CN114061574B - Position-invariant constraint and zero-speed correction-based coal mining machine pose-determining and orienting method - Google Patents
Position-invariant constraint and zero-speed correction-based coal mining machine pose-determining and orienting method Download PDFInfo
- Publication number
- CN114061574B CN114061574B CN202111380954.6A CN202111380954A CN114061574B CN 114061574 B CN114061574 B CN 114061574B CN 202111380954 A CN202111380954 A CN 202111380954A CN 114061574 B CN114061574 B CN 114061574B
- Authority
- CN
- China
- Prior art keywords
- inertial navigation
- navigation system
- representing
- matrix
- speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000003245 coal Substances 0.000 title claims abstract description 43
- 238000005065 mining Methods 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000012937 correction Methods 0.000 title claims abstract description 14
- 230000003068 static effect Effects 0.000 claims abstract description 18
- 239000011159 matrix material Substances 0.000 claims description 38
- 238000001914 filtration Methods 0.000 claims description 34
- 238000005259 measurement Methods 0.000 claims description 20
- 230000007704 transition Effects 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 4
- 239000004973 liquid crystal related substance Substances 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000009825 accumulation Methods 0.000 abstract description 2
- 230000001629 suppression Effects 0.000 abstract 1
- 238000004088 simulation Methods 0.000 description 9
- 230000002238 attenuated effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- 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
-
- 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
- G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
- G01C21/188—Compensation of inertial measurements, e.g. for temperature effects for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
Abstract
The invention discloses a coal mining machine pose orientation method based on position invariant constraint and zero-speed correction. Aiming at the problem that the attitude heading error diverges due to accumulation of errors of inertial devices after a strapdown inertial navigation system in a coal mining machine works for a long time, the invention provides a heading attitude error suppression method under two combined modes of taking speed as an observed quantity when the inertial navigation system is in a static state and taking a position unchanged as an observed quantity when the system works dynamically, the platform deflection angle and the errors of the inertial devices of the inertial navigation system are estimated, and the estimated platform deflection angle is utilized to correct the attitude heading of the inertial navigation system, so that the attitude and heading accuracy of the inertial navigation system during long-time working is improved. The invention provides a solution for the problem of the gesture course maintaining capability of the strapdown inertial navigation system in the coal mining machine after long-time working, and has strong practical value.
Description
Technical Field
The invention relates to a coal mining machine attitude determination and orientation method based on position invariant constraint and zero-speed correction, which is mainly suitable for improving the holding capacity of an inertial navigation system in attitude heading after long-time working, and belongs to the technical field of inertial navigation for coal mining machines.
Background
Coal is one of important energy sources in China, and automation, unmanned and intelligent coal mining become an important development trend of coal mining in China at present due to severe coal mining environments. Therefore, the positioning and orientation precision of the coal mining machine is improved.
At present, most of researches on coal mining machines improve the positioning accuracy by using methods such as wireless sensor networks and ultrasonic reflection, but as signals in a coal mine are weak, and as the working length is increased, the signals are attenuated, and the like, the positioning is deviated, and the gesture heading information of the coal mining machine cannot be acquired. After the initial information is obtained by the inertial navigation technology, autonomous navigation can be performed without other navigation modes, and not only can the real-time position information of the system be provided, but also the gesture heading information can be provided, so that the method becomes an effective solution for positioning and orienting the coal mining machine.
However, in practical application, the working time of the coal mining machine is long, and after the inertial navigation system works for a long time, the inertial navigation system can not accurately measure the gesture heading due to the accumulation of errors of inertial devices. In order to improve the long-endurance attitude heading maintaining capability of the inertial navigation system, the invention provides a platform deflection angle estimation method combining two measurement information of position and speed, so as to improve the attitude heading precision of the inertial navigation system after long-time navigation.
Disclosure of Invention
The invention solves the technical problems that: aiming at the problem that the inertial navigation system is difficult to maintain high-precision attitude heading after the coal mining machine works for a long time, the invention provides a coal mining machine attitude heading method based on position invariant constraint and zero speed correction, and under two modes of coal mining machine movement and static, the position invariant constraint and the zero speed are respectively selected as measurement, a Kalman filter is designed for filtering, the platform deflection angle of the system is estimated and compensated, and the attitude heading precision of the inertial navigation system in long-time heading can be effectively improved.
The technical solution of the invention is as follows: a coal mining machine attitude determination and orientation method based on position invariant constraint and zero-speed correction is characterized by comprising the following steps: by utilizing the characteristic of time stop of the coal mining machine, a Kalman filtering model for observing by utilizing the position and the speed under two modes of motion and static is designed, and the method can realize the estimation of the deflection angle of the platform of the inertial navigation system and reduce the attitude heading error of the inertial navigation system after compensation. The coal mining machine attitude determination and orientation method based on position invariant constraint and zero-speed correction comprises the following steps:
step one: performing navigation calculation on the strapdown inertial navigation system to obtain real-time position, speed and gesture heading information of the inertial navigation system;
step two: in the process of executing the first step, when the inertial navigation system is in a dynamic working state, as the overall travelling distance of the coal mining machine is about 200 meters, the position of the coal mining machine is basically unchanged, and therefore, the initial position information is taken as a reference, and the platform deflection angle in the motion process of the system is estimated by using a Kalman filtering model with the difference between the real-time position information and the initial position information of the inertial navigation system as an observation, so that an estimation result of the platform deflection angle of the inertial navigation system in the motion process is obtained; when the inertial navigation system is in a static state, taking zero speed as a reference, and using the difference between the speed of the inertial navigation system at the static moment and the zero speed as an observed quantity, carrying out Kalman filtering to estimate the platform deflection angle of the system in the static state, so as to obtain an estimation result of the platform deflection angle of the inertial navigation system in the static state;
step three: and compensating the estimation results of the platform deflection angles obtained in the two modes into the inertial navigation system to obtain corrected attitude and heading information of the inertial navigation system.
In the second step, a kalman filter model combining the position and speed information of the inertial navigation system as observables is used for estimating the platform deflection angle in the system movement process, wherein the process is as follows:
the Kalman filtering model establishes a state equation based on an error propagation equation and the error characteristic of an inertial device, wherein the state quantity comprises position errors, speed errors, east, north, an sky platform drift angle and body system gyro drift, and the total is 10 dimensions;
the Kalman filtering model is:
wherein F (t) is a state transition matrix, X (t) is a state vector, Z (t) is an observed quantity, H (t) is an observation matrix, W (t) and V (t) are system noise and observation noise respectively, and both are white noise;
wherein, delta lambda is used for controlling the temperature of the liquid crystal display,representing latitude and longitude errors, δV E ，δV N Representing east and north speed errors, delta phi E ，δφ N ,δφ U Platform declination, ε, representing east, north and sky directions X ，ε Y ，ε Z Indicating gyro drift under the machine system;
the state transition matrix is written in the form of a block matrix:
wherein: f (F) A (t) 7×7 Establishing the relation between navigation errors, F B (t) 7×3 A relation between the navigation error and the inertial device error is established, F C (t) 3×3 A model of inertial device errors is built.
Wherein,representing the components of the earth's rotation in the north and in the sky, R e Representing the earth radius>Representing local latitude, g representing gravitational acceleration, f E ，f N Representing accelerometer outputs in the east and north directions, τ represents the time associated with the gyro drift first order markov process. 0 m×n The zero matrix representing m rows and n columns, b being the body coordinate system of the inertial navigation system, n being the east-north-sky geographic system,>a directional cosine matrix of b series converted to n series;
when the system is in a motion state, the Kalman filtering model takes the difference between the real-time longitude and latitude and the initial longitude and latitude of the inertial navigation system as measurement, and a measurement equation is written as follows:
wherein the observable matrix H is a constant matrix, I 2×2 Representing a 2-order identity matrix;
when the inertial navigation system is in a static state, the reference speed is zero, the east, north and sky platform deflection angles are estimated by using a Kalman filtering model with the east and north speeds of the inertial navigation system as observations, and the observation equation is written as follows:
wherein the observable matrix H is a constant matrix;
and establishing a Kalman filtering model for filtering according to the state transition matrix and the observation matrix under different working modes to obtain estimation results of the east, north and sky direction platform deflection angles of the inertial navigation system.
Compared with the prior art, the invention has the advantages that:
(1) The invention utilizes the position and speed information and the speed information of the inertial navigation system, can realize the estimation of the deflection angle of the platform and the correction of the gesture heading information without external equipment, and has strong practicability.
(2) Aiming at the problem that the high-precision gesture heading is difficult to maintain during long-term navigation of the inertial navigation system, the invention provides the gesture heading correction method combining position measurement and speed measurement, and the Kalman filtering is performed by utilizing different quantity measurements of the coal mining machine in different working modes, so that the platform deflection angle can be estimated more accurately, and the gesture heading maintaining capability of the inertial navigation system is improved greatly.
Drawings
FIG. 1 is a flow chart of an implementation of the method of the present invention;
FIG. 2 is a simulation curve of the motion trajectory of the shearer and a speed and heading change curve;
FIG. 3 is a set of convergence curves for plateau deflection angles;
FIG. 4 is a graph of gesture heading comparisons before and after correction of platform yaw.
Detailed Description
The process according to the invention is described in detail below with reference to the drawings and to specific examples.
The invention relates to a coal mining machine attitude determination and orientation method based on position measurement and zero speed correction, which combines inertial navigation position measurement and speed measurement, estimates a platform deflection angle and further corrects attitude heading information of an inertial navigation system as shown in figure 1. Firstly, establishing a Kalman filtering state equation by utilizing an inertial navigation error propagation equation and error characteristics of an inertial device, and designing two observation modes according to the actual conditions of two modes of motion and rest when the coal mining machine works. When the coal mining machine moves, observing the difference between the real-time position and the initial position by using the inertial navigation system; when the coal mining machine is in a static state, the reference speed is considered to be zero, and the difference between the inertial navigation system speed and the reference speed is used for observation. And filtering according to a basic equation of Kalman filtering, estimating the deflection angle of the platform, and compensating the estimated deflection angle into an inertial navigation system, so that the maintenance accuracy of the gesture heading is improved. Based on the principle, the coal mining machine attitude determination and orientation method based on position measurement and zero-speed correction provided by the invention is subjected to simulation verification. The implementation process of the specific parts is as follows.
Step one: according to the characteristics that the coal mining machine can be in two working modes of motion and static, the motion trail shown in figure 2 is designed by considering acceleration, deceleration and course change of the coal mining machine in the long-time working process.
Step two: adding random constant value errors of gyro drift and accelerometer zero bias and a first-order Markov process in simulation; furthermore, at the initial moment, a random initial platform bias angle is added. The solution frequency of the inertial navigation system is 100Hz, and the filtering frequency is 1Hz. The inertial device errors for a given system are shown in table 1.
Table 1 simulation of given error terms
Step three: the platform deflection angle in the system motion process is estimated by combining a Kalman filtering model taking the inertial navigation system position and speed information as observables, and the related formulas are as follows:
the Kalman filtering model is:
wherein F (t) is a state transition matrix, X (t) is a state vector, Z (t) is an observed quantity, H (t) is an observation matrix, W (t) and V (t) are system noise and observation noise respectively, and both are white noise;
wherein, delta lambda is used for controlling the temperature of the liquid crystal display,representing latitude and longitude errors, δV E ，δV N Representing east and north speed errors, delta phi E ，δφ N ,δφ U Platform declination, ε, representing east, north and sky directions X ，ε Y ，ε Z Indicating gyro drift under the machine system;
the state transition matrix is written in the form of a block matrix:
wherein: f (F) A (t) 7×7 Establishing the relation between navigation errors, F B (t) 7×3 A relation between the navigation error and the inertial device error is established, F C (t) 3×3 A model of inertial device errors is built.
Wherein,representing the components of the earth's rotation in the north and in the sky, R e Representing the earth radius>Representing local latitude, g representing gravitational acceleration, f E ，f N Representing accelerometer outputs in the east and north directions, τ represents the time associated with the gyro drift first order markov process. 0 m×n The zero matrix representing m rows and n columns, b being the body coordinate system of the inertial navigation system, n being the east-north-sky geographic system,>a directional cosine matrix of b series converted to n series;
because the overall travelling distance of the coal mining machine is short and is about 200 meters, the position of the coal mining machine is basically unchanged, when the system is in a motion state, initial position information is taken as a reference, the Kalman filtering model takes the difference between the real-time longitude and latitude of the inertial navigation system and the initial longitude and latitude as measurement, and a measurement equation is written as follows:
wherein the observable matrix H is a constant matrix, I 2×2 Representing a 2-order identity matrix;
when the inertial navigation system is in a static state, the reference speed is zero, the east-direction and north-direction speeds of the inertial navigation system are used as observational quantities to carry out Kalman filtering, and a measurement equation is written as follows:
wherein the observable matrix H is a constant matrix;
and selecting a proper initial filtering value according to a basic equation of Kalman filtering. The initial value of P, Q, R array is selected according to the device type and precision of the system, and P, Q, R should be correspondingly changed if the device type and precision of the system are different. The P, Q, R parameter value method and the Kalman filtering basic equation are common knowledge of the person skilled in the art.
P 10×10 (k) Covariance at k-th timeThe non-zero terms of the initial values are:
P 0 (1,1)＝P 0 (2,2)＝(1m/R e ) 2
P 0 (3,3)＝P 0 (4,4)＝(1m/s) 2
P 0 (5,5)＝P 0 (6,6)＝(5") 2
P 0 (7,7)＝(30") 2
P 0 (8,8)＝P 0 (9,9)＝P 0 (10,10)＝(0.02°/h) 2
Q 10×10 (k) For the system noise matrix at the kth time, the non-zero terms of the initial value are:
Q 0 (8,8)＝Q 0 (9,9)＝Q 0 (10,10)＝(0.02°/h) 2
R 10×10 (k) For the measurement noise array at the kth moment, when the equivalent measurement is the difference between the real-time longitude and latitude and the initial longitude and latitude of the system motion state, the non-zero term of the initial value of the R array is as follows:
R 0 (1,1)＝R 0 (2,2)＝(20″) 2
when the equivalent measurement is the difference between the speed of the system in a static state and zero speed, the non-zero term of the initial value of the R array is as follows:
R 0 (1,1)＝R 0 (2,2)＝(0.02m/s) 2
after the initial value is set, a Kalman filtering process is executed, time updating is carried out at all times when the coal mining machine works, and when the coal mining machine is in a motion state, measurement updating is carried out by taking the position information of the inertial navigation system as an observed quantity; and when the coal mining machine is in a static state, measuring and updating by taking inertial navigation system speed information as observed quantity, and finishing estimation of the deflection angle of the platform. The results of the platform bias angle estimation for a set of simulation experiments are shown in fig. 3. It can be seen that the Kalman filtering is performed by the method, and the east, north and sky platform deflection angles can be well estimated.
Step four: and (3) correcting the attitude heading information by compensating the deflection angle of the platform obtained in the step (III) into an inertial navigation system, wherein fig. 4 is an attitude heading comparison diagram of a group of simulation experiment inertial navigation systems before and after compensation. It can be seen that after compensating the estimated east, north and sky platform deflection angles, the pitch, roll and heading errors of the system are obviously reduced, and the superiority of the attitude determination and orientation method provided by the invention is illustrated.
In order to illustrate the effectiveness of the invention for correcting the attitude heading information of the inertial navigation system, the steps are repeatedly executed for one to four times for 10 times to obtain the estimation result of each group of simulation experiments and compensate the attitude heading information, and the maximum error and standard deviation (1 sigma) of the inertial navigation system before and after the attitude heading compensation are respectively calculated, as shown in table 2.
Table 2-10 comparison of gesture heading before and after compensation of simulated inertial navigation system
As can be seen from Table 2, the inertial navigation system significantly reduces the maximum and standard deviation of pitch, roll and heading errors before and after compensating for the estimated east, north and heaven platform declination. The result of 10 simulation shows that after the system compensates the deflection angle of the platform, the gesture heading accuracy is greatly improved, and the effectiveness of the algorithm provided by the invention is verified.
In a word, the invention combines the position information and the speed information to observe according to the characteristic that the coal mining machine can work in two modes, and can finish correction of the attitude and heading information of the inertial navigation system by using Kalman filtering. Simulation results show that the method provided by the invention can obviously reduce the attitude heading error of the inertial navigation system and effectively improve the attitude heading maintaining precision of the inertial navigation system. The invention provides a solution to the problem of attitude and heading maintaining capability of the strapdown inertial navigation system in the coal mining machine after long-time working, and has strong practical value.
The invention, in part, is not disclosed in detail and is well known in the art.
While the foregoing describes illustrative embodiments of the present invention to facilitate an understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, but is to be construed as protected by the accompanying claims insofar as various changes are within the spirit and scope of the present invention as defined and defined by the appended claims.
Claims (1)
1. The coal mining machine attitude determination and orientation method based on position invariant constraint and zero-speed correction is characterized by comprising the following steps of:
step one: performing navigation calculation on the strapdown inertial navigation system to obtain real-time position, speed and gesture heading information of the inertial navigation system;
step two: in the process of executing the first step, when the inertial navigation system is in a dynamic working state, as the overall travelling distance of the coal mining machine is about 200 meters, the position of the coal mining machine is basically unchanged, and therefore, the initial position information is taken as a reference, and a Kalman filtering model with the difference between the real-time position information of the inertial navigation system and the initial position information as an observed quantity is used for estimating the platform deflection angle in the system movement process, so that an estimation result of the platform deflection angle of the inertial navigation system in the movement process is obtained; when the inertial navigation system is in a static state, taking zero speed as a reference, and using the difference between the speed of the inertial navigation system at the static moment and the zero speed as an observed quantity, carrying out Kalman filtering to estimate the platform deflection angle of the system in the static state, so as to obtain an estimation result of the platform deflection angle of the inertial navigation system in the static state;
in the step, a Kalman filtering model combining the position invariant constraint and the speed information of an inertial navigation system as observables is used for estimating a platform deflection angle in the system motion process, wherein the process is as follows:
the Kalman filtering model establishes a state equation based on an error propagation equation and the error characteristic of an inertial device, wherein the state quantity comprises position errors, speed errors, east, north, an sky platform drift angle and body system gyro drift, and the total is 10 dimensions;
the Kalman filtering model is:
Z(t)＝H(t)X(t)+V(t)
wherein F (t) is a state transition matrix, X (t) is a state vector,the differential of the state vector X (t), Z (t) is the observed quantity, H (t) is the observation matrix, W (t) and V (t) are the system noise and the observation noise respectively, and both are white noise;
wherein, delta lambda is used for controlling the temperature of the liquid crystal display,representing latitude and longitude errors, δV E ，δV N Representing east and north speed errors, delta phi E ，δφ N ，δφ U Platform declination, ε, representing east, north and sky directions X ，ε Y ，ε Z Indicating gyro drift under the machine system;
the state transition matrix is written in the form of a block matrix:
wherein: f (F) A (t) 7×7 Establishing the relation between navigation errors, F B (t) 7×3 A relation between the navigation error and the inertial device error is established, F C (t) 3×3 A model of the inertial device error is built,
wherein,representing the components of the earth's rotation in the north and in the sky, R e Representing the earth radius>Representing local latitude, g representing gravitational acceleration, h representing altitude of the location, f E ，f N Representing accelerometer outputs in the east and north directions, T representing the relative time of the gyro drift first order Markov process; 0 m×n The zero matrix representing m rows and n columns, b being the body coordinate system of the inertial navigation system, n being the east-north-sky geographic system,>a directional cosine matrix of b series converted to n series;
when the system is in a motion state, the Kalman filtering model takes the difference between the real-time longitude and latitude and the initial longitude and latitude of the inertial navigation system as measurement, and a measurement equation is written as follows:
wherein the observable matrix H is a constant matrix, I 2×2 Representing a 2-order identity matrix;
when the inertial navigation system is in a static state, the reference speed is zero, the east and north speeds of the inertial navigation system are used as observables, kalman filtering is carried out to estimate the east, north and sky platform deflection angles, and an observation equation is written as follows:
wherein the observable matrix H is a constant matrix;
establishing a Kalman filtering model for filtering according to the state transition matrix and the observation matrix under different working modes to obtain estimation results of the east, north and sky direction platform deflection angles of the inertial navigation system;
step three: and compensating the estimation results of the platform deflection angles obtained in the two modes into the inertial navigation system to obtain corrected attitude and heading information of the inertial navigation system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111380954.6A CN114061574B (en) | 2021-11-20 | 2021-11-20 | Position-invariant constraint and zero-speed correction-based coal mining machine pose-determining and orienting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111380954.6A CN114061574B (en) | 2021-11-20 | 2021-11-20 | Position-invariant constraint and zero-speed correction-based coal mining machine pose-determining and orienting method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114061574A CN114061574A (en) | 2022-02-18 |
| CN114061574B true CN114061574B (en) | 2024-04-05 |
Family
ID=80278643
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111380954.6A Active CN114061574B (en) | 2021-11-20 | 2021-11-20 | Position-invariant constraint and zero-speed correction-based coal mining machine pose-determining and orienting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114061574B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117214933B (en) * | 2023-11-07 | 2024-02-06 | 中国船舶集团有限公司第七〇七研究所 | Inertial navigation/Beidou tight coupling long-period inertial navigation speed quality improvement method for water surface ship |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106767900A (en) * | 2016-11-23 | 2017-05-31 | 东南大学 | A kind of online calibration method of the optical fibre SINS system based on integrated navigation technology |
| CN107655476A (en) * | 2017-08-21 | 2018-02-02 | 南京航空航天大学 | Pedestrian's high accuracy foot navigation algorithm based on Multi-information acquisition compensation |
| CN111220151A (en) * | 2019-12-20 | 2020-06-02 | 湖北航天技术研究院总体设计所 | Inertia and milemeter combined navigation method considering temperature model under load system |
| CN112378400A (en) * | 2020-10-30 | 2021-02-19 | 湖南航天机电设备与特种材料研究所 | Dual-antenna GNSS assisted strapdown inertial navigation integrated navigation method |
-
2021
- 2021-11-20 CN CN202111380954.6A patent/CN114061574B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106767900A (en) * | 2016-11-23 | 2017-05-31 | 东南大学 | A kind of online calibration method of the optical fibre SINS system based on integrated navigation technology |
| CN107655476A (en) * | 2017-08-21 | 2018-02-02 | 南京航空航天大学 | Pedestrian's high accuracy foot navigation algorithm based on Multi-information acquisition compensation |
| CN111220151A (en) * | 2019-12-20 | 2020-06-02 | 湖北航天技术研究院总体设计所 | Inertia and milemeter combined navigation method considering temperature model under load system |
| CN112378400A (en) * | 2020-10-30 | 2021-02-19 | 湖南航天机电设备与特种材料研究所 | Dual-antenna GNSS assisted strapdown inertial navigation integrated navigation method |
Non-Patent Citations (5)
| Title |
|---|
| 一种车载惯性导航系统误差抑制方法;颜开思;刘亚龙;张锐;;现代导航;20170415(第02期);全文 * |
| 低成本捷联惯导系统的静基座快速精对准方法;祝燕华;刘建业;钱伟行;赖际舟;;上海交通大学学报(第05期);全文 * |
| 祝燕华 ; 刘建业 ; 钱伟行 ; 赖际舟 ; .低成本捷联惯导系统的静基座快速精对准方法.上海交通大学学报.2008,(第05期),全文. * |
| 赵玉 ; 赵忠 ; 范毅 ; .零速修正技术在车载惯性导航中的应用研究.压电与声光.2012,(第06期),全文. * |
| 零速修正技术在车载惯性导航中的应用研究;赵玉;赵忠;范毅;;压电与声光(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114061574A (en) | 2022-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110487301B (en) | Initial alignment method of radar-assisted airborne strapdown inertial navigation system | |
| CN110398257B (en) | GPS-assisted SINS system quick-acting base initial alignment method | |
| CN113029199B (en) | System-level temperature error compensation method of laser gyro inertial navigation system | |
| CN111024064B (en) | SINS/DVL combined navigation method for improving Sage-Husa adaptive filtering | |
| CN111156994B (en) | INS/DR & GNSS loose combination navigation method based on MEMS inertial component | |
| CN102519470B (en) | Multi-level embedded integrated navigation system and navigation method | |
| CN105698822B (en) | Initial Alignment Method between autonomous type inertial navigation based on reversed Attitude Tracking is advanced | |
| CN105806363B (en) | The underwater large misalignment angle alignment methods of SINS/DVL based on SRQKF | |
| CN103822633A (en) | Low-cost attitude estimation method based on second-order measurement update | |
| CN110440830B (en) | Self-alignment method of vehicle-mounted strapdown inertial navigation system under movable base | |
| CN109596144B (en) | GNSS position-assisted SINS inter-travel initial alignment method | |
| CN102519485B (en) | Gyro information-introduced double-position strapdown inertial navigation system initial alignment method | |
| CN113063429B (en) | Self-adaptive vehicle-mounted integrated navigation positioning method | |
| CN103076026B (en) | A kind of method determining Doppler log range rate error in SINS | |
| CN111399023B (en) | Inertial basis combined navigation filtering method based on lie group nonlinear state error | |
| CN107289942B (en) | Relative navigation system and method for formation flight | |
| Fu et al. | Autonomous in-motion alignment for land vehicle strapdown inertial navigation system without the aid of external sensors | |
| CN109612460B (en) | Plumb line deviation measuring method based on static correction | |
| CN114739425A (en) | Coal mining machine positioning calibration system based on RTK-GNSS and total station and application method | |
| CN111189442A (en) | Multi-source navigation information state prediction method of unmanned aerial vehicle based on CEPF | |
| CN114877915B (en) | Device and method for calibrating g sensitivity error of laser gyro inertia measurement assembly | |
| CN111220151B (en) | Inertia and milemeter combined navigation method considering temperature model under load system | |
| CN110285830B (en) | SINS/GPS speed matching alignment method based on MEMS sensor | |
| CN114061574B (en) | Position-invariant constraint and zero-speed correction-based coal mining machine pose-determining and orienting method | |
| CN113916226B (en) | Minimum variance-based interference rejection filtering method for integrated navigation system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |