CN112378400A - Dual-antenna GNSS assisted strapdown inertial navigation integrated navigation method - Google Patents

Dual-antenna GNSS assisted strapdown inertial navigation integrated navigation method Download PDF

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CN112378400A
CN112378400A CN202011189897.9A CN202011189897A CN112378400A CN 112378400 A CN112378400 A CN 112378400A CN 202011189897 A CN202011189897 A CN 202011189897A CN 112378400 A CN112378400 A CN 112378400A
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speed
gnss
inertial navigation
strapdown inertial
error
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班镜超
段腾辉
李良君
吴志洁
张春梅
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Hunan Aerospace Institute of Mechanical and Electrical Equipment and Special Materials
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Hunan Aerospace Institute of Mechanical and Electrical Equipment and Special Materials
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • 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
    • G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
    • 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

Abstract

The invention discloses a strapdown inertial navigation combined navigation method assisted by a double-antenna GNSS, wherein azimuth, three-dimensional speed and three-dimensional position information acquired by the GNSS (including double antennas) are combined with azimuth, three-dimensional speed and three-dimensional position information resolved by the strapdown inertial navigation, a combined navigation state and an observation model are established, errors of the strapdown inertial navigation are estimated by adopting a Kalman filtering fast calculation method, and real-time online compensation is carried out on navigation errors and zero offset errors of a gyroscope and an accelerometer through feedback correction, so that the precision of the combined navigation is improved; the RAIM method is used for detecting and isolating abnormal observation information such as GNSS satellite signal loss or data jump, and the like, so that the combined navigation precision of the system is ensured. The combined navigation method can be suitable for occasions where the system is in static or dynamic motion at the initial moment, and has general applicability.

Description

Dual-antenna GNSS assisted strapdown inertial navigation integrated navigation method
Technical Field
The invention belongs to the technical field of integrated navigation, and particularly relates to a strapdown inertial navigation integrated navigation method assisted by a dual-antenna GNSS.
Background
The inertial navigation is a full-autonomous navigation mode, can work under various working conditions, is not limited by a motion environment, and causes error accumulation and divergence after long-term work. As an all-weather and high-precision Navigation positioning System, a GNSS (Global Navigation Satellite System) can provide relatively accurate Navigation positioning information, but in an urban canyon environment, signals are often shielded by high-rise buildings, trees, tunnels and the like or multipath scattering causes signal loss or insufficient satellites to acquire high-precision positioning information, reliability is reduced to some extent, and a problem of difficult positioning is faced.
Disclosure of Invention
The invention aims to provide a strapdown inertial navigation combined navigation method assisted by a dual-antenna GNSS, which aims to solve the problems of error accumulation and divergence caused by long-term work of inertial navigation and difficulty in positioning of the GNSS.
One or more of the above objects are solved by the solution of the independent claims of the present invention.
The invention solves the technical problems through the following technical scheme: a strap-down inertial navigation combined navigation method assisted by a dual-antenna GNSS comprises the following steps:
step 1: performing initial alignment on strapdown inertial navigation when a strapdown inertial navigation combined navigation system assisted by a dual-antenna GNSS is static and dynamic respectively to obtain an initial attitude, an initial speed and an initial position; and after initial alignment, the strapdown inertial navigation enters an integrated navigation state;
step 2: performing navigation resolving by using the strapdown inertial navigation to obtain a real-time attitude, a real-time speed and a real-time position;
and step 3: establishing a Kalman filtering model of a strapdown inertial navigation integrated navigation system assisted by a dual-antenna GNSS;
and 4, step 4: estimating the state quantity by using the Kalman filtering model to obtain a velocity error, an attitude error, a position error, a gyro zero-offset error and an accelerometer zero-offset error of the strapdown inertial navigation;
and 5: and (4) performing feedback correction on the real-time speed, the attitude matrix, the real-time position, the gyro output and the accelerometer output which are solved by the strapdown inertial navigation by using the speed error, the attitude error, the position error, the gyro zero-bias error and the accelerometer zero-bias error in the step (4) to obtain the corrected real-time speed, attitude matrix, real-time position, gyro output and accelerometer output.
In the invention, the GNSS provides information such as position, speed, direction and the like, so that the system can complete initial alignment and enter integrated navigation in a static or dynamic motion process, and the system has more universal applicability; and in the combination process, the navigation error, the gyro zero-offset error and the accelerometer zero-offset error are corrected on line, so that the navigation precision of the system is improved.
Further, in the step 1, when the dual-antenna GNSS-assisted strapdown inertial navigation integrated navigation system is stationary, the process of initially aligning the strapdown inertial navigation system includes:
if the direction output of the GNSS is invalid, acquiring the static output increment of the strapdown inertial navigation gyroscope and the static output increment of the accelerometer to obtain an initial attitude as follows:
α0＝atan(C12/C22)，β0＝asin(C32)，γ0＝atan(-C31/C33)
Figure BDA0002752473220000021
wherein alpha is0To an initial azimuth angle, beta0At an initial pitch angle, γ0To an initial roll angle, Cij(i 1,2,3, j 1,2,3) is an initial attitude matrix
Figure BDA0002752473220000022
Element of (5), t0As an initial moment of time, the time of day,
Figure BDA0002752473220000023
is an attitude matrix at the initial time, rn、rbAre all intermediate vectors, gnIs the projection of the gravitational acceleration on the northeast coordinate system n, gn＝[0 0 g0]T，g0In order to be the local gravitational acceleration,
Figure BDA0002752473220000024
is the projection of the rotational angular velocity of the earth in a coordinate system n in the northeast of the world,
Figure BDA0002752473220000025
ωieis the rotational angular velocity of the earth, L is the local latitude, fbThe static output increment of the accelerometer under the carrier coordinate system b,
Figure BDA0002752473220000026
Figure BDA0002752473220000027
respectively the static output increment average values of the three axial accelerometers in the carrier coordinate system b,
Figure BDA0002752473220000028
the static output increment of the gyroscope under the carrier coordinate system b,
Figure BDA0002752473220000029
Figure BDA00027524732200000210
Figure BDA00027524732200000211
respectively taking the static output increment average values of the three axial gyroscopes under the carrier coordinate system b;
if the GNSS azimuth output is valid, the azimuth angle measured by the GNSS is adopted
Figure BDA00027524732200000212
Substituted for alpha0；
The GNSS measured speed and position are an initial speed and initial position.
Preferably, in step 1, the implementation process of performing initial alignment on the strapdown inertial navigation system when the dual-antenna GNSS-assisted strapdown inertial navigation integrated navigation system is dynamic is as follows:
if the GNSS azimuth output is invalid, judging whether the movement speed is greater than a speed threshold value, and accelerating the movement when the movement speed is less than or equal to the speed threshold value until the movement speed is greater than the speed threshold value; when the motion speed is greater than the speed threshold, the initial posture is as follows:
Figure BDA0002752473220000031
wherein alpha is0To an initial azimuth angle, beta0At an initial pitch angle, γ0In order to be the initial roll angle,
Figure BDA0002752473220000032
and
Figure BDA0002752473220000033
east speed, north speed and sky speed measured by GNSS respectively;
if the GNSS azimuth output is valid, the azimuth angle measured by the GNSS is adopted
Figure BDA0002752473220000034
Substituted for alpha0；
The GNSS measured speed and position are an initial speed and initial position.
Setting the speed threshold avoids the problem of relatively large computation errors of the GNSS speed at low speeds.
Preferably, the speed threshold is 5 m/s.
Further, in step 2, the update equation of the attitude matrix is:
Figure BDA0002752473220000035
wherein, the subscript k is the current time, the subscript k-1 is the last time,
Figure BDA0002752473220000036
is the matrix of the attitude at the current time,
Figure BDA0002752473220000037
is the matrix of the attitude at the last moment,
Figure BDA0002752473220000038
for the change of the navigation matrix caused by the position change in the motion process of the integrated navigation system at the current moment, delta t is a calculation period, delta lambda is the longitude change in the calculation period, delta L is the latitude change in the calculation period, and L iskThe latitude of the current moment;
the update equation for real-time speed is:
Figure BDA0002752473220000039
wherein the content of the first and second substances,
Figure BDA00027524732200000310
is the speed under the coordinate system n of the northeast of the current moment,
Figure BDA00027524732200000311
Figure BDA00027524732200000312
and
Figure BDA00027524732200000313
respectively an east-direction speed, a north-direction speed and a sky-direction speed at the current moment,
Figure BDA00027524732200000314
is the speed under the coordinate system n of the northeast of the last moment,
Figure BDA00027524732200000315
the attitude matrix at the last moment is obtained,
Figure BDA00027524732200000316
the static output increment of the accelerometer under the carrier coordinate system b at the last moment,
Figure BDA00027524732200000317
the projection of the rotational angular velocity of the earth on the coordinate system n in the northeast is carried out at the last moment,
Figure BDA00027524732200000318
is the projection of the northeast coordinate system relative to the angular velocity of the earth's motion on the northeast coordinate system at the previous moment,
Figure BDA00027524732200000319
the projection of the gravity acceleration at the last moment in a coordinate system n in the northeast is shown;
the update equation for the real-time position is:
Figure BDA0002752473220000041
Figure BDA0002752473220000042
Figure BDA0002752473220000043
wherein，
Figure BDA0002752473220000044
And
Figure BDA0002752473220000045
respectively the north velocity, east velocity and sky velocity of the previous moment, Ry is the radius of the earth meridian, Rx is the radius of the earth prime circle, lambda is the radius of the earth prime circlekAs the longitude, h, of the current timekHeight at the present moment, Lk-1Is the latitude, λ, of the previous momentk-1Longitude, h, of the previous timek-1The height of the last moment.
Further, in step 3, the modeling process of the kalman filter model is as follows:
step 3.1: selecting a state quantity X which is as follows:
Figure BDA0002752473220000046
wherein, δ vE、δvN、δvUAn east direction speed error, a north direction speed error and a sky direction speed error respectively; delta beta, delta gamma,
Figure BDA0002752473220000047
Pitch angle error, roll angle error, azimuth angle error, respectively; δ L, δ λ, δ h are latitude error, longitude error, altitude error, respectively; epsilonE、εN、εURespectively an east gyroscope zero bias error, a north gyroscope zero bias error and a sky gyroscope zero bias error;
Figure BDA0002752473220000048
respectively is a zero offset error of an east accelerometer, a zero offset error of a north accelerometer and a zero offset error of a sky accelerometer;
step 3.2: establishing a state transition equation of a Kalman filtering model, wherein the state transition equation is as follows:
Xk＝Φk,k-1Xk-1+Γk-1Wk-1
wherein, Xk、Xk-1Respectively the state quantities, phi, of the current time and the previous timek,k-1State transition matrix, Γ, being a linear discrete kalmank-1Driving the matrix for system noise, Wk-1Is a state noise matrix;
step 3.3: establishing a Kalman filtering observation equation, wherein the Kalman filtering observation equation is as follows:
Figure BDA0002752473220000049
wherein Z iskAs Kalman filter observations, HkAs a matrix of observation coefficients, RkIn order to observe the noise matrix,
Figure BDA0002752473220000051
and
Figure BDA0002752473220000052
respectively an east speed, a north speed and a sky speed of the strapdown inertial navigation,
Figure BDA0002752473220000053
and
Figure BDA0002752473220000054
east, north and sky speed, L, respectively, as measured by GNSSk、λk、hkAnd
Figure BDA0002752473220000055
respectively latitude, longitude, altitude and azimuth of strapdown inertial navigation, Lk,GNSS、λk,GNSS、hk,GNSSAnd
Figure BDA0002752473220000056
respectively latitude, longitude, altitude and azimuth of the GNSS.
Further, in step 5, the real-time speed after correction is:
v'E＝vE-X(1)
v'N＝vN-X(2)
v'U＝vU-X(3)
wherein v isE、vNAnd vUEast, north and sky speeds before correction, v'E、v'NAnd v'UThe corrected east-direction speed, north-direction speed and sky-direction speed are respectively, X (1) is the 1 st state quantity estimated value, X (2) is the 2 nd state quantity estimated value, and X (3) is the 3 rd state quantity estimated value;
the corrected attitude matrix is:
Figure BDA0002752473220000057
wherein the content of the first and second substances,
Figure BDA0002752473220000058
to be the attitude matrix before the correction,
Figure BDA0002752473220000059
for the corrected attitude matrix, X (4), X (5), and X (6) are respectively the 4 th, 5 th, and 6 th of the state quantity estimation values;
the corrected real-time positions are as follows:
L'＝L-X(7)
λ'＝λ-X(8)
h'＝h-X(9)
wherein, L, λ and h are respectively latitude, longitude and altitude before correction, L ', λ ' and h ' are respectively latitude, longitude and altitude after correction, and X (7), X (8) and X (9) are respectively the 7 th, 8 th and 9 th of state quantity estimated values;
the corrected gyro output is:
Figure BDA00027524732200000510
the corrected accelerometer output is:
Figure BDA00027524732200000511
wherein the content of the first and second substances,
Figure BDA00027524732200000512
respectively the corrected gyro output and the accelerometer output,
Figure BDA00027524732200000513
the gyroscope output and the accelerometer output before correction are respectively epsilon,
Figure BDA0002752473220000061
Respectively, a gyroscope zero-offset error and an accelerometer zero-offset error.
Further, before the step 1, a step of installing strapdown inertial navigation and a dual-antenna GNSS is further included, and the specific installation step is as follows:
determining the direction of a GNSS dual-antenna connecting line according to the direction of an azimuth output shaft defined by the strapdown inertial navigation, so that the direction of the azimuth output shaft of the strapdown inertial navigation is parallel to the direction of the azimuth output shaft of the GNSS, and the distance between the two GNSS antennas is as long as possible; and (3) installing the GNSS main antenna right above the strapdown inertial navigation system, wherein the installation positions of the GNSS main antenna and the strapdown inertial navigation system are as close as possible.
The longer the distance between the GNSS double antennas is, the higher the azimuth precision is, and the GNSS main antenna is arranged right above the strapdown inertial navigation, so that the influence of lever arm errors under large azimuth maneuvering on the system precision can be reduced.
Further, a step of updating the state quantity and the covariance matrix in the kalman filter model is further included between step 3 and step 4, and a specific update formula is as follows:
Figure BDA0002752473220000062
Figure BDA0002752473220000063
Pm,k＝Pm,k-1-km,khmPm,k-1
wherein, subscript k is the current time, subscript k-1 is the last time, M is 1,2, …, M is kalman filtering observed quantity ZkNumber of (a), km,kA gain coefficient, P, corresponding to the mth Kalman filtering observation at the current momentm,k-1Is a covariance matrix, h, corresponding to the mth Kalman filtering observation at the previous momentmCoefficient, r, for the mth Kalman filtering observationmThe observation noise for the mth kalman filter observation,
Figure BDA0002752473220000064
the state quantity corresponding to the mth Kalman filtering observation quantity at the current moment,
Figure BDA0002752473220000065
the state quantity, Z, corresponding to the mth Kalman filtering observation quantity at the previous momentm,kFor the mth Kalman filtering observation at the present time, Pm,kAnd the covariance matrix corresponding to the mth Kalman filtering observation quantity at the current moment.
When the Kalman filtering model is updated, the solution of Kalman gain comprises matrix inversion operation, in order to avoid complex matrix inversion operation, scalar measurement sequential processing method is adopted to optimize the updating of the Kalman filtering model, and the efficiency of the combined navigation Kalman filtering calculation process is improved.
Further, after the state quantity and covariance matrix in the p-kalman filter model are updated, a step of detecting and isolating a system measurement value is further included before step 4, and the specific steps are as follows:
constructing RAIM (RAIM, Receiver Autonomous integration Monitoring) fault detection isolation observation information calculation value ymThe calculation value is calculated according to the formula:
ym＝Zm/(Pm+Rm)
wherein Z ismIs the m-th Kalman filtering observation, PmFor the mth Kalman filtering observation pairDiagonal elements of the corresponding covariance matrix, RmIs the mth diagonal element of the observed noise matrix;
and respectively setting a speed fault detection threshold value, a position fault detection threshold value and a direction fault detection threshold value, if the speed fault detection threshold value, the position fault detection threshold value and the direction fault detection threshold value exceed the corresponding fault detection threshold values, only carrying out pure inertial navigation resolving of strapdown inertial navigation, and otherwise, carrying out state quantity estimation by using a Kalman filtering model.
Advantageous effects
Compared with the prior art, the strapdown inertial navigation combined navigation method assisted by the double-antenna GNSS provided by the invention has the advantages that the GNSS provides information such as position, speed, direction and the like, so that the system can complete initial alignment and enter combined navigation in a static or dynamic motion process, and the system has more universal applicability; and in the combination process, the navigation error, the gyro zero-offset error and the accelerometer zero-offset error are corrected on line, so that the navigation precision of the system is improved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic diagram of a strapdown inertial navigation system with dual antenna GNSS assistance according to an embodiment of the present invention.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the strapdown inertial navigation system integrated navigation method assisted by a dual-antenna GNSS provided in this embodiment includes the following steps:
1. installation of strapdown inertial navigation system and dual-antenna GNSS
Determining the direction of a GNSS dual-antenna connecting line according to the direction of an azimuth output shaft defined by the strapdown inertial navigation, so that the direction of the azimuth output shaft of the strapdown inertial navigation is parallel to the direction of the azimuth output shaft of the GNSS, and the distance between the two GNSS antennas is as long as possible; and (3) installing the GNSS main antenna right above the strapdown inertial navigation system, wherein the installation positions of the GNSS main antenna and the strapdown inertial navigation system are as close as possible.
The longer the distance between the GNSS double antennas is, the higher the azimuth precision is, and the GNSS main antenna is arranged right above the strapdown inertial navigation, so that the influence of lever arm errors under large azimuth maneuvering on the system precision can be reduced.
2. Initial alignment of strapdown inertial navigation
Performing initial alignment on strapdown inertial navigation when a strapdown inertial navigation combined navigation system assisted by a dual-antenna GNSS is static and dynamic respectively to obtain an initial attitude, an initial speed and an initial position; and after initial alignment, the strapdown inertial navigation enters an integrated navigation state.
Defining a carrier coordinate system b where the strapdown inertial navigation is located, wherein a navigation coordinate system n is an northeast (ENU) coordinate system, i is an inertial coordinate system, and e is a terrestrial coordinate system.
2.1 initial alignment procedure in static state
If the direction output of the GNSS is invalid, namely the direction information of the GNSS is invalid at the moment, acquiring the static output increment of the strapdown inertial navigation gyroscope and the static output increment of the accelerometer within 5min to obtain an initial attitude as follows:
α0＝atan(C12/C22)，β0＝asin(C32)，γ0＝atan(-C31/C33)
Figure BDA0002752473220000081
wherein alpha is0To an initial azimuth angle, beta0At an initial pitch angle, γ0To an initial roll angle, Cij(i-1, 2,3, j-1, 2,3) isInitial attitude matrix
Figure BDA0002752473220000082
Element of (5), t0As an initial moment of time, the time of day,
Figure BDA0002752473220000083
is an attitude matrix at the initial time, rn、rbAre all intermediate vectors, gnIs the projection of the gravitational acceleration on the northeast coordinate system n, gn＝[0 0 g0]T，g0In order to be the local gravitational acceleration,
Figure BDA0002752473220000084
is the projection of the rotational angular velocity of the earth in a coordinate system n in the northeast of the world,
Figure BDA0002752473220000085
ωieis the rotational angular velocity of the earth, L is the local latitude, fbThe static output increment of the accelerometer under the carrier coordinate system b,
Figure BDA0002752473220000086
Figure BDA0002752473220000087
respectively the static output increment average values of the three axial accelerometers in the carrier coordinate system b,
Figure BDA0002752473220000088
the static output increment of the gyroscope under the carrier coordinate system b,
Figure BDA0002752473220000089
Figure BDA00027524732200000810
Figure BDA00027524732200000811
and respectively the static output increment average values of the three axial gyroscopes in the carrier coordinate system b. Static output increment of gyroscopeThe angular increment is the static output increment of the accelerometer, namely the velocity increment.
If the GNSS azimuth output is valid, the azimuth angle measured by the GNSS is adopted
Figure BDA00027524732200000812
Substituted for alpha0。
The GNSS measured speed and position are the initial speed and initial position.
2.2 initial alignment procedure in dynamic time
If the GNSS azimuth output is invalid, judging whether the movement speed is greater than a speed threshold, and accelerating the movement when the movement speed is less than or equal to the speed threshold until the movement speed is greater than the speed threshold; when the motion speed is greater than the speed threshold, the initial posture is:
Figure BDA0002752473220000091
wherein alpha is0To an initial azimuth angle, beta0At an initial pitch angle, γ0In order to be the initial roll angle,
Figure BDA0002752473220000092
and
Figure BDA0002752473220000093
east, north and sky speed, respectively, as measured by GNSS. In this embodiment, the speed threshold depends on the actual characteristics of the system, and the speed threshold may be 5 m/s. The GNSS has relatively large speed calculation error at low speed, and the speed threshold is set to avoid the problem of relatively large calculation error of the GNSS speed at low speed.
If the GNSS azimuth output is valid, the azimuth angle measured by the GNSS is adopted
Figure BDA0002752473220000094
Substituted for alpha0。
The GNSS measured speed and position are the initial speed and initial position.
3. And performing navigation calculation by using the strapdown inertial navigation to obtain a real-time attitude, a real-time speed and a real-time position.
And performing strapdown inertial navigation solution by using the following attitude matrix, speed and position update equation.
The update equation of the attitude matrix is:
Figure BDA0002752473220000095
wherein, the subscript k is the current time, the subscript k-1 is the last time,
Figure BDA0002752473220000096
is the matrix of the attitude at the current time,
Figure BDA0002752473220000097
is the matrix of the attitude at the last moment,
Figure BDA0002752473220000098
for the change of the navigation matrix caused by the position change in the motion process of the integrated navigation system at the current moment, delta t is a calculation period, delta lambda is the longitude change in the calculation period, delta L is the latitude change in the calculation period, and L iskThe latitude of the current time.
The update equation for real-time speed is:
Figure BDA0002752473220000099
wherein the content of the first and second substances,
Figure BDA00027524732200000910
is the speed under the coordinate system n of the northeast of the current moment,
Figure BDA00027524732200000911
Figure BDA00027524732200000912
and
Figure BDA00027524732200000913
respectively an east-direction speed, a north-direction speed and a sky-direction speed at the current moment,
Figure BDA00027524732200000914
is the speed under the coordinate system n of the northeast of the last moment,
Figure BDA00027524732200000915
the attitude matrix at the last moment is obtained,
Figure BDA00027524732200000916
the static output increment of the accelerometer under the carrier coordinate system b at the last moment,
Figure BDA00027524732200000917
the projection of the rotational angular velocity of the earth on the coordinate system n in the northeast is carried out at the last moment,
Figure BDA0002752473220000101
is the projection of the northeast coordinate system relative to the angular velocity of the earth's motion on the northeast coordinate system at the previous moment,
Figure BDA0002752473220000102
is the projection of the gravitational acceleration on the coordinate system n in the northeast of the day at the previous moment.
The update equation for the real-time position is:
Figure BDA0002752473220000103
Figure BDA0002752473220000104
Figure BDA0002752473220000105
wherein the content of the first and second substances,
Figure BDA0002752473220000106
and
Figure BDA0002752473220000107
respectively the north velocity, east velocity and sky velocity of the previous moment, Ry is the radius of the earth meridian, Rx is the radius of the earth prime circle, lambda is the radius of the earth prime circlekAs the longitude, h, of the current timekHeight at the present moment, Lk-1Is the latitude, λ, of the previous momentk-1Longitude, h, of the previous timek-1The height of the last moment.
4. And establishing a Kalman filtering model of the strapdown inertial navigation integrated navigation system assisted by the dual-antenna GNSS.
The modeling process of the Kalman filtering model comprises the following steps:
4.1: selecting a state quantity of a Kalman filtering model, wherein the state quantity X is as follows:
Figure BDA0002752473220000108
wherein, δ vE、δvN、δvUAn east direction speed error, a north direction speed error and a sky direction speed error respectively; delta beta, delta gamma,
Figure BDA0002752473220000109
Pitch angle error, roll angle error, azimuth angle error, respectively; δ L, δ λ, δ h are latitude error, longitude error, altitude error, respectively; epsilonE、εN、εURespectively an east gyroscope zero bias error, a north gyroscope zero bias error and a sky gyroscope zero bias error;
Figure BDA00027524732200001010
respectively, the zero offset error of an east accelerometer, the zero offset error of a north accelerometer and the zero offset error of a sky accelerometer.
4.2: establishing a state transition equation of a Kalman filtering model, wherein the state transition equation is as follows:
Xk＝Φk,k-1Xk-1+Γk-1Wk-1
wherein, Xk、Xk-1Respectively the state quantities, phi, of the current time and the previous timek,k-1State transition matrix, Γ, being a linear discrete kalmank-1Driving the matrix for system noise, Wk-1Is a state noise matrix. Specifically, reference may be made to the strapdown inertial navigation algorithm and the integrated navigation principle of Severe allergy and Weng Dredging.
4.3: establishing a Kalman filtering observation equation, wherein the Kalman filtering observation equation is as follows:
Figure BDA0002752473220000111
wherein Z iskAs Kalman filter observations, HkAs a matrix of observation coefficients, RkIn order to observe the noise matrix,
Figure BDA0002752473220000112
and
Figure BDA0002752473220000113
respectively an east speed, a north speed and a sky speed of the strapdown inertial navigation,
Figure BDA0002752473220000114
and
Figure BDA0002752473220000115
east, north and sky speed, L, respectively, as measured by GNSSk、λk、hkAnd
Figure BDA0002752473220000116
respectively latitude, longitude, altitude and azimuth of strapdown inertial navigation, Lk,GNSS、λk,GNSS、hk,GNSSAnd
Figure BDA0002752473220000117
respectively latitude, longitude, altitude and azimuth of the GNSS.
5. Updating state quantity and covariance matrix in Kalman filtering model
And after the strapdown inertial navigation resolving and the construction of a Kalman filtering model are completed, estimating the system state quantity by utilizing Kalman filtering. When the Kalman filtering model is updated, the solution of Kalman gain comprises matrix inversion operation, in order to avoid complex matrix inversion operation, the updating of the Kalman filtering model is optimized by adopting a scalar measurement sequential processing method, the efficiency of the combined navigation Kalman filtering calculation process is improved, and the processing result of the scalar measurement sequential processing method is consistent with that of a normal vector method and is irrelevant to the sequence of each scalar measurement value.
The update formula of the state quantity and covariance matrix is as follows:
Figure BDA0002752473220000118
Figure BDA0002752473220000119
Pm,k＝Pm,k-1-km,khmPm,k-1
wherein, subscript k is the current time, subscript k-1 is the last time, M is 1,2, …, M is kalman filtering observed quantity ZkM is 7, k in this embodimentm,kA gain coefficient, P, corresponding to the mth Kalman filtering observation at the current momentm,k-1Is a covariance matrix, h, corresponding to the mth Kalman filtering observation at the previous momentmCoefficient, r, for the mth Kalman filtering observationmThe observation noise for the mth kalman filter observation,
Figure BDA00027524732200001110
the state quantity corresponding to the mth Kalman filtering observation quantity at the current moment,
Figure BDA00027524732200001111
the state quantity, Z, corresponding to the mth Kalman filtering observation quantity at the previous momentm,kFor the mth Kalman filtering observation at the present time, Pm,kAnd the covariance matrix corresponding to the mth Kalman filtering observation quantity at the current moment. Specifically, the study of Beidou signal vector tracking algorithm research of the engineering Master academic thesis of Harbin engineering university can be referred to.
6. System measurement detection isolation
When some measured values of the system are wrong or seriously deviated, other channels are easily polluted, and even all the channels are unlocked in error tracking calculation. For example, when the satellite signal suddenly fails, the measured value becomes inaccurate, so that the fault detection and isolation of the system measured value become necessary.
Constructing RAIM (RAIM, Receiver Autonomous integration Monitoring) fault detection isolation observation information calculation value ymThe calculation value is calculated by the formula:
ym＝Zm/(Pm+Rm)
wherein Z ismIs the m-th Kalman filtering observation, PmDiagonal elements, R, of covariance matrix corresponding to mth Kalman filtering observationmTo observe the mth diagonal element of the noise matrix.
And respectively setting a speed fault detection threshold value, a position fault detection threshold value and a direction fault detection threshold value, if the speed fault detection threshold value, the position fault detection threshold value and the direction fault detection threshold value exceed the corresponding fault detection threshold values, only carrying out pure inertial navigation calculation of strapdown inertial navigation, and not carrying out integrated navigation of current observation information, so that the reliability of the system is improved, otherwise, estimating the state quantity by using a Kalman filtering model.
7. And estimating the state quantity by using a Kalman filtering model to obtain a velocity error, an attitude error, a position error, a gyro zero-offset error and an accelerometer zero-offset error of the strapdown inertial navigation.
8. And (4) performing feedback correction on the real-time speed, the attitude matrix, the real-time position, the gyro output and the accelerometer output which are solved by the strapdown inertial navigation by using the speed error, the attitude error, the position error, the gyro zero-bias error and the accelerometer zero-bias error in the step (7) to obtain the corrected real-time speed, attitude matrix, real-time position, gyro output and accelerometer output.
The corrected real-time speed is as follows:
v'E＝vE-X(1)
v'N＝vN-X(2)
v'U＝vU-X(3)
wherein v isE、vNAnd vUEast, north and sky speeds before correction, v'E、v'NAnd v'UThe corrected east-direction speed, north-direction speed and sky-direction speed are respectively, X (1) is the 1 st state quantity estimated value, X (2) is the 2 nd state quantity estimated value, and X (3) is the 3 rd state quantity estimated value;
the corrected attitude matrix is:
Figure BDA0002752473220000131
wherein the content of the first and second substances,
Figure BDA0002752473220000132
to be the attitude matrix before the correction,
Figure BDA0002752473220000133
for the corrected attitude matrix, X (4), X (5), and X (6) are respectively the 4 th, 5 th, and 6 th of the state quantity estimation values;
the corrected real-time positions are as follows:
L'＝L-X(7)
λ'＝λ-X(8)
h'＝h-X(9)
wherein, L, λ and h are respectively latitude, longitude and altitude before correction, L ', λ ' and h ' are respectively latitude, longitude and altitude after correction, and X (7), X (8) and X (9) are respectively the 7 th, 8 th and 9 th of state quantity estimated values;
the corrected gyro output is:
Figure BDA0002752473220000134
the corrected accelerometer output is:
Figure BDA0002752473220000135
wherein the content of the first and second substances,
Figure BDA0002752473220000136
respectively the corrected gyro output and the accelerometer output,
Figure BDA0002752473220000137
the gyroscope output and the accelerometer output before correction are respectively epsilon,
Figure BDA0002752473220000138
Respectively, a gyroscope zero-offset error and an accelerometer zero-offset error.
The invention relates to a strapdown inertial navigation combined navigation method assisted by a dual-antenna GNSS, wherein azimuth, three-dimensional speed and three-dimensional position information acquired by the GNSS (including dual antennas) are combined with azimuth, three-dimensional speed and three-dimensional position information resolved by strapdown inertial navigation to establish a combined navigation state and observation model, errors of the strapdown inertial navigation are estimated by adopting a Kalman filtering fast calculation method, and real-time online compensation is carried out on navigation errors and zero offset errors of a gyroscope and an accelerometer through feedback correction, so that the precision of the combined navigation is improved; the RAIM method is used for detecting and isolating abnormal observation information such as GNSS satellite signal loss or data jump, and the like, so that the combined navigation precision of the system is ensured. The combined navigation method can be suitable for occasions where the system is in static or dynamic motion at the initial moment, and has general applicability.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.

Claims (10)

1. A strap-down inertial navigation combined navigation method assisted by a dual-antenna GNSS is characterized by comprising the following steps:
step 1: performing initial alignment on strapdown inertial navigation when a strapdown inertial navigation combined navigation system assisted by a dual-antenna GNSS is static and dynamic respectively to obtain an initial attitude, an initial speed and an initial position; and after initial alignment, the strapdown inertial navigation enters an integrated navigation state;
step 2: performing navigation resolving by using the strapdown inertial navigation to obtain a real-time attitude, a real-time speed and a real-time position;
and step 3: establishing a Kalman filtering model of a strapdown inertial navigation integrated navigation system assisted by a dual-antenna GNSS;
and 4, step 4: estimating the state quantity by using the Kalman filtering model to obtain a velocity error, an attitude error, a position error, a gyro zero-offset error and an accelerometer zero-offset error of the strapdown inertial navigation;
and 5: and (4) performing feedback correction on the real-time speed, the attitude matrix, the real-time position, the gyro output and the accelerometer output which are solved by the strapdown inertial navigation by using the speed error, the attitude error, the position error, the gyro zero-bias error and the accelerometer zero-bias error in the step (4) to obtain the corrected real-time speed, attitude matrix, real-time position, gyro output and accelerometer output.
2. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of claim 1, wherein: in the step 1, when the dual-antenna GNSS-assisted strapdown inertial navigation integrated navigation system is stationary, the process of initially aligning the strapdown inertial navigation system includes:
if the direction output of the GNSS is invalid, acquiring the static output increment of the strapdown inertial navigation gyroscope and the static output increment of the accelerometer to obtain an initial attitude as follows:
α0＝atan(C12/C22)，β0＝asin(C32)，γ0＝atan(-C31/C33)
Figure FDA0002752473210000011
wherein alpha is0To an initial azimuth angle, beta0At an initial pitch angle, γ0To an initial roll angle, CijAs an initial attitude matrix
Figure FDA0002752473210000012
Wherein i is 1,2,3, j is 1,2,3, t0As an initial moment of time, the time of day,
Figure FDA0002752473210000013
is an attitude matrix at the initial time, rn、rbAre all intermediate vectors, gnIs the projection of the gravitational acceleration on the northeast coordinate system n, gn＝[0 0 g0]T，g0In order to be the local gravitational acceleration,
Figure FDA0002752473210000014
is the projection of the rotational angular velocity of the earth in a coordinate system n in the northeast of the world,
Figure FDA0002752473210000015
ωieis the rotational angular velocity of the earth, L is the local latitude, fbThe static output increment of the accelerometer under the carrier coordinate system b,
Figure FDA0002752473210000016
Figure FDA0002752473210000017
respectively three axial direction addition under a carrier coordinate system bThe static output delta average of the speedometer,
Figure FDA0002752473210000018
the static output increment of the gyroscope under the carrier coordinate system b,
Figure FDA0002752473210000019
Figure FDA00027524732100000110
Figure FDA00027524732100000111
respectively taking the static output increment average values of the three axial gyroscopes under the carrier coordinate system b;
if the GNSS azimuth output is valid, the azimuth angle measured by the GNSS is adopted
Figure FDA00027524732100000213
Substituted for alpha0；
The GNSS measured speed and position are an initial speed and initial position.
3. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of claim 1, wherein: in the step 1, the implementation process of performing initial alignment on the strapdown inertial navigation system when the strapdown inertial navigation system assisted by the dual-antenna GNSS is dynamic is as follows:
if the GNSS azimuth output is invalid, judging whether the movement speed is greater than a speed threshold value, and accelerating the movement when the movement speed is less than or equal to the speed threshold value until the movement speed is greater than the speed threshold value; when the motion speed is greater than the speed threshold, the initial posture is as follows:
Figure FDA0002752473210000021
γ0＝0
wherein alpha is0To an initial azimuth angle, beta0At an initial pitch angle, γ0In order to be the initial roll angle,
Figure FDA0002752473210000022
and
Figure FDA0002752473210000023
east speed, north speed and sky speed measured by GNSS respectively;
if the GNSS azimuth output is valid, the azimuth angle measured by the GNSS is adopted
Figure FDA00027524732100000214
Substituted for alpha0；
The GNSS measured speed and position are an initial speed and initial position.
4. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of claim 3, wherein: the speed threshold is 5 m/s.
5. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of claim 1, wherein: in step 2, the update equation of the attitude matrix is as follows:
Figure FDA0002752473210000024
wherein, the subscript k is the current time, the subscript k-1 is the last time,
Figure FDA0002752473210000025
is the matrix of the attitude at the current time,
Figure FDA0002752473210000026
is the matrix of the attitude at the last moment,
Figure FDA0002752473210000027
combining navigation system movements for current timeThe change of the navigation matrix caused by the position change in the process, wherein delta t is a calculation period, delta lambda is the longitude change in the calculation period, delta L is the latitude change in the calculation period, and L iskThe latitude of the current moment;
the update equation for real-time speed is:
Figure FDA0002752473210000028
wherein the content of the first and second substances,
Figure FDA0002752473210000029
is the speed under the coordinate system n of the northeast of the current moment,
Figure FDA00027524732100000210
Figure FDA00027524732100000211
and
Figure FDA00027524732100000212
respectively an east-direction speed, a north-direction speed and a sky-direction speed at the current moment,
Figure FDA0002752473210000031
is the speed under the coordinate system n of the northeast of the last moment,
Figure FDA0002752473210000032
the attitude matrix at the last moment is obtained,
Figure FDA0002752473210000033
the static output increment of the accelerometer under the carrier coordinate system b at the last moment,
Figure FDA0002752473210000034
the projection of the rotational angular velocity of the earth on the coordinate system n in the northeast is carried out at the last moment,
Figure FDA0002752473210000035
is the projection of the northeast coordinate system relative to the angular velocity of the earth's motion on the northeast coordinate system at the previous moment,
Figure FDA0002752473210000036
the projection of the gravity acceleration at the last moment in a coordinate system n in the northeast is shown;
the update equation for the real-time position is:
Figure FDA0002752473210000037
Figure FDA0002752473210000038
Figure FDA0002752473210000039
wherein the content of the first and second substances,
Figure FDA00027524732100000310
and
Figure FDA00027524732100000311
respectively the north velocity, east velocity and sky velocity of the previous moment, Ry is the radius of the earth meridian, Rx is the radius of the earth prime circle, lambda is the radius of the earth prime circlekAs the longitude, h, of the current timekHeight at the present moment, Lk-1Is the latitude, λ, of the previous momentk-1Longitude, h, of the previous timek-1The height of the last moment.
6. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of claim 1, wherein: in step 3, the modeling process of the kalman filter model is as follows:
step 3.1: selecting a state quantity X which is as follows:
Figure FDA00027524732100000312
wherein, δ vE、δvN、δvUAn east direction speed error, a north direction speed error and a sky direction speed error respectively; delta beta, delta gamma,
Figure FDA00027524732100000314
Pitch angle error, roll angle error, azimuth angle error, respectively; δ L, δ λ, δ h are latitude error, longitude error, altitude error, respectively; epsilonE、εN、εURespectively an east gyroscope zero bias error, a north gyroscope zero bias error and a sky gyroscope zero bias error;
Figure FDA00027524732100000313
respectively is a zero offset error of an east accelerometer, a zero offset error of a north accelerometer and a zero offset error of a sky accelerometer;
step 3.2: establishing a state transition equation of a Kalman filtering model, wherein the state transition equation is as follows:
Xk＝Φk,k-1Xk-1+Γk-1Wk-1
wherein, Xk、Xk-1Respectively the state quantities, phi, of the current time and the previous timek,k-1State transition matrix, Γ, being a linear discrete kalmank-1Driving the matrix for system noise, Wk-1Is a state noise matrix;
step 3.3: establishing a Kalman filtering observation equation, wherein the Kalman filtering observation equation is as follows:
Zk＝HkXk+Rk，
Figure FDA0002752473210000041
wherein Z iskFor Kalman filtering observationsAmount HkAs a matrix of observation coefficients, RkIn order to observe the noise matrix,
Figure FDA0002752473210000042
and
Figure FDA0002752473210000043
respectively an east speed, a north speed and a sky speed of the strapdown inertial navigation,
Figure FDA0002752473210000044
and
Figure FDA0002752473210000045
east, north and sky speed, L, respectively, as measured by GNSSk、λk、hkAnd
Figure FDA0002752473210000049
respectively latitude, longitude, altitude and azimuth of strapdown inertial navigation, Lk,GNSS、λk,GNSS、hk,GNSSAnd
Figure FDA00027524732100000410
respectively latitude, longitude, altitude and azimuth of the GNSS.
7. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of claim 1, wherein: in the step 5, the corrected real-time speed is as follows:
v'E＝vE-X(1)
v'N＝vN-X(2)
v'U＝vU-X(3)
wherein v isE、vNAnd vUEast, north and sky velocities, v 'before correction, respectively'E、v'NAnd v'UThe corrected east-direction speed, north-direction speed and sky-direction speed are respectively, and X (1) is a state quantity estimated valueX (2) is the 2 nd of state quantity estimated values, and X (3) is the 3 rd of state quantity estimated values;
the corrected attitude matrix is:
Figure FDA0002752473210000046
wherein the content of the first and second substances,
Figure FDA0002752473210000047
to be the attitude matrix before the correction,
Figure FDA0002752473210000048
for the corrected attitude matrix, X (4), X (5), and X (6) are respectively the 4 th, 5 th, and 6 th of the state quantity estimation values;
the corrected real-time positions are as follows:
L'＝L-X(7)
λ'＝λ-X(8)
h'＝h-X(9)
wherein, L, λ and h are respectively latitude, longitude and altitude before correction, L ', λ ' and h ' are respectively latitude, longitude and altitude after correction, and X (7), X (8) and X (9) are respectively the 7 th, 8 th and 9 th of state quantity estimated values;
the corrected gyro output is:
Figure FDA0002752473210000051
the corrected accelerometer output is:
Figure FDA0002752473210000052
wherein the content of the first and second substances,
Figure FDA0002752473210000053
respectively the corrected gyro output and the accelerometer output,
Figure FDA0002752473210000054
the gyroscope output and the accelerometer output before correction are respectively epsilon,
Figure FDA0002752473210000055
Respectively, a gyroscope zero-offset error and an accelerometer zero-offset error.
8. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of any of claims 1 to 7, wherein: before the step 1, the method further comprises the steps of installing strapdown inertial navigation and a dual-antenna GNSS, wherein the specific installation steps are as follows:
determining the direction of a GNSS dual-antenna connecting line according to the direction of an azimuth output shaft defined by the strapdown inertial navigation, so that the direction of the azimuth output shaft of the strapdown inertial navigation is parallel to the direction of the azimuth output shaft of the GNSS, and the distance between the two GNSS antennas is as long as possible; and (3) installing the GNSS main antenna right above the strapdown inertial navigation system, wherein the installation positions of the GNSS main antenna and the strapdown inertial navigation system are as close as possible.
9. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of any of claims 1 to 7, wherein: a step of updating the state quantity and the covariance matrix in the Kalman filtering model is also included between the step 3 and the step 4, and the specific updating formula is as follows:
Figure FDA0002752473210000056
Figure FDA0002752473210000057
Pm,k＝Pm,k-1-km,khmPm,k-1
wherein, subscript k is the current time, subscript k-1 is the last time, M is 1,2, …, M is kalman filtering observed quantity ZkNumber of (a), km,kFor the mth Kalman filter at the current momentGain factor, P, corresponding to the wave observed quantitym,k-1Is a covariance matrix, h, corresponding to the mth Kalman filtering observation at the previous momentmCoefficient, r, for the mth Kalman filtering observationmThe observation noise for the mth kalman filter observation,
Figure FDA0002752473210000058
the state quantity corresponding to the mth Kalman filtering observation quantity at the current moment,
Figure FDA0002752473210000059
the state quantity, Z, corresponding to the mth Kalman filtering observation quantity at the previous momentm,kFor the mth Kalman filtering observation at the present time, Pm,kAnd the covariance matrix corresponding to the mth Kalman filtering observation quantity at the current moment.
10. The dual-antenna GNSS assisted strapdown inertial navigation combined navigation method of claim 9, wherein: after the state quantity and the covariance matrix in the Kalman filtering model are updated, a step of detecting and isolating a system measurement value is further included before the step 4, and the specific steps are as follows:
constructing RAIM fault detection isolation observation information calculation value ymThe calculation value is calculated according to the formula:
ym＝Zm/(Pm+Rm)
wherein Z ismIs the m-th Kalman filtering observation, PmDiagonal elements, R, of covariance matrix corresponding to mth Kalman filtering observationmIs the mth diagonal element of the observed noise matrix;
and respectively setting a speed fault detection threshold value, a position fault detection threshold value and a direction fault detection threshold value, if the speed fault detection threshold value, the position fault detection threshold value and the direction fault detection threshold value exceed the corresponding fault detection threshold values, only carrying out pure inertial navigation resolving of strapdown inertial navigation, and otherwise, carrying out state quantity estimation by using a Kalman filtering model.
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