CN110806220B - Inertial navigation system initial alignment method and device - Google Patents
Inertial navigation system initial alignment method and device Download PDFInfo
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- CN110806220B CN110806220B CN201911160717.1A CN201911160717A CN110806220B CN 110806220 B CN110806220 B CN 110806220B CN 201911160717 A CN201911160717 A CN 201911160717A CN 110806220 B CN110806220 B CN 110806220B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
Abstract
The invention provides an inertial navigation system initial alignment method and a device, wherein the method comprises the following steps: after the inertial navigation system performs coarse alignment, performing Kalman filtering alignment according to the normal sampling and navigation resolving rate; before the alignment is finished, utilizing the stored inertial original data to carry out reverse navigation resolving and reverse Kalman filtering alignment; and performing reverse alignment to the initial rough alignment moment, and performing forward filtering alignment according to the stored inertial original data. By the scheme, the problem of long initial alignment time of the conventional inertial navigation system is solved, the azimuth error estimation convergence can be accelerated, and the alignment time is shortened.
Description
Technical Field
The invention relates to the field of navigation, in particular to an inertial navigation system initial alignment method and device.
Background
The initial alignment is generally used to determine the attitude relationship between the inertial measurement unit and the navigation coordinate system, i.e. the attitude matrix at the initial time of the navigation positioning operation. In the field of inertial navigation, the navigation accuracy and the response capability of an inertial navigation system are directly influenced by the initial alignment accuracy and the alignment time, and in practice, the navigation accuracy and the response capability of the inertial navigation system are mutually contradictory technical indexes, so that a longer alignment time is often needed to reach the ultimate alignment accuracy as soon as possible.
The general initial alignment process comprises coarse alignment and fine alignment, wherein the coarse alignment is used for determining the inertial navigation initial attitude, the inertial navigation initial attitude error meets a small angle condition, and the method is an application premise based on Kalman filtering fine alignment. The fine alignment process is mainly based on an inertial navigation error model, and navigation errors such as inertial navigation attitude misalignment angles are estimated by adopting a Kalman filtering algorithm. However, due to poor observability, in the fine alignment process, the convergence speed of the azimuth misalignment angle error estimation relative to the horizontal attitude misalignment angle estimation is slow, resulting in long initial alignment time.
Disclosure of Invention
In view of this, embodiments of the present invention provide an initial alignment method and an initial alignment device for an inertial navigation system, so as to solve the problem of long alignment time in a fine alignment process of an existing initial alignment method.
In a first aspect of the embodiments of the present invention, there is provided an inertial navigation system initial alignment method, including:
after the inertial navigation system performs coarse alignment, performing Kalman filtering alignment according to the normal sampling and navigation resolving rate;
before the alignment is finished, utilizing the stored inertial original data to carry out reverse navigation resolving and reverse Kalman filtering alignment;
and performing reverse alignment to the initial rough alignment moment, and performing forward filtering alignment according to the stored inertial original data.
In a second aspect of the embodiments of the present invention, there is provided an inertial navigation system initial alignment apparatus, including:
the first forward filtering module is used for performing Kalman filtering alignment according to the normal sampling and navigation resolving rate after the inertial navigation system performs coarse alignment;
the backward filtering module is used for performing backward navigation resolving and backward Kalman filtering alignment by using the stored inertial original data before the alignment is finished;
and the second forward filtering module is used for performing reverse alignment to the initial rough alignment moment and performing forward filtering alignment according to the stored inertia original data.
In the embodiment of the invention, after coarse alignment, the inertial navigation system carries out Kalman filtering alignment according to normal sampling and resolving speed, after the alignment is finished, reverse navigation resolving and reverse Kalman filtering alignment are carried out according to acquired and stored inertial original data, the alignment is carried out to the initial start, forward navigation resolving and Kalman filtering alignment are carried out again until the alignment is finished, the alignment precision can be continuously improved through repeated forward and reverse filtering, the convergence speed is accelerated, and the fine alignment time is shortened, so that the problems of low initial alignment speed and long time are solved, the azimuth error estimation convergence can be obviously accelerated based on the reverse Kalman filtering, and the alignment speed is improved while the alignment precision is ensured.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating an inertial navigation system initial alignment method according to an embodiment of the present invention;
FIG. 2 is another schematic flow chart of an inertial navigation system initial alignment method according to an embodiment of the present invention;
FIG. 3 is another schematic diagram of an inertial navigation system initial alignment method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an inertial navigation system initial alignment apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons skilled in the art without any inventive work shall fall within the protection scope of the present invention, and the principle and features of the present invention shall be described below with reference to the accompanying drawings.
The terms "comprises" and "comprising," when used in this specification and claims, and in the accompanying drawings and figures, are intended to cover non-exclusive inclusions, such that a process, method or system, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements.
Referring to fig. 1, fig. 1 is a schematic flow chart of an inertial navigation system initial alignment method according to an embodiment of the present invention, including:
s101, after the inertial navigation system performs coarse alignment, Kalman filtering alignment is performed according to normal sampling and navigation resolving rates;
the coarse alignment is used for preliminarily calculating the relation of the navigation system relative to the inertial navigation attitude, the Kalman filtering is an algorithm for carrying out optimal state estimation on the system according to observation data input and output by the system, and preliminary alignment is carried out on the inertial navigation system through Kalman filtering based on sampling data. There is also a large azimuth misalignment angle error, typically after the first kalman filtering. The existing filtering-based method is difficult to improve the estimation convergence of the azimuth misalignment angle to a large extent.
Defining a navigation computation period as TsThe forward navigation calculation recurrence update process comprises the following steps:
the speed update is performed according to equation (1):
performing attitude update according to formula (2):
the location update is performed according to equation (3):
wherein the content of the first and second substances,
a matrix of inertial navigation poses is represented,
expressing inertial navigation speed, L and lambda expressing inertial navigation latitude and precision, h expressing inertial navigation height,
and
respectively representing a gyro angular velocity measurement and an accelerometer specific force measurement; omegaieAnd g is the rotation angular rate of the earth and the magnitude of the local gravity acceleration respectively; rMAnd RNThe radius of the meridian and the radius of the unitary mortise of the local earth are respectively.
The forward Kalman filter state equation is:
Xk＝φk,k-1Xk-1+Wk-1 (4)
wherein, XkAnd representing the error state quantity to be estimated of the system, wherein the error state quantity comprises course, attitude error, speed error, position error, gyro measurement error and accelerometer measurement error.
S102, before the alignment is finished, reverse navigation resolving and reverse Kalman filtering alignment are carried out by using stored inertial original data;
the inertial raw data are data collected in the forward Kalman filtering process and comprise observation data, initial state data and the like. The reverse navigation is the reverse process of the forward navigation, namely the inertial navigation system is represented by t on the time sequencekBackward recursion to tk-1And (5) solving the time. It is noted that the reverse navigation solution and the forward navigation solution are at tk-1The navigation parameters at the moment are consistent.
Specifically, the reverse navigation solution process is as follows:
the pose update is performed according to equation (5):
the speed update is performed according to equation (6):
the location update is performed according to equation (7):
wherein the content of the first and second substances,
a matrix of inertial navigation poses is represented,
expressing inertial navigation speed, L and lambda expressing inertial navigation latitude and precision, h expressing inertial navigation height,
and
respectively representing a gyro angular velocity measurement and an accelerometer specific force measurement; omegaieAnd g is the rotation angular rate of the earth and the magnitude of the local gravity acceleration respectively; rMAnd RNThe radius of the meridian and the radius of the unitary mortise of the local earth are respectively.
And in the reverse navigation resolving process, firstly carrying out attitude updating and then carrying out speed updating.
The inverse Kalman filter alignment model mainly includes two parts: a state equation and a measurement equation. The reverse Kalman filtering alignment adopts the same observed quantity as the forward Kalman filtering for measurement updating, and is also the same as a forward Kalman filtering alignment measurement equation, wherein the state equation is derived as follows:
the difference between the reverse Kalman filtering alignment state equation and the forward Kalman filtering alignment state equation is that the state transition matrixes are reciprocal, the recursive calculation process is consistent with the forward Kalman filtering calculation process, and calculation can be performed according to the standard Kalman filtering process.
And S103, performing reverse alignment to the initial rough alignment moment, and performing forward filtering alignment according to the stored inertial original data.
And after the reverse navigation resolving and the reverse Kalman filtering are finished, forward filtering is carried out again according to a filtering result, and the azimuth misalignment angle error is further reduced.
Optionally, the stored inertial raw data is repeatedly subjected to inverse filtering alignment and forward filtering alignment to improve the initial alignment accuracy.
The reverse Kalman filtering and the forward Kalman filtering are repeated to gradually converge the azimuth error, so that the alignment accuracy can be improved, the alignment speed can be increased, and the alignment time can be shortened under the preset accuracy
Optionally, on the basis that the inertial navigation system is provided with the indexing mechanism, the inertial navigation system rotates around the Z-direction single axis in the filtering alignment process to increase observability of the inertial navigation system.
By the method provided by the embodiment, the attitude information with higher precision obtained by the conventional forward initial alignment is navigated to the initial moment in the reverse direction, the initial misalignment angle estimation error is further reduced based on the reverse filtering, the alignment is restarted by using the stored original data, the attitude misalignment angle error is optimally estimated by the forward and reverse filtering, meanwhile, on the premise of not increasing the alignment time, the error estimation is rapidly converged within a limited time by improving the utilization rate of the inertial original data, and the alignment time length in the fine alignment process is shortened.
Fig. 2 is another schematic flow chart of an inertial navigation system initial alignment method according to an embodiment of the present invention, and fig. 2 is a schematic flow chart of the inertial navigation system initial alignment method, taking an application condition of 5min initial alignment of uniaxial modulation laser inertial navigation as an example, in a 5min initial alignment process of the inertial navigation system:
-1 represents the backward filter alignment, 0 represents the coarse alignment, and 1 represents the forward filter alignment.
Performing analytic coarse alignment within 0 s-120 s of the initial alignment to complete determination of the inertial navigation initial attitude and orientation, and performing forward navigation calculation based on the more accurate inertial navigation initial attitude and orientation;
and performing primary forward Kalman filtering alignment for 120 s-300 s in the initial alignment, and estimating navigation errors such as inertial navigation directions, attitude misalignment angles and the like by taking the speed as an observed quantity.
And before the initial alignment is finished, performing reverse Kalman filtering alignment, specifically performing reverse navigation resolving and filtering estimation by reading the inertial original data stored in the memory.
And performing second forward Kalman filtering alignment when the reverse Kalman filtering alignment reaches the alignment starting moment, reading the inertial original data stored in the memory to perform forward navigation resolving and filtering estimation, and performing navigation error correction such as inertial navigation azimuth and attitude misalignment angle at 300s to complete initial alignment.
The coarse alignment process is performed according to equation (9):
wherein, gnAnd
g is obtained according to the gravity acceleration of the earth, the rotation angular velocity of the earth and the position of the carrier at the momentnFor the gravity vector g under the navigation systemn＝[0 0 g]T，
Tethered earth spin vector for navigation
The forward Kalman filtering alignment process comprises the following steps:
establishing a Kalman filtering initial alignment state space model:
wherein
The system state transition matrix is:
the system measurement matrix is:
the reverse navigation resolving process comprises the following steps:
the pose is updated as follows:
the speed update is as follows:
the location update is as follows:
the measurement is updated by adopting the same observed quantity as the forward Kalman filtering alignment, and the measurement equation is the same as the forward Kalman filtering alignment measurement equation, and the state equation is as follows:
as can be seen from FIG. 3, the 5min initial alignment azimuth error estimation curve shows that the azimuth error estimation is not converged yet in the 1 st forward filtering alignment, the azimuth estimation is gradually converged at the end of the 2 nd forward filtering alignment, the stability precision is better than 0.02 degrees, and the method can realize the azimuth error estimation convergence by improving the utilization rate of data and improve the alignment rapidity.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 4 is a schematic structural diagram of an inertial navigation system initial alignment apparatus according to an embodiment of the present invention, where the apparatus includes:
the first forward filtering module 410 is used for performing Kalman filtering alignment according to the normal sampling and navigation resolving rate after the inertial navigation system performs coarse alignment;
a backward filtering module 420, configured to perform backward navigation solution and backward Kalman filtering alignment by using the stored inertial raw data before the alignment is completed;
and a second forward filtering module 430, configured to perform backward filtering alignment to the initial coarse alignment time according to the stored inertial raw data.
Optionally, the performing forward filtering alignment according to the stored inertial raw data when the reverse alignment reaches the initial rough alignment time further includes:
and repeatedly carrying out reverse filtering alignment and forward filtering alignment on the stored inertial raw data so as to improve the initial alignment precision.
Optionally, the performing forward filtering alignment according to the stored inertial raw data when the reverse alignment reaches the initial rough alignment time further includes:
and on the basis of the indexing mechanism, the inertial navigation system rotates around a Z-direction single axis in the filtering alignment process to increase observability of the inertial navigation system.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by a program to instruct associated hardware, where the program may be stored in a computer-readable storage medium, and when the program is executed, the program includes steps S101 to S103, where the storage medium includes, for example: ROM/RAM, magnetic disk, optical disk, etc.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. An inertial navigation system initial alignment method is characterized by comprising the following steps:
after the inertial navigation system performs coarse alignment, performing Kalman filtering alignment according to the normal sampling and navigation resolving rate;
before the alignment is finished, utilizing the stored inertial original data to carry out reverse navigation resolving and reverse Kalman filtering alignment;
the reverse Kalman filtering alignment state equation and the forward Kalman filtering alignment state equation are different in that state transition matrixes are reciprocal, and the reverse Kalman filtering recursion calculation process is consistent with the forward Kalman filtering calculation process;
the forward Kalman filtering alignment process comprises the following steps:
establishing a Kalman filtering initial alignment state space model:
wherein
The system state transition matrix is:
the system measurement matrix is:
the reverse navigation resolving process comprises the following steps:
the pose is updated as follows:
the speed update is as follows:
the location update is as follows:
the measurement is updated by adopting the same observed quantity as the forward Kalman filtering alignment, and the measurement equation is the same as the forward Kalman filtering alignment measurement equation, and the state equation is as follows:
performing reverse alignment to the initial rough alignment moment, and performing forward filtering alignment according to the stored inertial original data;
and repeatedly carrying out reverse filtering alignment and forward filtering alignment on the stored inertial raw data so as to improve the initial alignment precision.
2. The method of claim 1, wherein the reverse alignment to an initial coarse alignment time, the forward filtering alignment from the stored inertial raw data further comprises:
and repeatedly carrying out reverse filtering alignment and forward filtering alignment on the stored inertial raw data so as to improve the initial alignment precision.
3. The method of claim 1, wherein the reverse alignment to an initial coarse alignment time, the forward filtering alignment from the stored inertial raw data further comprises:
and on the basis of the indexing mechanism, the inertial navigation system rotates around a Z-direction single axis in the filtering alignment process to increase observability of the inertial navigation system.
4. An inertial navigation system initial alignment device, comprising:
the first forward filtering module is used for performing Kalman filtering alignment according to the normal sampling and navigation resolving rate after the inertial navigation system performs coarse alignment;
the backward filtering module is used for performing backward navigation resolving and backward Kalman filtering alignment by using the stored inertial original data before the alignment is finished;
the reverse Kalman filtering alignment state equation and the forward Kalman filtering alignment state equation are different in that state transition matrixes are reciprocal, and the reverse Kalman filtering recursion calculation process is consistent with the forward Kalman filtering calculation process;
the forward Kalman filtering alignment process comprises the following steps:
establishing a Kalman filtering initial alignment state space model:
wherein
The system state transition matrix is:
the system measurement matrix is:
the reverse navigation resolving process comprises the following steps:
the pose is updated as follows:
the speed update is as follows:
the location update is as follows:
the measurement is updated by adopting the same observed quantity as the forward Kalman filtering alignment, and the measurement equation is the same as the forward Kalman filtering alignment measurement equation, and the state equation is as follows:
the second forward filtering module is used for performing reverse alignment to the initial rough alignment moment and performing forward filtering alignment according to the stored inertia original data;
and repeatedly carrying out reverse filtering alignment and forward filtering alignment on the stored inertial raw data so as to improve the initial alignment precision.
5. The apparatus of claim 4, wherein the reverse alignment to an initial coarse alignment time, the forward filtering alignment from the stored inertial raw data further comprises:
and repeatedly carrying out reverse filtering alignment and forward filtering alignment on the stored inertial raw data so as to improve the initial alignment precision.
6. The apparatus of claim 4, wherein the reverse alignment to an initial coarse alignment time, the forward filtering alignment from the stored inertial raw data further comprises:
and on the basis of the indexing mechanism, the inertial navigation system rotates around a Z-direction single axis in the filtering alignment process to increase observability of the inertial navigation system.
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| CN112697166B (en) * | 2020-11-04 | 2023-06-06 | 河北汉光重工有限责任公司 | Self-alignment method of strapdown inertial navigation system under motion state |
| CN112729332B (en) * | 2020-11-17 | 2022-10-28 | 中国船舶重工集团公司第七0七研究所 | Alignment method based on rotation modulation |
| CN112747772B (en) * | 2020-12-28 | 2022-07-19 | 厦门华源嘉航科技有限公司 | Request-based inertial odometer moving base coarse alignment method |
| CN113959462B (en) * | 2021-10-21 | 2023-09-12 | 北京机电工程研究所 | Quaternion-based inertial navigation system self-alignment method |
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