CN114563804A - Adaptive fault-tolerant method of GNSS/INS tightly-combined navigation system - Google Patents

Adaptive fault-tolerant method of GNSS/INS tightly-combined navigation system Download PDF

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CN114563804A
CN114563804A CN202111614506.8A CN202111614506A CN114563804A CN 114563804 A CN114563804 A CN 114563804A CN 202111614506 A CN202111614506 A CN 202111614506A CN 114563804 A CN114563804 A CN 114563804A
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fault
navigation system
gnss
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CN114563804B (en
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赵修斌
沈子涵
常浩伟
刘鑫贤
庞春雷
张良
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Air Force Engineering University of PLA
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    • 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
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/40—Correcting position, velocity or attitude
    • G01S19/41—Differential correction, e.g. DGPS [differential GPS]
    • 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
    • G01S19/235—Calibration of receiver components
    • 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
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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Abstract

The disclosure relates to a self-adaptive fault-tolerant method of a GNSS/INS tightly-combined navigation system. According to the method, after a local component detection method is used for detecting and identifying a fault dimension, the influence of the fault dimension on filtering precision is judged, an observation value prediction model is built by using a long-short term memory neural network, a long-short term memory neural network prediction value is obtained, and whether the long-short term memory neural network prediction value needs to be called to replace the fault observation value is judged according to an RDPOP value corresponding to the fault observation value. According to the method, the problem that the positioning accuracy of the whole system is reduced when the GNSS/INS tightly-combined navigation system processes fault observation is solved by introducing the long-short term memory neural network predicted value, the effectiveness of a fault detection function in a long fault period is ensured, and the integrity and the reliability of the GNSS/INS tightly-combined navigation system are improved.

Description

Adaptive fault-tolerant method of GNSS/INS tightly-combined navigation system
Technical Field
The disclosure relates to the technical field of neural network algorithms, in particular to a self-adaptive fault-tolerant method of a GNSS/INS tight combination navigation system.
Background
The Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) have good advantage complementarity, and the combination of the two Navigation systems can fully utilize the information of each subsystem to realize information fusion and complementation, thereby improving the overall Navigation accuracy and reliability of the System.
With the wide application of the GNSS/INS integrated navigation system, the fault-tolerant capability of the integrated navigation system is more and more emphasized by the increasingly complex use environment. The core of the fault-tolerant design is to perform system self-monitoring, so that not only can system faults be detected quickly, but also the faults need to be identified and effective treatment measures need to be taken. Most of the existing research focuses on how to quickly detect the fault, and the attention on a fault processing method is less. However, the accuracy of subsequent fault detection and system positioning is directly affected by whether fault processing is appropriate.
The traditional fault-tolerant method has insufficient adaptability, and can cause the problems of reduced positioning precision, failure detection and the like in a complex environment. Therefore, it is an urgent need to provide a method capable of avoiding the accuracy reduction due to the fault observation processing and ensuring the effectiveness of the fault detection function in the case of a long fault.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The embodiment of the disclosure aims to provide a self-adaptive fault-tolerant method for a GNSS/INS tight combination navigation system, so as to ensure positioning accuracy and subsequent fault detection performance and improve the integrity and reliability of the GNSS/INS tight combination navigation system.
According to a first aspect of the embodiments of the present disclosure, there is provided an adaptive fault-tolerant method for a GNSS/INS tightly-integrated navigation system, the method including the following steps:
aiming at a nonlinear environment, establishing a GNSS/INS tight integrated navigation model, which comprises a state equation and a measurement equation of the GNSS/INS tight integrated navigation system, wherein both the state function and the measurement function are nonlinear functions;
judging the fault existence condition of the GNSS/INS tight combination navigation system by adopting a local component fault detection method;
if the GNSS/INS tightly-combined navigation system is judged to have no fault, carrying out LSTM prediction model training on a current observed value to obtain an LSTM predicted value of a measured value of the tightly-combined navigation system;
if the GNSS/INS tightly-combined navigation system is judged to have a fault, the tightly-combined navigation system enters a fault tolerance stage, the tightly-combined navigation system respectively calculates the RDPOP value corresponding to each dimension fault observation value, compares the RDPOP value with a corresponding preset threshold, and finally judges whether the LSTM predicted value needs to be called to replace the fault observation value;
if the RDPOP value has a large influence on the positioning accuracy of the system, updating the measurement function of the GNSS/INS tight integrated navigation system by using the LSTM predicted value to obtain an updated measurement equation, and calculating the navigation parameter output at the next moment; otherwise, normal fault isolation operation is carried out;
and continuing to perform a Kalman filtering iteration process according to the navigation parameter output.
In an exemplary embodiment of the present disclosure, in the step of establishing a GNSS/INS compact integrated navigation model for a nonlinear environment, a state equation and a measurement equation of the GNSS/INS compact integrated navigation system respectively include:
Xk＝Φk，k-1Xk-1+Γk，k-1Wk-1 (1)
Zk＝HkXk+Vk (2)
wherein, XkIs tightly combinedSynthesizing a state vector of the navigation system; x is the number ofk-1Outputting a state vector for the filtering of the last compact combined navigation system; phik，k-1A state transition matrix for a tightly integrated navigation system; rk，k-1A noise matrix for a tightly integrated navigation system; zkIs a measurement vector; hkIs a measurement matrix; wk-1And VkRespectively noise and measurement noise of a tightly combined navigation system; and k is the number of filtering iterations.
In an exemplary embodiment of the present disclosure, when determining that a fault of the GNSS/INS tightly-combined navigation system exists by using a local component fault detection method, the method includes the following steps:
filtering the GNSS/INS tight combination navigation system through a Kalman filter, wherein a residual vector is the difference between a measurement value and a state prediction value and can be based on a fault detection function of the residual vector;
the value of the fault detection function and a detection threshold T corresponding to a preset false alarm rate alpha are compareddAnd comparing to judge whether the dimension of the GNSS/INS tight integrated navigation system has a fault.
In an exemplary embodiment of the disclosure, in the process of filtering the GNSS/INS tightly combined navigation system through the kalman filter, the calculation formula of the residual vector includes:
Figure BDA0003436056420000031
wherein the content of the first and second substances,
Figure BDA0003436056420000032
updating the result for the state of the tightly integrated navigation system; v. ofkThe corresponding covariance matrix formula includes:
Figure BDA0003436056420000033
wherein, Pk，k-1Predicting vectors for states one step
Figure BDA0003436056420000034
The covariance of (a);
Figure BDA0003436056420000035
transposing the measurement matrix; rkFor measuring noise VkThe covariance matrix of (a);
the calculation formula of the fault detection function comprises:
Figure BDA0003436056420000036
wherein v iskIs a residual vector; c. CiIs a unit vector with dimension i equal to 1;
Figure BDA0003436056420000037
is ciTransposing;
Figure BDA0003436056420000038
is v iskAn inverse of the corresponding covariance matrix; i is the dimension of the observation vector; n is the nth dimension of the observation vector.
In an exemplary embodiment of the disclosure, the value of the fault detection function is equal to a detection threshold T corresponding to a preset false alarm rate αdThe comparison process comprises: will be provided with
Figure BDA00034360564200000314
Detection threshold T corresponding to preset false alarm rate alphadA comparison is made, including the detection rule:
Figure BDA0003436056420000039
wherein, TdA detection threshold corresponding to the preset false alarm rate alpha is set.
In an exemplary embodiment of the disclosure, if the GNSS/INS tight combination navigation system has no fault, performing LSTM prediction model training on a current observation value to obtain the tight combination navigation systemThe LSTM predicted value of the integrated navigation system; wherein, the GNSS pseudo range of m time is divided into
Figure BDA00034360564200000310
And INS pseudorange
Figure BDA00034360564200000311
Respectively carrying out difference processing to obtain GNSS pseudo range increment of m time compared with m-1 time
Figure BDA00034360564200000312
And INS pseudorange increments
Figure BDA00034360564200000313
Inputting the two pseudo-range increments into the LSTM prediction model respectively for training; in the prediction process, when the GNSS pseudo-range at the time h has a fault and the duration is T, the GNSS pseudo-range increment prediction value expression at the time h + T includes:
Figure BDA0003436056420000041
wherein t is the time count after the fault occurs;
Figure BDA0003436056420000042
is the pseudo-moment of the GNSS at time h + T.
In an exemplary embodiment of the present disclosure, if there is a failure in the GNSS/INS tightly-integrated navigation system, where the RDPOP value is a relative differential positioning accuracy, the formula includes:
Figure BDA0003436056420000043
wherein the content of the first and second substances,
Figure BDA0003436056420000044
and
Figure BDA0003436056420000045
respectively represent covariance matrices
Figure BDA0003436056420000046
And PkThe jth diagonal of (a);
Figure BDA0003436056420000047
after the i-dimensional fault is isolated, filtering and outputting a covariance matrix of state estimation; pkA covariance matrix representing the state estimate of the filtered output without isolating the fault; i is the dimension of the observation vector; k represents the number of filtering iterations;
the RDPOP value and the preset threshold mu establish the following relation:
Figure BDA0003436056420000048
in an exemplary embodiment of the present disclosure, the navigation parameter output includes parameters corresponding to an attitude, a speed, a position, a gyroscope, an accelerometer, a clock error, and a clock frequency error of the satellite.
The technical scheme provided by the disclosure can comprise the following beneficial effects:
the invention provides a self-adaptive fault-tolerant method of a GNSS/INS tight combination navigation system, which can avoid the problem of positioning accuracy reduction caused by processing fault observation by adding an LSTM predicted value, ensure the effectiveness of a fault detection function under the condition of long fault and improve the integrity and reliability of the GNSS/INS tight combination navigation system.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is apparent that the drawings in the following description are only some embodiments of the present disclosure, and that other drawings can be derived from those drawings by a person of ordinary skill in the art without inventive effort.
FIG. 1 is a schematic diagram illustrating the steps of an adaptive fault tolerance method for a GNSS/INS tightly-integrated navigation system in an exemplary embodiment of the present disclosure;
FIG. 2 is a block diagram illustrating an adaptive fault tolerance method for a GNSS/INS tightly-integrated navigation system in an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a schematic flow chart of LSTM predictive model training in an exemplary embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating the movement trace of a carrier in measured data when performing performance verification of an LSTM predictive model in an exemplary embodiment of the disclosure;
FIG. 5 is a graph illustrating pseudorange delta predictions versus LSTM prediction model performance validation in an exemplary embodiment of the disclosure;
fig. 6 shows a comparison graph of the RDPOP values of the satellite G7 under two groups of the number of visible stars when performing the positioning accuracy verification in the environment a in the exemplary embodiment of the present disclosure;
FIG. 7 is a graph illustrating a comparison of positioning errors for two sets of numbers of visible stars during positioning accuracy verification in Environment A in an exemplary embodiment of the present disclosure;
fig. 8 shows a comparison of the RDPOP values for satellite G6 for 6 visible satellites in two configurations when performing location accuracy verification in environment B in an exemplary embodiment of the disclosure;
FIG. 9 illustrates a comparison plot of positioning errors for 6 visible stars in two configurations when performing a verification of positioning accuracy in Environment B in an exemplary embodiment of the present disclosure;
fig. 10 shows a diagram of the RDPOP values of 5 visible under-satellite G1 and a map of the positioning error when performing positioning accuracy verification in environment C in an exemplary embodiment of the present disclosure;
fig. 11 is a graph showing a comparison of the values of the fault detection function at two periods when the satellite G1 performs the fault detection performance verification in the environment C in the exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. The example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In the present exemplary embodiment, an adaptive error-tolerant method for a GNSS/INS tightly-integrated navigation system is provided, which is shown in fig. 1 and fig. 2, and may include the following steps:
step S101: aiming at a nonlinear environment, establishing a GNSS/INS tight integrated navigation model, which comprises a state equation and a measurement equation of the GNSS/INS tight integrated navigation system, wherein both the state function and the measurement function are nonlinear functions;
step S102: judging the fault existence condition of the GNSS/INS tight combination navigation system by adopting a local component fault detection method;
step S103: if the GNSS/INS tightly-combined navigation system is judged to have no fault, carrying out LSTM prediction model training on the current observation value to obtain an LSTM prediction value of the tightly-combined navigation system; LSTM is short for long-short term memory neural network;
if the GNSS/INS tightly-combined navigation system is judged to have a fault, the tightly-combined navigation system enters a fault tolerance stage, the tightly-combined navigation system respectively calculates the RDPOP value corresponding to each dimension fault observation value, compares the RDPOP value with a corresponding preset threshold, and finally judges whether the LSTM predicted value needs to be called to replace the fault observation value;
step S104: if the RDPOP value has a large influence on the positioning accuracy of the system, updating the measurement function of the GNSS/INS tight combination navigation system by using the LSTM predicted value to obtain an updated measurement equation, and calculating the navigation parameter output at the next moment; otherwise, normal fault isolation operation is carried out;
step S105: and continuing to perform a Kalman filtering iteration process according to the navigation parameter output.
Hereinafter, each step of the above-described method in the present exemplary embodiment will be described in more detail.
Referring to fig. 1 and fig. 2, in step S101, a tight-integrated GNSS/INS navigation model is built for a nonlinear environment, which includes a state equation and a measurement equation of the tight-integrated GNSS/INS navigation system, where the state function and the measurement function are both nonlinear functions.
In this example, the state equation and the measurement equation of the GNSS/INS tightly-integrated navigation system respectively include:
Xk＝Φk，k-1Xk-1+Γk，k-1Wk-1 (1)
Zk＝HkXk+Vk (2)
wherein, XkState vectors for tightly packed navigation systems; xk-1Outputting a state vector for the filtering of the last compact combined navigation system; phik，k-1A state transition matrix for a tightly integrated navigation system; gamma-shapedk，k-1A noise matrix for a tightly integrated navigation system; zkIs a measurement vector; hkIs a measurement matrix; wk-1And VkRespectively noise and measurement noise of a tightly combined navigation system; and k is the number of filtering iterations.
In general, assume their covariance matrix QkAnd RkIs zero-mean white gaussian noise.
Referring to fig. 1 and fig. 2, in the present example, the step S102, which uses a local component fault detection method to determine the existence of the fault in the GNSS/INS tightly combined navigation system, includes the following steps:
step S1021: filtering the GNSS/INS tightly combined navigation system through a Kalman filter, wherein the contained Kalman filtering algorithm is based on a covariance matrix of a residual vector, and a fault detection function based on the residual vector is constructed;
in step S1021, in the process of filtering the GNSS/INS tightly combined navigation system through the kalman filter, the calculation formula of the residual vector includes:
Figure BDA0003436056420000071
wherein the content of the first and second substances,
Figure BDA0003436056420000072
updating the result for the state of the tightly integrated navigation system; v. ofkThe corresponding covariance matrix formula includes:
Figure BDA0003436056420000073
wherein, Pk，k-1Predicting vectors for states one step
Figure BDA0003436056420000074
The covariance of (a);
Figure BDA0003436056420000075
transposing the measurement matrix; rkFor measuring noise VkThe covariance matrix of (a);
the calculation formula of the fault detection function comprises:
Figure BDA0003436056420000076
wherein v iskIs a residual vector; c. CiIs a unit vector with dimension i equal to 1;
Figure BDA0003436056420000077
is ciTransposing;
Figure BDA0003436056420000078
is v iskAn inverse of the corresponding covariance matrix; i is the dimension of the observation vector; n is the nth dimension of the observation vector.
Step S1022: the value of the fault detection function and a detection threshold T corresponding to a preset false alarm rate alpha are compareddAnd comparing to judge whether the dimension of the GNSS/INS tight integrated navigation system has a fault.
In step S1022, the present example is specifically explained as follows: assume a fault vector at time k of
Figure BDA0003436056420000081
The corresponding residual vector is
Figure BDA0003436056420000082
When the kinetic model is reliable, the corresponding test statistic can be constructed based on the individual components as:
Figure BDA0003436056420000083
wherein the content of the first and second substances,
Figure RE-GDA0003542125470000084
is a covariance matrix
Figure RE-GDA0003542125470000085
The ith diagonal element of (a) is,
Figure RE-GDA0003542125470000086
is that
Figure RE-GDA0003542125470000087
The (d) th-dimensional component of (a),
Figure RE-GDA0003542125470000088
may be referred to as the normalized residual of the ith dimension component. If no fault occurs on the ith dimension component, then there is a null hypothesis
Figure RE-GDA0003542125470000089
Otherwise, there is an alternative assumption
Figure RE-GDA00035421254700000810
Wherein, deltaiIs a non-centralized parameter that is a function of false alarm rate α and false alarm rate β. As used herein
Figure RE-GDA00035421254700000811
By absolute value of
Figure RE-GDA00035421254700000812
I.e. at the rear part
Figure RE-GDA00035421254700000813
Is equal to
Figure RE-GDA00035421254700000814
Thus, the decentralization parameter can be expressed as:
δ＝Nα/2(0，1)-N1-β(0，1)
the fault determination criteria include
Figure BDA00034360564200000815
That is to say that
Figure BDA00034360564200000816
Detection threshold T corresponding to preset false alarm rate alphadFor comparison, the following detection rules were constructed:
Figure BDA00034360564200000817
wherein, TdAnd is a detection threshold corresponding to the preset false alarm rate alpha.
χ commonly used in combined navigation2The residual error detection method is a system-level fault detection method, specific fault observation values cannot be identified, and the whole fault subsystem is usually isolated after a fault is detected, so that a lot of useful observation values are wasted. In the integrated navigation, the identification and isolation of the fault observation play an important role in the fault detection performance and the integrated navigation precision. Therefore, to identify specific fault observations while retaining more normal observation data, the present example employs a method based on local component detection for fault detection and identification.
Referring to fig. 3, in step S103 of the present example, the judgment result is divided into two cases, and the present example is processed separately:
in the first case, if the GNSS/INS tightly-combined navigation system is judged to have no fault, carrying out LSTM prediction model training on a current observation value to obtain an LSTM prediction value of a measurement value of the tightly-combined navigation system; the method comprises the following steps: then LSTM prediction model training is performed on the current observed value, wherein GNSS pseudo range of m time is used
Figure BDA0003436056420000091
And INS pseudorange
Figure BDA0003436056420000092
Respectively carrying out differential processing to obtain GNSS pseudo range increment of m time compared with m-1 time
Figure BDA0003436056420000093
And INS pseudorange increments
Figure BDA0003436056420000094
Inputting the two pseudo-range increments into the LSTM prediction model respectively for training; in the prediction process, when the GNSS pseudo range at the h moment has a fault and the duration is T, the GNSS pseudo range increment prediction at the h + T momentThe value expression includes:
Figure BDA0003436056420000095
wherein t is the time count after the fault occurs;
Figure BDA0003436056420000096
is the pseudo-moment of the GNSS at time h + T.
If the GNSS/INS tightly-combined navigation system is judged to have a fault, the tightly-combined navigation system enters a fault tolerance stage, the tightly-combined navigation system respectively calculates the RDPOP value corresponding to each dimension fault observation value, compares the RDPOP value with a corresponding preset threshold, and finally judges whether the LSTM predicted value needs to be called to replace the fault observation value;
the RDPOP value in this example is relative differential positioning accuracy, and the calculation formula includes:
Figure BDA0003436056420000097
wherein the content of the first and second substances,
Figure BDA0003436056420000098
and Pk jjRespectively represent covariance matrices
Figure BDA0003436056420000099
And PkThe jth diagonal of (a);
Figure BDA00034360564200000910
after the i-dimensional fault is isolated, filtering and outputting a covariance matrix of state estimation; pkA covariance matrix representing the state estimate of the filtered output without isolating the fault; i is the dimension of the observation vector; k denotes the number of filtering iterations.
The RDPOP value and the preset threshold mu establish the following relation:
Figure BDA00034360564200000911
in step S104, if the RDPOP value has a large influence on the positioning accuracy of the system, the measurement function of the GNSS/INS tightly-integrated navigation system is updated by using the LSTM prediction value to obtain an updated measurement equation, and the navigation parameter output at the next time is calculated; otherwise, normal fault isolation operation is carried out;
the navigation parameter output of this example includes parameters corresponding to the attitude, velocity, position, gyroscope, accelerometer, clock error, and clock frequency error of the satellite, respectively.
In step S105, the present example continues with the kalman filter iteration process using the navigation parameters.
To verify the effectiveness of the method provided by the exemplary embodiment, a set of INS raw data and GNSS data is collected by using SPAN-CPT tight combination navigation product, and the output frequencies are 100HZ and 1HZ, respectively. The experimental site is located in a certain school playground in xi' an city, Shaanxi province, China, and the parameter values of the inertial devices in the experiment are shown in Table 1:
table 1: parameter values of inertial device in experiment
Figure BDA0003436056420000101
First, since the prediction accuracy of the fault observation in the method provided in this exemplary embodiment may have a great influence on the fault tolerance performance, the performance of the LSTM prediction model needs to be verified first. Here, in this example, the conventional fault isolation method and the fault repair method are respectively denoted as M1 and M2, and the disclosed method is denoted as M3. Comparing the M1 method with the M2 method, and selecting GNSS pseudo ranges of satellites 9 in two different time periods of 131s-190s (time period 1) and 261s-320s (time period 2) in the measured data for prediction.
Referring to fig. 4, the motion trajectory of the carrier in the measured data can be seen, wherein the 131s-190s carrier mainly moves in a circular manner, and the 261s-320s carrier mainly moves in a straight line.
The prediction of the pseudorange increments of the two time periods by using the M1 and M2 methods is shown in fig. 5, and it can be seen that by using the M2 method, the satellite pseudorange change trend can be better tracked due to the introduction of inertial navigation information. Wherein the predicted value of the Model1 is a predicted value obtained by adopting an M1 method; the predicted value of the Model2 is the predicted value by adopting the M2 method
Meanwhile, prediction error values of two models using M1 and M2 methods are listed, wherein mad (Mean Absolute deviation) is Mean Absolute error and rmse (root Mean Squared error) is root Mean square error, as shown in table 2:
table 2: prediction error values for two models using M1 and M2 methods, respectively
Figure BDA0003436056420000111
From table 2, it can be found that, in the prediction of 60s in two periods, whether the carrier performs linear motion in period 2 or circular motion in period 1, the MAD and RMSE using the M2 method are much smaller than those using the M1 method, which indicates that the prediction has higher accuracy and can reflect the characteristics of the original data to a great extent. Therefore, by replacing the fault data with the prediction data of the M2 method, the filtering accuracy can be maintained while eliminating the effect of the fault.
Next, to verify the performance of the fault tolerance method (denoted as M3) in the present exemplary embodiment, we compared M3 with the aforementioned M1 and M2, and examine the performance advantage of the M3 method from the two aspects of fault detection capability and positioning accuracy.
Considering that the number of visible satellites, the geometric configuration and the fault duration all affect the fault tolerance of a GNSS/INS compact integrated navigation system (hereinafter referred to as "system") to different degrees, three different environments (denoted as environment a, environment B and environment C) are selected from measured data for simulation verification, as shown in table 3:
table 3: measured data under three different environments
Figure BDA0003436056420000112
In the experiment, alpha is set to 0.001 and TdThe error is set to 3.29, μ is set to 0.1, and the deviation at the time of satellite observation failure is set to 80 m.
Thirdly, the measured data of the three different environments are combined, and the positioning error of the system is different even if the observation is faultless under different satellite configurations, so that the positioning error of the system when the system is faultless is introduced when the positioning accuracy is analyzed. When the system fails, the performance of each algorithm can be more intuitively reflected by solving the relative Root Mean Square error RRMSE (relative Root Mean Square error) between each algorithm and the system failure-free solution.
The following description is provided for verification of positioning accuracy in three environments:
and environment A:
observations were from G1, G6, G7, G8, G9, G11, G13, G19 and G27, respectively, for a total of 9 visible stars with G7 failing, within 199s-219 s. In order to verify the fault tolerance performance under different numbers of visible stars, four visible stars of G8, G9, G13 and G19 are artificially isolated, and the observed values of the other 5 visible stars are reserved.
Referring to fig. 6 and 7, it can be seen that two sets of visible star number RDPOP values are compared to the positioning error values using three methods, M1, M2, and M3, respectively, thereby listing table 4, as shown below:
table 4: positioning error values respectively adopting different methods under two groups of different numbers of visible stars in environment A
Figure BDA0003436056420000121
As can be seen from fig. 6, the reduction in the number of satellites changes the RDPOP value for the same satellite. When the number of visible stars is 9, it can be found by combining fig. 7 and table 4 that G7 has little influence on the positioning accuracy, and the RRMSE using the three methods M1, M2 and M3 is lower.
However, when the number of visible satellites is reduced to 5, the RDPOP of G7 increases due to the reduction of the observation redundancy, and the influence on the positioning accuracy increases, and the positioning accuracy is reduced when the three methods are respectively adopted. Among them, the maximum values of RRMSE in three dimensions of the methods M1 and M2 reach 2.316mh and 4.989M, which shows that the positioning accuracy is seriously reduced.
Compared with M1 and M2, the method of M3 is adopted in the present exemplary embodiment, and the LSTM predicted value is used to replace the fault observed value after the RDPOP of G7 is detected to exceed the threshold value, so that the system provides effective observation information on the premise of isolating the influence of the fault, and therefore the positioning error is far smaller than that of the methods of M1 and M2, and the maximum error of three dimensions of the method of M3 is only 0.679M.
This shows that the M3 method provided in this example embodiment can overcome the problem of filtering divergence caused by fault-tolerant processing when the number of visible stars is small in the conventional method, and with the M3 method, the system can maintain high positioning accuracy under different numbers of visible stars.
And environment B:
g6 failed within 256s-276s, artificially isolated partially visible stars, and two geometric configurations with 6 visible stars were designed. Among them, configuration 1 is composed of G1, G6, G7, G9, G11, and G19, and configuration 2 is composed of G1, G6, G7, G8, G11, and G27.
Referring to fig. 8 and 9, two sets of RDPOP values for different visible star counts in different geometries and positioning error values using three methods, M1, M2, and M3, respectively, can be seen, thereby listing table 5, as follows:
table 5: positioning error values of two groups of different visible star numbers in environment B by respectively adopting different methods under different geometric configurations
Figure BDA0003436056420000131
Different satellite geometries may have different effects on the performance of the system. Referring to fig. 8, it can be seen that G6 has a RDPOP value in configuration 1 greater than configuration 2 and exceeds the threshold 5s after the start of the fault.
From an analysis of fig. 9 and table 5, it can be seen that when the fault-tolerant processing is performed on configuration 1 by using M1 and M2 methods, the RRMSE is increased by about 1 time compared with configuration 2. This shows that the performance of the system fluctuates greatly and the adaptability is not strong when different geometrical configurations are faced by adopting the methods M1 and M2.
Compared with M1 and M2, when the M3 method is adopted, the positioning error of the system in the two configurations is kept stable, and the positioning accuracy in the configuration 1 is obviously better than that in the M1 and the M2. This demonstrates that the proposed M3 method in the exemplary embodiment can accommodate different geometries while maintaining high positioning accuracy.
And environment C:
theoretically, as the duration of the fault increases, the filtering error of the system gradually accumulates, and the positioning accuracy becomes lower. The failure durations of the environment a and the environment B are both 20s, and in order to compare the influence of the failure on the three fault-tolerant methods, the failure occurrence period set in the environment C is 69s-119s, and the failure durations are 50s in total. During this period, the number of visible stars is 5, which are: g1, G3, G8, G9 and G19, and the failed satellite is G1.
Referring to fig. 10, the RDPOP values and the positioning error values using the three methods M1, M2, and M3, respectively, for this time period can be seen, and thus table 6 is set forth below:
table 6: positioning error values in environment C using three methods respectively
Figure BDA0003436056420000141
As can be seen with reference to fig. 10, within 50s of isolation G1, the positioning errors using the M1 and M2 methods increase gradually, wherein the maximum values of accuracy, dimension and height using the M1 and M2 methods reach 4.082M, 1.823M and 17.245M and 20.169M, 8.964M and 49.609M, respectively. This indicates that the system performance is degraded severely when the M1 and M2 methods are used, and the error generated by the M2 method cannot be converged after the fault is over.
Compared with M1 and M2, after 9s of failure, the method of M3 can detect that the RDPOP of G1 is larger than the threshold value, and at the moment, the LSTM predicted value is used for replacing the failure observed value, so that the RRMSE can be kept at a lower level. This shows that the application method M3 can well deal with the problem of positioning error divergence caused by long-time faults, so that the system can maintain high positioning accuracy.
Finally, the local component detection method adopted by the exemplary embodiment is to perform individual detection on each observed dimension, and the main influence on the fault detection performance is the positioning error of the system. In the three environments, the positioning error generated by the system is the largest when the three methods of M1, M2 and M3 are adopted in the environment C, so that the fault detection functions of the satellite G1 in the environment C at 20s and 50s of fault occurrence are recorded respectively. Referring to fig. 11 and as set forth in table 7 below:
table 7: number of false alarms of system adopting three different methods for satellite G1
Figure BDA0003436056420000151
Referring to fig. 11a, the values of the fault detection function using the methods M1 and M2 reach a maximum value just before the fault begins to appear, and then gradually decrease. This is because the local component detection method and the conventional residual χ2The detection methods are similar, the detection functions are all constructed through the statistical characteristics of residual vectors, and the reduction of the filtering precision can lead to the gradual reduction of the fault detection function values. The fault detection function using the M3 method also trends downward for the fault duration 9s, but after replacing the fault observed value with the LSTM predicted value, the fault detection function value increases again and remains relatively stable.
As can be seen from fig. 11b and table 7, when the failure time is extended by 30s, the values of the function of the failure detection using the methods M1 and M2 keep a downward trend, and in 50s of failure detection, the number of false alarms using the methods M1 and M2 reaches 12 times and 10 times, respectively. In addition, when the fault is over, due to the sharp change of the observed value of the G1, the detection function values of the two increase again and exceed the threshold value, and the system generates false alarm. That is, with the methods of M1 and M2, the system completely loses fault detection capability after the fault persists for 29s and 37s, respectively.
Compared with M1 and M2, the fault detection performance of the M3 method is kept relatively stable all the time, because the participation of the LSTM predicted value enables the filtering result to keep a certain precision level, and the filtering result does not drop greatly along with the extension of the fault time, so that the residual error convergence during the fault period is avoided, and the effectiveness of system fault detection is ensured.
In summary, in the exemplary embodiment, from the perspective that the positioning accuracy and the subsequent fault detection are affected by the fault processing of the GNSS/INS integrated navigation system, aiming at the problems that the conventional fault-tolerant method is insufficient in adaptability, the positioning accuracy is reduced and the fault detection fails in a complex environment, the fault-tolerant method of the GNSS/INS tightly integrated system based on the LSTM is provided. According to the method, an observation value prediction model is established by using the LSTM, and whether the LSTM prediction value needs to be called to replace the fault observation value is judged according to the RDPOP value corresponding to the fault observation value. Through the analysis of the positioning accuracy and the fault detection performance of the three error-tolerant methods in three different environments, the result shows that the addition of the LSTM predicted value avoids the problem of positioning accuracy reduction caused by the processing of fault observation, and meanwhile, the effectiveness of a fault detection function under the condition of long fault is ensured, so that the integrity and the reliability of the GNSS/INS tight combination system are improved.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (8)

1. A self-adaptive fault-tolerant method of a GNSS/INS tightly-combined navigation system is characterized by comprising the following steps:
aiming at a nonlinear environment, establishing a GNSS/INS tight integrated navigation model, which comprises a state equation and a measurement equation of the GNSS/INS tight integrated navigation system, wherein both the state function and the measurement function are nonlinear functions;
judging the fault existence condition of the GNSS/INS tight combination navigation system by adopting a local component fault detection method;
if the GNSS/INS tightly-combined navigation system is judged to have no fault, carrying out LSTM prediction model training on the current observation value to obtain an LSTM prediction value of the measurement value of the tightly-combined navigation system;
if the GNSS/INS tightly combined navigation system is judged to have a fault, the tightly combined navigation system enters a fault tolerance stage, the tightly combined navigation system respectively calculates an RDPOP value corresponding to each dimension fault observation value, compares the RDPOP value with a corresponding preset threshold, and finally judges whether the LSTM predicted value needs to be called to replace the fault observation value;
if the RDPOP value has a large influence on the positioning accuracy of the system, updating the measurement function of the GNSS/INS tight integrated navigation system by using the LSTM predicted value to obtain an updated measurement equation, and calculating the navigation parameter output at the next moment; otherwise, normal fault isolation operation is carried out;
and continuing to perform a Kalman filtering iteration process according to the navigation parameter output.
2. The adaptive fault-tolerant method according to claim 1, wherein in the step of building a tightly-integrated GNSS/INS navigation model for a nonlinear environment, the state equations and the measurement equations of the tightly-integrated GNSS/INS navigation system respectively comprise:
Xk＝Φk，k-1Xk-1+Γk，k-1Wk-1 (1)
Zk＝HkXk+Vk (2)
wherein, XkA state vector for a tightly integrated navigation system; xk-1Outputting a state vector for the last filtering of the combined navigation system; phik，k-1A state transition matrix for a tightly integrated navigation system; rk，k-1A noise matrix for a tightly integrated navigation system; zkIs a measurement vector; hkIs a measurement matrix; wk，k-1And VkRespectively noise and measurement noise of a tightly combined navigation system; and k is the number of filtering iterations.
3. The adaptive fault-tolerant method according to claim 2, wherein when the local component fault detection method is used to determine the existence of the fault in the GNSS/INS tightly-combined navigation system, the method comprises the following steps:
filtering the GNSS/INS tightly combined navigation system through a Kalman filter, wherein a residual vector is obtained by subtracting a measurement value and a state recursion value, and a fault detection function based on the residual vector can be obtained;
the value of the fault detection function and a detection threshold T corresponding to a preset false alarm rate alpha are compareddAnd comparing to judge whether the dimension of the GNSS/INS tight integrated navigation system has a fault.
4. The adaptive fault-tolerant method of claim 3, wherein during the filtering of the GNSS/INS tightly-integrated navigation system by the Kalman filter, the calculation formula of the residual vector comprises:
Figure FDA0003436056410000021
wherein the content of the first and second substances,
Figure FDA0003436056410000022
updating the result for the state of the tightly integrated navigation system; v. ofkThe corresponding covariance matrix formula contains:
Figure FDA0003436056410000023
wherein, Pk，k-1Predicting vectors for states one step
Figure FDA0003436056410000029
The covariance of (a);
Figure FDA0003436056410000024
transposing the measurement matrix; rkFor measuring noise VkThe covariance matrix of (a);
the calculation formula of the fault detection function comprises:
Figure FDA0003436056410000025
wherein v iskIs a residual vector; c. CiIs a unit vector with dimension i equal to 1;
Figure FDA0003436056410000026
is ciTransposing;
Figure FDA00034360564100000210
is v iskAn inverse of the corresponding covariance matrix; i is the dimension of the observation vector; n is the nth dimension of the observation vector.
5. The adaptive fault-tolerant method according to claim 4, characterized in that the value of the fault detection function corresponds to a detection threshold T corresponding to a preset false alarm rate αdThe comparison process comprises: will be provided with
Figure FDA0003436056410000027
Detection threshold T corresponding to preset false alarm rate alphadA comparison is made, including the detection rule:
Figure FDA0003436056410000028
wherein, TdA detection threshold corresponding to the preset false alarm rate alpha is set.
6. The adaptive fault-tolerant method according to claim 5, wherein if the GNSS/INS tightly-combined navigation system has no fault, performing LSTM prediction model training on a current observation value to obtain an LSTM prediction value of the tightly-combined navigation system; wherein, the GNSS pseudo range of m time is divided into
Figure FDA0003436056410000032
And INS pseudorange
Figure FDA0003436056410000033
Respectively carrying out differential processing to obtain GNSS pseudo range increment of m time compared with m-1 time
Figure FDA0003436056410000034
And INS pseudorange increments
Figure FDA0003436056410000035
Inputting the two pseudo-range increments into the LSTM prediction model respectively for training; in the prediction process, when the GNSS pseudo-range at the time h has a fault and the duration is T, the GNSS pseudo-range increment prediction value expression at the time h + T includes:
Figure FDA0003436056410000036
wherein t is the time count after the fault occurs;
Figure FDA0003436056410000037
is the pseudo-moment of the GNSS at time h + T.
7. The adaptive fault-tolerant method of claim 5, wherein if the GNSS/INS tightly-integrated navigation system has a fault, the RDPOP value is relative differential positioning accuracy, and the formula comprises:
Figure FDA0003436056410000038
wherein the content of the first and second substances,
Figure FDA0003436056410000039
and
Figure FDA00034360564100000310
respectively represent covariance matrices
Figure FDA00034360564100000313
And PkThe jth diagonal of (a);
Figure FDA00034360564100000311
after the i-dimensional fault is isolated, filtering and outputting a covariance matrix of state estimation; pkA covariance matrix representing the state estimate of the filtered output without isolating the fault; i is the dimension of the observation vector; k represents the number of filtering iterations;
the RDPOP value and the preset threshold mu establish the following relation:
Figure FDA00034360564100000312
8. the adaptive fault tolerant method of claim 1 wherein the navigational parameter outputs comprise parameters corresponding to attitude, velocity, position, gyroscope, accelerometer, clock error, and clock frequency error of the satellite, respectively.
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