An integrated method, device and product for inertial group alignment and calibration

Abstract

The present application discloses an integrated method, device and product for alignment and calibration of an inertial group, and relates to the field of calibration of an inertial group. The method comprises: constructing a gyro output error model of an inertial group based on the principle of equivalent rotating vectors, and performing linearization processing to obtain a linearized error model of the gyro output; solving the linearized error model by an iterative approximation solution method to obtain an estimated value of the misalignment angle of the sub-inertial navigation in the inertial group; obtaining a gyro output error rewriting model based on the estimated value of the misalignment angle of the sub-inertial navigation and the gyro output error model, and using a multi-sampling point data solution method to solve the gyro output error rewriting model under inequality constraints to obtain an estimated value of the gyro zero bias of the sub-inertial navigation, thereby achieving alignment and calibration of the inertial group. The present application is suitable for small and large misalignment angle conditions without relying on the prior information of INS or designing filters, and can improve the accuracy of alignment and calibration of the inertial group.

Description

Technical Field

[0001] The present application relates to the field of inertial group calibration, and in particular to an inertial group alignment and calibration integrated method, device and product. Background Art

[0002] Due to the limitations of payload and cost, autonomous drones are often equipped with low-precision inertial navigation systems (INS). The initial attitude error obtained by this type of system through self-alignment is large, and it is impossible to estimate the gyro bias through its own navigation parameters. The method based on laboratory environment estimation is not suitable for airborne INS (hereinafter referred to as sub-INS) due to the problems of complex INS disassembly, poor timeliness and high labor cost. After the autonomous drone is launched, the alignment algorithm using its own maneuvers and GPS (Global Positioning System) is not only time-consuming, but also consumes a lot of battery power, thereby reducing the duration of the drone's mission. In satellite denial environments, this method will fail. Therefore, in a disassembly-free and denial environment, it is crucial and challenging for the autonomous drone to achieve the initial attitude and gyro bias compensation of its onboard sub-INS through online compensation before being launched.

[0003] The proposed method of integrating IUG alignment and calibration is currently an effective method for solving the above problems. Currently, a variety of IUG alignment and calibration methods based on Kalman filter (KF), Extended KF (EKF) and Unscented KF (UKE) have been derived. These algorithms have shown great potential in improving the misalignment angle and gyro bias accuracy of sub-INS.

[0004] However, the existing integrated method of inertial group alignment and calibration has a complex processing process, and the various errors are coupled in the model and cannot be separated linearly. The only way to obtain the optimal estimate of the misalignment angle of the sub-INS is to use filtering. In addition, the filtering-based method needs to design the filter according to the performance parameters such as the device accuracy and noise characteristics of the sub-INS. Therefore, whether the device accuracy and noise characteristics are accurately evaluated will be reflected in the estimation accuracy of the misalignment angle and gyro zero bias of the sub-INS, and will also determine the quality of the filtering estimation results. Moreover, the filtering estimation is a process that gradually converges over time. The integrated method takes a long time, which increases the preparation time before the autonomous drone is launched and reduces the timeliness of the drone's mission execution. Most of the related algorithms known to the inventors are studies conducted on one of the working conditions of small misalignment angle or large misalignment angle, and there are few algorithms that can be applied to both working conditions. Summary of the invention

[0005] In order to solve the above-mentioned problems existing in the known technologies of the inventor, the present application provides an integrated method, device and product for alignment and calibration of an inertial group.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides an integrated method for alignment and calibration of an inertial group, comprising:

[0008] The gyro output error model of the inertial unit is constructed based on the principle of equivalent rotating vector;

[0009] Using an equivalent rotation vector to linearize the gyro output error model to obtain a linearized error model of the gyro output;

[0010] By adopting an iterative approximation solution method, a linearized error model of the gyro output is solved to obtain an estimated value of the misalignment angle of the neutron inertial navigation system of the inertial group;

[0011] Based on the misalignment angle estimation value of the sub-INS and the gyro output error model, a gyro output error rewriting model is obtained;

[0012] Construct the inequality constraint between the gyro bias of the main inertial navigation system and the gyro bias of the sub-inertial navigation system;

[0013] The data of the UAV launch vehicle during its driving process is sampled at different sampling points to obtain multi-sampling point data;

[0014] By adopting a multi-sampling point data solution method, under the inequality constraint, the gyro output error rewriting model is solved to obtain the gyro zero bias estimation value of the sub-inertial navigation system, thereby realizing the alignment and calibration of the inertial group.

[0015] Optionally, the gyro output error model is expressed as:

[0016] ;

[0017] In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system, represents the gyro measurement value of the sub-INS, represents the gyro measurement value of the main inertial navigation, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Represents the coordinate system change matrix from the sub-INS's own coordinate system to the main INS's own coordinate system, represents a higher-order term, Represents angular velocity.

[0018] Optionally, the linearized error model of the gyro output is expressed as:

[0019] ;

[0020] In the formula, Main Inertial Navigation Coordinate system to sub-INS The equivalent rotation vector of the coordinate system, express The gyro measurement value of the sub-inertial navigation at this moment, express The gyro measurement value of the main inertial navigation at this moment, represents a higher-order term, represents the angular velocity, [ , ] indicates the selected data segment interval, .

[0021] Optionally, when the UAV launch vehicle is driving and the UAV launch vehicle performs a large maneuver, there is a difference inequality constraint. At this time, the linearized error model of the gyro output is expressed as:

[0022] ;

[0023] In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system, represents the gyro measurement value of the sub-INS, Indicates the gyro measurement value of the main inertial navigation;

[0024] The large maneuvers include turning and erecting the UAV launcher.

[0025] Optionally, the difference inequality constraint is expressed as:

[0026] ;

[0027] In the formula, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Represents the coordinate system change matrix from the sub-INS's own coordinate system to the main INS's own coordinate system.

[0028] Optionally, the gyro output error rewriting model is expressed as:

[0029] ;

[0030] In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system, represents the gyro measurement value of the sub-INS, represents the gyro measurement value of the main inertial navigation, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Indicates The attitude matrix composed of the misalignment angle estimated by the iteration, represents a higher-order term, represents the angular velocity, Represents the number of iterations to estimate.

[0031] Optionally, the inequality constraint between the gyro bias of the main inertial navigation system and the gyro bias of the sub-inertial navigation system is expressed as:

[0032] ;

[0033] In the formula, Indicates The attitude matrix composed of the misalignment angle estimated by the iteration, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Represents the number of iterations to estimate.

[0034] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned integrated method for inertial group alignment and calibration.

[0035] In a third aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned inertial group alignment and calibration integrated method.

[0036] According to the specific embodiments provided in this application, this application has the following technical effects:

[0037] The present application provides an integrated method, device and product for alignment and calibration of an inertial navigation system. Under the premise of not losing the estimation accuracy, an integrated model of gyro output error is constructed based on the projection principle, and a linear characterization model of the misalignment angle and gyro zero bias of the sub-inertial navigation system is given by using an equivalent rotating vector approximation method. In order to avoid the phenomenon of large approximation errors in the process of using the linearized model under large misalignment angle conditions, the present application also adopts an iterative approximation solution method to solve the linearized error model of the gyro output, obtain the misalignment angle estimation value of the sub-inertial navigation system in the inertial navigation system, and adopts a multi-sampling point data solution method to solve the gyro output error rewriting model under inequality constraints to obtain the gyro zero bias estimation value of the sub-inertial navigation system. The above two-step iterative solution method can improve the calibration efficiency, and is suitable for small and large misalignment angle conditions without relying on the prior information of INS or designing filters, and can reduce the influence of the mounting lever arm of the sub-inertial navigation system relative to the main inertial navigation system on the misalignment angle estimation accuracy, thereby improving the accuracy of alignment and calibration of the inertial navigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a flow chart of an integrated method for alignment and calibration of an inertial group provided in one embodiment of the present application;

[0040] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0043] In an exemplary embodiment, the present application provides an integrated method for alignment and calibration of an inertial group, which is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation. Figure 1 As shown, the method includes the following steps 100 to 106.

[0044] Step 100: Construct a gyro output error model of the inertial group based on the equivalent rotation vector principle.

[0045] Assuming that under ideal conditions (i.e., without measurement error and measurement noise), the gyro output value of the main inertial navigation system (hereinafter referred to as the main inertial navigation) is , the output value of the sub-inertial navigation system (abbreviated as sub-INS) is , the coordinate system change matrix from the sub-INS's own coordinate system to the main INS's own coordinate system is , that is, the attitude matrix formed by the relative installation angle. Based on this, according to the projection principle, we can get and The relationship between them is:

[0046] (1)

[0047] In the formula, and Both The matrix of .

[0048] The error model of the INS gyro output is:

[0049] (2)

[0050] In the formula, It is composed of the scale factor error and the non-orthogonality error dimensional matrix, represents the true angular velocity of the carrier, is the gyro bias, represents the gyro measurement noise, Indicates gyro measurement value.

[0051] Substituting formula (2) into formula (1) yields:

[0052] (3)

[0053] In the formula, and Represent the matrices consisting of the scale factor error and non-orthogonal error of the gyro of the sub-INS and the main INS, and Represent the gyro measurement values ​​of the slave and main inertial navigation respectively, and Respectively represent the gyro bias of the slave and main inertial navigation systems, and They represent the gyro measurement noise of the slave and main inertial navigation systems respectively, Represents the transpose of a matrix.

[0054] Rewriting formula (3) into an expression for the gyro measurement value of the sub-INS, we have:

[0055] (4)

[0056] The matrix formed by the relative installation angle between the sub-INS and the main INS and the scale factor error and non-orthogonal error of the gyro of the sub-INS And the matrix composed of the scale factor error and non-orthogonal error of the main inertial navigation gyro are all fixed values, so is a constant matrix, then let:

[0057] (5)

[0058] In the formula, represents the constant matrix. Substituting formula (5) into formula (4) and simplifying it, we can get:

[0059] (6)

[0060] In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system.

[0061] However, the INS has already calculated the matrix of the scale factor error and non-orthogonal error of the gyro before leaving the factory. And the matrix composed of the scale factor error and non-orthogonal error of the main inertial navigation gyro Based on this, let the matrix of residual scale factor error and non-orthogonal error of main inertial navigation and sub-inertial navigation after factory be: and , are two small quantities. Then formula (5) can be rewritten as:

[0062] (7)

[0063] The residual scale factor error and non-orthogonal error matrix of the main inertial guidance after factory And the matrix consisting of residual scale factor error and non-orthogonal error after the sub-inertial is derived are all small quantities, then, according to the principle of equivalent rotation vector, the matrix and Approximately:

[0064] (8)

[0065] In the formula, and Represent the equivalent rotation vector of the main inertial navigation and the equivalent rotation vector of the sub-inertial navigation respectively and The matrix formed is an antisymmetric matrix. is the identity matrix.

[0066] Substituting formula (8) into formula (5) and simplifying it, we can obtain:

[0067] (9)

[0068] In the formula, Indicates the equivalent rotation vector of the main inertial navigation and the equivalent rotation vector of the sub-inertial navigation and The higher-order small terms.

[0069] Combining formula (9) and formula (6), the gyro output error model can be obtained as follows:

[0070] (10)

[0071] In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system, represents the gyro measurement value of the sub-INS, represents the gyro measurement value of the main inertial navigation, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Represents the coordinate system change matrix from the sub-INS's own coordinate system to the main INS's own coordinate system, represents a higher-order term, Represents angular velocity.

[0072] Step 101: linearize the gyro output error model using an equivalent rotation vector to obtain a linearized gyro output error model.

[0073] In order to realize the rapid estimation of the misalignment angle between the sub-INS and the main INS, for example, this step adopts the equivalent rotation vector method to linearize and approximate formula (10), and the expression of the linearized error model of the gyro output is obtained as follows:

[0074] (11)

[0075] in, yes Coordinate system to The equivalent rotation vector of the coordinate system.

[0076] From formula (11), it can be seen that the gyro bias and measurement noise of the sub-INS and main INS are the main error sources affecting the misalignment angle of the estimated sub-INS. and is the gyro measurement value of the sub-INS and the main INS, which is a known quantity. and is the unknown quantity, To measure noise, its influence on the estimation accuracy of misalignment angle can only be suppressed by filtering or data smoothing. When the inequality (i.e., the difference inequality constraint) of formula (12) holds, is a high-order small term and can be ignored.

[0077] (12)

[0078] Among them, during the driving process of the UAV launch vehicle, inequality (12) is only valid when the launch vehicle performs large maneuvers (for example, turning and erecting the UAV launcher). At this time, the expression shown in formula (13) can be obtained:

[0079] (13)

[0080] Based on the above description, the linearized error model of the gyro output can be expressed as:

[0081] (14)

[0082] In the formula, Main Inertial Navigation Coordinate system to sub-INS The equivalent rotation vector of the coordinate system, express The gyro measurement value of the sub-inertial navigation at this moment, express The gyro measurement value of the main inertial navigation at this moment, represents a higher-order term, represents the angular velocity, [ , ] indicates the selected data segment interval, .

[0083] Step 102: using an iterative approximation solution method to solve the linearized error model of the gyro output, and obtain an estimated value of the misalignment angle of the INS in the IMU.

[0084] According to the properties of antisymmetric matrices:

[0085] (15)

[0086] Based on formula (15), formula (14) can be rewritten as:

[0087] (16)

[0088] In the formula, , , express Time Master Inertial Navigation The measurement values ​​of the three-axis (i.e. xyz) gyroscope. , , They are the misalignment angles of the sub-INS in the x-axis, y-axis and z-axis directions respectively.

[0089] When the UAV launch vehicle makes a large-angle turn, the inequality in formula (17) holds true.

[0090] (17)

[0091] In the formula, is the time interval of the UAV launch vehicle heading maneuvering process. At this time, the estimated values ​​of the misalignment angles of the sub-INS in the x-axis and y-axis directions can be estimated as:

[0092] (18)

[0093] In addition, during the erection of the launcher of the UAV launch vehicle, there are:

[0094] (19)

[0095] In the formula, , is the time interval of the erection process of the UAV launcher. Substituting formula (19) into formula (16), the estimated value of the misalignment angle of the sub-INS in the z-axis direction is:

[0096] (20)

[0097] In summary, according to formula (17) to formula (20), it can be concluded that the large-angle turning maneuver of the UAV launch vehicle can assist in estimating the misalignment angle of the sub-INS relative to the main INS in the x-axis and y-axis directions, and the erection action of the transmitter can assist in estimating the misalignment angle of the sub-INS in the z-axis direction.

[0098] However, when the misalignment angle between the sub-INS and the main INS is small, the estimation accuracy is small when the approximation method of formula (11) is used. When the misalignment angle is large, the estimation accuracy will be increased, making the estimation accuracy of the misalignment angle not globally optimal. In order to solve this problem, an iterative approximation method is used on the basis of formula (10) to solve the problem of large estimation accuracy loss caused by the linear approximation of the misalignment angle estimation model. That is, the following iterative equation holds true:

[0099] (twenty one)

[0100] In the formula, represents the number of iterations, and , Indicates cumulative multiplication. Indicates The own coordinate system of the sub-INS estimated in the iteration. Indicates The coordinate system of the sub-INS estimated in the first iteration is The transformation matrix of the sub-INS's own coordinate system estimated in the iteration, Represents the transformation matrix from the sub-INS’s own coordinate system to the main INS’s own coordinate system estimated in the first iteration.

[0101] Formula (21) is not only applicable to the estimation of small misalignment angles, but also to the estimation of large misalignment angles. The iterative estimation solution of the misalignment angle is:

[0102] (twenty two)

[0103] In the formula, represents the attitude matrix formed by the misalignment angle estimated at the kth iteration. The initial attitude matrix formed by the misalignment angle is , is the identity matrix. express No. A row vector consisting of all elements of a row. , , Indicates The vector sum of the misalignment angles in the x-axis, y-axis and z-axis directions of the sub-INS is estimated by the iteration. , express Time Inertial Navigation The measured values ​​of the xy-axis gyro.

[0104] Step 103: Based on the misalignment angle estimation value of the sub-INS and the gyro output error model, a gyro output error rewriting model is obtained.

[0105] Among them, after estimating the misalignment angle of the sub-INS using formula (22), the gyro output error model shown in formula (10) is rewritten to obtain the gyro output error rewritten model as follows:

[0106] (twenty three)

[0107] Step 104: construct an inequality constraint between the gyro bias of the main inertial navigation system and the gyro bias of the sub-inertial navigation system.

[0108] Since the performance index of the main inertial navigation is more than one order of magnitude higher than that of the sub-inertial navigation, the inequality constraint between the gyro bias of the main inertial navigation and the gyro bias of the sub-inertial navigation is expressed as:

[0109] (twenty four)

[0110] Step 105: Sampling the data of the UAV launch vehicle during its driving process according to different sampling points to obtain multi-sampling point data.

[0111] Step 106: Using a multi-sampling point data solution method, under the inequality constraint, solve the gyro output error rewriting model to obtain the gyro zero bias estimation value of the sub-INS, so as to achieve the alignment and calibration of the INS.

[0112] In order to suppress the influence of noise on the estimated value of the sub-INS gyro bias, the method of summing the data of multiple sampling points is used to improve the signal-to-noise ratio. Combining formula (23) and formula (24), the estimated value of the sub-INS gyro bias is:

[0113] (25)

[0114] In the formula, and The interval formed It represents the time period of the launch vehicle's straight-line driving process.

[0115] Although the misalignment angle of the sub-INS can be effectively compensated by using formula (22), the residual small misalignment angle error will still produce a large cross-projection error of the measured value during the large-angle maneuver of the UAV launch vehicle, thereby affecting the estimation accuracy of the gyro bias. Therefore, only by using the data of the launch vehicle during straight-line driving to estimate the gyro bias of the sub-INS can the estimation accuracy be optimized.

[0116] Based on the above description, in order to not rely on the prior information of inertial devices and to improve the versatility and timeliness of the integrated method of inertial group alignment and calibration, the present application proposes a fast integrated method that is suitable for both small misalignment angles and large misalignment angles. The integrated method of inertial group alignment and calibration provided in the present application adopts a mathematical analytical approach, combining the three-dimensional (xyz) space projection principle and the equivalent rotating vector principle to construct a gyro output error model, and abstracts the integrated method process into a linear model without losing the estimation accuracy. In addition, in order to avoid the phenomenon of large approximation errors in the process of abstracting the gyro output error model into a linearized model using an equivalent rotating vector under large misalignment angle conditions, the two-cycle method of iterative approximation and multi-sampling point data summation is used to achieve accurate estimation of the misalignment angle and gyro zero bias of the sub-inertial navigation.

[0117] The integrated method for inertial group alignment and calibration provided in this application can be applied to the entire process of the drone being transported to the destination by the launch vehicle and launching the drone. In this application scenario, satellite signals are denied, and the launch vehicle of the autonomous drone is equipped with a high-precision inertial navigation system as the main inertial navigation. The drone is equipped with a low-precision inertial navigation system as the sub-inertial navigation. The main inertial navigation is installed in the drone launch tube, and the sub-inertial navigation is installed on the geometric center of the drone. Using the integrated method for inertial group alignment and calibration provided in this application, there is no need for the unmanned vehicle to perform additional maneuvers to assist in estimating the misalignment angle and gyro zero bias of the sub-inertial navigation. The alignment and calibration of the inertial group can be completed by relying only on the turning maneuvers of the launch vehicle during driving and the maneuvers of the launch tube to launch the drone. And the integrated method for inertial group alignment and calibration provided in this application can complete the error estimation of the sub-inertial navigation after the launch tube is erected, without the need for additional time to wait for the operation of the algorithm, which can improve the efficiency of inertial group alignment and calibration.

[0118] In summary, the integrated method for alignment and calibration of the inertial group provided by the present application has the following advantages over the prior art:

[0119] 1. This application innovatively proposes an integrated method that combines the projection principle with the gyro output error model, and uses the equivalent rotating vector approximation method to give a linear characterization model of the sub-inertial navigation misalignment angle and gyro zero bias.

[0120] 2. The method proposed in this application adopts a two-step iterative method, which can improve the efficiency of inertial group alignment and calibration, does not rely on the prior information of INS, does not design filters, and is applicable to conditions with small and large misalignment angles.

[0121] 3. Compared with the traditional method, the method provided by the present application has a simple process, does not require navigation solution to be implemented, and is not based on the speed of the main inertial navigation system, thereby effectively avoiding the influence of the mounting arm of the sub-inertial navigation system relative to the main inertial navigation system on the estimation accuracy of the misalignment angle.

[0122] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 2 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. In the formula, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. In the formula, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the integrated alignment and calibration data of the inertial group. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an integrated alignment and calibration method of the inertial group is implemented.

[0123] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0124] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0125] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In the formula, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0128] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0129] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An integrated method for alignment and calibration of an inertial group, characterized in that: The integrated method for inertial group alignment and calibration comprises: The gyro output error model of the inertial unit is constructed based on the principle of equivalent rotating vector; The gyro output error model is linearized by using an equivalent rotation vector to obtain a linearized error model of the gyro output; the linearized error model of the gyro output is expressed as: ； In the formula, Main Inertial Navigation Coordinate system to sub-INS The equivalent rotation vector of the coordinate system, express The gyro measurement value of the sub-inertial navigation at this moment, express The gyro measurement value of the main inertial navigation at this moment, represents a higher-order term, represents the angular velocity, [ , ] indicates the selected data segment interval, ; By adopting an iterative approximation solution method, a linearized error model of the gyro output is solved to obtain an estimated value of the misalignment angle of the neutron inertial navigation system of the inertial group; Based on the misalignment angle estimation value of the sub-INS and the gyro output error model, a gyro output error rewriting model is obtained; Construct an inequality constraint between the gyro bias of the main inertial navigation system and the gyro bias of the sub-inertial navigation system; the inequality constraint between the gyro bias of the main inertial navigation system and the gyro bias of the sub-inertial navigation system is expressed as: ； In the formula, Indicates The attitude matrix composed of the misalignment angle estimated by the iteration, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, represents the number of iterative estimates; The data of the UAV launch vehicle during its driving process is sampled at different sampling points to obtain multi-sampling point data; By adopting a multi-sampling point data solution method, under the inequality constraint, the gyro output error rewriting model is solved to obtain the gyro zero bias estimation value of the sub-inertial navigation system, thereby realizing the alignment and calibration of the inertial group.

2. The integrated method for inertial group alignment and calibration according to claim 1, characterized in that: The gyro output error model is expressed as: ； In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system, represents the gyro measurement value of the sub-INS, represents the gyro measurement value of the main inertial navigation, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Represents the coordinate system change matrix from the sub-INS's own coordinate system to the main INS's own coordinate system, represents a higher-order term, Represents angular velocity.

3. The integrated method for inertial group alignment and calibration according to claim 1, characterized in that: When the UAV launch vehicle is driving and performs a large maneuver, there is a difference inequality constraint. At this time, the linearized error model of the gyro output is expressed as: ； In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system, represents the gyro measurement value of the sub-INS, Indicates the gyro measurement value of the main inertial navigation; The large maneuvers include turning and erecting the UAV launcher.

4. The integrated method for inertial group alignment and calibration according to claim 3, characterized in that: The difference inequality constraint is expressed as: ； In the formula, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Represents the coordinate system change matrix from the sub-INS's own coordinate system to the main INS's own coordinate system.

5. The integrated method for inertial group alignment and calibration according to claim 1, characterized in that: The gyro output error rewriting model is expressed as: ； In the formula, The new noise represents the synthesis of the gyro measurement noise of the main inertial navigation system and the sub-inertial navigation system, represents the gyro measurement value of the sub-INS, represents the gyro measurement value of the main inertial navigation, represents the gyro bias of the sub-INS and, Indicates the gyro bias of the main inertial navigation system, Indicates The attitude matrix composed of the misalignment angle estimated by the iteration, represents a higher-order term, represents the angular velocity, Represents the number of iterations to estimate.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the inertial group alignment and calibration integrated method according to any one of claims 1 to 5.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the inertial group alignment and calibration integrated method described in any one of claims 1 to 5 is implemented.

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