CN109596139B - Vehicle-mounted navigation method based on MEMS - Google Patents

Vehicle-mounted navigation method based on MEMS Download PDF

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CN109596139B
CN109596139B CN201910058603.XA CN201910058603A CN109596139B CN 109596139 B CN109596139 B CN 109596139B CN 201910058603 A CN201910058603 A CN 201910058603A CN 109596139 B CN109596139 B CN 109596139B
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mems
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CN109596139A (en
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闫勇伟
王青江
付迪
于翔
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CETC 13 Research Institute
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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
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
    • 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
    • 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 provides a vehicle-mounted navigation method based on an MEMS, which is applied to the technical field of navigation positioning. The method comprises the following steps: initializing a vehicle navigation system; preprocessing the measurement data of an inertial measurement unit IMU of a micro electro mechanical system MEMS to determine the output information of the IMU; performing strapdown inertial navigation resolving according to the output information of the IMU; and realizing vehicle navigation according to the strapdown inertial navigation resolving result. The vehicle-mounted navigation method based on the MEMS can ensure the vehicle-mounted navigation precision under the condition that the GNSS signal is frequently unlocked.

Description

Vehicle-mounted navigation method based on MEMS
Technical Field
The invention belongs to the technical field of navigation and positioning, and particularly relates to a vehicle-mounted navigation method based on an MEMS.
Background
At present, the increasing demand for vehicle navigation applications and the development of autonomous vehicles put new demands on navigation systems: high precision, high resolution, high reliability, low cost, low power consumption and miniaturization. In modern Navigation systems, Global Navigation Satellite System (GNSS) is the most widespread Navigation means due to its Global, all-weather, and high-precision advantages. However, GNSS is susceptible to satellite signal blockage, multipath reflections, and the like. An Inertial Navigation System (INS) is an autonomous Navigation System that does not rely on external information, can provide all motion parameters of a carrier, such as position, speed, attitude, and the like, and has the advantages of high data update rate, good short-term accuracy and stability, and the like. The GNSS and the INS have good complementarity, and the combination of the GNSS and the INS can enable the navigation system to provide stable and reliable navigation information.
With the maturity of Micro-Electrical-Mechanical systems (MEMS) processing technology, MEMS inertial devices (MEMS accelerometers and gyroscopes) have been developed, and are widely applied to integrated navigation systems due to their advantages of low price, light weight, small volume, low power consumption, etc. However, the existing MEMS device has low precision, and in a complex urban environment, frequent unlocking of satellite signals can cause the increase of the independent working time of the MEMS-INS, thereby causing the rapid accumulation of navigation information errors.
Disclosure of Invention
The invention aims to provide a vehicle-mounted navigation method based on an MEMS (micro-electromechanical system) so as to solve the technical problem that the precision of navigation information is rapidly reduced due to frequent unlocking of GNSS (global navigation satellite system) signals in the prior art.
In order to achieve the purpose, the invention adopts the technical scheme that: the vehicle-mounted navigation method based on the MEMS comprises the following steps:
initializing a vehicle navigation system;
preprocessing the measurement data of an inertial measurement unit IMU of a micro electro mechanical system MEMS to determine the output information of the IMU;
performing strapdown inertial navigation resolving according to the output information of the IMU;
and realizing vehicle navigation according to the strapdown inertial navigation resolving result.
Optionally, the preprocessing the inertial measurement unit IMU data includes:
carrying out temperature compensation on IMU measurement data according to a linear interpolation method;
and performing cross coupling compensation on IMU measurement data according to a six-position method.
Optionally, the preprocessing the inertial measurement unit IMU data further includes:
the IMU measurement data is low pass filtered.
Optionally, the temperature compensating the IMU measurement data according to the linear interpolation method includes:
acquiring output information and temperature output data of the IMU under a preset temperature step length in a preset temperature range;
establishing a linear relation between the IMU output information and the temperature output data in the current temperature step range within a preset temperature step;
and carrying out temperature compensation on IMU measurement data according to the linear relation.
Optionally, the performing cross-coupling compensation on IMU measurement data according to a six-position method includes:
determining an acceleration ideal output matrix and an actual output matrix of the IMU according to a six-position method and the output information of the IMU;
and performing cross coupling compensation on IMU measurement data according to the ideal output matrix and the actual output matrix.
Optionally, before determining the acceleration ideal output matrix and the acceleration actual output matrix of the IMU according to the six-position method, the method further includes:
and placing the IMU gyroscope according to six positions to acquire the output information of the IMU.
Optionally, the performing strapdown inertial navigation solution according to the output information of the IMU includes:
performing strapdown inertial navigation resolving on output information of the IMU at the current moment;
predicting a state observation quantity and a state covariance matrix through extended Kalman filtering EKF;
and repeating the step of performing strapdown inertial navigation calculation on the output information of the IMU at the current moment until the IMU moment information meets a preset time condition, and determining a strapdown inertial navigation calculation result according to the output information of the IMU at the current moment, the predicted state observed quantity and the state covariance matrix.
Optionally, before predicting the state observation and the state covariance matrix through the extended kalman filter EKF, the method further includes:
modeling parameters of the extended Kalman filter EKF estimation as constant values, wherein the parameters of the extended Kalman filter EKF estimation comprise a GNSS lever arm, a GNSS installation angle, an IMU lever arm and an IMU installation angle.
Optionally, the state observation includes a vehicle northeast velocity component, an acceleration component, and a location coordinate component.
Optionally, the state observation further includes a vehicle wheel speed, a heading angle rate, and vehicle gear information.
The vehicle navigation method based on the MEMS has the beneficial effects that: the vehicle-mounted navigation method based on the MEMS initializes the vehicle-mounted navigation system, preprocesses IMU (inertial measurement unit) measurement data of the MEMS of the micro electro mechanical system to determine the output information of the IMU, then carries out strapdown inertial navigation calculation according to the output information of the IMU, and finally realizes the vehicle-mounted navigation according to the strapdown inertial navigation calculation result. The vehicle-mounted navigation method based on the MEMS effectively improves the precision of vehicle-mounted navigation by preprocessing IMU measurement data and performing strapdown inertial navigation resolving on IMU output information. Furthermore, by increasing the state observed quantity in the strapdown inertial navigation resolving process, the divergence of the navigation course after the satellite navigation is invalid is effectively inhibited, so that the vehicle-mounted navigation precision is ensured under the condition that the GNSS signal is frequently unlocked.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for vehicle navigation based on MEMS according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for vehicle navigation based on MEMS according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a method for vehicle navigation based on MEMS according to still another embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating a method for vehicle navigation based on MEMS according to another embodiment of the present invention;
fig. 5 is a schematic flow chart of a MEMS-based vehicle navigation method according to another embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a vehicle navigation method based on MEMS according to an embodiment of the present invention. The vehicle-mounted navigation method based on the MEMS comprises the following steps:
s101: and initializing the vehicle-mounted navigation system.
In the embodiment, the initialization of the vehicle navigation system includes, but is not limited to, hardware configuration such as system clock and interrupt, and setting of various operating parameters in the components of the inertial measurement unit.
S102: and preprocessing the measurement data of an inertial measurement unit IMU of the MEMS to determine the output information of the IMU.
In this embodiment, an inertial measurement unit IMU is disposed in a MEMS, and preprocessing data measured by the inertial measurement unit IMU mainly includes three aspects: temperature compensation of IMU component measurement data, cross coupling compensation of IMU gyroscope measurement data, namely calibration of the IMU gyroscope, and low-pass filtering of the measurement data after temperature compensation and cross coupling compensation.
S103: and performing strapdown inertial navigation resolving according to the output information of the IMU.
In this embodiment, the output information of the IMU is the preprocessed IMU measurement data, and the embodiment may perform strapdown inertial navigation calculation according to the IMU output information. For example, strapdown inertial navigation solution may be performed on output information of the IMU at the current time, and then the state observed quantity and the state covariance matrix may be predicted through the EKF. And then, repeatedly executing the step of performing strapdown inertial navigation calculation on the output information of the IMU at the current moment until the IMU moment information meets a preset time condition, and determining a strapdown inertial navigation calculation result according to the output information of the IMU at the current moment, the predicted state observed quantity and the state covariance matrix.
The accuracy of vehicle navigation can be guaranteed by increasing the state observation quantity.
S104: and realizing vehicle navigation according to the strapdown inertial navigation resolving result.
In the present embodiment, the in-vehicle real-time navigation can be realized by continuously executing step S103.
Optionally, referring to fig. 2, as a specific implementation manner of the vehicle navigation method based on MEMS according to an embodiment of the present invention, on the basis of the foregoing embodiment, step S102 may be detailed as follows:
s201: and carrying out temperature compensation on the IMU measurement data according to a linear interpolation method.
In this embodiment, the output information of the IMU is linear with the temperature change within a certain temperature change range, so in order to ensure the accuracy of the output information of the IMU, a linear difference method may be adopted to compensate the temperature data in the IMU measurement data.
S202: and performing cross coupling compensation on IMU measurement data according to a six-position method.
In this embodiment, cross-coupling compensation is performed on IMU measurement data, that is, the IMU is calibrated, so that the IMU component can be calibrated based on the least square method by using static data at six positions.
Optionally, referring to fig. 2, as a specific implementation manner of the vehicle navigation method based on the MEMS according to the embodiment of the present invention, on the basis of the foregoing embodiment, step S102 may further include:
s203: the IMU measurement data is low pass filtered.
In this embodiment, the IMU measurement data subjected to temperature compensation and cross-coupling compensation is low-pass filtered to remove high-frequency noise, thereby reducing noise of the IMU original state observed quantity.
Optionally, referring to fig. 3, as a specific implementation manner of the vehicle navigation method based on the MEMS according to the embodiment of the present invention, on the basis of the foregoing embodiment, step S201 may be detailed as follows:
s301: and acquiring output information and temperature output data of the IMU under a preset temperature step length in a preset temperature range.
In this embodiment, a temperature-variable box can be used to collect a series of temperature points and the output of the components at the temperature points. For example, IMU output information and temperature output data may be collected at 10 ℃ intervals in the range of-45 ℃ to +85 ℃. That is, in this embodiment, the preset temperature range is-45 ℃ to +85 ℃, and the preset temperature step is 10 ℃.
S302: and establishing a linear relation between the IMU output information and the temperature output data in the current temperature step range within the preset temperature step.
In this embodiment, assuming that 13 sets of data are collected, in a preset temperature step, establishing a linear relationship between the output information of the IMU and the temperature output data in the current temperature step range may be:
within every 10 ℃ of adjacent temperature points, the output information and the temperature output data of the IMU are fitted into a straight line yi＝aiT+bi(i is 1,2,3 …,13), and the temperature is compensated by a coefficient ai，biAnd (5) storing.
S303: and carrying out temperature compensation on the IMU measurement data according to the linear relation.
In this embodiment, the IMU measurement data may be temperature compensated according to a linear relationship. For example, if the current IMU component temperature sensor value is TtempThen according to TtempThe value of (a) is searched for corresponding to the temperature compensation coefficient aj，bjThen, the temperature output of the IMU after temperature compensation is: y isoffset＝yj-(ajTtemp+bj)。
Optionally, referring to fig. 4, as a specific implementation manner of the MEMS-based vehicle navigation method according to the embodiment of the present invention, on the basis of the foregoing embodiment, step S202 may be detailed as follows:
s401: and determining an acceleration ideal output matrix and an actual output matrix of the IMU according to a six-position method and the output information of the IMU.
In this embodiment, the output of the IMU triaxial accelerometer can be expressed as:
Figure BDA0001953386400000061
wherein the content of the first and second substances,
Figure BDA0001953386400000062
respectively the output values of three axes of the accelerometer, sx，sy，szScale factors of the three axes of the accelerometer, bax，bay，bazRespectively, zero offset, m, of the three axes of the accelerometeryx，mzx，mxy，mzy，mxz，myzFor cross-axis coupling of three-axis accelerometers, fx，fy，fzIs the ideal output value of the triaxial accelerometer.
The position of the IMU is adjusted using a six-position method, i.e., x-axis up, x-axis down, y-axis up, y-axis down, z-axis up, z-axis down. The ideal output value of the accelerometer can be expressed as:
Figure BDA0001953386400000071
wherein g is the acceleration of gravity. Therefore, the construction matrix applying the least squares method, i.e. the ideal output matrix, can be expressed as:
Figure BDA0001953386400000072
the accelerometer sensor output values represent a matrix, i.e. the actual output matrix is:
L＝[l1 l2 l3 l4 l5 l6]
wherein the content of the first and second substances,
Figure BDA0001953386400000073
wherein l1Representing the actual output value, l, with the x-axis up2Representing the actual output value with the x-axis down, and so on.
S402: and performing cross coupling compensation on IMU measurement data according to the ideal output matrix and the actual output matrix.
In this embodiment, the cross-coupling compensation parameter can be obtained by using least squares estimation solution according to L ═ MA:
M＝LAT(AAT)-1
optionally, referring to fig. 4, as a specific implementation manner of the MEMS-based vehicle navigation method according to the embodiment of the present invention, on the basis of the foregoing embodiment, step S202 may further include:
s403: and placing the IMU gyroscope according to six positions to acquire the output information of the IMU.
Optionally, referring to fig. 5, as a specific implementation manner of the vehicle navigation method based on MEMS according to an embodiment of the present invention, on the basis of the foregoing embodiment, step S103 may be detailed as:
s501: and performing strapdown inertial navigation calculation on the output information of the IMU at the current moment.
S502: the state observations and the state covariance matrix are predicted by extended kalman filtering EKFs.
S503: and repeatedly executing the step of performing strapdown inertial navigation calculation on the output information of the IMU at the current moment until the IMU moment information meets a preset time condition, and determining a strapdown inertial navigation calculation result according to the output information of the IMU at the current moment, the predicted state observed quantity and the state covariance matrix.
In this embodiment, the purpose of repeatedly performing the step of performing strapdown inertial navigation solution on the output information of the IMU at the current time is to acquire the latest IMU output information, so as to avoid that the output information of the IMU acquired for the first time changes due to updating of the speed information, the attitude information, and the position information output by the IMU.
Optionally, referring to fig. 5, as a specific implementation manner of the vehicle navigation method based on the MEMS according to the embodiment of the present invention, on the basis of the foregoing embodiment, step S103 may further include:
s504: and modeling parameters of the extended Kalman filter EKF estimation as constant values, wherein the parameters of the extended Kalman filter EKF estimation comprise a GNSS lever arm, a GNSS installation angle, an IMU lever arm and an IMU installation angle.
In this embodiment, the GNSS lever arm, the GNSS mounting angle, the IMU lever arm, and the IMU mounting angle may be used as parameters for EKF filtering estimation to estimate in real time, so as to reduce the influence of mounting angle errors and lever arm errors on the integrated navigation system, improve the navigation accuracy of the system, and reduce the workload of the conventional integrated navigation system that needs to accurately measure the GNSS lever arm and the IMU lever arm. In this embodiment, the GNSS lever arm, the GNSS installation angle, the IMU lever arm, and the IMU installation angle are modeled as constant values, that is:
Figure BDA0001953386400000081
wherein lg、lbGNSS and IMU lever arms, ag、abGNSS and IMU mounting angles, respectively.
Optionally, as a specific implementation manner of the MEMS-based vehicle navigation method provided by the embodiment of the present invention, the state observation quantity includes a vehicle northeast velocity component, an acceleration component, and a position coordinate component.
Optionally, as a specific implementation manner of the vehicle navigation method based on the MEMS provided by the embodiment of the present invention, the state observation further includes a vehicle wheel speed, a heading angle rate, and vehicle gear information.
In this embodiment, on the basis of using kinematic constraints such as a vehicle northeast speed component, an acceleration component, a position coordinate component, and the like, information such as a wheel speed, a gear, a steering wheel angle, and the like in the vehicle body CAN information may be further fused, and a filtering observed quantity may be added to enhance the stability of the integrated navigation system.
The land vehicle comprises 2 kinematic constraints, the speed of the vehicle being 0 in the direction of the rotation axis of the wheels and perpendicular to the road or track surface. The forward speed of the vehicle CAN be corrected and compensated using the wheel speed in the body CAN message. Wherein the observation quantity correction model can be expressed as:
Figure BDA0001953386400000091
wherein c is a vehicle rear wheel coordinate system, b is an IMU coordinate system, vodoCAN wheel speed of the vehicle body.
For a differential odometer, the steering angle rate, i.e., heading angle rate, can be further determined by the wheel speed difference. The method for solving the speed of the rear wheel comprises the following steps:
Figure BDA0001953386400000092
wherein v isl，vlSpeeds, ω, of the left and right rear wheels, respectivelyodoIs the heading angular rate.
The observed quantity model of course angular rate is
Figure BDA0001953386400000093
If the vehicle body CAN information contains the steering wheel turn angle, a course angle observed quantity model CAN be constructed according to the steering wheel turn angle in the CAN information:
Figure BDA0001953386400000094
wherein h isodoThe steering wheel angle in the CAN message.
The gear signal can be used for pre-judging the acceleration state of the vehicle, and is used as a basis for adjusting and observing the noise variance matrix, so that the gear signal can also be used as one of state observed quantities.
From the above description, the embodiment of the invention constructs the observed quantity of the three-dimensional speed of the vehicle body by using the kinematic constraint of the vehicle and the wheel speed, and the observed quantity effectively maintain the measurement update of the combined navigation filter after the satellite navigation fails. Meanwhile, under the measurement and correction of the speed and the angle of the wheel speed course angle, the accuracy of the vehicle navigation course can be effectively improved, and the divergence of the course after the satellite navigation is invalid is restrained.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A vehicle navigation method based on MEMS is characterized by comprising the following steps:
initializing a vehicle navigation system;
preprocessing the measurement data of an inertial measurement unit IMU of a micro electro mechanical system MEMS to determine the output information of the IMU;
performing strapdown inertial navigation resolving according to the output information of the IMU;
realizing vehicle navigation according to the strapdown inertial navigation resolving result;
wherein, the calculating the strapdown inertial navigation according to the output information of the IMU comprises:
performing strapdown inertial navigation resolving on output information of the IMU at the current moment;
predicting a state covariance matrix through extended Kalman filtering EKF and state observation quantity;
repeatedly executing the step of performing strapdown inertial navigation solution on the output information of the IMU at the current moment until the IMU moment information meets a preset time condition, and determining a strapdown inertial navigation solution result according to the output information, the state observed quantity and the state covariance matrix of the IMU at the current moment;
the state observation quantity comprises a vehicle northeast direction speed component, an acceleration component position coordinate component, a vehicle wheel speed, a course angle speed and vehicle gear information;
wherein the preprocessing the IMU measurement data of the MEMS comprises the following steps:
carrying out temperature compensation on IMU measurement data according to a linear interpolation method;
performing cross coupling compensation on IMU measurement data according to a six-position method;
wherein the performing cross-coupling compensation on IMU measurement data according to a six-position method comprises:
determining an acceleration ideal output matrix and an actual output matrix of the IMU according to a six-position method and the output information of the IMU;
performing cross coupling compensation on IMU measurement data according to the ideal output matrix and the actual output matrix;
the method for determining the cross-coupling compensation parameter M comprises the following steps:
M＝LAT(AAT)-1
where A is the ideal output matrix and L is the actual output matrix.
2. The MEMS-based vehicle navigation method of claim 1, wherein the pre-processing inertial measurement unit IMU data further comprises:
the IMU measurement data is low pass filtered.
3. The MEMS-based vehicle navigation method of claim 1, wherein the temperature compensating IMU measurement data according to a linear interpolation method comprises:
acquiring output information and temperature output data of the IMU under a preset temperature step length in a preset temperature range;
establishing a linear relation between the IMU output information and the temperature output data in the current temperature step range within a preset temperature step;
and carrying out temperature compensation on IMU measurement data according to the linear relation.
4. The MEMS-based vehicle navigation method of claim 1, further comprising, prior to determining the acceleration ideal output matrix and the actual output matrix of the IMU according to a six-position method:
and placing the IMU gyroscope according to six positions to acquire the output information of the IMU.
5. The MEMS-based vehicle navigation method of claim 1, further comprising, prior to predicting the state covariance matrix by extended kalman filtering, EKF, and the state observations:
modeling parameters of the extended Kalman filter EKF estimation as constant values, wherein the parameters of the extended Kalman filter EKF estimation comprise a GNSS lever arm, a GNSS installation angle, an IMU lever arm and an IMU installation angle.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI724686B (en) * 2019-12-13 2021-04-11 國立成功大學 Positioning and orientation system and positioning and orientation method using high definition maps

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103712620A (en) * 2013-11-27 2014-04-09 北京机械设备研究所 Inertia autonomous navigation method by utilizing vehicle body non-integrity constraint
CN108168574A (en) * 2017-11-23 2018-06-15 东南大学 A kind of 8 position Strapdown Inertial Navigation System grade scaling methods based on speed observation

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101701825A (en) * 2009-09-28 2010-05-05 龙兴武 High-precision laser gyroscope single-shaft rotating inertial navigation system
RU2593432C1 (en) * 2015-05-19 2016-08-10 Акционерное общество "Московский институт электромеханики и автоматики" (АО "МИЭА") Method of increasing accuracy of strapdown inertial navigation system
CN107643534B (en) * 2017-09-11 2019-07-12 东南大学 A kind of dual rate kalman filter method based on GNSS/INS deep integrated navigation
CN107894232A (en) * 2017-09-29 2018-04-10 湖南航天机电设备与特种材料研究所 A kind of accurate method for locating speed measurement of GNSS/SINS integrated navigations and system
CN108535755B (en) * 2018-01-17 2021-11-19 南昌大学 GNSS/IMU vehicle-mounted real-time integrated navigation method based on MEMS

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103712620A (en) * 2013-11-27 2014-04-09 北京机械设备研究所 Inertia autonomous navigation method by utilizing vehicle body non-integrity constraint
CN108168574A (en) * 2017-11-23 2018-06-15 东南大学 A kind of 8 position Strapdown Inertial Navigation System grade scaling methods based on speed observation

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