A GNSS-assisted air precision alignment method and system for missile-borne inertial navigation system

Abstract

The application provides a GNSS auxiliary-based air precision alignment method and system for a missile-borne inertial navigation system, and relates to the technical field of integrated navigation, wherein the method comprises the following steps: acquiring system state data, position data and GNSS auxiliary navigation data of the missile-borne inertial navigation system, and constructing a measurement equation of the missile-borne inertial navigation system according to the system state data; determining position error parameters and system state error parameters according to the measurement equation and data obtained in S1; inputting the data into a position prediction model for prediction to obtain corresponding state prediction results and position prediction results; correcting the state prediction results and the position prediction results according to the system state error parameters and the position error parameters, and completing air precision alignment of the missile-borne inertial navigation system; and the application realizes air precision alignment of the missile-borne inertial navigation system through multiple ways such as fusing multiple source data, considering error models and constructing decision models, and improves alignment precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated navigation, more particularly to a GNSS-assisted air precision alignment method and system for missile-borne inertial navigation systems. BACKGROUND

[0002] At present, inertial navigation systems have the characteristics of complete autonomy, high concealment, and high data update rate, and play a very important role in modern precision guided bombs. For missile-borne strapdown inertial navigation systems, high-precision initial alignment plays an important role in improving the navigation accuracy of the inertial navigation system.

[0003] However, the traditional missile-borne inertial navigation system needs to perform a long static alignment process when starting on the ground to ensure the accuracy of its initial attitude and position information. However, during flight, due to the influence of complex and variable environmental conditions such as dynamic acceleration, angular rate changes, and other factors, the traditional method is difficult to provide sufficient precision, especially in long-time flight missions. In addition, the traditional alignment method usually depends on static or quasi-static conditions, and has poor environmental adaptability.

[0004] Therefore, how to provide a missile-borne inertial navigation system air precision alignment method that can solve the above problems is a problem that those skilled in the art need to solve. SUMMARY

[0005] Therefore, the present application provides a GNSS-assisted air precision alignment method and system for missile-borne inertial navigation systems, which realizes the air precision alignment of missile-borne inertial navigation systems by fusing multi-source data, considering error models, and constructing decision models, and improves the alignment accuracy.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] A GNSS-assisted air precision alignment method for missile-borne inertial navigation systems, comprising the following steps:

[0008] S1: obtaining system state data, position data, and GNSS-assisted navigation data of the missile-borne inertial navigation system, and constructing a measurement equation of the missile-borne inertial navigation system according to the system state data;

[0009] S2: determining position error parameters and system state error parameters according to the measurement equation and the data obtained in S1;

[0010] S3: constructing a prediction model and inputting the data obtained in S1 into the position prediction model for prediction to obtain corresponding state prediction results and position prediction results;

[0011] S4: correcting the state prediction result and the position prediction result obtained by S4 according to the system state error parameter and the position error parameter obtained by S2, to complete the air fine alignment of the missile-borne inertial navigation system.

[0012] Preferably, the system state data in S1 comprises misalignment angle data, velocity data and acceleration data.

[0013] Preferably, S2 specifically comprises:

[0014] S21: constructing a Kalman filter, inputting the misalignment angle data, the velocity data, the acceleration data, the position data and the GNSS auxiliary navigation data into the Kalman filter for processing to obtain corresponding misalignment angle error parameters, velocity error parameters and acceleration error parameters;

[0015] S22: determining corresponding position error data according to the position data and the GNSS auxiliary navigation data.

[0016] Preferably, S3 specifically comprises:

[0017] S31: obtaining historical system state data and device parameters of the missile-borne inertial navigation system, pre-processing the historical system state data, taking the device parameters as labels to form a corresponding data set;

[0018] S32: constructing a prediction model and dividing the data set into a training set and a test set according to a preset proportion;

[0019] S33: training the prediction model by using the training set, testing the prediction model by using the test set, calculating model loss, and stopping training when the model loss is the smallest;

[0020] S34: inputting the data obtained in S1 into the trained prediction model for prediction to obtain corresponding state prediction results and position prediction results.

[0021] Preferably, S4 further comprises:

[0022] The position prediction result after correction is corrected again by acquiring real-time GNSS information.

[0023] The application further provides a GNSS auxiliary-based air fine alignment system of a missile-borne inertial navigation system, comprising:

[0024] An acquisition module is configured to acquire system state data, position data and GNSS auxiliary navigation data of a missile-borne inertial navigation system, and construct a measurement equation of the missile-borne inertial navigation system according to the system state data.

[0025] an error determination module configured to determine position error parameters and system state error parameters according to the measurement equation and the data obtained in S1;

[0026] a prediction module configured to construct a prediction model and input the data obtained in S1 into the position prediction model for prediction to obtain corresponding state prediction results and position prediction results;

[0027] a correction module configured to correct the state prediction results and position prediction results obtained in S4 according to the system state error parameters and position error parameters obtained in S2, to complete the air precision alignment of the missile-borne inertial navigation system.

[0028] According to the technical solution described above, compared with the prior art, the present disclosure provides a GNSS-assisted missile-borne inertial navigation system air precision alignment method and system, which has the following beneficial effects:

[0029] (1) Improved alignment accuracy: The present disclosure combines the actual attitude data and actual motion parameter data of the missile-borne inertial navigation system, and fuses the misalignment angle, velocity, acceleration and GNSS data in real time through the Kalman filter to accurately estimate the system error parameters (such as misalignment angle error, velocity error, etc.), thereby improving the alignment accuracy through the fusion and processing of multiple data sources;

[0030] (2) Optimized decision-making process: The present disclosure constructs a prediction model and trains the prediction model (such as a machine learning model) using historical data to predict the system state and position in advance, thereby reducing real-time calculation delay, making the alignment process more intelligent and automated, and being able to adapt to different flight environments and task requirements;

[0031] (3) The present disclosure realizes the air precision alignment of the missile-borne inertial navigation system through the fusion of multiple data sources, the construction of a dynamic simulation model, the consideration of error models and the construction of a decision-making model, thereby improving the alignment accuracy, optimizing the decision-making process and improving the combat effectiveness, which is of great significance for improving the overall performance and combat effectiveness of the missile weapon system. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present disclosure, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0033] Figure 1 The present disclosure provides a GNSS-assisted missile-borne inertial navigation system air precision alignment method, and the overall flowchart of the method is shown in the figure;

[0034] Figure 2 A timing diagram of the discrete reverse Kalman filtering algorithm provided for the embodiment of the present application is shown in the figure;

[0035] Figure 3 A structural principle block diagram of a GNSS-assisted missile-borne inertial navigation system air precision alignment system provided for the present application is shown in the figure. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0037] Referring to Figure 1 The embodiment of the present application discloses a GNSS-assisted missile-borne inertial navigation system air precision alignment method, which comprises the following steps:

[0038] S1: acquiring system state data, position data and GNSS-assisted navigation data of a missile-borne inertial navigation system, and constructing a measurement equation of the missile-borne inertial navigation system according to the system state data, wherein the specific expression of the measurement equation is:

[0039]

[0040] In the formula, H = [0 3×3 0 3×3 I 3×3 0 3×3 0 3×3 ].

[0041] S2: determining position error parameters and system state error parameters according to the measurement equation and the data obtained in S1;

[0042] S3: constructing a prediction model, inputting the data obtained in S1 into the position prediction model for prediction, and obtaining corresponding state prediction results and position prediction results;

[0043] S4: correcting the state prediction results and the position prediction results obtained in S4 according to the system state error parameters and the position error parameters obtained in S2, and completing air precision alignment of the missile-borne inertial navigation system.

[0044] In one specific embodiment, the system state data in S1 comprises misalignment angle data, velocity data and acceleration data.

[0045] In one specific embodiment, S2 specifically comprises:

[0046] S21: Construct a Kalman filter, input the misalignment angle data, velocity data, acceleration data, the position data and GNSS auxiliary navigation data into the Kalman filter for processing to obtain corresponding misalignment angle error parameters, velocity error parameters and acceleration error parameters, wherein the Kalman filter can be a discrete inverse Kalman filtering algorithm, and the algorithm timing diagram is shown in Figure 2 ;

[0047] S22: Determine corresponding position error data according to the position data and the GNSS auxiliary navigation data.

[0048] Specifically, the discrete inverse Kalman filtering algorithm is shown in Table 1.

[0049]

[0050]

[0051] For convenience of calculation, in the discrete inverse Kalman filtering algorithm, the gyroscopic measurement information and the earth rotation angular velocity can be taken inversely, and the obtained related navigation results are used to solve F * (t k+1 ), at the same time, the corresponding coefficient of F * (t k+1 ) with the gyroscopic constant drift ε b should be taken

[0052] In one specific embodiment, the S3 specifically comprises:

[0053] S31: Obtain historical system state data and device parameters of a missile-borne inertial navigation system, and pre-process the historical system state data, wherein the device parameters are used as labels to form a corresponding data set;

[0054] S32: Construct a prediction model, and divide the data set into a training set and a test set according to a preset proportion;

[0055] S33: Train the prediction model using the training set, test the prediction model using the test set, and calculate the model loss, wherein the training is stopped when the model loss is the smallest;

[0056] S34: Input the data obtained in the S1 into the trained prediction model for prediction to obtain corresponding state prediction results and position prediction results.

[0057] Specifically, the prediction model can include a filter combining a cubature Kalman filter and a particle filter, and a model combining an LSTM neural network, which can significantly improve the filtering performance.

[0058] In one specific embodiment, the S4 further comprises:

[0059] The real-time GNSS information is used to correct the corrected position prediction result.

[0060] Referring to Figure 3 The embodiments of the present application also provide a system using the above-mentioned GNSS-assisted missile-borne inertial navigation system air precision alignment method, which comprises:

[0061] An acquisition module is configured to acquire system state data, position data and GNSS-assisted navigation data of the missile-borne inertial navigation system, and construct a measurement equation of the missile-borne inertial navigation system according to the system state data;

[0062] An error determination module is configured to determine position error parameters and system state error parameters according to the measurement equation and the data obtained by the S1;

[0063] A prediction module is configured to construct a prediction model, and input the data obtained by the S1 into the position prediction model to obtain corresponding state prediction results and position prediction results;

[0064] A correction module is configured to correct the state prediction results and the position prediction results obtained by the S4 according to the system state error parameters and the position error parameters obtained by the S2, so as to complete the air precision alignment of the missile-borne inertial navigation system.

[0065] In order to fully compare the effects of the CKF algorithm and the velocity+attitude matching transfer alignment algorithm based on the inverse Kalman filter in the large azimuth misalignment angle transfer alignment, four groups of experimental data are verified by the experimental method adopted in this section, and the experimental estimation results are shown in Table 2.

[0066] Table 2 Comparison of estimation results of laboratory semi-physical simulation large azimuth misalignment angle transfer alignment

[0067]

[0068]

[0069] Note: The misalignment angle error in the table refers to the estimation value of the misalignment angle of the sub-inertial navigation system at the beginning of navigation by the RTS smoothing algorithm, and the installation error angle error is obtained by comparing the installation error angle estimated by the CKF and the installation error angle estimated by the second precision alignment in the transfer alignment based on the inverse Kalman filter with the reference value.

[0070] It can be seen that in the large misalignment angle transfer alignment, the CKF method is poor in estimating the misalignment angle and installation error angle in the horizontal direction, and the velocity + attitude matching transfer alignment method based on the inverse Kalman filter can effectively estimate each error of the sub-inertial navigation, and is obviously superior to the CKF method in estimation accuracy.

[0071] The various embodiments are described in the specification with progressive progression, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0072] The above description of disclosed embodiments enables those skilled in the art to carry out or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for precise airborne alignment of a missile-borne inertial navigation system based on GNSS assistance, characterized in that, Includes the following steps: S1: Acquire the system status data, position data, and GNSS-assisted navigation data of the missile-borne inertial navigation system, and construct the measurement equations of the missile-borne inertial navigation system based on the system status data; S2: Determine the position error parameters and system state error parameters based on the measurement equation and the data obtained in S1; S3: Construct a prediction model and input the data obtained in S1 into the prediction model to make a prediction, and obtain the corresponding state prediction result and position prediction result; S4: Based on the system state error parameters and position error parameters obtained in S2, correct the state prediction results and position prediction results obtained in S3 to complete the airborne inertial navigation system's precise alignment.

2. The airborne precision alignment method for a GNSS-assisted missile-borne inertial navigation system according to claim 1, characterized in that, The system status data in S1 includes: misalignment angle data, velocity data, and acceleration data.

3. The airborne precision alignment method for a GNSS-assisted missile-borne inertial navigation system according to claim 2, characterized in that, S2 specifically includes: S21: Construct a Kalman filter by inputting the misalignment angle data, velocity data, acceleration data, position data, and GNSS-assisted navigation data into the Kalman filter for processing, and obtaining the corresponding misalignment angle error parameters, velocity error parameters, and acceleration error parameters. S22: Determine the corresponding position error data based on the position data and the GNSS-assisted navigation data.

4. The airborne precision alignment method for a GNSS-assisted missile-borne inertial navigation system according to claim 3, characterized in that, S3 specifically includes: S31: Obtain historical system state data and equipment parameters of the missile-borne inertial navigation system, and preprocess the historical system state data, using the equipment parameters as labels to form a corresponding dataset; S32: Construct a prediction model and divide the dataset into a training set and a test set according to a preset ratio; S33: Train the prediction model using the training set and test the prediction model using the test set, calculate the model loss, and stop training when the model loss is minimized. S34: Input the data obtained in S1 into the trained prediction model for prediction, and obtain the corresponding state prediction result and position prediction result.

5. The airborne precision alignment method for a GNSS-assisted missile-borne inertial navigation system according to claim 2, characterized in that, S4 further includes: The location prediction results, which have already been corrected, are further revised by acquiring real-time GNSS information.

6. A system utilizing the GNSS-assisted airborne inertial navigation system airborne precision alignment method according to any one of claims 1-5, characterized in that, include: The acquisition module is used to acquire system status data, position data, and GNSS-assisted navigation data of the missile-borne inertial navigation system, and to construct the measurement equations of the missile-borne inertial navigation system based on the system status data. An error determination module is used to determine position error parameters and system state error parameters based on the measurement equation and the data obtained by the acquisition module. The prediction module is used to construct a prediction model and input the data obtained from the acquisition module into the prediction model to make predictions, thereby obtaining the corresponding state prediction results and position prediction results. The correction module is used to correct the state prediction results and position prediction results based on the system state error parameters and position error parameters, so as to complete the airborne inertial navigation system's precise alignment.

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