CN108931244B - Inertial navigation error suppression method and system based on train motion constraint - Google Patents

Inertial navigation error suppression method and system based on train motion constraint Download PDF

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CN108931244B
CN108931244B CN201810790065.9A CN201810790065A CN108931244B CN 108931244 B CN108931244 B CN 108931244B CN 201810790065 A CN201810790065 A CN 201810790065A CN 108931244 B CN108931244 B CN 108931244B
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inertial navigation
navigation error
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CN108931244A (en
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陈光武
杨菊花
刘射德
邢东峰
李鹏
程鉴皓
石建强
包成启
司涌波
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Lanzhou Jiaotong University
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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
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled

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Abstract

The invention discloses an inertial navigation error suppression method and system based on train motion constraint, wherein the inertial navigation error suppression method based on train motion constraint comprises the following steps: judging whether the satellite signal is shielded; if the satellite signal is shielded, real-time judgment is carried out on the maneuvering state of the train so as to judge whether the train has larger acceleration disturbance; if the train has no large acceleration disturbance, the inertial navigation error model is corrected; and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train. And processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm, thereby inhibiting error divergence and ensuring the accuracy of the following vehicle positioning under the condition of only depending on an inertial navigation system.

Description

Inertial navigation error suppression method and system based on train motion constraint
Technical Field
The invention relates to the field of train positioning, in particular to an inertial navigation error suppression method and system based on train motion constraint.
Background
At present, the existing train positioning methods mainly include the following methods: an axle counter, a cross induction loop, a transponder, an orbital circuit, an SINS (inertial navigation System) and a GNSS (Global navigation satellite System). The track circuit can only distinguish the section to which the train belongs, cannot distinguish the specific position, and has large error; the axle counter is easy to interfere, needs to be reset directly or reset in advance, and if the power failure phenomenon occurs in the axle counting process, the number of incoming and outgoing axles of a train is unequal, and the positioning result is interfered; the cross induction loop needs to be realized by laying a large number of cables along the line, and the later maintenance workload is large; the transponder cannot realize continuous positioning on the train; the SINS error is accumulated continuously along with time, and long-time train positioning cannot be realized; GNSS satellite signals are susceptible to interference. With the development of railways in China, a plurality of technologies for supporting high-speed running of the railways are increasingly perfected, wherein the importance of real-time and accurate position information service is increased in a control system, a dispatching system and a safety early warning system for train running safety and efficiency, and the trains are autonomously positioned in high precision, high real time and strong continuity, so that the use of ground equipment can be greatly reduced, the construction and operation maintenance cost is reduced, and the train running efficiency is improved.
The SINS/GNSS-based train combined positioning method makes full use of the characteristics of all weather and high precision of a satellite navigation system and simple structure and strong autonomy of the SINS, reduces the cost of the train positioning system, provides accurate position service for a train and has important significance for the development of a train operation control system. However, the dependence degree of the SINS/GNSS integrated navigation system on the integrity of the GNSS signal is too large, the satellite signal is in an unavailable state for a long time in the area where the train passes through the tunnel and the like and the satellite signal is shielded, and the error is rapidly dispersed.
Disclosure of Invention
The present invention aims to provide a method and a system for suppressing inertial navigation error based on train motion constraint to solve at least some of the problems in the prior art.
In order to achieve the purpose, the invention adopts the technical scheme that:
an inertial navigation error suppression method based on train motion constraint comprises the following steps:
judging whether the satellite signal is shielded;
if the satellite signal is shielded, real-time judgment is carried out on the maneuvering state of the train so as to judge whether the train has larger acceleration disturbance;
if the train has no large acceleration disturbance, the inertial navigation error model is corrected;
and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
Preferably, the method further comprises acquiring real-time data, the real-time data including inertial sensor data and GNSS satellite receiver data.
Preferably, the method further comprises processing the inertial sensor data to obtain velocity, acceleration and attitude information of the inertial sensor.
Preferably, if the satellite signal is blocked, the real-time determination of the maneuvering state of the train is specifically as follows: and judging the maneuvering state of the train in real time by adopting a Lyte criterion based on biased estimation.
Preferably, the real-time judgment of the maneuvering state of the train by using the reiter criterion based on the biased estimation includes:
setting a G-M model of the measured data;
the G-M model is as follows: l is AX-V,
wherein, L is a measurement vector, A is a design matrix, X is a undetermined parameter, and V is a measurement residual vector.
Preferably, the inertial navigation error correction model specifically includes:
and correcting the inertial navigation error model by using a train motion constraint model.
Preferably, the processing of the data obtained by the modified inertial navigation error model by using an unscented kalman filter algorithm to obtain the optimal estimation of the position, the speed and the attitude of the train includes:
and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm to obtain error data, and performing error compensation on the speed, acceleration and attitude information of the inertial sensor by using the error data to obtain the optimal estimation of the position, the speed and the attitude of the train.
Preferably, the processing of the data obtained by the modified inertial navigation error model by using an unscented kalman filter algorithm to obtain the optimal estimation of the position, the speed and the attitude of the train includes:
according to the corrected inertial navigation error model, taking train position error, attitude error, speed error and inertial sensor drift data as state quantities and taking the inertial sensor data as measurement to construct a nonlinear state space model;
and carrying out time updating and measurement updating on the nonlinear state space model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
Meanwhile, the technical scheme of the invention also discloses an inertial navigation error suppression system based on train motion constraint, which comprises the following steps:
satellite signal judges the module: the device is used for judging whether the satellite signal is shielded;
the real-time judgment module of the maneuvering state: the system is used for judging the maneuvering state of the train in real time if the satellite signal is shielded so as to judge whether the train has larger acceleration disturbance;
an inertial navigation error model correction module: the inertial navigation error model is corrected if the train has no larger acceleration disturbance;
a calculation module: and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
Preferably, the data acquisition module: for obtaining real-time data including inertial sensor data and GNSS satellite receiver data.
The technical scheme of the invention has the following beneficial effects:
according to the technical scheme, the data obtained by the corrected inertial navigation error model are processed by using an unscented Kalman filtering algorithm, so that error divergence is restrained, and the accuracy of train positioning under the condition of only depending on an inertial navigation system is ensured.
The technical scheme of the invention also has the following advantages:
1. the invention provides a method for judging the maneuvering state of the train in real time based on the biased estimation and the Latt criterion, and the judgment result is used as the premise of whether to correct, thereby improving the reliability of train positioning.
2. The invention introduces the running characteristic of the train into an inertial navigation error model, directly corrects the measured data of the train before the SINS calculates the speed and the position of the train, and inhibits the accumulative error of the SINS.
3. The invention utilizes Unscented Kalman Filtering (UKF) to carry out filtering estimation without solving a nonlinear model, thereby realizing the estimation of the recursive mean and variance of the system state.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flowchart of an inertial navigation error suppression method based on train motion constraint according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram illustrating a train motion constraint model based inertial navigation error model modification according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of train acceleration suppression according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating the suppression of the angular velocity of the train according to the embodiment of the present invention;
FIG. 5 is a positioning trajectory diagram processed by KF, UKF and UKF algorithm with introduced error model according to the embodiment of the present invention;
FIGS. 5a and 5b are enlarged views at a and b, respectively, on the trajectory diagram shown in FIG. 5;
FIG. 6 is an X-axis velocity error result diagram processed by KF, UKF and UKF algorithm with introduced error model according to the embodiment of the present invention;
FIG. 6a is an enlarged view of the error result plot shown in FIG. 6 at a;
FIG. 7 is a Y-axis velocity error result diagram processed by KF, UKF and UKF algorithm with introduced error model according to the embodiment of the invention;
FIG. 7a is an enlarged view of the error result plot shown in FIG. 7 at a;
FIG. 8 is a diagram showing the result of longitude errors after being processed by KF, UKF and UKF algorithm with introduced error model according to the embodiment of the present invention;
FIG. 8a is an enlarged view of the error result plot shown in FIG. 8 at a;
FIG. 9 is a latitude error result diagram processed by KF, UKF and UKF algorithm with introduced error model according to the embodiment of the present invention;
fig. 9a is an enlarged view of a point a on the error result graph shown in fig. 9.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
As shown in fig. 1, an inertial navigation error suppression method based on train motion constraint includes:
s102: judging whether the satellite signal is shielded;
s103: if the satellite signal is shielded, real-time judgment is carried out on the maneuvering state of the train so as to judge whether the train has larger acceleration disturbance;
s104: if the train has no large acceleration disturbance, the inertial navigation error model is corrected;
s105: and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
Preferably, the method further includes, S101: real-time data is acquired, the real-time data including inertial sensor data and GNSS satellite receiver data.
Real-time data is acquired, the real-time data including inertial sensor data and GNSS satellite receiver data.
The step of acquiring real-time data is performed before determining whether the satellite signal is occluded.
In a specific application, whether the satellite signal is blocked or not can be judged through the number of satellites in the GNSS satellite receiver data, for example, the number n of available satellites is calculated, whether n is greater than or equal to 4 or not is judged, if n is greater than or equal to 4, the satellite signal is not blocked, and if not, the satellite signal is blocked. The method can judge whether the satellite signal is shielded according to the fact that whether a GNSS satellite receiver on the train can receive the satellite signal.
Preferably, the method further comprises processing the inertial sensor data to obtain velocity, acceleration and attitude information of the inertial sensor.
Preferably, if the satellite signal is blocked, the real-time determination of the maneuvering state of the train is specifically as follows: and judging the maneuvering state of the train in real time by adopting a Lyte criterion based on biased estimation.
The method for judging the maneuvering state of the train in real time by adopting the Lyter criterion based on the biased estimation specifically comprises the following steps:
the measured data of train positioning can be represented by a G-M model:
L＝AX-V (1)，
wherein, L is a measurement vector, A is a design matrix, X is a undetermined parameter, and V is a measurement residual vector.
Due to the nonlinear ill-conditioned problem, the method adopts a ridge estimation biased estimation method to improve the estimation precision of the parameters. On the basis of least squares, the estimation criterion of ridge estimation is:
VTPV+αXTX＝min (2)，
wherein, P is a weight matrix of the observed value L, alpha is a ridge parameter, and X is a parameter to be estimated. Thereby to optimize the estimation result
Figure BDA0001734603660000061
And the covariance F is:
Figure BDA0001734603660000062
Figure BDA0001734603660000063
wherein, I is a unit array,
Figure BDA0001734603660000064
representing the variance of the sample data. Measuring sequence x output by accelerometer at time ki(i ═ 0,1, 2.., k) is the accelerometer output modulus, and the result at time k +1 with a biased estimate is
Figure BDA0001734603660000071
The output sequence at time k is represented as
Figure BDA0001734603660000072
Calculating the arithmetic mean of X
Figure BDA0001734603660000073
Because the output value of the accelerometer contains the local gravity g, the vector value output by the triaxial accelerometer is compared with the local gravity g for each datum to obtain the difference value between the vector value and the local gravity g
Figure BDA0001734603660000074
Namely, it is
Figure BDA0001734603660000075
The residual error of the ith data is
Figure BDA0001734603660000076
The squared difference σ of the entire set of data is calculated:
Figure BDA0001734603660000077
therefore, assume that the output value at the time k +1 is
Figure BDA0001734603660000078
According to the Lait criterion theory, if the residual error is more than three times of the standard deviation, the acceleration change amplitude is considered to be large at the moment, the train has a large horizontal acceleration maneuvering state, and the low acceleration mode is not applicable and is expressed as:
Figure BDA0001734603660000079
preferably, the inertial navigation error correction model specifically includes:
and correcting the inertial navigation error model by using a train motion constraint model.
And introducing a train motion constraint model to correct the inertial navigation error model. Let "east-north-sky" be the navigation coordinate system (n system), the carrier coordinate system (b system) to the direction cosine matrix of the navigation coordinate system:
Figure BDA00017346036600000710
wherein psi is a course angle, theta is a pitch angle, and gamma is a roll angle. MEMS measures angular rate omegabAngular velocity of train operation
Figure BDA00017346036600000711
The following relationships exist:
Figure BDA0001734603660000081
wherein,
Figure BDA0001734603660000082
is the gyroscope triaxial noise. The MEMS measured train acceleration is the vector sum of the train acceleration and the earth gravity acceleration in the three-axis projection, and the train axle acceleration is assumed to be
Figure BDA0001734603660000083
The accelerometer measurements are then:
Figure BDA0001734603660000084
wherein,
Figure BDA0001734603660000085
is the three-axis accelerometer noise. The sensor bias may be expressed as an error between the measured value and the actual value, if used
Figure BDA0001734603660000086
Which represents the gyro-related drift and,
Figure BDA0001734603660000087
representing accelerometer-dependent bias, wAnd
Figure BDA00017346036600000810
representing gyro and accelerometer related noise, then:
Figure BDA0001734603660000088
when the train runs in the tunnel, because the train is limited by track constraint and low-acceleration maneuvering state, only the heading angle of the attitude of the train changes, and the output of the accelerometer only changes relatively on the forward shaft of the train, namely:
Figure BDA0001734603660000089
by substituting equation (11) into equations (8) and (9), the accelerometer and gyroscope errors ω can be derivedbAnd fbThereby converting the MEMS error suppression problem into an optimal estimation problem of the measurement error.
Preferably, the processing of the data obtained by the modified inertial navigation error model by using an unscented kalman filter algorithm to obtain the optimal estimation of the position, the speed and the attitude of the train includes:
and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm to obtain error data, and performing error compensation on the speed, acceleration and attitude information of the inertial sensor by using the error data to obtain the optimal estimation of the position, the speed and the attitude of the train.
Preferably, the processing of the data obtained by the modified inertial navigation error model by using an unscented kalman filter algorithm to obtain the optimal estimation of the position, the speed and the attitude of the train includes:
according to the corrected inertial navigation error model, taking train position error, attitude error, speed error and inertial sensor drift data as state quantities and taking the inertial sensor data as measurement to construct a nonlinear state space model;
the nonlinear state space model is:
Figure BDA0001734603660000091
in the formula,
Figure BDA0001734603660000092
if it is used
Figure BDA0001734603660000093
And
Figure BDA0001734603660000094
representing zero offset drift rates of the gyroscope and accelerometer, the noise vector of the state equation is
Figure BDA0001734603660000095
If the train operation meets the motion constraint, after the virtual observation condition is introduced, the train pitch angle and the roll angle measured by the gyroscope are both measurement noises, the measurement values of the accelerometer on the X axis and the Z axis are the vector sum of the noises and the gravity acceleration, and the measurement form of the system is as follows:
Figure BDA0001734603660000096
and carrying out time updating and measurement updating on the nonlinear state space model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
The time update and the measurement update of the UKF are specifically carried out as follows:
(1) initializing variables:
Figure BDA0001734603660000101
(2) and (3) time updating:
Figure BDA0001734603660000102
(3) measurement updating:
Figure BDA0001734603660000103
wherein:
Figure BDA0001734603660000104
where L is the dimension of the state quantity, α represents the distribution of Sigma points around their mean (typically a small positive value), k is a scale factor (set to 0 for state estimation and 3-L for parameter estimation), and β is used to incorporate a priori knowledge of the state distribution, with an optimum of 2 for satisfying the gaussian distribution.
As shown in fig. 2, a positioning manner of a train in a GNSS unlocked condition such as a tunnel is described. And (3) performing maneuvering judgment on the train motion state by using train acceleration information measured by the IMU, modifying the positioning model if the train is in a low acceleration state at the moment, performing error compensation on the position and speed information of the train by using a UKF algorithm, and outputting optimal estimation of the position, speed and posture of the train.
As shown in fig. 3 and 4, which describe a model of train motion constraint, when a train runs in a tunnel, since the train is limited by track constraint and low-acceleration maneuvering state, only the heading angle of the attitude of the train changes, and the accelerometer output only changes relatively on the forward axis of the train running.
Fig. 5, 5a and 5b are positioning track diagrams of positioning data processed by KF, UKF and UKF algorithm introducing error model. Fig. 6, 6a, 7a, 8a, 9 and 9a are comparison graphs of X, Y speed error and longitude and latitude error processed by three algorithms. Compared with the traditional KF and UKF, the technical scheme of the invention has better effect in processing IMU strong nonlinear data, and the IMU resolving precision is greatly improved after train constraint conditions are introduced into an error model of the UKF.
Meanwhile, the technical scheme of the invention also discloses an inertial navigation error suppression system based on train motion constraint, which comprises the following steps:
satellite signal judges the module: the device is used for judging whether the satellite signal is shielded;
the real-time judgment module of the maneuvering state: the system is used for judging the maneuvering state of the train in real time if the satellite signal is shielded so as to judge whether the train has larger acceleration disturbance;
an inertial navigation error model correction module: the inertial navigation error model is corrected if the train has no larger acceleration disturbance;
a calculation module: and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
Preferably, the data acquisition module: for obtaining real-time data including inertial sensor data and GNSS satellite receiver data.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. An inertial navigation error suppression method based on train motion constraint is characterized by comprising the following steps:
judging whether the satellite signal is shielded;
if the satellite signal is shielded, real-time judgment is carried out on the maneuvering state of the train so as to judge whether the train has larger acceleration disturbance; the method specifically comprises the following steps: judging the maneuvering state of the train in real time by adopting a Lyte criterion based on biased estimation;
if the train has no large acceleration disturbance, the inertial navigation error model is corrected;
and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
2. The method of inertial navigation error suppression based on train motion constraint according to claim 1, further comprising obtaining real-time data, the real-time data comprising inertial sensor data and GNSS satellite receiver data.
3. The method for inertial navigation error suppression based on train motion constraint according to claim 2, further comprising processing the inertial sensor data to obtain velocity, acceleration and attitude information of the inertial sensor.
4. The inertial navigation error suppression method based on train motion constraint according to claim 3, wherein the real-time judgment of the maneuvering state of the train by using the reiter criterion based on biased estimation comprises:
setting a G-M model of the measured data;
the G-M model is as follows: l is AX-V,
wherein, L is a measurement vector, A is a design matrix, X is a undetermined parameter, and V is a measurement residual vector.
5. The inertial navigation error suppression method based on train motion constraint according to claim 2, wherein the modified inertial navigation error model specifically comprises:
and correcting the inertial navigation error model by using a train motion constraint model.
6. The method for inertial navigation error suppression based on train motion constraint according to claim 2, wherein the step of processing the data obtained by the modified inertial navigation error model by using an unscented kalman filter algorithm to obtain the optimal estimation of the train position, speed and attitude comprises:
and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm to obtain error data, and performing error compensation on the speed, acceleration and attitude information of the inertial sensor by using the error data to obtain the optimal estimation of the position, the speed and the attitude of the train.
7. The method for inertial navigation error suppression based on train motion constraint according to claim 6, wherein the step of processing the data obtained by the modified inertial navigation error model by using an unscented Kalman filtering algorithm to obtain the optimal estimation of the train position, speed and attitude comprises the following steps:
according to the corrected inertial navigation error model, taking train position error, attitude error, speed error and inertial sensor drift data as state quantities and taking the inertial sensor data as measurement to construct a nonlinear state space model;
and carrying out time updating and measurement updating on the nonlinear state space model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
8. An inertial navigation error suppression system based on train motion constraint is characterized by comprising:
satellite signal judges the module: the device is used for judging whether the satellite signal is shielded;
the real-time judgment module of the maneuvering state: the system is used for judging the maneuvering state of the train in real time if the satellite signal is shielded so as to judge whether the train has larger acceleration disturbance; the method specifically comprises the following steps: judging the maneuvering state of the train in real time by adopting a Lyte criterion based on biased estimation;
an inertial navigation error model correction module: the inertial navigation error model is corrected if the train has no larger acceleration disturbance;
a calculation module: and processing the data obtained by the corrected inertial navigation error model by using an unscented Kalman filtering algorithm so as to obtain the optimal estimation of the position, the speed and the attitude of the train.
9. The inertial navigation error suppression system based on train motion constraint according to claim 8, further comprising a data acquisition module: for obtaining real-time data including inertial sensor data and GNSS satellite receiver data.
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