Online estimation transfer alignment method for the main inertial navigation two-stage movable lever arm of the pod

Abstract

The present invention discloses an online estimation transfer alignment method for a main-sub inertial navigation two-stage movable lever arm of a pod, which relates to the field of aircraft navigation, guidance and control, and comprises the following steps: starting the sub-inertial navigation, and using the main inertial navigation to the sub-inertial navigation to send the main inertial navigation information; after receiving the main inertial navigation information, binding the main inertial navigation information into the initial attitude, initial speed and initial position of the sub-inertial navigation; based on the collected attitude, speed, position and pulse synchronization signal, recording the speed output by the sub-inertial navigation, and recording the attitude matrix and angular velocity output by the sub-inertial navigation; after the sub-inertial navigation completely receives the main inertial navigation information, using the sub-inertial navigation to record the speed output by the main inertial navigation, and calculating the observed quantity, while recording the attitude matrix and angular velocity output by the main inertial navigation, calculating the measurement matrix, and performing Kalman filtering to fuse the main and sub-inertial navigation information. The present invention solves the problem that the traditional movable lever arm introduces speed error in the transfer alignment process, which seriously affects the speed matching transfer alignment accuracy.

Description

Technical Field

[0001] The invention relates to the field of aircraft navigation, guidance and control, and in particular to an online estimation transfer alignment method for a main inertial navigation two-stage movable lever arm of a pod. Background Art

[0002] The onboard main inertial navigation information is one of the alignment and navigation information commonly used by onboard payload equipment to transmit alignment and integrated navigation. With the miniaturization and low cost of inertial navigation, as well as the diversification of the functions of onboard payload equipment, the utilization rate of inertial navigation systems in onboard payloads has gradually increased. Since the payload mounting points are usually on the belly and under the wings, it is difficult for the payload to receive satellite navigation signals through its own antenna. The onboard main inertial navigation is usually an inertial / satellite combination mode, which has the ability to provide stable navigation for a long time. Therefore, the payload equipped with inertial navigation needs to rely on the aircraft's main inertial navigation to provide navigation information for stable navigation.

[0003] Commonly used methods for transfer alignment include "speed + attitude" matching, "speed + position" matching and position matching. The integrated navigation is similar to the transfer alignment process, and usually uses the main inertial navigation "speed + position" or only uses the position for integrated navigation. The navigation models are basically the same. In theory, the "speed + attitude" matching has a fast convergence speed and makes full use of information, followed by "speed + position" matching, and then position matching. Position matching is the most robust transfer alignment method and the least sensitive to system modeling errors. When using attitude matching, it is necessary to model and compensate for the installation error between the main inertial navigation and the sub-inertial navigation, the angular motion caused by the shock absorber and other errors in the system. Speed ​​matching is insensitive to shock absorbers, and the lever arm effect is the main source of error. When the carrier rotates, the linear velocity involved in the angular motion often makes the speed of the sub-inertial navigation inconsistent with the speed of the main inertial navigation. Proper processing of the lever arm can ensure the accuracy and reliability of speed matching transfer alignment. In some complex usage scenarios, such as photoelectric detectors that need to be scanned in space, there is a movable scanning mechanism between the main inertial navigation system and the sub-inertial navigation system. A longer arm is fixedly connected to the main inertial navigation system, and a smaller arm is fixedly connected to the sub-inertial navigation system. This smaller arm is movable relative to the carrier and is called a movable arm. Although the movable arm is short, its continuous movement will cause the sub-inertial navigation system to move continuously relative to the main inertial navigation system, introducing speed error during the transfer alignment process, seriously affecting the accuracy of the speed matching transfer alignment. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the online estimation transfer alignment method for the main inertial navigation two-stage movable lever arm of the pod provided by the present invention solves the problem that the traditional movable lever arm introduces speed error in the transfer alignment process, which seriously affects the speed matching transfer alignment accuracy.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for online estimation transfer alignment of the main inertial navigation two-stage movable lever arm of the pod, comprising the following steps:

[0006] S1: Start the sub-INS and use the main INS to send the main INS information to the sub-INS;

[0007] S2: After the sub-INS receives the main INS information, it binds the main INS information into the initial attitude, initial velocity and initial position of the sub-INS, and establishes a Kalman filter to put the sub-INS into working state;

[0008] S3: Use the sub-INS to collect the attitude, speed, position and pulse synchronization signals periodically sent by the main INS;

[0009] S4: Based on the collected attitude, speed, position and pulse synchronization signal, record the speed output by the sub-INS , and record the attitude matrix output by the sub-INS and angular velocity , multiply to get the first matrix ;

[0010] S5: After the sub-INS receives the main INS information completely, the sub-INS records the speed output by the main INS. , and according to the speed output by the sub-INS and the speed output by the main inertial navigation system Calculate Observables , and record the attitude matrix output by the main inertial navigation system and angular velocity , multiply to get the second matrix ;

[0011] S6: Based on Observations , the first matrix and the second matrix , calculate the measurement matrix , and perform Kalman filtering to fuse the master and slave inertial navigation information;

[0012] S7: Repeat the above steps S2 to S6 until the alignment time is over, and the online estimation transfer alignment of the second-stage active lever arm of the main inertial navigation of the pod is completed.

[0013] Furthermore, the Kalman filter is established in S2 as:

[0014] State variables of Kalman filter for:

[0015]

[0016] in, The angle of misalignment is is the inertia velocity error, are the latitude, longitude and altitude errors, is the gyro zero bias, Add zero bias to the sub-habit, is the sub-inertial navigation segment of the movable arm, is the main inertial navigation section of the movable arm, is the transpose of the matrix;

[0017] The filtering equation is:

[0018]

[0019] in, for State variables at time , superscript represents the derivative, for The state equation at this moment, for The measurement equation of time, for Observable quantity at time.

[0020] Furthermore, the observation in S5 The calculation formula is:

[0021]

[0022] in, For speed observation, For position observation.

[0023] Furthermore, the speed observation for:

[0024]

[0025] in, is the sub-inertial navigation velocity, and the superscript ~ indicates the construction velocity observation The sub-inertial guidance speed used in the test has an inertial guidance error. is the main inertial navigation speed, superscript Represents structural velocity observation The main inertial navigation speed used has measurement errors.

[0026] Furthermore, the position observation for:

[0027]

[0028] in, is the position of the sub-INS, and the superscript ~ indicates the construction velocity observation The sub-INS position has an INS error, is the main inertial navigation position, superscript Represents structural velocity observation The main inertial navigation position has measurement errors.

[0029] Furthermore, the measurement matrix in S6 The calculation formula is:

[0030]

[0031] in, is the velocity measurement matrix, is the position measurement matrix.

[0032] Furthermore, the velocity measurement matrix for:

[0033]

[0034] in, is a 3×3 zero matrix, is a 3×3 unit diagonal matrix, is a 3×9 zero matrix.

[0035] Furthermore, the position measurement matrix for:

[0036]

[0037] in, is a 3×6 zero matrix, is the transformation matrix;

[0038]

[0039] in, is the principal radius of curvature of the meridian, is the principal curvature radius of the Maoyou circle, is the height of the ellipsoid.

[0040] Furthermore, Kalman filtering is performed in S6, including time update and measurement update, and the time update is:

[0041]

[0042]

[0043] in, for Status at all times One-step prediction, is the state transfer matrix, for The optimal estimate of the state at time for The variance matrix of for The variance matrix of is the noise driving matrix, is the identity matrix, for The state equation at time, for The next moment;

[0044] The measurement update is:

[0045]

[0046] in, for The optimal estimate of the state at time for The one-step prediction value state at time , is the filter gain matrix, for The variance matrix of is the measurement noise variance matrix, for The variance matrix of .

[0047] The beneficial effects of the present invention are as follows: the present invention proposes an online estimation transfer alignment method for the two-stage active lever arm of the main sub-inertial navigation of the pod, which can complete the rapid estimation of the active lever arm in the process of speed / position matching transfer alignment, and at the same time can improve the accuracy of transfer alignment and integrated navigation. It is suitable for the estimation and compensation of the lever arm effect of transfer alignment and integrated navigation when the sub-inertial navigation of airborne active equipment uses speed measurement, effectively reducing the transmission error of speed observation transfer alignment in complex environments, improving the alignment and navigation accuracy, and shortening the time required for navigation error convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a flow chart of an online estimation transfer alignment method for a main inertial navigation two-stage movable lever arm of a pod. DETAILED DESCRIPTION

[0049] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0050] like Figure 1 As shown, a method for online estimation and transfer alignment of the main inertial navigation two-stage movable lever arm of a pod includes the following steps:

[0051] S1: Start the sub-INS and use the main INS to send the main INS information to the sub-INS;

[0052] S2: After the sub-INS receives the main INS information, it binds the main INS information into the initial attitude, initial velocity and initial position of the sub-INS, and establishes a Kalman filter to put the sub-INS into working state;

[0053] S3: Use the sub-INS to collect the attitude, speed, position and pulse synchronization signals periodically sent by the main INS;

[0054] S4: Based on the collected attitude, speed, position and pulse synchronization signal, record the speed output by the sub-INS , and record the attitude matrix output by the sub-INS and angular velocity , multiply to get the first matrix ;

[0055] S5: After the sub-INS receives the main INS information completely, the sub-INS records the speed output by the main INS. , and according to the speed output by the sub-INS and the speed output by the main inertial navigation system Calculate Observables , and record the attitude matrix output by the main inertial navigation system and angular velocity , multiply to get the second matrix ;

[0056] S6: Based on Observations , the first matrix and the second matrix , calculate the measurement matrix , and perform Kalman filtering to fuse the master and slave inertial navigation information;

[0057] S7: Repeat the above steps S2 to S6 until the alignment time is over, and the online estimation transfer alignment of the second-stage active lever arm of the main inertial navigation of the pod is completed.

[0058] In one embodiment of the present invention, the relationship between the master and slave inertial navigation velocities in an inertial system is:

[0059]

[0060] in, for The inertial velocity of the system, for The principal inertial velocity of the system, is the angular velocity vector of the navigation system relative to the inertial rotation, It is the arm fixedly connected to the main inertia section. Output of the main inertial navigation gyro, is the arm fixedly connected to the sub-inertia segment, is the output of the sub-inertial navigation gyroscope, is the angular velocity involved, is the main inertial navigation attitude array, given by the main inertial navigation, is the sub-INS attitude matrix, given by the sub-INS, Represents the cross product operation;

[0061] Ignore the speed involved, and , the speed relationship of the master inertial navigation is:

[0062]

[0063] Next, we establish the Kalman filter equation. First, we select the state variable as 21 dimensions, including the sub-inertial misalignment angle , inertia velocity error , latitude, longitude and altitude errors , gyro bias , the zero bias of the sub-inertia addition , the movable arm of the sub-inertial navigation section , main inertial navigation arm . Set the selection state variable for:

[0064]

[0065] The state equation is constructed based on the 15-dimensional state equation commonly used in the geographic navigation system of the inertial navigation system, expanded to 21 dimensions, and the expanded part of the matrix is ​​filled with zeros.

[0066] The filtering equation is:

[0067]

[0068] in, for State variables at time , superscript represents the derivative, for The state equation at this moment, for The measurement equation of time, for Observable quantity at time.

[0069] The state equation is constructed based on the 15-dimensional state equation commonly used in the geographic navigation system of the inertial navigation system, which is expanded to 21 dimensions, and the expanded part of the matrix is ​​filled with zeros, that is:

[0070]

[0071] is the Kalman filter state equation of the traditional integrated navigation without the lever arm effect, is a 15×6 zero matrix, is a 6×15 zero matrix, is a 6×6 zero matrix.

[0072] The Kalman filter time update formula is:

[0073]

[0074] in, is the filter state transfer matrix, for The optimal estimated state at time for The variance matrix of , that is, the one-step prediction variance, is the state transfer matrix, for The variance matrix of The optimal estimated variance matrix at time, is the noise driving matrix.

[0075] The observation quantity and measurement equation of the speed information transmitted by the main inertial navigation are established. The speed observation quantity is:

[0076]

[0077] When establishing the measurement equation, the arm velocity caused by the main inertial navigation and sub-inertial navigation arm is considered.

[0078]

[0079] in, , is the quantity to be estimated, estimated online during transfer alignment, is the speed of the sub-INS output, The speed output by the main inertial navigation system.

[0080] Establish the velocity measurement equation:

[0081]

[0082] in is the main inertial navigation velocity noise, is the speed measurement matrix, and its expression is as follows:

[0083]

[0084] Establish the observation quantity and measurement equation of the position information transmitted by the main inertial navigation:

[0085]

[0086] in, Output latitude and longitude for the sub-INS. Output latitude and longitude for main inertial navigation.

[0087] Considering the position relationship between the master and slave inertial navigation systems:

[0088]

[0089] Change the location to latitude and longitude (denoted as and )express:

[0090]

[0091] The measurement equation is:

[0092]

[0093] in is the main inertial navigation position noise, is the position measurement matrix.

[0094] Based on the above derivation process, we can get:

[0095] S5 mesoscopic measurement The calculation formula is:

[0096]

[0097] in, For speed observation, For position observation.

[0098] Speed ​​Observation for:

[0099]

[0100] in, is the sub-inertial navigation velocity, and the superscript ~ indicates the construction velocity observation The sub-inertial guidance speed used in the test has an inertial guidance error. is the main inertial navigation speed, superscript Represents structural velocity observation The main inertial navigation speed used has measurement errors.

[0101] Position observation for:

[0102]

[0103] in, is the position of the sub-INS, and the superscript ~ indicates the construction velocity observation The sub-INS position has an INS error, is the main inertial navigation position, superscript Represents structural velocity observation The main inertial navigation position has measurement errors.

[0104] S6 Measurement Matrix The calculation formula is:

[0105]

[0106] in, is the velocity measurement matrix, is the position measurement matrix.

[0107] Speed ​​measurement matrix for:

[0108]

[0109] in, is a 3×3 zero matrix, is a 3×3 unit diagonal matrix, is a 3×9 zero matrix.

[0110] Position measurement matrix for:

[0111]

[0112] in, is a 3×6 zero matrix, is the transformation matrix;

[0113]

[0114] in, is the principal radius of curvature of the meridian, is the principal curvature radius of the Maoyou circle, is the height of the ellipsoid.

[0115] Kalman filtering is performed in S6, including time update and measurement update, and the time update is:

[0116]

[0117]

[0118] in, for Status at all times One-step prediction, is the state transfer matrix, for The optimal estimate of the state at time for The variance matrix of , that is, the one-step prediction variance, for The variance matrix of , that is, the optimal estimated variance matrix at time k-1, is the noise driving matrix, is the identity matrix, for The state equation at time, for The next moment;

[0119] The measurement update is:

[0120]

[0121] in, for The optimal estimate of the state at time for The one-step prediction value state at time , is the filter gain matrix, for The variance matrix of is the measurement noise variance matrix, for The variance matrix of . Then perform the Kalman filter time update and measurement update to complete the (Right now ) estimates.

[0122] The present invention proposes an estimation method for a main inertial navigation two-stage movable lever arm for a pod, which can realize online estimation and compensation of a fixed lever arm and a movable lever arm, and is used for calibration and compensation of the lever arm during online navigation and post-processing.

[0123] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A method for online estimation and transfer alignment of the main inertial navigation two-stage movable lever arm of a pod, characterized in that: The following steps are involved: S1: Start the sub-INS and use the main INS to send the main INS information to the sub-INS; S2: After the sub-INS receives the main INS information, it binds the main INS information into the initial attitude, initial velocity and initial position of the sub-INS, and establishes a Kalman filter to put the sub-INS into working state; S3: Use the sub-INS to collect the attitude, speed, position and pulse synchronization signals periodically sent by the main INS; S4: Based on the collected attitude, speed, position and pulse synchronization signal, record the speed output by the sub-INS , and record the attitude matrix output by the sub-INS and angular velocity , multiply to get the first matrix ; S5: After the sub-INS receives the main INS information completely, the sub-INS records the speed output by the main INS. , and according to the speed output by the sub-INS and the speed output by the main inertial navigation system Calculate Observables , and record the attitude matrix output by the main inertial navigation system and angular velocity , multiply to get the second matrix ; S6: Based on Observations , the first matrix and the second matrix , calculate the measurement matrix , and perform Kalman filtering to fuse the master and slave inertial navigation information; S7: Repeat the above steps S2 to S6 until the alignment time is over, and the online estimation transfer alignment of the second-stage active lever arm of the main inertial navigation of the pod is completed.

2. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 1 is characterized in that: The Kalman filter established in S2 is: State variables of Kalman filter for: in, The angle of misalignment is is the inertia velocity error, are the latitude, longitude and altitude errors, is the gyro zero bias, Add zero bias to the sub-habit, is the sub-inertial navigation segment of the movable arm, is the main inertial navigation section of the movable arm, is the transpose of the matrix; The filtering equation is: in, for State variables at time , superscript represents the derivative, for The state equation at time, for The measurement equation of time, for Observable quantity at time.

3. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 2 is characterized in that: The S5 mesoscale The calculation formula is: in, For speed observation, For position observation.

4. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 3 is characterized in that: The speed observation for: in, is the sub-inertial navigation velocity, and the superscript ~ indicates the construction velocity observation The sub-inertial guidance speed used in the test has an inertial guidance error. is the main inertial navigation speed, superscript Represents structural velocity observation The main inertial navigation speed used has measurement errors.

5. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 4 is characterized in that: The position observation for: in, is the position of the sub-INS, and the superscript ~ indicates the construction velocity observation The sub-INS position has an INS error, is the main inertial navigation position, superscript Represents structural velocity observation The main inertial navigation position has measurement errors.

6. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 5 is characterized in that: The measurement matrix in S6 The calculation formula is: in, is the velocity measurement matrix, is the position measurement matrix.

7. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 6 is characterized in that: The velocity measurement matrix for: in, is a 3×3 zero matrix, is a 3×3 unit diagonal matrix, is a 3×9 zero matrix.

8. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 7 is characterized in that: The position measurement matrix for: in, is a 3×6 zero matrix, is the transformation matrix; in, is the principal radius of curvature of the meridian, is the principal curvature radius of the Maoyou circle, is the height of the ellipsoid.

9. The method for online estimation, transfer and alignment of the main inertial navigation two-stage movable lever arm for a pod according to claim 8, characterized in that: The Kalman filter is performed in S6, including time update and measurement update, and the time update is: in, for Status at all times One-step prediction, is the state transfer matrix, for The optimal estimate of the state at time for The variance matrix of for The variance matrix of is the noise driving matrix, is the identity matrix, for The state equation at time, for The next moment; The measurement update is: in, for The optimal estimate of the state at time for The one-step prediction value state at time , is the filter gain matrix, for The variance matrix of is the measurement noise variance matrix, for The variance matrix of .

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