CN110780326A - Vehicle-mounted integrated navigation system and positioning method - Google Patents
Vehicle-mounted integrated navigation system and positioning method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—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
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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
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—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
Abstract
The invention provides a vehicle-mounted integrated navigation system, which comprises an inertia measuring device, a navigation module and a navigation module, wherein the inertia measuring device comprises a gyroscope and an accelerometer; the integrated navigation computer is connected with the inertia measuring device, the satellite navigation module and the odometer, and an inertia navigation subsystem and a Kalman filter are arranged on the integrated navigation computer; the inertial navigation subsystem carries out inertial navigation resolving on the inertial navigation signal to obtain inertial navigation parameters; the Kalman filter can be used for dynamically estimating the errors of the inertial navigation parameters for the observation signals by switchably using information provided by the satellite navigation module or using the carrier motion speed provided by the odometer, and correcting the navigation parameters according to the errors. The vehicle-mounted integrated navigation system provided by the invention combines inertial navigation, satellite navigation and a mileometer, so that the resolving precision of the inertial navigation can be improved in a region with good satellite navigation signals, and high-precision position information can still be provided for vehicles in a region with reduced or unavailable satellite navigation positioning precision.
Description
Technical Field
The invention relates to a navigation system and a positioning method, in particular to a vehicle-mounted integrated navigation system and a positioning method.
Background
Navigation and positioning are important means for guaranteeing human traffic and military activities, and play a key role in controlling vehicles, aircrafts, ships and even underwater vehicles. For example, in a railway train control system, it is a basic requirement of the train control system to know the train position in real time with high accuracy. Particularly, under the conditions that the train running speed is faster and higher train number density is higher and higher, the whole network train is monitored and managed safely and efficiently without the support of a high-precision train positioning method and equipment. For another example, an autonomous vehicle does not leave a high-precision position information acquisition technique.
At present, the high-precision land vehicle positioning mainly adopts satellite navigation positioning technology. The Satellite Navigation System (GNSS) refers to a technology for performing Navigation and positioning on users on the ground, the sea, the air and the space by using a Navigation Satellite, and common Satellite Navigation systems include the american GPS System and the chinese beidou Navigation System. Satellite navigation has the advantages of all weather, high precision and the like, but has a plurality of defects. Firstly, satellite navigation signals are easy to be shielded, and positioning accuracy is deteriorated or even disabled in scenes such as indoor scenes, tunnels, urban high-rise forest road surfaces and the like; secondly, the output frequency of satellite navigation is limited, generally 1Hz to 5Hz, and the requirements under some scenes with rapidness and strong mobility cannot be met. In order to solve the problems of insufficient satellite navigation positioning output frequency and reduced usability in partial scenes, a combined navigation technology combining satellite navigation and inertial navigation appears.
An Inertial Navigation System (INS), also called an Inertial reference System, is an autonomous Navigation System that does not rely on external information, nor radiates energy to the outside (as in radio Navigation). The inertial navigation system belongs to the dead reckoning navigation mode, i.e. the position of the next point is reckoned from the position of a known point according to the continuously measured course angle and speed of the moving body, thus the current position of the moving body can be continuously measured. In an inertial navigation system, it is generally composed of a gyroscope for measuring attitude changes and an accelerometer for measuring acceleration changes. The output of the gyroscope is processed, and the current attitude information of the motion can be obtained. The acceleration of the moving body measured by the accelerometer is subjected to one-time integration on time to obtain the speed, and the speed is subjected to one-time integration on time to obtain the displacement.
Various modes such as flexible inertial navigation, optical fiber inertial navigation, laser inertial navigation, micro solid-state inertial instrument and the like have been developed for the inertial navigation system at present. The gyroscope is developed from a traditional wire-wound gyroscope to an electrostatic gyroscope, a laser gyroscope, a fiber optic gyroscope, a micromechanical gyroscope and the like. The laser gyroscope has the advantages of wide dynamic measurement range, good linearity, stable performance, good temperature stability and repeatability and always occupies a leading position in the high-precision application field. For vehicle navigation positioning equipment, although a laser gyro can provide better positioning accuracy, the expansion of the application field is limited due to the high price of the laser gyro.
The inertial navigation system is divided into a platform type inertial navigation system (the inertial measurement unit is arranged on a platform body of the inertial platform) and a strapdown type inertial navigation system (the inertial measurement unit is directly arranged on the carrier) according to the installation mode of the inertial measurement unit on the carrier. The latter saves a platform, the working condition of the instrument is poor (the precision is influenced), and the calculation workload is large.
At present, for vehicle navigation positioning equipment, the strapdown inertial navigation composed of low-cost MEMS (micro electro mechanical system) inertial measurement units (IMU, including gyroscopes, accelerometers, and the like) is a main application mode, and with the increasing precision of MEMS inertial units, the MEMS-based strapdown inertial navigation equipment will be more and more popular.
The inertial navigation system has the following advantages: 1. because the system is an autonomous system which does not depend on any external information and does not radiate energy to the outside, the system has good concealment and is not influenced by external electromagnetic interference; 2. the all-weather all-time working platform can work in the air, on the earth surface or even underwater all day long; 3. the navigation system can provide position, speed, course and attitude angle data, and the generated navigation information has good continuity and low noise; 4. high data updating rate, short-term precision and good stability. However, inertial navigation systems have some drawbacks: 1. the navigation information is generated through integration, so that the positioning error is increased along with the time, and the long-term accuracy is poor; 2. a long initial alignment time is required before each use; 3. the price of the equipment is expensive; 4. time information cannot be given.
Since the respective advantages and disadvantages of satellite navigation and inertial navigation have strong complementarity, the combined navigation technology that combines satellite navigation and inertial navigation has gained wide application in recent years [ stringent. vehicle-mounted autonomous positioning and orientation system research [ D ]. seian: northwest university of industry 2006, [ husband, liu fuga, gao shi, yang yi ] improved strong tracking UKF algorithm and its application in INS/GPS combined navigation [ J ] china technical inertial reports 2014,22(5):634 + 639 ], [ zhang + MEMS-based unmanned aerial vehicle GPS/SINS combined navigation [ D ]. beijing university of technology, 2015 ], and [ Jaradat m., [ a ] -afez.m. improved MEMS-IMU/GPS integrated systems for land vehicle navigation [ J ]. IEEE Sensors Journal 2014,14(5):1545 + 1554 ]. The GNSS/INS integrated navigation system uses an integrated navigation algorithm to integrate the information of the inertial navigation unit and the information of the satellite navigation system to compensate the error of the inertial element and correct the position, the speed and the attitude signal of the carrier, thereby forming the integrated navigation system with high precision, compact structure and low cost.
However, the GNSS/INS integrated navigation system still has problems in practical applications. In the area where the GNSS signal is weak or lost, the accurate reference signal is not provided by the GNSS, and the INS can only navigate alone, so that the error of the INS is accumulated continuously, and the positioning error is dispersed. For some scenarios where the accuracy requirement is high and areas where the satellite navigation system is not available are experienced, the GNSS/INS integrated navigation system becomes unsuitable. For example, the above-mentioned train may pass through a region where the satellite navigation accuracy is reduced or unavailable, such as a valley, a tunnel or a station yard with a ceiling, and the GNSS/INS combined navigation may not provide sufficient accuracy.
Disclosure of Invention
The invention aims to provide a novel integrated navigation method and equipment to solve the problem of high-precision positioning of an integrated navigation system combining satellite navigation and inertial navigation when the satellite navigation availability is reduced or unavailable.
In order to achieve the above object, the present invention provides a vehicle-mounted integrated navigation system, which is mounted on a carrier, and includes: inertial measurement devices including gyroscopes and accelerometers; a satellite navigation module configured to receive and process satellite navigation signals; the odometer is set to output the motion speed of the carrier; the integrated navigation computer is connected with the inertial measurement device, the satellite navigation module and the odometer, and an inertial navigation subsystem and a Kalman filter are installed on the integrated navigation computer; the inertial navigation subsystem is set to carry out inertial navigation solution on the inertial navigation signal measured by the inertial measurement device to obtain inertial navigation parameters; the Kalman filter is set up to switch to dynamically estimate the error of the inertial navigation parameter by using the position and speed information provided by the satellite navigation module or the carrier motion speed provided by the odometer as an observation signal, and correct the inertial navigation parameter according to the error.
The satellite navigation module comprises a satellite navigation antenna, a differential data transmission antenna and a satellite navigation board card connected with the satellite navigation antenna and the differential data transmission antenna.
The gyroscope is an MEMS gyroscope, the accelerometer is an MEMS accelerometer, and the inertial navigation subsystem is a strapdown inertial navigation system.
In another aspect, the present invention provides a vehicle-mounted integrated navigation positioning method, including:
s1: the vehicle-mounted integrated navigation system is built on a carrier, initialization and alignment of the vehicle-mounted integrated navigation system are completed, and initial parameters of a Kalman filter of the vehicle-mounted integrated navigation system are set;
s2: measuring the angular velocity of a carrier coordinate system by adopting a gyroscope of the vehicle-mounted integrated navigation system, measuring the specific force of the carrier coordinate system by adopting an accelerometer of the vehicle-mounted integrated navigation system, then carrying out inertial navigation calculation by adopting an inertial navigation subsystem and outputting inertial navigation parameters, wherein the inertial navigation parameters comprise attitude, velocity and position;
s3: receiving satellite navigation signals by adopting a satellite navigation module of the vehicle-mounted integrated navigation system, and judging whether available satellite navigation signals exist or not;
s4: according to the judgment result, when available satellite navigation signals exist, the satellite navigation signals are processed to obtain the position and the speed of the carrier measured by the satellite navigation module, and the integrated navigation calculation of the satellite navigation module and the inertial navigation subsystem is executed, otherwise, the integrated navigation calculation of the inertial navigation subsystem and the odometer is executed;
s5: obtaining a Kalman filter of the vehicle-mounted integrated navigation system, comprising:
s51: determining a state equation of a Kalman filter;
s52: determining a state transition matrix F (t), a system noise distribution matrix G (t) and a system white noise vector W (t) of a state equation of the Kalman filter;
s53: discretizing the system and performing Kalman filtering to obtain an updated Kalman filtering state vector;
s6: and correcting the inertial navigation parameters according to the updated Kalman filtering state vector, wherein the correction comprises the correction of platform angle, speed and position, and the correction of gyroscope zero offset and accelerometer zero offset.
The inertial navigation solution comprises an attitude update solution, a speed update solution and a position update solution, the attitude update solution is carried out by adopting a quaternion method or a rotating vector method, the speed update solution is calculated by utilizing the specific force of a carrier coordinate system measured by an accelerometer, and the position update settlement comprises the step of updating the position of the navigation coordinate system according to the change speed of the position of the navigation coordinate system.
In step S4, a combined navigation solution of the satellite navigation module and the inertial navigation subsystem is performed, including:
s41: constructing measurement information taking the position and the speed of the carrier measured by the satellite navigation module as reference, wherein the measurement information comprises the position and the speed of the carrier measured by the satellite navigation module;
s42: establishing a position observation equation and a velocity observation equation to estimate errors of the position observation quantity and the velocity observation quantity by taking a difference between the position in the inertial navigation parameters in the step S2 and the position measured by the satellite navigation module as a position observation quantity and taking a difference between the velocity in the inertial navigation parameters in the step S2 and the velocity measured by the satellite navigation module as a velocity observation quantity;
the position observation equation is Z
p(t)＝H
p(t)X(t)+V
p(t), where X (t) is the Kalman filtering state vector, H
p(t) is the observation matrix of the position observation equation, V
p(t) is the observation noise of the position observation equation;
the velocity observation equation is Z
v(t)＝H
v(t)X(t)+V
v(t), X (t) is the Kalman filtering state vector, H
v(t) is the observation matrix of the velocity observation equation, V
v(t) is the observation noise of the velocity observation equation.
In S42, the position observed quantity zp (t) is:
wherein L is
I、λ
I、h
IIs the position in the inertial navigation parameter in the unit of meter, L
G、λ
G、h
GThe measured position of the satellite navigation module is measured in meters, and L, lambda and h are latitude, longitude and altitude of a navigation coordinate system in meters and R
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter;
and the velocity observed quantity Z
v(t) is
Wherein the content of the first and second substances,
error of velocity, δ v, measured for the satellite navigation module 2
nIs the error in velocity in the inertial navigation parameters.
In step S4, the performing a combined navigation solution of the inertial navigation subsystem and the odometer includes:
step S41': constructing measurement information taking the carrier motion speed output by the odometer as reference, wherein the measurement information is a transformation result of the carrier motion speed output by the odometer under a navigation coordinate system;
step S42': at a velocity in the inertial navigation parameter
Speed of movement of the carrier in conjunction with odometer output
The difference is used as the observed speed Z
v(t) establishing a velocity observation equation in the kalman filter to estimate the error of the velocity observation; the velocity observation equation is Z
v(t)＝H
v(t)X(t)+V
v(t), X (t) is the Kalman filtering state vector, H
v(t) is the observation matrix of the velocity observation equation, V
v(t) is a velocity observationObservation noise of the equation; the velocity observed quantity is
Wherein the content of the first and second substances,
measuring error, delta v, for odometer under navigation coordinate system
nIs the error in velocity in the inertial navigation parameters.
In the step S51, the state vector of the kalman filter is
Wherein the content of the first and second substances,
and
respectively representing platform angle errors of an east direction E, a north direction N and a sky direction U; delta v
E、δv
NAnd δ v
URespectively representing the speed errors of an east direction E, a north direction N and a sky direction U; δ L, δ λ, and δ h represent errors of latitude L, longitude λ, and altitude h, respectively;
and
respectively representing zero offset of the gyroscope in an x axis, a y axis and a z axis;
and
respectively representing zero offset of the accelerometer on an x axis, a y axis and a z axis;
and the state equation of the Kalman filter is as follows:
X(t+1)＝F(t)X(t)+G(t)W(t)，
wherein t is time in seconds; f (t) is a state transition matrix of 15 × 15 dimensions; g (t) is a 15 × 6-dimensional system noise distribution matrix, and W (t) is a 6 × 1-dimensional system white noise vector.
In the step S52, the state transition matrix F
INSThe method is constructed by adopting an error propagation equation, wherein the error propagation equation comprises a platform angle error equation, a speed error equation, a position error equation, a constant value error equation and a noise equation.
The vehicle-mounted integrated navigation system provided by the invention combines the inertial navigation, the satellite navigation and the odometer, so that the vehicle-mounted integrated navigation system can provide high-precision position information in a region with good satellite navigation signals through the combination of the satellite navigation and the inertial navigation.
Drawings
FIG. 1 is a block diagram of the components of an in-vehicle integrated navigation system according to one embodiment of the present invention;
FIG. 2 is a flowchart of a vehicle-mounted integrated navigation positioning method according to an embodiment of the present invention;
fig. 3 is a detailed logic block diagram of the vehicle-mounted integrated navigation positioning method shown in fig. 2.
Detailed Description
The present invention will be further described with reference to the following specific examples. It should be understood that the following examples are illustrative only and are not intended to limit the scope of the present invention.
Fig. 1 shows an on-board integrated navigation system according to an embodiment of the present invention, which is installed on a carrier (usually a vehicle) and realizes the acquisition of high-precision position information of a train or a car by combining satellite navigation, strapdown inertial navigation and odometer, and comprises an inertial measurement unit 1, a satellite navigation module 2:
the inertial measurement unit 1 is used for measuring inertial navigation signals and comprises a gyroscope 11 and an accelerometer 12; wherein the gyroscope 11 is a MEMS gyroscope for measuring the angular velocity (turning or rolling) of the carrier, and the accelerometer 12 is a MEMS accelerometer for measuring the specific force (i.e. acceleration) of the carrier.
The satellite navigation module 2 is configured to receive and process satellite navigation signals, and includes a satellite navigation antenna 21, a differential data transmission antenna 22, and a satellite navigation board 23 connected to both the satellite navigation antenna 21 and the differential data transmission antenna 22, where the satellite navigation antenna 21 is configured to receive the satellite navigation signals; the differential data transmission antenna 22 is used for receiving differential data of satellite navigation; the satellite navigation board card 21 processes the satellite navigation signal and the differential data of the satellite navigation, and outputs corresponding information such as position, speed, course angle and the like.
The odometer 3 is arranged to output linear velocity information of the vehicle movement, i.e. the carrier movement velocity.
And the integrated navigation computer 4 is connected with the inertial measurement device 1, the satellite navigation module 2 and the odometer 3, and is provided with an inertial navigation subsystem 41 and a Kalman filter 42. The integrated navigation computer 4 processes the inertial navigation signal measured by the inertial measurement device 1, the position, speed, course angle information output by the satellite navigation module 2 and the linear velocity information output by the odometer 3, the inertial navigation subsystem 41 is configured to perform inertial navigation solution on the inertial navigation signal measured by the inertial measurement device 1 to obtain inertial navigation parameters, the kalman filter 41 is configured to perform integrated navigation filtering calculation, that is, the kalman filter 41 is configured to switchably use the position and speed information provided by the satellite navigation module 2 or the carrier motion speed provided by the odometer 3 as an observation signal to dynamically estimate the error of the inertial navigation parameters, and corrects the inertial navigation parameters according to the error to obtain the integrated navigation parameters, wherein the integrated navigation parameters comprise information such as position, speed and the like. The inertial navigation subsystem 41 is preferably a strapdown inertial navigation system.
And the power supply module 5 is used for supplying power to the inertia measurement device 1, the satellite navigation module 2, the odometer 3 and the combined navigation computer 4.
Therefore, the vehicle-mounted integrated navigation method combines the existing inertial navigation technology with the satellite navigation module 2, combines the inertial navigation technology with the odometer 3, and adopts the Kalman filter 41 to be unified, so that the structure is simple, and the combination precision is higher.
Fig. 2-3 show the vehicle-mounted integrated navigation positioning method implemented based on the vehicle-mounted integrated navigation system described above. The method realizes high-precision positioning of the train or the automobile in various line scenes by combining the satellite navigation, the strapdown inertial navigation and the odometer. The method comprises the following steps:
step S1: the system initialization and alignment specifically comprises: the vehicle-mounted integrated navigation system is built on a carrier, initialization and alignment of the vehicle-mounted integrated navigation system are completed, and initial parameters of a Kalman filter 41 of the vehicle-mounted integrated navigation system are set;
step S2: the angular velocity of a carrier coordinate system is measured by adopting the gyroscope 11 of the vehicle-mounted integrated navigation system, the specific force of the carrier coordinate system is measured by adopting the accelerometer 12 of the vehicle-mounted integrated navigation system, then the inertial navigation subsystem 41 is adopted to carry out inertial navigation calculation and output inertial navigation parameters, the inertial navigation calculation comprises attitude updating calculation, velocity updating calculation and position updating calculation, and the inertial navigation parameters comprise the attitude, the velocity and the position output by the inertial navigation subsystem 41.
The attitude updating and resolving refers to acquiring an attitude angle and an attitude matrix of the carrier by using measurement data of the gyroscope 11 and the accelerometer 12. And setting the three attitude angles of the carrier as a roll angle gamma, a pitch angle theta and a course angle psi. The attitude updating calculation can be carried out by adopting a quaternion method or a rotating vector method.
The speed updating calculation means that the speed of the carrier is updated by using the specific force measured by the accelerometer. Let the speed of the carrier be v
n＝[v
E,v
N,v
U]
T，v
E,v
N,v
UThe velocity components of the vector are east E, north N and sky U, respectively. Velocity update solutionThe differential of the velocity, i.e. the specific force of the carrier coordinate system measured by the accelerometer 12, is used for the calculation. Specifically, the specific force equation can be used to obtain the acceleration of the carrier:
wherein the content of the first and second substances,
is the specific force of a carrier coordinate system measured by an accelerometer and has the unit of meter/second
2；
The unit is radian/second which is the rotation angular velocity of the navigation coordinate system rotating along with the earth,
the angular speed of the navigation coordinate system relative to the rotation of the earth is expressed in radian/second; g
nIs the acceleration of gravity in meters per second
2；
Is a transformation matrix from the carrier coordinate system (subscript b) to the navigation coordinate system (superscript n) with 3 rows and 3 columns.
The specific force of the carrier coordinate system measured by the accelerometer
Rotation angular velocity of navigation coordinate system rotating with earth
Angular velocity of navigation coordinate system relative to earth rotation
Acceleration of gravity g
nRespectively as follows:
g
n＝[0,0,-g]
T，
wherein, L, lambda and h are respectively latitude, longitude and height of the navigation coordinate system, the unit of the latitude and longitude is radian, and the unit of the height is meter; r
M、R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter; omega
ieIs the angular velocity of rotation of the earth in radians.
Acceleration of the carrier
The velocity increment of the carrier can be obtained by integrating once.
And the position updating settlement comprises updating the position of the navigation coordinate system according to the change speed of the position of the navigation coordinate system, wherein the position of the navigation coordinate system comprises latitude L, longitude lambda and altitude h of the navigation coordinate system. Wherein, the change speed of the position of the navigation coordinate system is as follows:
wherein the content of the first and second substances,
respectively the change speeds of the latitude L, the longitude lambda and the altitude h of the navigation coordinate system; r
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter; l, h is the latitude and altitude of the navigational coordinate system; v. of
E,v
N,v
UThe velocity components of the vector are east E, north N and sky U, respectively.
Further, the step S2 further includes: after updating the position of the navigation coordinate system, the meridian and prime unitary curvature radius R is calculated according to the following formula
M、R
NAnd (6) updating.
Meridian and prime radius of curvature R
M、R
NRespectively as follows:
wherein R is
eThe length of the earth's major semi-axis is in meters; f is the ellipticity of the earth.
Step S3: receiving satellite navigation signals by adopting a satellite navigation module 2 of the vehicle-mounted integrated navigation system, and judging whether available satellite navigation signals exist or not;
step S4: according to the judgment result, when available satellite navigation signals exist, the satellite navigation signals are processed to obtain the position and the speed of the carrier measured by the satellite navigation module 2, the combined navigation calculation of the satellite navigation module 2 and the inertial navigation subsystem 41 is executed, and otherwise, the combined navigation calculation of the inertial navigation subsystem 41 and the odometer 3 is executed.
The executing of the integrated navigation solution of the satellite navigation module 2 and the inertial navigation subsystem 41 specifically includes:
step S41: constructing measurement information with reference to the position and velocity of the carrier measured by the satellite navigation module 2, the measurement information including the position and velocity of the carrier measured by the satellite navigation module 2, the position including latitude, longitude and altitude, and the velocity may be 3 velocity components v of the carrier
n＝[v
E,v
N,v
U]
TIt may also be the linear velocity v of the carrier in the direction of advance.
Wherein, when the speed measured by the satellite navigation module 2 is the linear speed v of the carrier in the forward direction, the step S4 further includes: with the result of the attitude update calculation in step S2, the linear velocity v is decomposed into 3 velocity components v of the carrier
n＝[v
E,v
N,v
U]
T. Whereby the carrier has 3 velocity components v
nComprises the following steps:
wherein v is
nFor the 3 velocity components of the carrier,
v is the attitude matrix, i.e. the solved attitude matrix of the carrier, and v is the linear velocity of the carrier in the forward direction measured by the satellite navigation module 2.
Step S42: estimating an error of the observed quantity by an indirect method, comprising: establishing a position observation equation and a velocity observation equation to estimate errors of the position observation amount and the velocity observation amount, with a difference between the position in the inertial navigation parameters in step S2 and the position measured by the satellite navigation module 2 in step S3 as a position observation amount, and a difference between the velocity in the inertial navigation parameters in step S2 and the velocity measured by the satellite navigation module 2 as a velocity observation amount;
the position observation equation is Z
p(t)＝H
p(t)X(t)+V
p(t)。
Wherein X (t) is a Kalman filtering state vector, H
p(t) is the observation matrix of the position observation equation, V
p(t) is the observation noise of the position observation equation.
Observation matrix H of position observation equation
p(t) and observed noise V
p(t) is determined by the following method:
if X is the state vector of the kalman filter to be estimated, which is a column vector of 15 rows and 1 columns, the state vector X of the kalman filter is:
of which the first 3 components
Platform angle errors of east E, north N and sky U, respectively; delta v
E,δv
N,δv
UVelocity errors of east E, north N and sky U, respectively; δ L, δ λ, δ h are errors of latitude L, longitude λ and altitude h, respectively;
is the 3-axis zero-offset of the gyroscope 11;
is the 3-axis zero offset of the accelerometer 12.
Then, when the satellite navigation is available, the difference between the position in the inertial navigation parameters and the position measured by the satellite navigation module 2 is used as the position observed quantity Z of the position observed equation
p(t)；
Setting the position in the inertial navigation parameter as L due to error between the two outputs
I、λ
I、h
IThe position measured by the satellite navigation module 2 is L
G、λ
G、h
GTheir relationships with the real positions L, λ, h are respectively:
where δ L, δ λ, δ h are positioning errors of the inertial navigation subsystem 41, and the unit is meter, δ L ═ δ L [ [ δ L ═ δ L [ ]
Eδl
Nδl
U]Is the positioning error of the satellite navigation module 2, in meters, R
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter.
The position observation Z
p(t) is:
wherein L is
I、λ
I、h
IIs the position in the inertial navigation parameter in the unit of meter, L
G、λ
G、h
GThe position measured by the satellite navigation module 2 is expressed in meters, and L, lambda and h are real positions, namely latitude, longitude and altitude of a navigation coordinate systemIs located at the position of rice, R
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter.
The variable-to-position observed quantity Z adopting the two formulas
p(t) formula, then the position observation Z
p(t) is:
wherein L is
I、λ
I、h
IIs the position in the inertial navigation parameter in the unit of meter, L
G、λ
G、h
GThe position measured by the satellite navigation module 2 is expressed in meters, and L, lambda and h are real positions, i.e. latitude, longitude and altitude of the navigation coordinate system, expressed in meters, R
MAnd R
NThe radius of curvature is meridian and prime unitary curvature with the unit of meter and delta l
E、δl
N、δl
UIs the positioning error of the satellite navigation module 2 in meters.
As can be seen from the above equation (4), the observation matrix H of the position observation equation
p(t) is:
observation noise V of position observation equation
p(t)＝δl
TEach dimension is white noise.
The velocity observation equation is Z
v(t)＝H
v(t)X(t)+V
v(t)，
Wherein X (t) is a Kalman filtering state vector, H
v(t) is the observation matrix of the velocity observation equation, V
v(t) is the observation noise of the velocity observation equation.
Observation matrix H of velocity observation equation
v(t) and observed noise V
v(t) is determined as follows:
setting the speed in the inertial navigation parameters as
Satellite navigation module 2 measurementsTo a speed of
It has a relation with the real speed of
Wherein the content of the first and second substances,
for errors in the velocity in the inertial navigation parameters,
is the velocity in the inertial navigation parameters,
the velocity measured by the satellite navigation module 2.
And taking the difference between the speed in the inertial navigation parameters and the speed measured by the satellite navigation module 2 as the speed observed quantity of the speed observation equation. Velocity observed quantity Z
v(t) is:
wherein the content of the first and second substances,
error of velocity, δ v, measured for the satellite navigation module 2
nIs the error in velocity in the inertial navigation parameters.
Observation matrix H
v(t) can be obtained from the above formula. Observation matrix H of velocity observation equation
v(t) is:
observation noise of velocity observation equation
Is white noise.
In performing the combined navigation solution of the satellite navigation module 2 and the inertial navigation subsystem 41, the step S4 may further include: the position observation equation and the velocity observation equation are combined to obtain a combined observation equation, and the combined observation equation can simultaneously estimate the difference between the position in the inertial navigation parameters and the position measured by the satellite navigation module 2 and the corresponding velocity difference. Thus, when satellite navigation signals are available, the combined observation equation is:
wherein Z is
p(t) is a position observed quantity, H
p(t) is the observation matrix of the position observation equation, V
p(t) is the observation noise of the position observation equation, Z
v(t) is the velocity observed quantity, H
v(t) is the observation matrix of the velocity observation equation, V
v(t) is the observation noise of the velocity observation equation.
The integrated navigation calculation of the inertial navigation subsystem 41 and the odometer 3 specifically includes:
step S41': and constructing measurement information by taking the carrier motion speed output by the odometer as a reference, wherein the measurement information is a transformation result (comprising 3 speed components) of the carrier motion speed output by the odometer 3 in a navigation coordinate system.
The carrier motion speed is the linear speed of the carrier advancing, and decomposition is carried out by means of the attitude angle when processing is carried out under the navigation coordinate system.
Firstly, because the moving direction of the land automobile or the train is always collinear with the forward direction of the carrier coordinate system, only the carrier moving speed v equal to the output of the odometer is arranged under the carrier coordinate system
DI.e. the speed of movement of the carrier as output by the odometer 3 in the coordinate system of the carrier
Can be expressed as
Reusing attitude matrices
And converting the carrier motion speed output by the odometer 3 into a navigation coordinate system to obtain measurement information under the navigation coordinate system. The measurement information is:
wherein the content of the first and second substances,
is the carrier movement speed output by the odometer 3,
in the form of a matrix of poses,
as a result of the transformation of the vehicle motion speed output by the odometer 3 in the navigation coordinate system,
step S42': at a velocity in the inertial navigation parameter
Speed of carrier movement output by odometer 3
The difference is used as the observed speed Z
v(t) establishing a velocity observation equation in the kalman filter 42 to estimate the error of the velocity observation;
the velocity observation equation is Z
v(t)＝H
v(t)X(t)+V
v(t)，
Wherein X (t) is a Kalman filtering state vector, H
v(t) is the observation matrix of the velocity observation equation, V
v(t) is the observation noise of the velocity observation equation.
The observed quantity of velocity is
Wherein the content of the first and second substances,
the measurement error of the odometer under the navigation coordinate system is satisfied
σ
DIs v is
DStandard deviation of (d), δ v
nIs the error in velocity in the inertial navigation parameters.
The observation matrix of the corresponding velocity observation equation is:
step S5: obtaining the kalman filter 42 of the vehicle-mounted integrated navigation system specifically includes:
step S51: determining a state equation of a Kalman filter 42 of the vehicle-mounted integrated navigation system;
since the present application adopts the 15-dimensional vector described below as the state vector of kalman filtering,
wherein the content of the first and second substances,
and
respectively representing platform angle errors of an east direction E, a north direction N and a sky direction U; delta v
E、δv
NAnd δ v
URespectively representing the speed errors of an east direction E, a north direction N and a sky direction U; δ L, δ λ, and δ h represent errors of latitude L, longitude λ, and altitude h, respectively;
and
respectively representing zero offset of the gyroscope in an x axis, a y axis and a z axis;
and
representing the zero offset of the accelerometer in the x, y and z axes, respectively.
Thus, the state equation for the Kalman filter 42 is:
X(t+1)＝F(t)X(t)+G(t)W(t)
wherein t is time in seconds; f (t) is a state transition matrix of 15x15 dimensions, determined by an error equation; g (t) is a 15 × 6-dimensional system noise distribution matrix, and W (t) is a 6 × 1-dimensional system white noise vector.
Step S52: determining a state transition matrix F (t), a system noise distribution matrix G (t) and a system white noise vector W (t) of the state equation of the Kalman filter 42;
determination of the state transition matrix f (t):
Wherein, F
INSIs a state transition matrix corresponding to the first 9 components of the 9 basic navigation parameters (i.e., corresponding to the first 9 attitude angle parameters, 3 velocity parameters and 3 position parameters, 9 dimensions in total) in the state vector x (t) of kalman filtering, and the state transition matrix F
INSAnd adopting an error propagation equation construction.
The error propagation equations comprise a platform angle error equation, a speed error equation, a position error equation, a constant value error equation and a noise equation.
Wherein the errors in the platform angle are the carrier coordinatesThe angular difference between the system and the inertial navigation platform coordinate system, in phi
n＝[φ
Eφ
Nφ
U]
TRepresenting the platform angle, since the expression for the platform angle error is:
wherein phi is
n＝[φ
Eφ
Nφ
U]
TIs the angle of the platform, omega
ieIs the rotational angular velocity of the earth. L is the latitude of the carrier position (i.e. the position of the navigation coordinate system) in radians;
the vector is shown as moving near the earth's surface, and the symbol δ represents the error of the corresponding quantity, which is the rotation of the navigation system caused by the curvature of the earth's surface.
The error of the rotational angular velocity of the earth is as follows:
wherein, ω is
ieIs the rotational angular velocity of the earth, and L is the latitude of the navigation coordinate system.
The error of the navigation system rotation is:
wherein the content of the first and second substances,
representing the rotation of the navigation system caused by the curvature of the earth surface when the carrier moves near the earth surface; r
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter; l, h is the latitude and altitude of the navigational coordinate system; v. of
E,v
NThe east E and north N velocity components of the carrier, respectively.
Accordingly, a platform error angle equation is obtained, which is:
wherein, ω is
ieIs the rotational angular velocity of the earth; r
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter; l, h is the latitude and altitude of the navigational coordinate system; v. of
E,v
N,v
UThe velocity components of the vector are east E, north N and sky U, respectively.
The velocity error is derived from the error of the velocity differential:
and expanding the component form to obtain a speed error equation, wherein the speed error equation is as follows:
wherein, ω is
ieIs the rotational angular velocity of the earth; r
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter; l, h is the latitude and altitude of the navigational coordinate system; v. of
E,v
N,v
UThe velocity components phi of the carrier in the east direction E, the north direction N and the sky direction U respectively
n＝[φ
Eφ
Nφ
U]
TIs a platform angle.
By
The position error equation can be found as:
wherein R is
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter; l, h is the latitude and altitude of the navigational coordinate system; v. of
E,v
N,v
UThe velocity components of the vector are east E, north N and sky U, respectively.
The constant error refers to the random drift of the gyroscope and the zero offset of the accelerometer, and the random error refers to the Gaussian noise of the gyroscope and the accelerometer. Since both the constant error and the random error are relative to the carrier, modeling in the navigation coordinate system should also translate the errors into the navigation coordinate system.
Let the random drift of the gyroscope be ε
bThe noise is omega
g(ii) a The accelerometer has zero bias of
The noise being omega
aThen, the constant error equation and the noise equation in the navigation coordinate system are respectively:
from this, the state transition matrix f (t) can be determined according to the error propagation equation described above.
The system noise distribution matrix g (t) is:
the system white noise vector W (t) is:
W(t)＝[ω
gxω
gyω
gzω
axω
ayω
az]
Twherein, ω is
gx、ω
gy、ω
gzWhite noise, omega, for gyroscopes
ax、ω
ay、ω
azIs the white noise of the accelerometer.
Step S53: and carrying out discretization and Kalman filtering of the system to obtain an updated Kalman filtering state vector.
When the discretization of the system is carried out, the discretized system state equation and the discretized observation equation are respectively as follows:
X
k＝Φ
k/k-1X
k-1+Γ
k-1W
k-1
Z
k＝H
kX
k+V
k
where k denotes the current time of discretization, k-1 denotes the last update time of discretization, Φ denotes the state transition matrix of discretization, and Γ denotes the system noise propagation matrix of discretization. Z
kIs a measured value, H
kIs a measurement matrix, V
kTo measure noise.
Performing Kalman filtering, specifically comprising the following steps:
and (3) state one-step prediction: x
k/k-1＝φ
k/k-1X
k-1
One-step prediction of mean square error:
and (3) calculating a filtering gain:
and (3) state estimation: x
k＝X
k/k-1+K
k(Z
k-H
kX
k/k-1)
Mean square error estimation:
wherein the output X of the Kalman filter 42 is obtained
kI.e. the new state vector after kalman filter estimation.
X
k-1Is the state estimate at the last time instant (k-1); x
kIs the updated (current time k) state estimate; x
k/k-1Is based on the state estimate X at the last time k-1
k-1And making a state predicted value of the current time k.
The matrix P is a variance matrix (15X15 dimensions) of the state vector X, with P ═ E { XX
T}. Accordingly, P
k-1Is the variance matrix at the last time instant (k-1); p
kIs the updated (current time k) variance matrix.
K
kIs a kalman filter gain matrix.
Q
k-1The system noise variance matrix is set at system initialization. R
kThe noise variance matrix is measured and set during system initialization or dynamically determined according to the measurement precision indication of the position and the speed provided by the satellite navigation module 2; or according to the speed measurement precision of the odometer provided by the odometer.
Step S6: and correcting the parameters, specifically comprising: and correcting the inertial navigation parameters according to the updated Kalman filtering state vector, wherein the correction comprises the correction of platform angle, speed and position, and the correction of gyroscope zero offset and accelerometer zero offset.
Let x (k) be the output of the kalman filter 42, i.e., the updated state vector of the kalman filter:
since the correction of the platform angle is embodied in the correction of the attitude matrix, the correction of the platform angle includes: error vector of platform angle
Converting to a platform error attitude matrix
Then with the current attitude matrix
Multiplying to obtain a corrected attitude matrix: that is to say that the first and second electrodes,
wherein the content of the first and second substances,
i is an identity matrix of 3 rows and 3 columns for the platform angle error vector.
2) Correction of velocity, comprising: the velocity v of the previous moment
E(k-1),v
N(k-1),v
U(k-1) subtracting the velocity error δ v in the updated Kalman filter state vector
E,δv
N,δv
UObtaining a corrected velocity v
E(k),v
N(k),v
U(k) In that respect That is to say that the first and second electrodes,
3) correction of position, comprising: and subtracting the position errors delta L, delta lambda and delta h in the updated Kalman filter state vector from the position L (k-1), lambda (k-1) and h (k-1) at the previous moment to obtain the corrected positions L (k), lambda (k) and h (k). That is to say that the first and second electrodes,
4) correction of gyroscope zero bias, comprising: zero offset of last time
Subtracting gyroscope zero bias error in updated Kalman filter state vector
Obtaining the corrected zero offset of the gyroscope
That is to say that the first and second electrodes,
5) accelerometer zero offset correction, comprising: zero-bias the accelerometer at the last moment
Subtracting accelerometer zero bias error in updated Kalman filter state vector
Obtaining a new accelerometer zero offset
That is to say that the first and second electrodes,
the above embodiments are merely preferred embodiments of the present invention, which are not intended to limit the scope of the present invention, and various changes may be made in the above embodiments of the present invention. All simple and equivalent changes and modifications made according to the claims and the content of the specification of the present application fall within the scope of the claims of the present patent application. The invention has not been described in detail in order to avoid obscuring the invention.
Claims (10)
1. A vehicle-mounted integrated navigation system, which is arranged on a carrier, is characterized by comprising:
an inertial measurement device (1) comprising a gyroscope (11) and an accelerometer (12);
a satellite navigation module (2) arranged to receive and process satellite navigation signals;
the odometer (3) is set to output the motion speed of the carrier; and
the integrated navigation computer (4) is connected with the inertial measurement device (1), the satellite navigation module (2) and the odometer (3), and is provided with an inertial navigation subsystem (41) and a Kalman filter (42); the inertial navigation subsystem (41) is arranged to carry out inertial navigation calculation on the inertial navigation signal measured by the inertial measurement device (1) to obtain inertial navigation parameters; the Kalman filter (41) is arranged to estimate dynamically the errors of the inertial navigation parameters by switchably using the position and speed information provided by the satellite navigation module (2) or the carrier motion speed provided by the odometer (3) as observation signals, and to correct the inertial navigation parameters according to the errors.
2. The vehicle-mounted integrated navigation system according to claim 1, wherein the satellite navigation module (2) comprises a satellite navigation antenna (21), a differential data transmission antenna (22), and a satellite navigation board card (23) connected with the satellite navigation antenna (21) and the differential data transmission antenna (22).
3. The on-board integrated navigation system according to claim 1, characterized in that the gyroscope (11) is a MEMS gyroscope, the accelerometer (12) is a MEMS accelerometer, and the inertial navigation subsystem (41) is a strapdown inertial navigation system.
4. A vehicle-mounted integrated navigation positioning method is characterized by comprising the following steps:
step S1: building a vehicle-mounted integrated navigation system according to one of claims 1 to 3 on a carrier, completing initialization and alignment of the vehicle-mounted integrated navigation system, and completing setting of initial parameters of a Kalman filter (41) of the vehicle-mounted integrated navigation system;
step S2: measuring the angular velocity of a carrier coordinate system by adopting a gyroscope (11) of the vehicle-mounted integrated navigation system, measuring the specific force of the carrier coordinate system by adopting an accelerometer (12) of the vehicle-mounted integrated navigation system, then carrying out inertial navigation calculation by adopting an inertial navigation subsystem (41) and outputting inertial navigation parameters, wherein the inertial navigation parameters comprise attitude, velocity and position;
step S3: receiving satellite navigation signals by adopting a satellite navigation module (2) of the vehicle-mounted integrated navigation system, and judging whether available satellite navigation signals exist or not;
step S4: according to the judgment result, when available satellite navigation signals exist, the satellite navigation signals are processed to obtain the position and the speed of the carrier measured by the satellite navigation module (2), and the combined navigation calculation of the satellite navigation module (2) and the inertial navigation subsystem (41) is executed, otherwise, the combined navigation calculation of the inertial navigation subsystem (41) and the odometer (3) is executed;
step S5: obtaining a Kalman filter (42) of the on-board integrated navigation system, comprising:
step S51: determining an equation of state for a Kalman filter (42);
step S52: determining a state transition matrix F (t), a system noise distribution matrix G (t) and a system white noise vector W (t) of a state equation of the Kalman filter (42);
step S53: discretizing the system and performing Kalman filtering to obtain an updated Kalman filtering state vector;
step S6: and correcting the inertial navigation parameters according to the updated Kalman filtering state vector, wherein the correction comprises the correction of platform angle, speed and position, and the correction of gyroscope zero offset and accelerometer zero offset.
5. The on-vehicle integrated navigation positioning method according to claim 4, wherein the inertial navigation solution includes an attitude update solution, a velocity update solution and a position update solution, the attitude update solution is performed by a quaternion method or a rotation vector method, the velocity update solution is calculated by using a specific force of the carrier coordinate system measured by the accelerometer (12), and the position update settlement includes updating the position of the navigation coordinate system according to a change speed of the position of the navigation coordinate system.
6. The integrated navigation-positioning method according to claim 4, characterized in that in step S4, a combined navigation solution of the satellite navigation module (2) and the inertial navigation subsystem (41) is performed, comprising:
step S41: constructing measurement information taking the position and the speed of the carrier measured by the satellite navigation module (2) as reference, wherein the measurement information comprises the position and the speed of the carrier measured by the satellite navigation module (2);
step S42: establishing a position observation equation and a velocity observation equation to estimate errors of the position observation quantity and the velocity observation quantity by taking a difference between the position in the inertial navigation parameters in the step S2 and the position measured by the satellite navigation module (2) as a position observation quantity and a difference between the velocity in the inertial navigation parameters in the step S2 and the velocity measured by the satellite navigation module (2) as a velocity observation quantity;
the position observation equation is Z
p(t)＝H
p(t)X(t)+V
p(t), where X (t) is the Kalman filtering state vector, H
p(t) is the observation matrix of the position observation equation, V
p(t) is the observation noise of the position observation equation;
the velocity observation equation is Z
v(t)＝H
v(t)X(t)+V
v(t), where X (t) is the Kalman filtering state vector, H
v(t) is the observation matrix of the velocity observation equation, V
v(t) is the observation noise of the velocity observation equation.
7. The vehicle-mounted integrated navigation and positioning method according to claim 6, wherein in the S42, the position observed quantity Z
p(t) is:
wherein L is
I、λ
I、h
IIs the position in the inertial navigation parameter in the unit of meter, L
G、λ
G、h
GThe position measured by the satellite navigation module (2) is measured in meters, L, lambda and h are latitude, longitude and altitude of a navigation coordinate system, and the unit is meter and R
MAnd R
NThe radius of curvature is meridian radius and prime radius, and the unit is meter;
and the velocity observed quantity Z
v(t) is
Wherein the content of the first and second substances,
error of velocity, delta v, measured for satellite navigation module (2)
nIs the error in velocity in the inertial navigation parameters.
8. The integrated navigation positioning method according to claim 4, wherein in the step S4, the performing integrated navigation solution of the inertial navigation subsystem (41) and the odometer (3) comprises:
step S41': constructing measurement information taking the carrier motion speed output by the odometer as reference, wherein the measurement information is a transformation result of the carrier motion speed output by the odometer (3) in a navigation coordinate system;
step S42': at a velocity in the inertial navigation parameter
The carrier motion speed output by the odometer (3)
The difference is used as the observed speed Z
v(t) establishing a velocity observation equation in a kalman filter (42) to estimate an error of the velocity observation; the velocity observation equation is Z
v(t)＝H
v(t)X(t)+V
v(t), where X (t) is the Kalman filtering state vector, H
v(t) is the observation matrix of the velocity observation equation, V
v(t) is the observation noise of the velocity observation equation;
the velocity observed quantity is
Wherein the content of the first and second substances,
measuring error, delta v, for odometer under navigation coordinate system
nIs the error in velocity in the inertial navigation parameters.
9. The integrated vehicle navigation and positioning method according to claim 4, wherein in the step S51, the Kalman filtering state vector is
Wherein the content of the first and second substances,
and
respectively representing platform angle errors of an east direction E, a north direction N and a sky direction U; delta v
E、δv
NAnd δ v
URespectively representing the speed errors of an east direction E, a north direction N and a sky direction U; δ L, δ λ, and δ h represent errors of latitude L, longitude λ, and altitude h, respectively;
and
respectively representing zero offset of the gyroscope in an x axis, a y axis and a z axis;
and
respectively representing zero offset of the accelerometer on an x axis, a y axis and a z axis;
and the state equation of the Kalman filter (42) is:
X(t+1)＝F(t)X(t)+G(t)W(t)，
wherein t is time in seconds; f (t) is a state transition matrix of 15 × 15 dimensions; g (t) is a 15 × 6-dimensional system noise distribution matrix, and W (t) is a 6 × 1-dimensional system white noise vector.
10. The integrated navigation positioning method according to claim 9, wherein in the step S52, the state transition matrix F
INSThe method is constructed by adopting an error propagation equation, wherein the error propagation equation comprises a platform angle error equation, a speed error equation, a position error equation, a constant value error equation and a noise equation.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011122921A (en) * | 2009-12-10 | 2011-06-23 | Mitsubishi Electric Corp | Position location apparatus, position location method, position location program, velocity vector calculation apparatus, velocity vector calculation method, and velocity vector calculation program |
| CN102519470A (en) * | 2011-12-09 | 2012-06-27 | 南京航空航天大学 | Multi-level embedded integrated navigation system and navigation method |
| CN104181574A (en) * | 2013-05-25 | 2014-12-03 | 成都国星通信有限公司 | Strapdown inertial navigation system/global navigation satellite system combined based navigation filter system and method |
| RU2539131C1 (en) * | 2013-08-02 | 2015-01-10 | Олег Степанович Салычев | Strapdown integrated navigation system of average accuracy for mobile onshore objects |
| CN107390247A (en) * | 2017-07-27 | 2017-11-24 | 河南省科学院应用物理研究所有限公司 | A kind of air navigation aid, system and navigation terminal |
-
2019
- 2019-09-26 CN CN201910919768.1A patent/CN110780326A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011122921A (en) * | 2009-12-10 | 2011-06-23 | Mitsubishi Electric Corp | Position location apparatus, position location method, position location program, velocity vector calculation apparatus, velocity vector calculation method, and velocity vector calculation program |
| CN102519470A (en) * | 2011-12-09 | 2012-06-27 | 南京航空航天大学 | Multi-level embedded integrated navigation system and navigation method |
| CN104181574A (en) * | 2013-05-25 | 2014-12-03 | 成都国星通信有限公司 | Strapdown inertial navigation system/global navigation satellite system combined based navigation filter system and method |
| RU2539131C1 (en) * | 2013-08-02 | 2015-01-10 | Олег Степанович Салычев | Strapdown integrated navigation system of average accuracy for mobile onshore objects |
| CN107390247A (en) * | 2017-07-27 | 2017-11-24 | 河南省科学院应用物理研究所有限公司 | A kind of air navigation aid, system and navigation terminal |
Non-Patent Citations (1)
| Title |
|---|
| 李兵等: "基于GPS/SINS/里程计的车载组合导航研究", 《测控技术》 * |
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