BDS / INS navigation method and device based on innovation differential fault detection

Abstract

The invention provides a BDS / INS navigation method and device based on innovation differential fault detection, and belongs to the technical field of navigation positioning. Determining a BDS pseudo-range measurement predicted value of the visual satellite based on the BDS ephemeris; calculating pseudo-range information according to the BDS pseudo-range measurement predicted value and pseudo-range measurement, and forming a current information set; the current innovation set and the historical preferred pseudo-range innovation set form a dynamic innovation data set; performing pairwise difference on pseudo-range information in the current information set and the dynamic information data set, and calculating information difference statistics of the N visual satellites based on a difference value; and then fault detection is carried out through iteration, and unavailable pseudo-range information is eliminated by comparing the information difference statistics with a fault detection threshold value in each iteration. And calculating a weight based on the available pseudo-range information, and carrying out Kalman filtering measurement updating to obtain navigation error state estimation. By using the method, the fault detection failure rate under the complex environment interference condition and the system error accumulation condition can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of navigation positioning, in particular to a BDS / INS navigation method and device based on innovation difference fault detection. BACKGROUND

[0002] The BDS (Beidou Satellite Navigation System) signal is affected by the shielding and reflection of electromagnetic waves by the surrounding environment, resulting in multipath error and NLOS (Non-Line-of-Sight) error, which affects the positioning accuracy. Therefore, during the navigation solution process, it is very important to accurately and efficiently detect and exclude BDS measurement outliers while ensuring the robustness of fault detection in poor observation conditions.

[0003] Traditional fault detection methods for satellite pseudorange measurements include RAIM (Receiver Autonomous Integrity Monitoring) algorithm, and improved MHSS (Multiple Hypothesis Solution Separation) algorithm, ARAIM (Advanced RAIM) algorithm, etc. However, the fault detection method of the single BDS system requires high satellite redundancy, and the increase in the number of subsets of the multi-fault hypothesis method also leads to a decrease in computational efficiency. The information fusion of the BDS / INS integrated navigation system is mainly based on Kalman filtering technology, and the prior information obtained by INS solution is used to predict the measurement information of BDS, and the fault is efficiently detected by comparing the BDS measurement with the prior information.

[0004] Using the method based on filter prior for BDS fault detection, first, the innovation test statistic needs to be constructed, and further based on chi-square distribution and hypothesis testing for fault detection; the robust estimation algorithm reweights the remaining measurements according to the value of the innovation test statistic on the basis of testing and removing outliers, increasing the availability of the system.

[0005] The existing BDS fault detection scheme based on INS prior information can efficiently detect BDS outliers, but has the following two shortcomings:

[0006] (1) The fault detection threshold is usually a fixed value, and lacks consideration of INS cumulative error, poor observation environment, and system navigation state variable pollution.

[0007] (2) In a long-time poor observation scenario, a high proportion of BDS measurements with high outliers may cause error accumulation of INS or pollution of the system. At this time, the decrease in the accuracy of the prior information will lead to a decrease in the performance of the fault detection method. SUMMARY

[0008] In view of this, the present invention provides a BDS / INS navigation method and device based on innovation differential fault detection. Its fault detection technology based on innovation differential can reduce the problem of fault detection failure under complex environmental interference conditions and conditions with system error accumulation.

[0009] In order to solve the above technical problems, the present invention is implemented as follows.

[0010] A BDS / INS navigation method based on innovation differential fault detection, comprising:

[0011] Step 1: Based on the inertial measurement information, the Kalman filter prediction step is used to predict the navigation error state; based on the navigation error state prediction result, the a priori corrected navigation state is obtained;

[0012] Step 2: Based on the BeiDou Satellite Navigation System (BDS) ephemeris and the a priori corrected navigation state, determine the BDS pseudorange measurement prediction value of each visible satellite. ;

[0013] Step 3: Predict the BDS pseudorange values ​​based on the N visible satellites and pseudorange measurements , calculate N pseudorange updates , forming the current new information set ; Calculate the fault detection threshold Th based on the INS cumulative error, environmental impact, and BDS receiver background noise;

[0014] Step 4: Collect the current new information and historical optimized pseudorange innovation set , forming a dynamic new information data set ; Collect the current new information Dynamic Innovation Dataset The new information of the pseudo-range in the new information is differentiated pairwise, and the new information difference statistics of N visible satellites are calculated based on the difference values ;

[0015] Step 5: Iterate fault detection: Each iteration, determine the current new information set Innovation difference statistics corresponding to each visible satellite Whether the maximum value of is greater than the fault detection threshold Th;

[0016] If it is greater than the fault detection threshold Th, the pseudo-range innovation of the satellite corresponding to the maximum value of the innovation difference statistic is removed from the current innovation set and dynamic innovation datasets Eliminate; according to the new current information set Recalculate the difference and update the innovation difference statistics Then, based on the new current innovation set fault detection is performed;

[0017] Otherwise, the iteration is completed, and the current innovation set The remaining r available pseudo-range innovations are added to the history preferred pseudo-range innovation set The r pseudo-range innovations of old time are deleted from the history preferred pseudo-range innovation set

[0018] Step 6: The r available pseudo-range innovations are re-weighted using a robust function to obtain a weight vector in the Kalman filter measurement update formula

[0019] Step 7: Based on the navigation error state prediction result of step 1, Kalman filter measurement update is performed to obtain a BDS / INS integrated navigation error state estimation result, and the navigation state calculated by the INS is feedback corrected to realize BDS / INS integrated navigation.

[0020] Preferably, in step 3, the fault detection threshold Th is calculated by combining the inertial navigation system (INS) cumulative error, the environmental influence degree, and the background noise of the BDS receiver, and is:

[0021] The position error information entropy EP is calculated using the state error covariance matrix of one-step prediction in Kalman filtering , which is used to represent the influence of the INS cumulative error on the position estimation;

[0022] An environmental factor based on the BDS measurement signal-to-noise ratio is used to represent the influence of the environment on the BDS measurement information and the pollution degree of the navigation state variable, and to reflect the accuracy of the BDS measurement;

[0023] Then, the fault detection threshold Th is constructed as:

[0024]

[0025]

[0026] wherein, is a correction coefficient, is a reference coefficient representing the background noise of the BDS receiver, and N is the number of currently visible satellites, is the BDS measurement signal-to-noise ratio of the i-th visible satellite.

[0027] Preferably, the position error information entropy EP is calculated as:

[0028] ​​​​​

[0029] wherein, is a sub-matrix with size , is a determinant operation, is a natural constant.

[0030] Preferably, the reference coefficient is calculated according to the average pseudo-range error of the BDS fault-free measurement collected in an open environment:

[0031] ;

[0032] wherein, is an expectation operation, and N is the current number of visible satellites, is a pseudo-range error vector in an open environment.

[0033] Preferably, in step 6, the r available pseudo-range innovations are re-weighted using a robust function to obtain a weight vector in the Kalman filter measurement update formula:

[0034] The r available pseudo-range innovations are represented as ; the r available pseudo-range innovations are corrected by centering to eliminate the influence of common mode errors in the weighting process:

[0035] ;

[0036] wherein, is the mean of the r available pseudo-range innovations; is the available pseudo-range innovation at time k after correction;

[0037] The available pseudo-range innovation after correction is normalized to obtain a normalized available pseudo-range innovation :

[0038] ;

[0039] wherein, is the BDS measurement matrix at time k, is the BDS measurement covariance matrix, is the state error covariance matrix of one-step prediction in the Kalman filter;

[0040] The normalized available pseudo-range innovations are weighted using a robust function to obtain a weight vector of the normal measurements after removing faults, wherein the i-th element in the weight vector .

[0041] Preferably, the robust function employs a Tukey function or an IGG-III robust weight function.

[0042] The application also provides a BDS / INS navigation device based on innovation difference fault detection, comprising a Kalman filtering module, an innovation of pseudo-range calculation module, a fault detection threshold calculation module, an innovation difference statistical quantity calculation module, a fault detection module, and a weighting module.

[0043] The Kalman filtering module employs a Kalman filtering algorithm of the BDS / INS integrated navigation, performs navigation error state prediction in the filtering prediction stage, obtains an a priori corrected navigation state based on the prediction result of the navigation error state, and provides the navigation state to the innovation of pseudo-range calculation module; in the measurement update stage of the Kalman filtering, the BDS / INS integrated navigation error state estimation result is obtained, and the navigation state calculated by the INS is corrected by feedback, so as to realize the BDS / INS integrated navigation.

[0044] The innovation of pseudo-range calculation module is used to determine the BDS pseudo-range measurement prediction value of each visible satellite based on the BDS ephemeris and the a priori corrected navigation state provided by the Kalman filtering module. According to the BDS pseudo-range measurement prediction value and the pseudo-range measurement , the N pseudo-range innovations are calculated to form a current innovation set , which is provided to the innovation difference statistical quantity calculation module.

[0045] The innovation difference statistical quantity calculation module is used to form a dynamic innovation data set by the current innovation set and the historical optimal pseudo-range innovation set ; the pseudo-range innovations in the current innovation set and the dynamic innovation data set are pairwise differentiated, the innovation difference statistical quantities of the N visible satellites are calculated based on the difference values , and the innovation difference statistical quantities are provided to the fault detection module.

[0046] The fault detection threshold calculation module is used to calculate the fault detection threshold Th by combining the INS cumulative error, the environmental influence degree, and the noise floor of the BDS receiver, and provide the fault detection threshold Th to the fault detection module.

[0047] The fault detection module is used to iteratively perform fault detection: in each iteration, it is judged whether the maximum value of the innovation difference statistical quantity corresponding to each visible satellite in the current innovation set is greater than the fault detection threshold Th; if yes, the pseudo-range innovation of the satellite corresponding to the maximum value of the innovation difference statistical quantity is removed from the current innovation set and the dynamic innovation data set . Eliminate it according to the new information set Recalculate the difference and update the innovation difference statistics , then based on the new current information set Perform fault detection; otherwise, complete the iteration and the current new information set The remaining r available pseudorange innovations are added to the historical preferred pseudorange innovation set , and according to the time from old to new Delete r pseudo-range updates from the old time;

[0048] A weighting module is used to reweight the r available pseudorange updates using a robust function to obtain a weight vector in the Kalman filter measurement update formula. , update the Kalman filter algorithm in the Kalman filter module.

[0049] Preferably, the fault detection threshold Th used by the fault detection threshold calculation module is constructed in the following manner:

[0050] Using the state error covariance matrix of one-step prediction in Kalman filtering , calculate the position error information entropy EP, which is used to characterize the impact of INS cumulative error on position estimation;

[0051] Environmental factors calculated based on the signal-to-noise ratio of BDS measurements , characterizes the degree of influence of the environment on BDS measurement information and the pollution degree of navigation state variables;

[0052] Then the fault detection threshold Th is constructed as:

[0053] ;

[0054] ;

[0055] in, is the correction factor, is the reference coefficient to characterize the background noise of the BDS receiver, N is the number of currently visible satellites, is the BDS measurement signal-to-noise ratio of the i-th visible satellite.

[0056] Preferably, in the fault detection threshold calculation module, the position error information entropy EP is calculated as follows:

[0057] ;

[0058] in, for The upper left corner size is The sub-matrix of is the determinant operation, is a natural constant;

[0059] reference coefficient The average pseudo-range error of the BDS fault-free measurement collected in an open environment is calculated according to:

[0060]

[0061] wherein, is a desired operation, is a pseudo-range error vector in an open environment.

[0062] Advantages:

[0063] (1) The present application is based on traditional Kalman filtering, and uses the prediction of INS for the BDS measurement to construct pseudo-range innovation, and through mutual difference of the pseudo-range innovation, the biasing influence of clock difference, clock drift and system common mode error on measurement fault detection is eliminated, and therefore the reference satellite reliability does not need to be considered.

[0064] (2) Considering that in a long-time poor observation environment, a high proportion of abnormal BDS measurement may cause error accumulation of INS or system pollution, when calculating the innovation difference, the historical optimal pseudo-range innovation is added, so as to solve the problem that in the case of insufficient satellite measurement, the innovation difference result is unstable due to the high proportion of measurement abnormal values. Further, the historical optimal pseudo-range innovation is constantly updated, and the old is removed to make way for the new, so as to avoid the problem that the performance verification of the fault detection method is reduced due to the decrease of the precision of prior information.

[0065] (3) The threshold value of fault detection does not use a fixed value, but uses the INS cumulative error based on the state error covariance matrix and the environment factor based on the measurement signal-to-noise ratio to dynamically calculate the threshold value; wherein the INS cumulative error calculated based on the state error covariance matrix can represent the untrustworthiness of the INS system; and the environment factor based on the measurement signal-to-noise ratio can represent the influence of the environment on the BDS measurement information and the pollution degree of the navigation state variable. It can be seen that the construction of the threshold value increases the consideration of the INS cumulative error and the pollution of the system navigation state variable, and ensures the accuracy of the threshold value for removing fault data. Further, the threshold value calculation also considers the severity of the observation environment, and adds the background noise of the BDS receiver, so that the threshold value setting has certain environmental adaptability. The improvement of environmental adaptability makes the threshold value be tightened in a good observation environment with high redundancy, so as to improve the navigation precision; and the threshold value is relaxed in a poor observation environment, so as to ensure the system usability.

[0066] ​(4) Traditional fault detection methods may introduce contamination or over-rejection in the binary classification of satellite faults. This invention combines fault detection with robust weighted addition, using fault detection as a pre-step to robust weighting. Typical faults are eliminated using fault detection, and the remaining available measurements are corrected using the mean of available BDS measurements and then re-weighted using a robust function to introduce system measurements. Compared with traditional fault detection methods, this method improves the availability and robustness of the system in complex interference environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Flowchart of a BDS / INS navigation method based on innovation differential fault detection in an embodiment of the present invention.

[0068] Figure 2 4 is a structural diagram of a BDS / INS navigation device based on innovation differential fault detection in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0070] The embodiment of the present invention provides a BDS / INS navigation method based on innovation differential fault detection, such as Figure 1 As shown, the method includes the following steps:

[0071] Step 1: Based on the inertial measurement information, the Kalman filter prediction step is used to predict the navigation error state. Based on the navigation error state prediction results, the a priori corrected navigation state is obtained, including the navigation position prediction value.

[0072] Step 101: Navigation error state prediction.

[0073] The present invention is a BDS / INS integrated navigation solution based on Kalman filtering. Since there are errors in pure inertial solution, it is necessary to estimate the navigation error to achieve the correction of inertial measurement. Therefore, the state variable of Kalman filtering is Constructed as navigation error state variable:

[0074] ;

[0075] in is the error state quantity of longitude, latitude and altitude, and are the velocity and attitude error state quantities in the navigation coordinate system respectively; They are the acceleration bias and gyroscope bias state quantities of the IMU respectively; and are the BDS-related clock error and clock drift state variables respectively.

[0076] The formula of the Kalman filter prediction step is constructed as follows:

[0077] ;

[0078] ;

[0079] wherein is a state one-step transition matrix, represents a k-time navigation error state prediction obtained according to a k-1-time state prediction, is a one-step predicted state error covariance matrix. and are a k-1-time navigation error state matrix and an error covariance matrix. is a k-1-time system noise covariance matrix.

[0080] The measurement update formula of the Kalman filter is constructed as follows:

[0081] ;

[0082] ;

[0083] ;

[0084] In the formula, and respectively represent a state estimation and a mean square error matrix of a current epoch k, is a filter gain, is a unit matrix, is a k-time measurement covariance matrix, is a k-time BDS measurement matrix, is a weight vector. is a measurement prediction error, i.e., a new information.

[0085] The navigation error state prediction is performed by using the Kalman filter prediction step to obtain a navigation error state prediction which is used for the correction of the inertial navigation state in step 102 and the measurement update of the Kalman filter in step 6.

[0086] Step 102: Based on the navigation error state prediction result , a priori corrected navigation state is obtained.

[0087] The navigation state prediction is performed according to an inertial navigation mechanics arrangement and a state space model.

[0088] wherein the correction calculation of the navigation position in the navigation state is as follows: the longitude latitude altitude and the velocity in east, north, and sky directions and the attitude . Further, the corrected position prediction value in the Earth-Centered Earth-Fixed (ECEF) coordinate system is obtained as:

[0089] ;

[0090] where R is the radius of the prime vertical, and e2is the square of the first eccentricity (about 0.00669438). The corrected position prediction value is used for the pseudo-range measurement prediction in step 2.

[0091] Step 2: Based on the BDS ephemeris, the BDS pseudo-range measurement prediction value of each visible satellite is determined in combination with the prior corrected navigation position prediction value . .

[0092] In combination with the BDS ephemeris, the orbital position of the i-th visible satellite in the Earth-Centered Earth-Fixed (ECEF) coordinate system can be calculated as , , . In combination with the prior corrected navigation position prediction value (x, y, z) in the ECEF coordinate system and the BDS error estimate, the BDS pseudo-range measurement prediction value is obtained as .

[0093] The prediction formula is:

[0094] ;

[0095] where c is the speed of light, is the receiver clock bias obtained by prior correction of the navigation error state variable.

[0096] Step 3: According to the BDS pseudo-range measurement prediction value of N visible satellites and the actual satellite pseudo-range measurement, the pseudo-range innovation of N visible satellites is calculated as to form the current innovation set ; in combination with the inertial navigation system (INS) cumulative error, the environmental impact degree, and the background noise of the BDS receiver, the fault detection threshold Th is calculated.

[0097] In this step, the pseudo-range innovation of the i-th satellite is calculated as:

[0098] ;

[0099] where​​​ the pseudo-range measurement of the actual i-th satellite, the pseudo-range innovation of the i-th satellite.

[0100] The fault detection threshold Th is constructed in the following manner:

[0101] Considering the one-step predicted state error covariance matrix The error accumulation of the IMU can represent the influence on the position estimate value, and the signal-to-noise ratio (SNR) of the BDS measurement can reflect the existence of non-line-of-sight signals or multi-path signals to a certain extent, representing the influence of the environment on the BDS measurement information and the pollution degree of the navigation state variable. Therefore, according to the one-step predicted state error covariance matrix and the SNR of the observed satellite, the fault detection threshold is calculated to ensure the adaptive adjustment ability of the threshold to the observation scene.

[0102] The fault detection threshold Th is constructed in the following manner:

[0103] ;

[0104] wherein, is the position error information entropy; is the environmental factor; is the correction coefficient, the numerical value of which is set by actual data; is the reference coefficient representing the background noise of the BDS receiver.

[0105] In the Th calculation formula, the position error information entropy EP is calculated in the following manner:

[0106] ;

[0107] wherein, is a sub-matrix with a size of at the top left corner, is the determinant operation.

[0108] In the Th calculation formula, the environmental factor is calculated in the following manner:

[0109] ;

[0110] wherein, N is the current number of visible satellites, is the BDS measurement signal-to-noise ratio of the i-th visible satellite.

[0111] In the Th calculation formula, can be calculated according to the average pseudo-range error of the fault-free BDS measurement collected in an open environment, and the calculation formula is as follows:

[0112] ;

[0113] wherein, N is the number of satellites at the current time, is the pseudo-range error vector in an open environment, and is about 0.5m~2.5m, which varies according to the performance of the receiver and the antenna. In addition, the coefficient varies according to the performance of different INS devices and BDS receivers and antennas.

[0114] It can be seen that in this step, the fault detection threshold is not a fixed value, but the consideration of the INS cumulative error, the degree of observation environment, and the pollution of the system navigation state variable is added, so as to ensure the accuracy of the threshold value for removing fault data. The calculation scheme of the fault detection threshold can also ensure environmental adaptability, so as to tighten the threshold value in a good observation environment with high redundancy, improve the navigation accuracy, and relax the threshold value in a poor observation environment, ensure the system usability.

[0115] Step 4: Combine the historical optimal pseudo-range innovation set to calculate the innovation difference statistics .

[0116] In this step, the current innovation set and the historical optimal pseudo-range innovation set form a dynamic innovation data set ; the pseudo-range innovations in the current innovation set and the dynamic innovation data set are pairwise differenced, and the innovation difference statistics of the N visible satellites are calculated based on the difference values .

[0117] wherein, the historical optimal pseudo-range innovation set is essentially a sliding window, and the number of innovations in the sliding window is L (L>20), so the total length of the sliding window is L, stores the historical optimal pseudo-range innovations (i.e., available pseudo-range innovations) before the k time. After the fault detection and removal of the fault innovations, the remaining available pseudo-range innovations are added to the set , and the pseudo-range information that is relatively old in time is removed from the set , so that the data amount in the set is maintained to be L.

[0118] In the difference calculation, not only the innovations in the current innovation set are pairwise differenced, but also the historical optimal pseudo-range innovations are added to combine and to construct a new dynamic innovation data set .

[0119] Assume that N satellite signals are received at time k, the current innovation set includes N pseudorange innovations, denoted as , ; the dynamic innovation dataset includes N+L pseudorange innovations, denoted as , . Then the pseudorange innovations in the current innovation set and the dynamic innovation dataset are pairwise differenced as follows:

[0120] ;

[0121] This step constructs the dynamic innovation dataset to perform difference calculation. Considering that the number of visible satellites at some time may be relatively small, innovations are themselves pairwise differenced, and the abnormality of the selected outliers is relatively large, so the difference calculation adds historical preferred innovations, and the historical data is used to compensate for the case where the number of searched satellites is small, thereby solving the problem of unstable innovation difference results caused by a high proportion of abnormal values.

[0122] Based on the pseudorange innovation difference , the innovation difference statistics of the pseudorange measurement of each satellite i are calculated as follows:

[0123] ;

[0124] In the process of solving the innovation difference quantity, the measurement innovation in the dataset is introduced, and the denominator of the innovation difference statistics formula is increased from N to ; when the value of N is small, the difference innovation result of normal measurement will not significantly increase due to individual outliers. Therefore, the stability of the difference result at the current time can be ensured.

[0125] Step 5: Iterative fault detection:

[0126] In this step, in each iteration, it is determined whether the maximum value of the innovation difference statistics corresponding to each visible satellite in the current innovation set is greater than the fault detection threshold Th; if so, the pseudorange innovation corresponding to the satellite with the maximum innovation difference statistics is removed from the current innovation set and the dynamic innovation dataset ; and the current innovation set The remaining pseudo-range innovations in the calculation of the innovation differential statistics of the satellites not removed at the current moment; then based on the new current innovation set Perform fault detection; if If the value is not greater than the threshold Th, the iteration is completed and the BDS measurement abnormal value removal is completed. Assume that the current new information set There are r available pseudorange updates remaining in the dynamic update data set. The order of the data time in the data is as follows: r old time data are removed from the old to the new, and r available pseudorange updates are added to the historical preferred pseudorange update set , so that the historical optimal pseudo-range innovation set Get updated.

[0127] Step 6: Use the robust function to reweight the r available pseudorange updates to obtain the weight vector in the Kalman filter measurement update formula .

[0128] In this step, the r available pseudorange updates are expressed as ; Perform centralization correction on the r available pseudorange innovations to eliminate the influence of common mode error in the weighting process:

[0129] ;

[0130] in, is the mean of r available pseudorange innovations; is the available pseudorange information at time k after correction.

[0131] Get the normalized available pseudorange information for:

[0132] ;

[0133] in, is the BDS measurement matrix at time k, is the BDS measurement covariance matrix.

[0134] Using robust functions The normalized available pseudo-range information is weighted to obtain the weight of the normal measurement after removing the fault. .

[0135] Here, robust weighting can use various truncated robust functions, such as Tukey function and IGG-III robust weight function. Weight vector Only the available innovation measurement weights are included, and the unavailable parts have been eliminated through the fault detection method.

[0136] Step 7: The weight vector obtained in step 6 Substitute the filter gain into the Kalman filter measurement update formula Based on the navigation error state prediction result of step 1, Kalman filter measurement update is performed to obtain the navigation error state estimation result of the BDS / INS fusion, the position calculated by the INS is feedback corrected to obtain the navigation state, and the BDS / INS integrated navigation is realized.

[0137] Thus, the present process ends.

[0138] Based on the above method, the present application further provides a BDS / INS navigation device based on innovation difference fault detection, as shown in the figure, which comprises a Kalman filter module, a pseudo-range innovation calculation module, a fault detection threshold calculation module, an innovation difference statistical quantity calculation module, a fault detection module and a weighting module. Figure 2

[0139] The Kalman filter module adopts the Kalman filter algorithm of the BDS / INS integrated navigation to obtain the navigation error state prediction in the filter prediction stage; based on the navigation error state prediction result, the corrected navigation state is obtained and provided to the pseudo-range innovation calculation module; in the Kalman filter measurement update stage, the navigation error state estimation result of the BDS / INS fusion is obtained, the navigation state calculated by the INS is feedback corrected to realize the BDS / INS integrated navigation.

[0140] The pseudo-range innovation calculation module is used to determine the BDS pseudo-range measurement prediction value of each visible satellite based on the BDS ephemeris, in combination with the navigation position prediction value and the clock error prediction value in the prior corrected navigation state provided by the Kalman filter module ; the N pseudo-range innovations are calculated according to the BDS pseudo-range measurement prediction value and the pseudo-range measurement , the current innovation set is composed, and provided to the innovation difference statistical quantity calculation module.

[0141] The innovation difference statistical quantity calculation module is used to form the dynamic innovation data set by the current innovation set and the historical optimal pseudo-range innovation set ; the pseudo-range innovations in the current innovation set and the dynamic innovation data set are pairwise differentiated, the innovation difference statistical quantities of the N visible satellites are calculated based on the difference values , and provided to the fault detection module.

[0142] The fault detection threshold calculation module is used to calculate the fault detection threshold Th in combination with the INS cumulative error, the environmental influence degree and the background noise of the BDS receiver, and provide the fault detection threshold Th to the fault detection module.

[0143] ​Fault detection module, used for iterative fault detection: each iteration, judge the current new information set Innovation difference statistics corresponding to each visible satellite Is the maximum value of the innovation difference statistic greater than the fault detection threshold Th? If so, remove the pseudo-range innovation of the satellite corresponding to the maximum value of the innovation difference statistic from the current innovation set and dynamic innovation datasets Eliminate it according to the new information set Recalculate the difference and update the innovation difference statistics, then based on the new current innovation set Perform fault detection; otherwise, complete the iteration and the current new information set The remaining r available pseudorange innovations are added to the historical preferred pseudorange innovation set , and according to the time from old to new Delete the r pseudorange updates at the old time.

[0144] A weighting module is used to reweight the r available pseudorange updates using a robust function to obtain a weight vector in the Kalman filter measurement update formula. , update the Kalman filter algorithm in the Kalman filter module.

[0145] In a preferred embodiment, the fault detection threshold Th used by the fault detection threshold calculation module is constructed by using the one-step prediction covariance matrix of the Kalman filter , calculate the position covariance information entropy EP, which is used to characterize the impact of INS cumulative error on position estimation; use the environmental factor calculated based on the BDS measurement signal-to-noise ratio Characterize the degree of impact of the environment on BDS measurement information and the degree of pollution of navigation state variables.

[0146] Then the fault detection threshold Th is constructed as:

[0147] ;

[0148] in, is the correction factor, It is the reference coefficient that characterizes the background noise of BDS receiver. The calculation formula of position error index EP, environmental factor The calculation formula and benchmark coefficient The method for determining has been described in detail above and will not be repeated here.

[0149] The above specific embodiments only describe the design principles of the present application, and the shapes and names of the components in the description can be different and are not limited. Therefore, those skilled in the art of the present application can modify or equivalently replace the technical solutions described in the foregoing embodiments; and these modifications and replacements do not deviate from the purpose and technical solutions of the present application, and should all belong to the protection scope of the present application.

Claims

1. A BDS / INS navigation method based on innovation difference fault detection, characterized in that, Comprise: Step 1: based on inertial measurement information, adopt Kalman filter prediction step to predict navigation error state; Based on the prediction result of navigation error state, obtain the prior modified navigation state; Step 2: Based on BDS ephemeris of Beidou satellite navigation system, combined with prior corrected navigation state, determine the BDS pseudorange measurement prediction value of each visible satellite ; Step 3: Calculate the predicted value of the pseudo-range measurement of the N visible satellites according to the BDS pseudo-range measurement of the N visible satellites and the pseudo-range measurement , calculate the N pseudo-range innovations , and form the current innovation set ; combine the accumulated error of the inertial navigation system (INS), the degree of environmental influence, and the noise floor of the BDS receiver to calculate the fault detection threshold Th; Step 4: Collect the current new information and historical optimized pseudorange innovation set , forming a dynamic new information data set ; Collect the current new information Dynamic Innovation Dataset The new information of the pseudo-range in the new information is differentiated pairwise, and the new information difference statistics of N visible satellites are calculated based on the difference values ; Step 5: Iterative fault detection: At each iteration, determine whether the current innovation set of the respective visible satellites has a maximum value greater than a fault detection threshold Th; If greater than the failure detection threshold Th, the pseudorange innovation for the satellite corresponding to the maximum of the innovation difference statistics is removed from the current innovation set and the dynamic innovation data set according to the new current innovation set re-perform the differencing calculation and update the innovation difference statistics and then perform failure detection based on the new current innovation set ; Otherwise, complete the iteration, the current set of innovations The remaining r available pseudorange innovations are added to the set of history preferred pseudorange innovations The r pseudorange innovations of the oldest time are removed from the set of history preferred pseudorange innovations; and The r pseudorange innovations of the oldest time are removed from the set of history preferred pseudorange innovations; and Step 6: reweight the r available pseudorange innovations using the robust function to obtain the weight vector in the Kalman filter measurement update formula ; Step 7: based on the prediction result of navigation error state in step 1, carry out Kalman filter measurement update, obtain the navigation error state estimation result of BDS / INS fusion, and carry out feedback correction to the navigation state calculated by INS, realize the BDS / INS integrated navigation.

2. The BDS / INS navigation method based on innovation differential fault detection according to claim 1, characterized in that: In step 3, the fault detection threshold Th is calculated by combining the inertial navigation system INS cumulative error, the environmental influence degree and the background noise of the BDS receiver: Utilizing a state error covariance matrix of one-step prediction in Kalman filtering calculating position error information entropy EP for representing the influence of INS accumulated error on position estimation; Adopting the environmental factor based on the signal-to-noise ratio calculated by BDS measurement , which represents the influence of environment on BDS measurement information and the pollution degree of navigation state variable, and reflects the accuracy of BDS measurement. Then the fault detection threshold Th is constructed as: ； ； wherein, is a correction coefficient, is a reference coefficient representing the background noise of the BDS receiver, and N is the current number of visible satellites, is the BDS measurement signal-to-noise ratio of the i-th visible satellite. 3.The BDS / INS navigation method based on innovation difference fault detection of claim 2, wherein, The calculation method of the position error information entropy EP is: ； wherein is the upper left corner size of the sub-matrix, is the determinant operation, is a natural constant. 4.The BDS / INS navigation method based on innovation difference fault detection according to claim 2 or 3, characterized in that, Benchmark Coefficient The average pseudorange error computed from the BDS fault-free measurements collected in the open environment is: ； wherein, N is the number of currently visible satellites, is the pseudorange error vector in an open sky scenario.

5. The BDS / INS navigation method based on innovation difference fault detection of claim 1, wherein, In step 6, the r available pseudo-range innovations are re-weighted using the robust function to obtain the weight vector in the Kalman filter measurement update formula is: r available pseudo-range innovations are expressed as ; the r available pseudo-range innovations are centering corrected to eliminate the common-mode error impact in the weighting process: ； wherein, is the mean of the r available pseudorange innovations; is the modified pseudorange innovation at time k. The normalized available pseudo-range innovation is obtained by normalizing the modified available pseudo-range innovation : ； wherein, is the BDS measurement matrix at time k, is the BDS measurement covariance matrix, is the state error covariance matrix of one-step prediction in Kalman filter. Using robust functions The weight vector of the normal measurements excluding faults is obtained by weighting the normalized available pseudo-range innovations , is the i-th element in the weight vector ​ 6. The BDS / INS navigation method based on innovation difference fault detection of claim 5, wherein, The robust function adopts Tukey function or IGG-III robust weight function. 7.A BDS / INS navigation device based on innovation difference fault detection, characterized in that, Comprise Kalman filter module, pseudo-range innovation calculation module, fault detection threshold calculation module, innovation difference statistics calculation module, fault detection module, weighting module; The Kalman filter module adopts the Kalman filter algorithm of BDS / INS integrated navigation to predict the navigation error state in the filter prediction stage; Based on the prediction result of navigation error state, obtain the prior modified navigation state, and provide it to the pseudo-range innovation calculation module; In the Kalman filter measurement update stage, the navigation error state estimation result of BDS / INS fusion is obtained, and the navigation state calculated by INS is feedback corrected to realize the BDS / INS integrated navigation; The pseudo-range innovation calculation module is configured to determine a BDS pseudo-range measurement prediction value of each visible satellite based on the BDS ephemeris and the prior modified navigation state provided by the Kalman filter module ; calculate N pseudo-range innovations from the BDS pseudo-range measurement prediction value and the pseudo-range measurement , to form a current innovation set , and provide the innovation difference statistical quantity calculation module; a newness difference statistics calculation module, configured to calculate newness difference statistics of the N visible satellites based on the difference values obtained by pairwise differencing the current newness set and the pseudorange newness in the dynamic newness dataset and the historical preferred pseudorange newness set to form a dynamic newness dataset ; and pairwise difference the current newness set and the pseudorange newness in the dynamic newness dataset to form a dynamic newness dataset ; and provide the newness difference statistics of the N visible satellites to the fault detection module The fault detection threshold calculation module is used to combine the INS cumulative error, the environmental influence degree and the background noise of the BDS receiver to calculate the fault detection threshold Th, which is provided to the fault detection module; Fault detection module, used for iterative fault detection: each iteration, judge the current new information set Innovation difference statistics corresponding to each visible satellite Is the maximum value of greater than the fault detection threshold Th? If yes, the pseudorange innovation of the satellite corresponding to the maximum innovation difference statistic is removed from the current innovation set and the dynamic innovation dataset , the differential calculation is performed again according to the new innovation set and the innovation difference statistic is updated , and then the fault detection is performed based on the new current innovation set ; otherwise, the iteration is completed, and the remaining r available pseudorange innovations in the current innovation set are added to the history preferred pseudorange innovation set , and the r pseudorange innovations of the old time are deleted from in chronological order from old to new. The weighting module is configured to reweight the r available pseudo-range innovations using a robust function to obtain a weight vector in a Kalman filter measurement update formula and update a Kalman filter algorithm in the Kalman filter module.

8. The BDS / INS navigation device based on innovation difference fault detection of claim 7, wherein, The construction method of the fault detection threshold Th adopted by the fault detection threshold calculation module is: Utilizing a state error covariance matrix of one-step prediction in Kalman filtering calculating position error information entropy EP for representing the influence of INS accumulated error on position estimation; Adopting the environmental factor based on the signal-to-noise ratio calculated by BDS measurement , which represents the influence degree of environment on BDS measurement information and the pollution degree of navigation state variable; Then the fault detection threshold Th is constructed as: ； ； wherein, is a correction coefficient, is a reference coefficient representing the background noise of the BDS receiver, and N is the current number of visible satellites, is the BDS measurement signal-to-noise ratio of the i-th visible satellite.

9. The BDS / INS navigation device based on innovation difference fault detection of claim 8, wherein, The calculation method of the position error information entropy EP in the fault detection threshold calculation module is: ； wherein is a top-left corner size of a sub-matrix, is a determinant operation, is a natural constant; Benchmark Coefficient The average pseudorange error computed from the BDS fault-free measurements collected in the open environment is: ； wherein, is the desired operation, is the pseudorange error vector in an open field.

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