Rapid transfer alignment method and system based on reverse / forward navigation

Abstract

The invention discloses a fast transfer alignment method and system based on reverse / forward navigation, and relates to the technical field of transfer alignment of inertial navigation systems. Comprising the following steps: receiving navigation data sent by comprehensive navigation equipment through an information interface combination; preprocessing the navigation data; carrying out transfer alignment by utilizing volume Kalman filtering based on the preprocessed navigation data, and storing main inertial navigation data and sub inertial navigation data; performing reverse filtering based on the main inertial navigation data and the sub inertial navigation data, returning to a transfer alignment starting point, and performing reverse Kalman filtering transfer alignment; and large azimuth misalignment angle transfer alignment is realized through inverse Kalman filtering. According to the method, the inverse Kalman filter is added to implement airborne master-slave inertial navigation rapid transfer alignment, so that the estimation precision of large azimuth misalignment angle transfer alignment is improved on the premise of not increasing additional computing resources.

Description

Technical Field

[0001] The present invention relates to the technical field of transfer alignment of an inertial navigation system, and in particular to a rapid transfer alignment method and system based on reverse / forward navigation. Background Art

[0002] There are two types of inertial navigation systems: platform-based and strapdown-based. The former has a physical platform that tracks the navigation coordinate system. The inertial instrument is installed on the platform. The speed and position information can be obtained by integrating the accelerometer signal. The attitude information is provided by the attitude angle sensor on the platform ring frame. The inertial navigation platform can isolate the angular motion of the carrier, thereby reducing the dynamic error, but it has the disadvantages of large size, low reliability, high cost, and inconvenient maintenance. In addition to the advantages of simple structure, low cost, small size and weight, short preparation time, and long MTBF, the strapdown inertial navigation system has more information available than the platform-based inertial navigation system, which is very important for transmission alignment. Therefore, it is an inevitable trend for the strapdown inertial navigation system to replace the platform-based inertial navigation system. The simplification of the physical structure of the strapdown inertial navigation system is actually exchanged for the complex design of algorithms and software. The rapid improvement of the current computer level provides a guarantee for the realization of this complex design. Therefore, the algorithm is the core of the strapdown inertial navigation. When the hardware configuration of the inertial device and other hardware is fixed, the algorithm determines the performance of the strapdown inertial navigation system.

[0003] The strapdown inertial navigation system has no physical platform. The inertial instrument is directly connected to the carrier. The inertial platform function is realized by computer software, and the attitude angle is obtained by calculation. It is also called a "mathematical platform". Since the inertial instrument in the strapdown system is subject to the influence of the angular motion of the carrier, it is required to have a large dynamic range, wide frequency bandwidth, good environmental adaptability, etc., and has high requirements on the speed and capacity of the navigation computer. The strapdown system has the advantages of compact structure, high reliability, light weight, small size, low power consumption, convenient maintenance, and low cost. It is also easy to integrate and integrate with other navigation systems or equipment. It has become the mainstream solution for the development of modern inertial system technology. Inertial sensors such as gyroscopes and accelerometers are the core components of the inertial navigation system and play a decisive role in the accuracy of the system. Among them, the gyroscope is used to obtain the angular velocity of the motion and measure its angle change. The angular velocity is used to obtain the direction information, which plays the role of attitude solution and auxiliary positioning in inertial navigation.

[0004] The initial alignment process of the strapdown inertial navigation system fast compass is a feedback control process. The accuracy and speed of the initial alignment are mutually restricted. Improving the accuracy requires collecting more data, which reduces the speed. In general initial alignment, data processing has the problem of being discarded after use. The data involved in the rough alignment calculation process cannot be used in the fine alignment calculation. The fine alignment process must re-collect new data, which prolongs the initial alignment time. The collected data is stored in time series and can be used at different stages of the initial alignment. The saved data can also be reversed, which is equivalent to obtaining an equal amount of new data, improving the utilization rate, reducing the amount of data collection, and indirectly reducing the time used for alignment.

[0005] The usual method to improve alignment accuracy is to reduce interference during alignment and increase alignment time, so that the proportion of initial alignment error in navigation error increases accordingly, making it easier to identify. However, interference during alignment is usually difficult to avoid, and increasing alignment time is also inconsistent with the requirement of rapid alignment. By making full use of the navigation error information contained in the sampled data within the alignment time, rather than simply increasing the alignment time, the sampled data used for alignment is stored, and the forward and reverse sequences are formed after being sorted forward and reversely in chronological order; then the forward sequence is roughly aligned using the anti-disturbance inertial system alignment algorithm to obtain a rough alignment result with a smaller error; secondly, the reverse sequence and the forward sequence are iteratively aligned according to the reverse navigation parameter identification method, ultimately achieving the purpose of accelerating the convergence speed of the error parameters and improving the heading angle alignment accuracy.

[0006] The real-time performance of the initial alignment represents the rapid response capability of the navigation system. It is difficult to meet both alignment accuracy and alignment speed at the same time. However, by using and mining the inertial navigation data multiple times, a higher-precision alignment can be achieved in the same time. The reverse alignment method can use the stored sensor data multiple times for forward and reverse calculations, which improves data utilization and shortens alignment time. Therefore, the forward and reverse navigation method can achieve the purpose of accelerating the convergence speed of the filter parameters and improving the heading angle alignment accuracy.

[0007] In large azimuth misalignment angle transfer alignment, although the cubature Kalman filter can effectively estimate the heading misalignment angle, the estimation effect of the horizontal misalignment angle is poor. Therefore, a fast transfer alignment method and system based on reverse / forward navigation is proposed to solve the difficulties existing in the prior art, which is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0008] In view of this, the present invention provides a rapid transfer alignment method and system based on reverse / forward navigation, improves the existing linear relative inertial navigation transfer alignment system model, re-derives a nonlinear relative inertial navigation transfer alignment system suitable for when the initial relative attitude error angle is large, and verifies the original relative inertial navigation transfer alignment with actual inertial navigation data.

[0009] In order to achieve the above object, the present invention adopts the following technical solution:

[0010] A rapid transfer alignment method based on reverse / forward navigation comprises the following steps:

[0011] Acquiring data: receiving navigation data sent by integrated navigation equipment through information interface combination;

[0012] Preprocessing data: preprocessing the navigation data to obtain preprocessed navigation data;

[0013] Secondary transfer alignment: Based on the pre-processed navigation data, the first transfer alignment is performed using the volumetric Kalman filter, and the main inertial navigation data and the sub-inertial navigation data are stored; based on the main inertial navigation data and the sub-inertial navigation data, reverse filtering is performed, and the starting point of the transfer alignment is returned to perform reverse Kalman filter transfer alignment;

[0014] Alignment implementation: A second transfer alignment is performed based on the inverse Kalman filter to achieve transfer alignment with a large azimuth misalignment angle.

[0015] In the above method, optionally, the navigation data includes position information, speed information and attitude information; the position information includes longitude, latitude and altitude; the speed information includes north speed, east speed and vertical speed; the attitude information includes heading angle, pitch angle, roll angle and yaw angle.

[0016] The above method may optionally perform point removal, position limiting and filtering preprocessing on the navigation data to obtain preprocessed navigation data.

[0017] The above method optionally uses a volumetric Kalman filter to perform a first transfer alignment based on the preprocessed navigation data, and stores the main inertial navigation data and the sub-inertial navigation data; performs inverse filtering based on the main inertial navigation data and the sub-inertial navigation data, and returns to the starting point of the transfer alignment. The specific steps of performing the inverse Kalman filter transfer alignment include:

[0018] Based on the preprocessed navigation data, the first transfer alignment is performed using the cubature Kalman filter, and the navigation data at the end of the transfer alignment is used as the initial information of the reverse transfer alignment process, while the initial velocities of the main inertial navigation and the sub-inertial navigation are reversed;

[0019] The stored main inertial navigation data and sub-inertial navigation data are used to perform the reverse transfer alignment process. In the reverse update process of the attitude and speed of the sub-inertial navigation, the gyro angular rate information and the earth's rotation angular rate information are inverted, and the coefficients corresponding to the gyro constant zero bias and the flexural deformation angular rate are adjusted in the state matrix of the reverse filter to obtain the reverse Kalman filter;

[0020] After the reverse transfer alignment process traces back to the starting point, the second transfer alignment is performed using the inverse Kalman filter.

[0021] The above method can optionally adopt a speed+attitude matching method to achieve transfer alignment with a large azimuth misalignment angle.

[0022] A rapid transfer alignment system based on reverse / forward navigation, applying any one of the rapid transfer alignment methods based on reverse / forward navigation, comprising: a data acquisition module, a data preprocessing module, a secondary transfer alignment module and an alignment implementation module;

[0023] A data acquisition module is connected to the input end of the pre-processing data module and is used to receive the navigation data sent by the integrated navigation device through the information interface combination;

[0024] A preprocessing data module is connected to the input end of the secondary transfer alignment module and is used to preprocess the navigation data to obtain preprocessed navigation data;

[0025] The secondary transfer alignment module is connected to the input end of the alignment realization module, and is used to perform the first transfer alignment based on the preprocessed navigation data by using the volumetric Kalman filter, and store the main inertial navigation data and the sub-inertial navigation data; perform inverse filtering based on the main inertial navigation data and the sub-inertial navigation data, return to the starting point of the transfer alignment, and perform inverse Kalman filter transfer alignment;

[0026] The alignment realization module is connected to the output end of the secondary transfer alignment module and is used to perform the second transfer alignment based on the inverse Kalman filter to realize the transfer alignment with a large azimuth misalignment angle.

[0027] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a method and system for rapid transfer alignment based on reverse / forward navigation, which has the following beneficial effects:

[0028] (1) The present invention aims to implement rapid transfer alignment of airborne master inertial navigation by adding an inverse Kalman filter, thereby improving the estimation accuracy of large azimuth misalignment angle transfer alignment without increasing additional computing resources;

[0029] (2) The present invention first uses the cubature Kalman filter to perform the fine alignment process in the transfer alignment and stores the main and sub-inertial navigation data of this stage, and then uses the stored data for reverse filtering to return to the starting point of the transfer alignment. At this time, the entire system has satisfied the linear model. Finally, the Kalman filter fine alignment is performed on the entire process again to achieve large azimuth misalignment angle transfer alignment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0031] Figure 1 A flow chart of a rapid transfer alignment method based on reverse / forward navigation provided by the present invention;

[0032] Figure 2 A structural block diagram of a rapid transfer alignment system based on reverse / forward navigation provided by the present invention;

[0033] Figure 3 It is a schematic diagram of the simulation results of the large azimuth misalignment angle transfer alignment inverse Kalman filtering process provided by the present invention, wherein 3a is a misalignment angle estimation effect diagram, 3b is an installation error angle estimation effect diagram, 3c is a dynamic deflection deformation angle estimation effect diagram, and 3d is a deflection deformation angular rate estimation effect diagram;

[0034] Figure 4 It is a schematic diagram of the simulation results of the second precise alignment of the large azimuth misalignment angle transfer alignment provided by the present invention, wherein 4a is a misalignment angle estimation effect diagram, 4b is an installation error angle estimation effect diagram, 4c is a dynamic flexural deformation angle estimation effect diagram, and 4d is a flexural deformation angular rate estimation effect diagram. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Reference Figure 1 As shown, the present invention discloses a rapid transfer alignment method based on reverse / forward navigation, comprising the following steps:

[0037] Acquiring data: receiving navigation data sent by integrated navigation equipment through information interface combination;

[0038] Preprocessing data: preprocessing the navigation data to obtain preprocessed navigation data;

[0039] Secondary transfer alignment: Based on the pre-processed navigation data, the first transfer alignment is performed using the volumetric Kalman filter, and the main inertial navigation data and the sub-inertial navigation data are stored; based on the main inertial navigation data and the sub-inertial navigation data, reverse filtering is performed, and the starting point of the transfer alignment is returned to perform reverse Kalman filter transfer alignment;

[0040] Alignment implementation: A second transfer alignment is performed based on the inverse Kalman filter to achieve transfer alignment with a large azimuth misalignment angle.

[0041] Furthermore, the navigation data includes position information, speed information and attitude information; the position information includes longitude, latitude and altitude; the speed information includes north speed, east speed and vertical speed; the attitude information includes heading angle, pitch angle, roll angle and yaw angle.

[0042] Furthermore, the navigation data is preprocessed by point removal, position limiting and filtering to obtain preprocessed navigation data.

[0043] Further, based on the preprocessed navigation data, the first transfer alignment is performed using the volumetric Kalman filter, and the main inertial navigation data and the sub-inertial navigation data are stored; based on the main inertial navigation data and the sub-inertial navigation data, inverse filtering is performed to return to the starting point of the transfer alignment. The specific steps of performing the inverse Kalman filter transfer alignment include:

[0044] Based on the preprocessed navigation data, the first transfer alignment is performed using the cubature Kalman filter, and the navigation data at the end of the transfer alignment is used as the initial information of the reverse transfer alignment process, while the initial velocities of the main inertial navigation and the sub-inertial navigation are reversed;

[0045] The stored main inertial navigation data and sub-inertial navigation data are used to perform the reverse transfer alignment process. In the reverse update process of the attitude and speed of the sub-inertial navigation, the gyro angular rate information and the earth's rotation angular rate information are inverted, and the coefficients corresponding to the gyro constant zero bias and the flexural deformation angular rate are adjusted in the state matrix of the reverse filter to obtain the reverse Kalman filter;

[0046] After the reverse transfer alignment process traces back to the starting point, the second transfer alignment is performed using the inverse Kalman filter.

[0047] Furthermore, a speed + posture matching method is adopted to achieve the transfer alignment of large azimuth misalignment angles.

[0048] Specifically, see Table 1, which is a specific formula of a discrete forward Kalman filter algorithm and a discrete inverse Kalman filter algorithm.

[0049] Table 1 Discrete forward Kalman filter algorithm and discrete inverse Kalman filter algorithm

[0050]

[0051]

[0052] in, represents the error state estimate at time k, the posterior estimate based on time k+1, f represents the forward filtering process, k represents a constant, k-1 represents a constant, F represents the state transfer matrix, b represents the inverse filtering process, P k|k+1 represents the error covariance matrix at time k, the posterior estimate based on time k+1, and reflects the uncertainty of state estimation. The T at the subscript represents matrix transposition. T in the equation represents the time interval, i.e., the sampling period. Q represents the covariance matrix of process noise. F* represents the state transfer matrix of inverse filtering. K represents the Kalman gain. H represents the observation matrix, describing the relationship between the error state and the observation value. R represents the covariance matrix of observation noise. Z represents the observation vector, i.e., the external observation information. I represents the unit matrix. k represents time k, t k+1 represents the k+1 time.

[0053] In a specific embodiment, the following contents are included:

[0054] The information acquisition interface combination receives the navigation data sent by the integrated navigation device; pre-processes the navigation data to obtain the pre-processed navigation data; based on the pre-processed navigation data, uses the volumetric Kalman filter to perform transfer alignment and stores the main inertial navigation data and the sub-inertial navigation data; performs inverse filtering based on the main inertial navigation data and the sub-inertial navigation data, returns to the starting point of the transfer alignment, and performs inverse Kalman filtering transfer alignment; performs transfer alignment based on the inverse Kalman filter to achieve large azimuth misalignment angle transfer alignment.

[0055] The velocity + attitude matching transfer alignment algorithm based on inverse Kalman filtering is as follows:

[0056] (1) Information Storage

[0057] In the process of velocity + attitude matching transfer alignment using cubature Kalman filter (CKF), the main and sub-INS navigation information required by the filter from the start time to the end time is stored in real time, and the sub-INS error estimated by CKF filtering is obtained at the end time, and the misalignment angle error of the sub-INS is compensated.

[0058] (2) Reverse transfer alignment process

[0059] The navigation information at the end of the cubature Kalman filter transfer alignment is used as the initial information of the reverse transfer alignment process, and the initial speeds of the main and sub inertial navigation systems need to be reversed.

[0060] The stored main and sub-INS navigation information is used to carry out the reverse transfer alignment process. Here, only the linear model velocity + attitude matching transfer alignment is needed. In the reverse update process of the sub-INS attitude and velocity, the gyro angular rate information and the earth's rotation angular rate information need to be inverted, and the coefficients corresponding to the gyro constant zero bias and the flexural deformation angular rate need to be changed in the state matrix of the reverse filter. The arm effect calculation compensation method also needs to be changed; after the reverse transfer alignment process is traced back to the starting point, the estimation result of the reverse Kalman filter transfer alignment is used to compensate the error of the sub-INS again.

[0061] (3) Second transfer alignment

[0062] From the beginning, the velocity + attitude matching transfer alignment process is executed again. After the sub-INS error compensation of CKF filtering and inverse Kalman filtering process, the transfer alignment model is already a linear model. The estimation accuracy of the second transfer alignment will be greatly improved.

[0063] When the second transfer alignment process is executed to the end time, the inverse Kalman filtering and the second transfer alignment process will consume a certain amount of computing time. Therefore, the second transfer alignment process needs to be executed for a certain amount of computing time before the navigation state can be entered, that is, the navigation computer is required to store the main and sub-inertial navigation information within the time period of the end time and the end time + a certain amount of computing time.

[0064] Although the present invention can solve the problem of poor horizontal misalignment angle estimation effect in large azimuth misalignment angle transfer alignment and improve the transfer alignment accuracy, it needs to store the navigation data of the main and sub-inertial navigation. At present, microcomputer and chip technology are advancing by leaps and bounds. Since the time of transfer alignment is very short, the navigation computer built into the sub-inertial navigation can realize data storage, and the powerful computing power can complete the reverse Kalman filtering calculation process and the second transfer alignment calculation process in a short time, so the certain calculation time is very short.

[0065] The simulation experiment analysis is carried out for the present invention:

[0066] The simulation indicators are as follows: for the sub-inertial navigation (gyro accuracy is better than 0.02° / h) alignment accuracy, the alignment time will reach <3min; alignment accuracy: azimuth is better than 3 minutes (1sigma), horizontal attitude is better than 0.3 minutes (1sigma).

[0067] The installation error angles between the master and slave inertial navigation systems are 0.5°, 0.5°, and -0.5°, respectively. The standard deviations of the processes are set to 6′, -10′, and 7′, respectively. The arm lengths are set to 2m, 2m, and 1m, respectively. The initial misalignment angles of the slave inertial navigation systems are set to 1°, 1°, and -30°.

[0068] The following conclusions were drawn from the simulation results of velocity + posture matching transfer alignment based on inverse filtering:

[0069] Figure 3 The present invention provides a schematic diagram of the simulation results of the reverse Kalman filtering process for the large azimuth misalignment angle transfer alignment, wherein 3a is a misalignment angle estimation effect diagram, 3b is an installation error angle estimation effect diagram, 3c is a dynamic deflection deformation angle estimation effect diagram, and 3d is a deflection deformation angular rate estimation effect diagram; after the misalignment angle compensation of the CKF transfer alignment, the misalignment angle of the sub-inertial navigation system has met the small misalignment angle condition, so the estimation effect of the reverse Kalman filtering process on the sub-inertial navigation system misalignment angle, installation error angle, dynamic deflection deformation angle and deflection deformation angular rate are better than those of the CKF transfer alignment process, wherein the estimated error of the pitch misalignment angle is -2.86′, the estimated error of the roll misalignment angle is -0.89′, and the estimated error of the heading misalignment angle is 4.67′.

[0070] Figure 4 It is a schematic diagram of the simulation results of the second precise alignment of the large azimuth misalignment angle transfer alignment provided by the present invention, wherein 4a is the misalignment angle estimation effect diagram, 4b is the installation error angle estimation effect diagram, 4c is the dynamic deflection deformation angle estimation effect diagram, and 4d is the deflection deformation angular rate estimation effect diagram; various error parameters of the sub-inertial navigation can be effectively tracked, wherein the estimated error of the pitch misalignment angle is 2.16′, the estimated error of the roll misalignment angle is -1.84′, and the estimated error of the heading misalignment angle is 4.37′, which is much better than the result of CKF filtering.

[0071] From the above simulation results, it can be seen that when solving the large azimuth misalignment angle transfer alignment problem, the new velocity + attitude matching transfer alignment algorithm based on inverse Kalman filtering effectively improves the estimation accuracy of each error term of the sub-inertial navigation compared with the CKF method.

[0072] and Figure 1 Corresponding to the method described above, an embodiment of the present invention further provides a rapid transfer alignment system based on reverse / forward navigation for Figure 1 The specific implementation of the method is shown in the following diagram: Figure 2 As shown, it specifically includes: a data acquisition module, a data preprocessing module, a secondary transfer alignment module and an alignment implementation module;

[0073] A data acquisition module is connected to the input end of the pre-processing data module and is used to receive the navigation data sent by the integrated navigation device through the information interface combination;

[0074] A preprocessing data module is connected to the input end of the secondary transfer alignment module and is used to preprocess the navigation data to obtain preprocessed navigation data;

[0075] The secondary transfer alignment module is connected to the input end of the alignment realization module, and is used to perform the first transfer alignment based on the preprocessed navigation data by using the volumetric Kalman filter, and store the main inertial navigation data and the sub-inertial navigation data; perform inverse filtering based on the main inertial navigation data and the sub-inertial navigation data, return to the starting point of the transfer alignment, and perform inverse Kalman filter transfer alignment;

[0076] The alignment realization module is connected to the output end of the secondary transfer alignment module and is used to perform the second transfer alignment based on the inverse Kalman filter to realize the transfer alignment with a large azimuth misalignment angle.

[0077] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0078] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fast transfer alignment method based on reverse / forward navigation, characterized in that: The following steps are involved: Acquiring data: receiving navigation data sent by integrated navigation equipment through information interface combination; Preprocessing data: preprocessing the navigation data to obtain preprocessed navigation data; Second pass alignment: Based on the preprocessed navigation data, the cubature Kalman filter is used to perform the first pass alignment and store the main inertial navigation data and the sub-inertial navigation data; Perform inverse filtering based on the main inertial navigation data and the sub-inertial navigation data, return to the starting point of the transfer alignment, and perform inverse Kalman filter transfer alignment; Alignment implementation: A second transfer alignment is performed based on the inverse Kalman filter to achieve transfer alignment with a large azimuth misalignment angle.

2. A rapid transfer alignment method based on reverse / forward navigation according to claim 1, characterized in that: Navigation data includes position information, speed information and attitude information; position information includes longitude, latitude and altitude; speed information includes north speed, east speed and vertical speed; attitude information includes heading angle, pitch angle, roll angle and yaw angle.

3. The method for rapid transfer alignment based on reverse / forward navigation according to claim 1, characterized in that: The navigation data is preprocessed by point removal, position limiting and filtering to obtain preprocessed navigation data.

4. The method for rapid transfer alignment based on reverse / forward navigation according to claim 1, characterized in that: Based on the preprocessed navigation data, the cubature Kalman filter is used to perform the first pass alignment and store the main inertial navigation data and the sub-inertial navigation data; Based on the main inertial navigation data and the sub-inertial navigation data, reverse filtering is performed to return to the starting point of the transfer alignment. The specific steps of the reverse Kalman filter transfer alignment include: Based on the preprocessed navigation data, the first transfer alignment is performed using the cubature Kalman filter, and the navigation data at the end of the transfer alignment is used as the initial information of the reverse transfer alignment process, while the initial velocities of the main inertial navigation and the sub-inertial navigation are reversed; The stored main inertial navigation data and sub-inertial navigation data are used to perform the reverse transfer alignment process. In the reverse update process of the attitude and speed of the sub-inertial navigation, the gyro angular rate information and the earth's rotation angular rate information are inverted, and the coefficients corresponding to the gyro constant zero bias and the flexural deformation angular rate are adjusted in the state matrix of the reverse filter to obtain the reverse Kalman filter; After the reverse transfer alignment process traces back to the starting point, the second transfer alignment is performed using the inverse Kalman filter.

5. The method for rapid transfer alignment based on reverse / forward navigation according to claim 1, characterized in that: The speed + posture matching method is adopted to achieve the transfer alignment of large azimuth misalignment angles.

6. A rapid transfer alignment system based on reverse / forward navigation, characterized in that: A rapid transfer alignment method based on reverse / forward navigation according to any one of claims 1 to 5 is applied, comprising: a data acquisition module, a data preprocessing module, a secondary transfer alignment module and an alignment implementation module; A data acquisition module is connected to the input end of the pre-processing data module and is used to receive the navigation data sent by the integrated navigation device through the information interface combination; A preprocessing data module is connected to the input end of the secondary transfer alignment module and is used to preprocess the navigation data to obtain preprocessed navigation data; The secondary transfer alignment module is connected to the input end of the alignment realization module, and is used to perform the first transfer alignment based on the preprocessed navigation data by using the volumetric Kalman filter, and store the main inertial navigation data and the sub-inertial navigation data; perform inverse filtering based on the main inertial navigation data and the sub-inertial navigation data, return to the starting point of the transfer alignment, and perform inverse Kalman filter transfer alignment; The alignment realization module is connected to the output end of the secondary transfer alignment module and is used to perform the second transfer alignment based on the inverse Kalman filter to realize the transfer alignment with a large azimuth misalignment angle.

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