CN113405563B - Inertial measurement unit alignment method - Google Patents
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Abstract
The application discloses an inertial measurement unit alignment method, which comprises the following steps: collecting output data of the inertial device in a shaking environment; decomposing the gesture matrix based on an inertial solidification principle; solving a posture matrix based on a double-vector posture determination TRIAD algorithm and a recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result; establishing a Kalman filtering error model by taking a coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filtering alignment by using inertial device data stored in the past moment to obtain an initial alignment result; outputting an initial alignment result in a shaking environment; the problem of low alignment precision of the traditional coarse alignment method under the condition of large-amplitude shaking interference in the related art can be solved; according to the initial alignment result of the vehicle-mounted positioning and orientation system under the shaking base, the vehicle-mounted positioning and orientation system can be effectively adapted to a strong shaking interference environment, and optimal alignment precision is realized while alignment time is shortened.
Description
Technical Field
The application belongs to the technical field of inertial navigation, and relates to an inertial measurement unit alignment method.
Background
As an important branch in the field of inertial navigation, the vehicle-mounted positioning and orientation system opens up a wide application prospect in the fields of strategic military, commercial and civil use and the like, and plays an important role in national defense construction and national economy development. In the vehicle-mounted positioning and orientation system, the inertial navigation technology is a process of integrating and calculating the posture, speed and position of a carrier based on measurement information of an inertial sensor, and serves as a core link of the inertial navigation technology, and an initial alignment task is to determine a carrier posture matrix and provide an accurate initial integral value for navigation operation. In order to ensure the maneuverability of the carrier, the vehicle-mounted positioning and orientation system needs to have the capability of quickly starting a navigation task; meanwhile, the attitude misalignment angle not only affects the system error in the form of attitude information, but also is reflected in the acquisition of speed and position information, and the navigation accuracy of the system is directly affected. Thus, initial alignment requires implementation of both critical indicators of rapidity and accuracy, which are often contradictory. Therefore, the research of completing high-precision initial alignment in a short time is of great significance in improving the reaction capability and positioning and orientation precision of the vehicle-mounted system.
According to the principle of the alignment process, the method can be divided into two stages of coarse alignment and fine alignment, the coarse alignment stage utilizes the measurement information of an inertial sensor to preliminarily solve a gesture matrix, a common method mainly comprises analytic coarse alignment and inertial system coarse alignment, the analytic coarse alignment utilizes projections of gravitational acceleration and earth rotation angular rate under different coordinate systems to obtain the gesture matrix, and under general conditions, the geographic position of an alignment place is known, so that the projections of the gravitational acceleration and earth rotation angular rate vectors under a navigation coordinate system are also known, and the gesture matrix can be obtained through the conversion relation between the gravitational acceleration and earth rotation angular rate vectors and the navigation system.
However, the analytic coarse alignment method is only applicable to static base environments, takes the attitude matrix as a constant value, and assumes that the gyroscopic measurement output is the earth rotation angular rate vector and the accelerometer measurement output is the gravitational acceleration vector. In fact, in a strong interference environment, the gesture matrix is not fixed, while the large interference angular rate makes it difficult to extract the effective information in the gyro output, and the above assumption no longer holds. Therefore, alignment accuracy is low in a severe environment with large interference, and effective initial alignment cannot be completed.
The inertial system coarse alignment firstly decomposes the gesture matrix, and indirectly utilizes the dual-vector gesture determination to calculate the gesture; the fine alignment stage is based on the initial attitude value calculated by coarse alignment, the attitude misalignment angle is assumed to be a small angle error, the inertial device error and the navigation error are taken as observables, and the misalignment angle is optimally estimated to obtain an accurate attitude matrix.
The inertial system coarse alignment method can adapt to a shaking environment, but does not effectively utilize all measurement information, and cannot achieve optimal alignment accuracy. Based on the existing alignment method, how to finish high-precision initial alignment under the shaking interference environment is a key problem of focusing of the application.
Disclosure of Invention
In order to solve the problem that the conventional coarse alignment method is low in alignment accuracy under the condition of large-amplitude shaking interference in the related art, the embodiment of the application provides an inertial measurement unit alignment method. The application completes coarse alignment through TRIAD (the three-axis attitude determination, double-vector gesture determination) and REQUEST (quaternion estimation, recursive quaternion estimation) algorithms, performs Kalman filtering fine alignment based on inertial device measurement information at the stored historical moment, and provides a combined alignment method which can be suitable for a shaking and swinging environment and effectively balance alignment time and alignment precision. The technical proposal is as follows:
collecting output data of the inertial device in a shaking environment;
decomposing the gesture matrix based on an inertial solidification principle;
solving the gesture matrix based on a double-vector gesture determination TRIAD algorithm and a recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result;
establishing a Kalman filtering error model by taking the coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filtering alignment by using inertial device data stored in the past moment to obtain the initial alignment result;
and outputting the initial alignment result in the shaking environment.
Optionally, the decomposing the gesture matrix based on the principle of inertial solidification is represented by:
wherein n represents a navigation coordinate system, namely a local geographic coordinate system; b represents a carrier coordinate system fixedly connected to the carrier;the navigation inertial coordinate system is a coordinate system which is determined according to the inertial solidification principle and is aligned with the navigation coordinate system at the initial moment; />The inertial coordinate system of the carrier is shown and is a coordinate system which is determined according to the inertial solidification principle and is aligned with the initial moment carrier coordinate system; />Representing a posture conversion matrix from a navigation inertial system to a navigation system, varying with time, and passing through alignment time and resolved position informationAcquiring update; />Representing an attitude conversion matrix from a carrier system to a carrier inertial system, and obtaining and updating through gyro output angular rate information according to time variation; />A posture conversion matrix representing a posture from the carrier inertial system to the navigation inertial system is a constant matrix corresponding to an alignment start time;
wherein the gesture matrixThe method comprises the following steps of:
in the formula,indicating the initial alignment time t 0 The earth coordinate system is a coordinate system determined according to the principle of inertial solidification.
Optionally, the saidThe solving process of (1) comprises:
using gyro angular velocityProjection of alternative rotation angular rate on the vector line +.>
From the alignment start time, the quaternion is solved and updated in each sampling interval;
completing the gesture matrix according to the conversion relation between the quaternion and the gesture matrixIs updated according to the update of the update program.
Optionally, the solving the gesture matrix based on the dual vector pose-determining TRIAD algorithm and the recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result includes:
determining an attitude transformation matrix from a carrier inertial system to a navigation inertial system based on a TRIAD algorithm
Optimizing the REQUEST algorithm based on the REQUESTObtain the optimal estimate of the pose matrix +.>
Optionally, determining an attitude transformation matrix from the carrier inertial frame to the navigation inertial frame based on the TRIAD algorithmComprising the following steps:
based on the gesture conversion matrix from navigation inertial system to navigation systemAnd a posture-switching matrix from carrier system to carrier inertial system +.>Acquiring projections of gravitational acceleration under a navigation inertial system and a carrier inertial system at the current moment;
integrating the acceleration vectors at different moments to obtain projections of the velocity vectors under a navigation inertial system and a carrier inertial system respectively;
determining the attitude transformation matrix according to the TRIAD principle and the projection of the velocity vector under the navigation inertial system and the carrier inertial system
Optionally, the current time t k The projection of the lower gravitational acceleration under the navigation inertial system and the carrier inertial system is represented by the following formula:
wherein, the projection g of the gravity acceleration under the navigation system n ＝[0 0 -g]Projection g of gravitational acceleration under a carrier system b Available accelerometer outputA representation;
accordingly, at different times t 1 and t2 The projection of the velocity vector under the navigation inertial system and the carrier inertial system is represented by:
the gesture conversion matrixRepresented by the formula:
optionally, the optimizing the REQUEST algorithmObtain the optimal estimate of the pose matrix +.>Comprising the following steps:
selecting the integral of the specific force as an observation vector, and at t k The time of day, the observation information is represented by the following formula:
initializing an initial value of a parameter, and setting m 0 ＝0，K 0 ＝0 4×4 ，
At t k Time of day, calculated using local latitude L and alignment time tOutput angular rate using gyroscopes>Calculation of
Solving t according to a calculation formula of observation information k The observation vector at moment is used for obtaining the current increment matrix delta K of the matrix K k ，ΔK k Represented by the formula:
z k ＝b k ×r k ；
σ k ＝tr(B k )；
according to the influence of different observation vectors on the K matrix, selecting a proper weight coefficient, and updating t k The K matrix at the moment is updated as follows:
m k ＝m k-1 +α k
wherein ,αk As the weight coefficient, the same weight coefficient is adopted for each observation vector;
solving a feature vector corresponding to the K maximum feature value, and updating the estimated value of the attitude matrix
Judging whether the alignment time is reached;
if the alignment is not finished, the process is again performed at t k Time of day, calculated using local latitude L and alignment time tOutput angular rate using gyroscopes>Calculate->Is carried out by the steps of (a);
if the alignment is finished, outputting the current attitude matrix estimated value
Optionally, the establishing a kalman filter error model by using the coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filter alignment by using inertial device data stored in the past moment, so as to obtain the initial alignment result, including:
selecting attitude misalignment angle of system errorVelocity error δv n Zero offset epsilon of gyroscope b Zero offset of accelerometer>As state quantity, a 12-dimensional system state equation is established:
wherein, the 12-dimensional state quantity is:
the state transition matrix is known according to the state quantity:
the system noise is:
wherein ,gyro angular rate noise and accelerometer specific force noise, respectively;
and when the carrier is stationary, the navigation solution speed is the system speed error. Taking the speed error as an observed quantity, and establishing a system observation equation:
Z＝HX+V
wherein, the observation matrix h= [0 ] 3×3 I 3×3 0 3×3 0 3×3 ]The observation noise is the velocity measurement noise v= [ V E V N V U ]。
Optionally, the establishing a kalman filter error model by using the coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filter alignment by using inertial device data stored in the past moment, so as to obtain the initial alignment result, including:
in the storage filtering alignment stage, sequentially reading 5 groups of stored inertial device data each time of calculation;
iterative filtering alignment is carried out by utilizing the gyro angle increment and accelerometer specific force increment data at the past moment;
when all stored inertial device data are processed, continuously reading inertial device data updated in real time;
and carrying out real-time filtering alignment by utilizing the angle increment and specific force increment data at the current moment.
Optionally, the method further comprises:
and constructing the shaking environment so that the shaking environment simulates an actual shaking scene.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart of a method for inertial measurement unit alignment provided by one embodiment of the present application;
FIG. 2 is a flow chart of a coarse alignment REQUEST algorithm provided by one embodiment of the present application;
FIG. 3 is a schematic diagram of alignment time allocation provided by one embodiment of the present application;
FIG. 4 is a graph of inertial measurement unit test output data in a sloshing environment provided by one embodiment of the present application;
FIG. 5 is a graph of alignment results of sloshing base data provided by one embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The embodiment of the application provides an inertial measurement unit alignment method, as shown in fig. 1, which at least comprises the following steps:
and step 101, acquiring output data of the inertial device in a shaking environment.
Before this step, the shaking environment needs to be constructed so that it simulates the actual shaking scene.
In one example, the inertial measurement unit is mounted on a test vehicle to keep the engine on and personnel get on the vehicle to create a sloshing interference environment. The inertial measurement unit was connected to a dc regulated power supply and a test computer using a test cable, setting a 24V dc supply. After the test preparation is completed, the inertia measurement combination is powered on, the navigation software of the test computer is started, and the measurement output data of the triaxial fiber optic gyroscope and the triaxial accelerometer are received and recorded in real time.
According to the test content, 5 minutes of inertial device data are continuously collected. After the acquisition is completed, the vehicle position is adjusted, and multiple groups of tests are repeated under the same test condition. The inertial device test output data under the sloshing base is shown in fig. 5. As can be seen from fig. 5, the gyro and accelerometer outputs have large shake interference at each moment, and the inertial device error will directly cause a large alignment error, so that an effective initial attitude value cannot be obtained.
Step 102, decomposing the gesture matrix based on the principle of inertial solidification.
The double-vector pose determination (TRIAD) is a process of solving a pose conversion matrix between two coordinate systems by observing projections of two non-collinear vectors under different coordinate systems. The vector in the double-vector attitude determination method is a determination vector, so that the method is only suitable for obtaining a constant attitude matrix. Under the swinging or swaying environment, the carrier gesture matrix can change along with time, and the effective earth rotation information is submerged by the larger interference angular velocity, so the double-vector gesture determination method is not fully applicable any more. Based on the above, the decomposing gesture matrix based on the principle of inertial solidification is represented by the following formula:
wherein n represents a navigation coordinate system, namely a local geographic coordinate system; b represents a carrier coordinate system fixedly connected to the carrier;the navigation inertial coordinate system is a coordinate system which is determined according to the inertial solidification principle and is aligned with the navigation coordinate system at the initial moment; />The inertial coordinate system of the carrier is shown and is a coordinate system which is determined according to the inertial solidification principle and is aligned with the initial moment carrier coordinate system; />Representing a posture conversion matrix from a navigation inertial system to a navigation system, changing along with time, and acquiring and updating through alignment time and resolved position information; />Representing an attitude conversion matrix from a carrier system to a carrier inertial system, and obtaining and updating through gyro output angular rate information according to time variation; />The posture conversion matrix representing the carrier inertial system to the navigation inertial system is a constant matrix corresponding to the alignment start time.
When the carrier position is not moving, the navigation system rotates along with the rotation of the earth,only with the local latitude L and the earth rotation angle omega in the current time ie t is related. Based on this, wherein the gesture matrix +.>The method comprises the following steps of:
in the formula,indicating the initial alignment time t 0 The earth coordinate system is a coordinate system determined according to the principle of inertial solidification.
The solving process of (1) comprises: using gyro angular velocity +.>Projection of alternative rotation angular rate on the vector line +.>From the alignment start time, the quaternion is solved and updated in each sampling interval; finishing the gesture matrix according to the conversion relation of the quaternion and the gesture matrix>Is updated according to the update of the update program. />The output may be measured using a gyroscope.
From the above, it can be seen that a pose matrix is determinedThe key to (1) is solving->Because the earth rotation information under the shaking interference is not effective any more, the gravity acceleration vectors at two moments can be selected, and the gravity acceleration vectors at different moments can be observed in the navigation inertial system respectively>And carrier inertial system->Is indirectly utilized to acquire +.f by the principle of double vector gesture determination>
And step 103, solving the gesture matrix based on a double-vector gesture determination TRIAD algorithm and a recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result.
The traditional inertial system coarse alignment method only uses acceleration observation information at different moments, and the solving precision is limited by abrupt disturbance at the selected moment. Therefore, how to effectively utilize the measurement information and eliminate abrupt interference at different moments is to solveThe problem of great concern is required. Based on the method, the application designs a coarse alignment scheme based on the TRIAD and REQUEST algorithm, and determines +.>In the late stage of the coarse alignment phase, the REQUEST algorithm is applied to optimize +.>Obtaining an optimal estimate of the pose matrix>
Specifically, the solving the gesture matrix based on the dual-vector gesture determination TRIAD algorithm and the recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result comprises the following steps: determining an attitude transformation matrix from a carrier inertial system to a navigation inertial system based on a TRIAD algorithmOptimizing said +.>Obtain the optimal estimate of the pose matrix +.>
The method determines an attitude conversion matrix from a carrier inertia system to a navigation inertia system based on a TRIAD algorithmComprising the following steps:
first, according to the posture conversion matrix from the navigation inertial system to the navigation systemAnd a posture-switching matrix from carrier system to carrier inertial system +.>Acquiring projections of gravitational acceleration under a navigation inertial system and a carrier inertial system at the current moment; at the current time t k For example, specifically expressed by the following formula:
wherein, the projection g of the gravity acceleration under the navigation system n ＝[0 0 -g]Projection g of gravitational acceleration under a carrier system b Available accelerometer outputAnd (3) representing.
Secondly, because the periodic disturbance acceleration in the shaking environment can cause larger alignment error, in order to reduce the influence of disturbance, the acceleration vectors are selected at different moments to carry out integral operation, and projections of the velocity vectors under a navigation inertial system and a carrier inertial system are respectively obtained; at different moments t 1 and t2 The following is specifically expressed by the following formula:
finally, determining the attitude transformation matrix according to the TRIAD principle and the projection of the velocity vector under the navigation inertial system and the carrier inertial systemSpecifically, the gesture conversion matrix +.>Represented by the formula:
the REQUEST algorithm has the meaning of recursion quaternion estimation and is derived from a batch processing algorithm, and the principle is that quaternion quantization gesture matrix parameters are utilized to convert a quaternion update problem into a matrix eigenvalue solving problem. The REQUEST algorithm can be effectively applied to parameter estimation under the condition of more observation vectors, and the key of the application of the REQUEST algorithm is to recursively solve the four-dimensional matrix K.
The process of recursively solving using the REQUEST algorithm is illustrated with reference to FIG. 2, the optimization of the REQUEST algorithm based on the REQUEST algorithmObtain the optimal estimate of the pose matrix +.>The method comprises the following steps:
step 21, selecting the integral of the specific force as the observation vector, at t k The time of day, the observation information is represented by the following formula:
step 22, initializing the initial value of the parameter, and setting m 0 ＝0，K 0 ＝0 4 × 4 ，
Step 23, at t k Time of day, calculated using local latitude L and alignment time tOutput angular rate using gyroscopesCalculate->
Step 24, solving t according to the calculation formula of the observation information k The observation vector at moment is used for obtaining the current increment matrix delta K of the matrix K k ，ΔK k Represented by the formula:
z k ＝b k ×r k ；
σ k ＝tr(B k )；
step 25, selecting proper weight coefficient according to the influence of different observation vectors on the K matrix, and updating t k The K matrix at the moment is updated as follows:
m k ＝m k-1 +α k
wherein ,αk As the weight coefficient, the same weight coefficient is adopted for each observation vector;
step 26, solving the eigenvector corresponding to the K maximum eigenvalue, and updating the estimated value of the attitude matrix
Step 27, judging whether the alignment time is reached; if the alignment is not finished, the step of initializing the initial value of the parameter is performed again, namely, step 23 is performed again; if the alignment is finished, outputting the current attitude matrix estimated value
And 104, establishing a Kalman filtering error model by taking the coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filtering alignment by applying inertial device data stored in the past moment to obtain the initial alignment result.
The step of establishing a Kalman filtering error model by taking the coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filtering alignment by applying inertial device data stored in the past moment to obtain the initial alignment result, comprises the following steps: in the storage filtering alignment stage, sequentially reading 5 groups of stored inertial device data each time of calculation; iterative filtering alignment is carried out by utilizing the gyro angle increment and accelerometer specific force increment data at the past moment; when all stored inertial device data are processed, continuously reading inertial device data updated in real time; and carrying out real-time filtering alignment by utilizing the angle increment and specific force increment data at the current moment.
Specifically, after the coarse alignment stage of step 103, the pose matrix has been preliminarily acquired. Since the current alignment result still has an attitude misalignment angle, the attitude angle error needs to be further corrected by using a precise alignment process. SelectingAttitude misalignment angle of systematic errorVelocity error δv n Zero offset epsilon of gyroscope b Zero offset of accelerometer>As state quantity, a 12-dimensional system state equation is established:
wherein, the 12-dimensional state quantity is:
the state transition matrix is known according to the state quantity:
the system noise is:
wherein ,gyro angular rate noise and accelerometer specific force noise, respectively;
and when the carrier is stationary, the navigation solution speed is the system speed error. Taking the speed error as an observed quantity, and establishing a system observation equation:
Z＝HX+V
wherein, the observation matrix h= [0 ] 3×3 I 3×3 0 3×3 0 3×3 ]The observation noise is the velocity measurement noise v= [ V E V N V U ]。
And in the storage filtering alignment stage, multiple groups of inertial device data can be processed by each calculation, compared with real-time filtering, more times of filtering calculation are completed in the same time, and the alignment precision is improved by increasing the alignment times. The storage filter alignment time is determined by the coarse alignment time, is only one fifth of the coarse alignment time, and ensures the accuracy of an alignment result while compressing the alignment time.
And step 105, outputting the initial alignment result in the shaking environment.
Assume that the geographical coordinates of the place where the binding test is located: latitude 39.8122 °, longitude 116.1515 °, and altitude 60m. The initial alignment total time was set to 270 seconds and the inertial coarse alignment time was set to 210 seconds. The stored filter alignment time, determined by the coarse alignment time, is 42 seconds with the remaining period of time being the real-time filter alignment. The alignment time allocation diagram is shown in fig. 3. After each parameter configuration is completed, the inertial device test data (shown in fig. 4) collected in the step 101 are input into the alignment algorithm described in the steps 102-104, and the optimal estimation of the gesture matrix can be obtained by applying the coarse alignment of the TRIAD/REQUEST algorithm and the precise alignment process of the memory kalman filter, and the initial alignment result of the system in the shaking environment is output. The initial alignment results of applying the method of the present application are shown in fig. 5. Other sets of inertial device data under the same test conditions were input to an alignment algorithm with north-seeking repeatability of the sets of data less than 0.1 °, as shown in table 1.
Table 1:
| index (I) | Alignment time(s) | Course angle (°) |
| Group 1 | 269.8199 | 88.8436 |
| Group 2 | 269.9399 | 88.8255 |
| Group 3 | 269.7399 | 88.8511 |
| Group 4 | 269.7799 | 88.8301 |
| Group 5 | 269.7899 | 88.8546 |
| Group 6 | 269.7399 | 88.8436 |
| Repeatability of | —— | 0.035 |
In summary, according to the inertial measurement unit alignment method provided by the embodiment of the application, output data of an inertial device is collected in a shaking environment; decomposing the gesture matrix based on an inertial solidification principle; solving a posture matrix based on a double-vector posture determination TRIAD algorithm and a recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result; establishing a Kalman filtering error model by taking a coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filtering alignment by using inertial device data stored in the past moment to obtain an initial alignment result; outputting an initial alignment result in a shaking environment; the problem of low alignment precision of the traditional coarse alignment method under the condition of large-amplitude shaking interference in the related art can be solved; according to the initial alignment result of the vehicle-mounted positioning and orientation system under the shaking base, the vehicle-mounted positioning and orientation system can be effectively adapted to a strong shaking interference environment, and optimal alignment precision is realized while alignment time is shortened.
Meanwhile, the gesture matrix to be solved is determined based on double-vector gesture determination, and the gesture matrix is estimated and optimized based on recursive quaternion, so that the anti-interference capability of a coarse alignment algorithm is improved. Meanwhile, an iterative filtering fine alignment scheme is innovatively designed by using stored inertial device data, attitude correction is completed based on inertial component data at the past moment, and reasonable balance of alignment time and alignment precision is achieved.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (4)
1. An inertial measurement unit alignment method, comprising:
collecting output data of the inertial device in a shaking environment;
decomposing the gesture matrix based on an inertial solidification principle;
solving the gesture matrix based on a double-vector gesture determination TRIAD algorithm and a recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result;
establishing a Kalman filtering error model by taking the coarse alignment result obtained in the coarse alignment stage as an initial value, and performing iterative filtering alignment by using inertial device data stored in the past moment to obtain an initial alignment result;
outputting the initial alignment result in the shaking environment;
the inertial solidification principle-based decomposing gesture matrix is represented by the following formula:
wherein n represents a navigation coordinate system, namely a local geographic coordinate system; b represents a carrier coordinate system fixedly connected to the carrier;the navigation inertial coordinate system is a coordinate system which is determined according to the inertial solidification principle and is aligned with the navigation coordinate system at the initial moment; />The inertial coordinate system of the carrier is shown and is a coordinate system which is determined according to the inertial solidification principle and is aligned with the initial moment carrier coordinate system; />Representing a posture conversion matrix from a navigation inertial system to a navigation system, changing along with time, and acquiring and updating through alignment time and resolved position information; />Representing an attitude conversion matrix from a carrier system to a carrier inertial system, and obtaining and updating through gyro output angular rate information according to time variation; />A posture conversion matrix representing a posture from the carrier inertial system to the navigation inertial system is a constant matrix corresponding to an alignment start time;
wherein the gesture matrixThe method comprises the following steps of:
in the formula,indicating the initial alignment time t 0 The earth coordinate system is a coordinate system determined according to the principle of inertial solidification;
the saidThe solving process of (1) comprises:
using gyro angular velocityProjection of alternative rotation angular rate on the vector line +.>
From the alignment start time, the quaternion is solved and updated in each sampling interval;
completing the gesture matrix according to the conversion relation between the quaternion and the gesture matrixIs updated according to the update of (a);
the solving of the gesture matrix based on the double-vector gesture determination TRIAD algorithm and the recursive quaternion estimation REQUEST algorithm to obtain a coarse alignment result comprises the following steps:
determining an attitude transformation matrix from a carrier inertial system to a navigation inertial system based on a TRIAD algorithm
Optimizing the REQUEST algorithm based on the REQUESTObtaining an attitude matrixOptimal estimation of +.>
The method determines an attitude conversion matrix from a carrier inertia system to a navigation inertia system based on a TRIAD algorithmComprising the following steps:
based on the gesture conversion matrix from navigation inertial system to navigation systemAnd a posture-switching matrix from carrier system to carrier inertial system +.>Acquiring projections of gravitational acceleration under a navigation inertial system and a carrier inertial system at the current moment;
integrating the acceleration vectors at different moments to obtain projections of the velocity vectors under a navigation inertial system and a carrier inertial system respectively;
determining the attitude transformation matrix according to the TRIAD principle and the projection of the velocity vector under the navigation inertial system and the carrier inertial system
The current time t k The projection of the lower gravitational acceleration under the navigation inertial system and the carrier inertial system is represented by the following formula:
wherein, the projection g of the gravity acceleration under the navigation system n ＝[00-g]Projection g of gravitational acceleration under a carrier system b Available accelerometer outputA representation;
accordingly, at different times t 1 and t2 The projection of the velocity vector under the navigation inertial system and the carrier inertial system is represented by:
the gesture conversion matrixRepresented by the formula:
the optimizing the REQUEST algorithmObtain the optimal estimate of the pose matrix +.>Comprising the following steps:
selecting the integral of the specific force as an observation vector, and at t k The time of day, the observation information is represented by the following formula:
initializing an initial value of a parameter, and setting m 0 ＝0，K 0 ＝0 4×4 ，
At t k Time of day, calculated using local latitude L and alignment time tOutput angular rate using gyroscopes>Calculation of
Solving t according to a calculation formula of observation information k The observation vector at moment is used for obtaining the current increment matrix delta K of the matrix K k ，ΔK k Represented by the formula:
z k ＝b k ×r k ；
σ k ＝tr(B k )；
according to the influence of different observation vectors on the K matrix, selecting a proper weight coefficient, and updating t k The K matrix at the moment is updated as follows:
wherein ,αk As the weight coefficient, the same weight coefficient is adopted for each observation vector;
solving a feature vector corresponding to the K maximum feature value, and updating the estimated value of the attitude matrix
Judging whether the alignment time is reached;
if the alignment is not finished, the process is again performed at t k Time of day, calculated using local latitude L and alignment time tOutput angular rate using gyroscopes>Calculate->Is carried out by the steps of (a);
if the alignment is finished, outputting the current attitude matrix estimated value
2. The method according to claim 1, wherein the establishing a kalman filter error model with the coarse alignment result obtained in the coarse alignment stage as an initial value, performing iterative filter alignment by using inertial device data stored in the past moment, and obtaining the initial alignment result includes:
selection systemError attitude misalignment angleVelocity error δv n Zero offset epsilon of gyroscope b Zero offset of accelerometer>As state quantity, a 12-dimensional system state equation is established:
wherein, the 12-dimensional state quantity is:
the state transition matrix is known according to the state quantity:
the system noise is:
wherein ,gyro angular rate noise and accelerometer specific force noise, respectively;
when the carrier is stationary, the navigation solution speed is the system speed error, the speed error is taken as the observed quantity, and a system observation equation is established:
Z＝HX+V
wherein, the observation matrix h= [0 ] 3×3 I 3×3 0 3×3 0 3×3 ]The observation noise is the velocity measurement noise v= [ V E V N V U ]。
3. The method according to claim 1, wherein the establishing a kalman filter error model with the coarse alignment result obtained in the coarse alignment stage as an initial value, performing iterative filter alignment by using inertial device data stored in the past moment, and obtaining the initial alignment result includes:
in the storage filtering alignment stage, sequentially reading 5 groups of stored inertial device data each time of calculation;
iterative filtering alignment is carried out by utilizing the gyro angle increment and accelerometer specific force increment data at the past moment;
when all stored inertial device data are processed, continuously reading inertial device data updated in real time;
and carrying out real-time filtering alignment by utilizing the angle increment and specific force increment data at the current moment.
4. The method according to claim 1, wherein the method further comprises:
and constructing the shaking environment so that the shaking environment simulates an actual shaking scene.
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