CN114993346A - Initial alignment method of strapdown inertial navigation system suitable for cross-sea-air medium - Google Patents

Initial alignment method of strapdown inertial navigation system suitable for cross-sea-air medium Download PDF

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CN114993346A
CN114993346A CN202210554149.9A CN202210554149A CN114993346A CN 114993346 A CN114993346 A CN 114993346A CN 202210554149 A CN202210554149 A CN 202210554149A CN 114993346 A CN114993346 A CN 114993346A
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王刚
余夕林
管练武
齐钊琳
张子斌
王鹏
林开宏
谷秀毅
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Harbin Engineering University
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Abstract

The invention discloses an initial alignment method of a strapdown inertial navigation system suitable for a cross-sea and air medium, and relates to the technical field of inertial navigation; the method comprises the following steps: the speed and acceleration information output by the inertial measurement element is adopted and calculated to obtain speed and position information, meanwhile, the speed and position information provided by the satellite navigation system is fed back to the inertial navigation system, and estimation of the related parameter error of the inertial navigation system is realized through an algorithm. Firstly, a multiple iteration coarse alignment method is realized through a data compression technology, and the coarse alignment precision is improved to the maximum extent before the inertial navigation enters a navigation mode; secondly, estimating variable parameters of an inertial navigation system such as zero errors of inertial devices by an improved filtering technology and optimization of a state covariance matrix and measuring speed and position errors; and finally, compensating the error obtained by estimation back to the inertial navigation system so as to improve the navigation precision of the system.

Description

Initial alignment method of strapdown inertial navigation system suitable for cross-sea-air medium
Technical Field
The invention belongs to the technical field of inertial navigation, and particularly relates to an initial alignment method of a strapdown inertial navigation system suitable for a cross-sea and air medium.
Background
The inertial navigation device needs to be initially aligned at the initial working moment, and the high-precision measuring element corrects the speed and position information of the inertial device. Although the inertial navigation devices used in ships and aviation have the same principle and similar functions, the inertial navigation devices have great difference in data processing and performance indexes in actual work. The marine inertial navigation device-optical fiber compass and the aviation inertial navigation device-airborne navigation system can not be interchanged, for example: the traditional optical fiber compass (a marine inertial navigation device) is used on an aircraft, and the application effect is poor due to large maneuverability. The traditional airborne inertial navigation is not suitable for the shaking environment caused by sea waves, so that the initial alignment error is large.
At present, with the rise of difficulty required by polar operation and the rise of amphibious aircrafts in water paths, an inertial navigation device capable of working on a marine ship and a sky aircraft is urgently needed, and a strapdown inertial navigation algorithm is correspondingly needed for working. Meanwhile, the initial alignment time of the marine inertial navigation device is different from the initial alignment time of the machine inertial navigation device; in different alignment times, achieving different use required accuracy is the key point for improving the applicability.
Disclosure of Invention
The method aims to solve the problems that the traditional Kalman filtering speed is low, the cross-medium inertial navigation alignment performance is poor and the like; the invention aims to provide an initial alignment method of a strapdown inertial navigation system suitable for a cross-sea and air medium, and can realize a cross-medium alignment algorithm suitable for aligning with a sea surface environment and a high-mobility aviation environment. Coarse alignment is realized within 5 minutes, and the aligned index is reached within 1 hour; the course accuracy can meet the requirement index after 5 minutes.
The invention discloses an initial alignment method of a strapdown inertial navigation system applicable to a cross-sea and air medium, which comprises the following steps:
angular velocity and acceleration information output by an inertial measurement element are adopted and calculated to obtain velocity and position information, and meanwhile, the velocity and position information provided by the satellite navigation is fed back to the inertial navigation system to estimate errors of relevant parameters of the inertial navigation system, so that the navigation precision is improved; estimating the initial alignment model obtained in the past through a correlation algorithm, reducing the dimension of a system state covariance matrix as much as possible, and optimizing the system matrix; and while the Kalman filtering is adopted to estimate part of errors, iterative estimation for recycling the data fed back in the initial alignment period is adopted, and the iterative estimation also optimizes the state covariance matrix of the system.
The initial alignment method of the strapdown inertial navigation system suitable for the cross-sea-air medium specifically comprises the following steps:
the method comprises the following steps: establishing initial alignment of the inertial navigation system by using the acquired high-precision initial alignment data, wherein the model comprises the following steps:
Figure BDA0003654220950000021
taking a geographic coordinate system t system;
Figure BDA0003654220950000022
respectively the east, north and azimuth attitude angle errors of the geographic coordinate system. Delta V E 、δV N 、δV U The velocity errors in the east, north and sky directions of the geographic system, δ L and δ λ are the longitude and dimension errors, ε x 、ε y 、ε z In order to measure the gyro drift of the device,
Figure BDA0003654220950000023
zero offset is given to the accelerometer, and the gyro drift and the accelerometer zero offset are constant errors;
the state equation for the initial alignment is:
Figure BDA0003654220950000024
wherein A represents a state coefficient matrix of 14X14 dimensions, G represents a noise state coefficient matrix of the integrated navigation system, and W represents an integrated navigation system noise vector;
step two: fusing data of the high-precision navigation device and inertial navigation elements to obtain a measurement equation, and obtaining a matrix z of related information of speed and position;
z＝Hx+η
wherein z:
z＝[δL δλ δV E δV N ] T
h is a measurement matrix, and eta is measurement noise;
step three: estimating by adopting an initial alignment model used by Kalman filtering, adjusting the dimension of a covariance matrix of a system, optimizing a state equation of the system, and estimating the model as follows:
X k ＝φ k∣k-1 X k-1 +Γ k∣k-1 W k-1
Z k ＝H k X k +V k
wherein X k-1 、W k-1 Is the state vector known at the last moment; phi is a k∣k-1 、Γ k∣k-1 And H k Is a system structure parameter; x k 、Z k The state and the measurement equation at the moment are shown; iterative updating is carried out on the state observation equation by adopting a Kalman method, and error estimation can be carried out on the model:
and obtaining the state at the next moment from the state at the previous moment:
X k|k-1 ＝φ k∣k-1 X k-1
and (3) solving a state prediction covariance matrix at the next moment:
Figure BDA0003654220950000031
and (3) calculating Kalman filtering gain:
Figure BDA0003654220950000032
and (3) carrying out state estimation:
X k ＝X k∣k-1 +K k (z k -H k X k∣k-1 )
the state covariance matrix of the system is:
P k ＝(I-K K H k )P K|K-1
state covariance matrix P for the initial system 0 Designing to reduce the dimension of a state covariance matrix of the system;
step four: performing iterative processing on the data, including: timely adjusting a state covariance matrix P of a system 0 The dimension of (2) is to perform iterative estimation on the zero offset of the accelerometer to form an accelerometer closed loop, so that the zero offset of the accelerometer can be reduced; iterative estimation is carried out on the zero offset of the gyroscope, and the zero offset of the gyroscope can be reduced; and then carrying out further iterative estimation, wherein the iterative estimation is required to reduce the zero offset error of the gyroscope and the accelerometer by one half of the zero offset error of the accelerometer and the gyroscope before alignment.
Compared with the prior art, the invention has the beneficial effects that:
the initial alignment method of the strapdown inertial navigation system for the ship-machine is provided, and the method realizes the optimization and improvement of a state error matrix from the perspective of adjusting covariance; the dimension reduction processing is carried out on the state equation, and meanwhile, the defect that the traditional error equation is slow in convergence is overcome, so that the error precision of the system is greatly improved, and the precision and time of initial alignment are improved.
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For ease of illustration, the invention is described in detail by the following detailed description and the accompanying drawings.
FIG. 1 is a flow chart of the present invention.
Detailed Description
In order that the objects, aspects and advantages of the invention will become more apparent, the invention will be described by way of example only, and in connection with the accompanying drawings. It is to be understood that this description is made only by way of example and not as a limitation on the scope of the invention. The structure, proportion, size and the like shown in the drawings are only used for matching with the content disclosed in the specification, so that the person skilled in the art can understand and read the description, and the description is not used for limiting the limit condition of the implementation of the invention, so the method has no technical essence, and any structural modification, proportion relation change or size adjustment still falls within the range covered by the technical content disclosed by the invention without affecting the effect and the achievable purpose of the invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present invention are shown in the drawings, and other details not so relevant to the present invention are omitted.
The specific implementation mode adopts the following technical scheme: because the adoption of Kalman filtering can cause the problem of slow convergence of inertial devices, the method adopts an optimized state covariance matrix to improve an initial alignment model of the system, improve the alignment precision of the system and reduce the alignment time; meanwhile, the inertia navigation system can keep high-precision work for a long time by combining with a high-precision sensing device.
The first embodiment is as follows: an initial alignment method of a strapdown inertial navigation system suitable for a cross-sea-air medium is disclosed, and a specific flow is shown in figure 1, and an initial alignment model of the ship strapdown inertial navigation system and an initial alignment model of the aircraft strapdown inertial navigation system are constructed based on an error propagation rule of the ship strapdown inertial navigation system and initial information provided by an external high-precision sensor; estimating the model by using a Kalman filtering method, adjusting the dimensionality of a state defense matrix of the system, and performing iterative estimation on part of error quantity of the alignment model; and after the iterative estimation is finished, adjusting the state defense matrix of the system in time, and optimizing the state defense matrix of the system to carry out further iterative estimation.
The specific embodiment provides a strapdown inertial navigation system based on ships and airplanes, and an initial alignment method based on state matrix adjustment is adopted; angular velocity and acceleration information acquired by a gyroscope and an accelerometer realize initial alignment of strapdown inertial navigation through Kalman filtering, and high-precision alignment of an inertial navigation system is realized. Specifically, a Kalman filtering mode is adopted to adjust the state equation by the system state quantity, namely speed error, position error, gyroscope and accelerometer zero offset, generated by an inertial device and a high-precision sensor, and the state equation is subjected to dimension reduction treatment and then further iteration is carried out on the state quantity, so that the filtering process is accelerated and the error of the system is reduced.
Example two: an initial alignment method of a strapdown inertial navigation system suitable for a cross-sea and air medium specifically comprises the following steps:
the method comprises the following steps: and establishing an initial alignment model of the ship and the airplane according to the propagation rule of the error amount of the inertial navigation of the ship and the airplane and information provided by other high-precision sensors.
The initial alignment model can be expressed as:
Figure BDA0003654220950000061
taking a geographic coordinate system t system;
Figure BDA0003654220950000062
the east, north and azimuth attitude angle errors of the geographic coordinate system are respectively. Delta V E 、δV N 、δV U The velocity errors in the east, north and sky directions of the geographic system, δ L and δ λ are the longitude and dimension errors, ε x 、ε y 、ε z In order to measure the gyro drift of the device,
Figure BDA0003654220950000063
the accelerometer has zero offset, and the gyro drift and the accelerometer have zero offset which is a constant error.
The state equation for the initial alignment is:
Figure BDA0003654220950000064
wherein A represents a state coefficient matrix of 14X14 dimensions, G represents a noise state coefficient matrix of the integrated navigation system, and W represents an integrated navigation system noise vector.
Step two: obtaining a matrix z of velocity and position related information from an initial alignment model
z＝HX+η
Wherein z:
z＝[δL δλ δV E δV N ] T
h is the measurement matrix, and η is the measurement noise.
Step three: estimating an initial alignment model by adopting Kalman filtering, adjusting the dimension of a covariance matrix of a system, optimizing a state equation of the system, and estimating the model as follows:
X k ＝φ k∣k-1 X k-1 +Γ k∣k-1 W k-1
Z k ＝H k X k +V k
wherein X k-1 、W k-1 Is the last known state vector; phi is a k∣k-1 、Γ k∣k-1 And H k Is a system structure parameter; x k 、Z k The state and the measurement equation at the moment are shown; iterative updating is carried out on the state observation equation by adopting a Kalman method, and error estimation can be carried out on the model:
and obtaining the state of the next moment from the state of the previous moment:
X k|k-1 ＝φ k∣k-1 X k-1
and (3) solving a state prediction covariance matrix at the next moment:
Figure BDA0003654220950000071
and (3) calculating Kalman filtering gain:
Figure BDA0003654220950000072
and (3) carrying out state estimation:
X k ＝X k∣k-1 +K k (z k -H k X k∣k-1 )
the state covariance matrix of the system is:
P k ＝(I-K K H k )P K|K-1
state covariance matrix P for initial system 0 And designing to reduce the dimension of the state covariance matrix of the system. Because the time requirements of the ship and the airplane for initial alignment are different, the initial error model is estimated, so that the effect of reducing the initial alignment time of the system is achieved.
Step four: and correcting the accumulated error generated by the inertial navigation device by introducing an external high-precision sensor. Because the angular velocity information and the acceleration information measured by the inertial device have constant zero offset errors, the errors have great influence on the accuracy of the navigation device along with the accumulation of time, and finally the speed and position information of the whole system is diverged. The speed and position information is obtained by adopting an external high-precision measuring device, and the zero offset error of the system is calculated by adopting a multi-iteration mode.
In the actual zero offset estimation process, because the zero offset error of the accelerometer is estimated simply and the zero offset error of the gyroscope is estimated difficultly, the state matrix of the system is optimized by the zero offset error of the accelerometer preferentially to reduce the dimension of the system. Then, the zero offset error of the gyroscope can be estimated.
The embodiment can provide a high-precision alignment method of a ship and an airplane, an initial alignment model of a system is established according to an error propagation rule of a marine inertial navigation device and a machine inertial navigation device, the dimension of a state covariance matrix of the system needs to be further optimized due to the requirement of alignment time, and a part of error amount needs to be estimated firstly; performing iteration again on the data on the basis of the existing error amount estimation to optimize a state matrix of the system; therefore, the error of the accelerometer can be quickly estimated, and the gyroscope can be quickly converged, so that the alignment precision of the inertial navigation system is ensured; the use specification of the inertial navigation device can be reached in a short time.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims (2)

1. The initial alignment method of the strapdown inertial navigation system suitable for the cross-sea and air medium is characterized by comprising the following steps of: the method comprises the following steps:
speed and acceleration information output by an inertial measurement element is adopted and calculated to obtain speed and position information, and meanwhile, the speed and position information provided by the satellite navigation system is fed back to the inertial navigation system to estimate errors of relevant parameters of the inertial navigation system, so that the navigation precision is improved; estimating the initial alignment model obtained before through a correlation algorithm, reducing the dimension of a system state covariance matrix as much as possible, and optimizing the system matrix; and while the Kalman filtering is adopted to estimate part of errors, iterative estimation for recycling the data fed back in the initial alignment period is adopted, and the iterative estimation also optimizes the state covariance matrix of the system.
2. The initial alignment method of the strapdown inertial navigation system suitable for the cross-sea and air medium is characterized by comprising the following steps of: the method specifically comprises the following steps:
the method comprises the following steps: establishing initial alignment of the inertial navigation system by using the acquired high-precision initial alignment data, wherein the model comprises the following steps:
Figure FDA0003654220940000014
taking a geographic coordinate system t system;
Figure FDA0003654220940000011
respectively representing east, north and azimuth attitude angle errors of the geographic coordinate system; delta V E 、δV N 、δV U The velocity errors in the east, north and sky directions of the geographic system, δ L and δ λ are the longitude and dimension errors, ε x 、ε y 、ε z In order to measure the gyro drift of the device,
Figure FDA0003654220940000012
zero offset is given to the accelerometer, and the gyro drift and the accelerometer zero offset are constant errors;
the state equation for the initial alignment is:
Figure FDA0003654220940000013
wherein A represents a state coefficient matrix of 14X14 dimensions, G represents a noise state coefficient matrix of the integrated navigation system, and W represents a system noise vector of the integrated navigation system;
step two: fusing data of the high-precision navigation device and inertial navigation elements to obtain a measurement equation, and obtaining a matrix z of related information of speed and position;
z＝Hx+η
wherein z:
z＝[δL δλ δV E δV N ] T
h is a measurement matrix, eta is measurement noise;
step three: estimating by adopting an initial alignment model used by Kalman filtering, adjusting the dimension of a covariance matrix of a system, optimizing a state equation of the system, and estimating the model as follows:
X k ＝φ k∣k-1 X k-1 +Γ k∣k-1 W k-1
Z k ＝H k X k +V k
wherein X k-1 、W k-1 Is the state vector known at the last moment; phi is a unit of k∣k-1 、Γ k∣k-1 And H k Is a system structure parameter; x k 、Z k The state and the measurement equation at the moment are shown; iterative updating is carried out on the state observation equation by adopting a Kalman method, and error estimation can be carried out on the model:
and obtaining the state at the next moment from the state at the previous moment:
X k|k-1 ＝φ k∣k-1 X k-1
and (3) solving a state prediction covariance matrix at the next moment:
Figure FDA0003654220940000021
and (3) calculating Kalman filtering gain:
Figure FDA0003654220940000022
and (3) carrying out state estimation:
X k ＝X k∣k-1 +K k (z k -H k X k∣k-1 )
the state covariance matrix of the system is:
P k ＝(I-K K H k )P K|K-1
state covariance matrix P for initial system 0 Designing to reduce the dimension of a state covariance matrix of the system;
step four: iteratively processing the data iteratively, comprising: timely adjusting the state covariance matrix P of the system 0 The dimension of (2) is to perform iterative estimation on the zero offset of the accelerometer to form an accelerometer closed loop, so that the zero offset of the accelerometer can be reduced; iterative estimation is carried out on the zero offset of the gyroscope, and the zero offset of the gyroscope can be reduced; and then performing near-further iterative estimation, wherein the zero offset error of the gyroscope and the accelerometer can be reduced to one half of the zero offset error of the accelerometer and the gyroscope before alignment by the iterative estimation.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116026324A (en) * 2023-02-10 2023-04-28 北京大学 Cross-domain navigation system and method for water-air cross-medium craft

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116026324A (en) * 2023-02-10 2023-04-28 北京大学 Cross-domain navigation system and method for water-air cross-medium craft

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