square parameter in steps (4) and (5) and the slope factor. It is mainly used to cover the horizontal position error introduced by the satellite's gradual fault, which is related to the positioning error caused by the fault and its detection difficulty.

[0031] (7) Compare the HPL with the horizontal warning threshold HAL required by the flight path. If HPL If HAL is enabled, the inertial-assisted RAIM algorithm is available; otherwise, it is not.

[0032] In a second aspect, the present invention provides a terminal device, comprising: Memory, used to store at least one instruction executed by a processor; A processor is used to execute instructions stored in memory to implement the methods described above.

[0033] Thirdly, the present invention provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method described above.

[0034] Example: This invention relates to an inertial-assisted RAIM availability monitoring method based on a tightly coupled system, the specific implementation steps of which are as follows: 1. Construct a tightly coupled GNSS / IRS Kalman filter architecture, defining the filter state variables as: (1) in, For the attitude error of the inertial navigation system, For speed error, For positional error, For zero bias of the gyroscope, To achieve zero bias in the accelerometer, The equivalent distance error caused by receiver clock error. This refers to the error in the equivalent distance change rate caused by clock frequency error.

[0035] If pseudorange error and pseudorange rate error are used as measurements, then the system measurement equation is: (2) 2. Fault detection statistics for Kalman filters s Defined as: (3) in, For Kalman filter information, Let be the new information variance matrix.

[0036] Null hypothesis Assuming the satellite is fault-free, Since the sequence is a zero-mean Gaussian white noise sequence, the test statistic follows a chi-square distribution, i.e. ,in n Let be the number of satellites used for tight coupling. At this point, if the test statistic... s If the value is greater than the detection threshold, it indicates that the system has made a false detection. Therefore, the detection threshold can be calculated according to equation (4). .

[0037] (4) Right now , This represents the false detection rate of the RAIM system.

[0038] 3. Based on the integrity risk of the RAIM system, calculate the inflation factor using the inverse function relationship of the Gaussian distribution. K : (5) in, It is the inverse function of the Gaussian distribution. In this embodiment, the probability of integrity risk is considered. Set to 10 -7 .

[0039] 4. Alternative Hypothesis Assuming the satellite is faulty, the test statistic follows a non-central chi-square distribution, i.e. At this point, if the test statistic... s Less than the detection threshold If the result is negative, it indicates that the system has missed detections. Therefore, the non-central chi-square parameter can be calculated according to equation (6). .

[0040] (6) in, This represents the false negative rate of the RAIM system.

[0041] 5. Introduce a unit slowly varying fault of 1 m / s into the Kalman filter measurement information. For each satellite, perform Kalman filter estimation using both the original and new measurement information. The difference in horizontal position error between the two estimations is... Based on the new interest rate spread corresponding to the two estimates The difference between the corresponding test statistics is calculated to be ;in i This represents the satellite's index value.

[0042] A unit slowly varying fault is introduced into the measurement information of each satellite, and the slope factor of each satellite is calculated. ,but .

[0043] 6. Calculate the Horizontal Protection Level (HPL) according to formula (7).

[0044] (7) in, is the standard deviation of the largest eigenvalue of the horizontal position covariance matrix of the Kalman filter.

[0045] 7. Simulation verification of the HPL's protection effect on horizontal position error, such as... Figure 3 As shown, the HPL is less than the HAL (2 nautical miles) specified for the flight path, indicating that the inertial-assisted RAIM algorithm is usable.

[0046] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A method for inertial-assisted RAIM availability monitoring based on tightly coupled systems, comprising: Construct a tightly coupled GNSS / IRS Kalman filter; The assumption that the satellite is fault-free is taken as the original assumption. Satellite malfunction was set as the alternative hypothesis. Under the original hypothesis Under the premise of [condition], the fault detection statistic of the constructed Kalman filter s It satisfies a chi-square distribution; Based on the false detection rate of the RAIM system, the calculation of the system under the null hypothesis is performed. Detection threshold for false positives under certain conditions ; Based on the integrity risk of the RAIM system, the inflation factor is calculated using the inverse function relationship of the Gaussian distribution. K ; Based on the false negative rate of the RAIM system and the system's performance under the null hypothesis Detection threshold for false positives under certain conditions The computing system in the alternative hypothesis Non-central chi-square parameter when a missed detection occurs ; A unit slowly varying fault is introduced into the measurement information of the Kalman filter, and the slope factor is calculated. ; Combined expansion factor K Non-central chi-square parameters Slope factor Calculate the Horizontal Protection Level (HPL); The availability of the inertial-assisted RAIM algorithm is determined based on the calculated values ​​of HPL.

2. The inertial-assisted RAIM availability monitoring method based on a tightly coupled system according to claim 1, characterized in that: Fault detection statistics for Kalman filters s Specifically: in, For Kalman filter information, Let be the new information variance matrix.

3. The inertial-assisted RAIM availability monitoring method based on a tightly coupled system according to claim 1, characterized in that: Based on the false detection rate of the RAIM system, the calculation of the system under the null hypothesis is performed. Detection threshold for false positives under certain conditions The solution equation is: , The false detection rate of the RAIM system; n This represents the number of satellites used for tight coupling.

4. The inertial-assisted RAIM availability monitoring method based on a tightly coupled system according to claim 1, characterized in that: Based on the integrity risk of the RAIM system, the inflation factor is calculated using the inverse function relationship of the Gaussian distribution. K Specifically: in, It is the inverse function of the Gaussian distribution. This refers to the probability of integrity risk.

5. The inertial-assisted RAIM availability monitoring method based on a tightly coupled system according to claim 1, characterized in that: Based on the false negative rate of the RAIM system, and The computing system in the alternative hypothesis Non-central chi-square parameter when a missed detection occurs The solution equation is: This represents the false negative rate of the RAIM system.

6. The inertial-assisted RAIM availability monitoring method based on a tightly coupled system according to claim 1, characterized in that: A unit slowly varying fault is introduced into the measurement information of the Kalman filter, and the slope factor is calculated. hour: For each satellite, Kalman filter estimation is performed using both the original and new measurement information. The difference in horizontal position error between the two estimations is... ; Based on the new interest rate spread corresponding to the two estimates The difference between the corresponding test statistics is calculated to be ;in i Indicates the satellite's index value; The slope factor for each satellite is: ; Final slope factor for: .

7. The inertial-assisted RAIM availability monitoring method based on a tightly coupled system according to claim 1, characterized in that: The Horizontal Protection Level (HPL) is as follows: in: is the standard deviation of the largest eigenvalue of the horizontal position covariance matrix of the Kalman filter.

8. The inertial-assisted RAIM availability monitoring method based on a tightly coupled system according to claim 1, characterized in that: When determining whether the inertial-assisted RAIM algorithm is available based on the calculated value of HPL: Compare the HPL with the horizontal warning threshold HAL required by the flight path. If the HPL If HAL is enabled, the inertial-assisted RAIM algorithm is available; otherwise, it is not.

9. A terminal device, characterized in that, include: Memory, used to store at least one instruction executed by a processor; A processor for executing instructions stored in memory to implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-8.

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