Transfer alignment method, device and equipment for real-time dynamic parameter updating

Abstract

The invention discloses a real-time dynamic parameter updating transfer alignment method, device and equipment, belongs to the technical field of inertial navigation systems, and particularly relates to transfer alignment of carriers such as airborne weapons and unmanned aerial vehicles in a mounting state dynamic change scene. The problems of static parameter hypothesis, historical data pollution and insufficient real-time performance existing in the current transfer alignment technology are solved; the method comprises the following steps: setting a sliding window starting interval; judging whether the mounting state suddenly changes or not; and updating the dynamic installation error angle in real time. The real-time dynamic parameter updating transfer alignment method, device and equipment are suitable for real-time navigation parameter transfer alignment of carriers such as airborne missiles (weapons) and unmanned aerial vehicles in high-speed maneuvering flight in a mounting state dynamic change scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of inertial navigation system, and particularly relates to transfer alignment of a carrier such as an airborne weapon or a UAV in a scenario where the mounting state dynamically changes. BACKGROUND

[0002] Before an airborne missile performs a combat task, it needs to complete accurate initial alignment of an inertial navigation system (INS), and transfer alignment technology as a key link directly determines the guidance accuracy and strike effectiveness of the missile. In the prior art, a master-slave inertial navigation architecture is usually adopted: a high-precision strapdown inertial navigation system (SINS) is carried by a carrier aircraft as a master inertial navigation system, and a medium-low precision inertial navigation system is provided for the airborne missile as a slave inertial navigation system. The master inertial navigation system assists the slave inertial navigation system to complete initial alignment by transferring real-time navigation initial parameters such as attitude, velocity and position. However, in a complex flight environment, this transfer alignment technology faces multiple interference factors: Inertial device inherent error: parameter drift caused by gyro drift and accelerometer zero offset; Structural deformation influence: dynamic lever arm effect caused by wing deflection deformation; Time-varying interference: mechanical vibration characteristic mutation caused by missile mounting / demounting; Time delay: time-space mismatch caused by data transmission and processing lag.

[0003] In order to accurately estimate the installation error angle of the slave inertial navigation system, the current transfer alignment method usually takes some dynamic parameters such as dynamic lever arm error as part of the state quantity to estimate by Kalman filtering; in the filtering process, the value of the state quantity parameter gradually approaches the true value; after the more accurate dynamic lever arm error value is estimated, the dynamic lever arm can be compensated to obtain a more accurate installation error angle of the slave inertial navigation system. However, for dynamic parameters such as dynamic lever arm error, the value is prone to sudden change with the change of the mounting state, and if the Kalman filtering process at the previous time point when no mutation occurs is still performed at this time, error estimation lag and even Kalman filtering divergence will occur, resulting in low transfer alignment accuracy, inability to guarantee real-time performance, and even transfer alignment failure.

[0004] Based on the above analysis, the current transfer alignment technology has three major systemic defects: (1) Static parameter assumption: The installation error angle is assumed to be fixed, and the mechanical deformation and vibration characteristic change caused by mounting / demounting (such as missile launch) are ignored.

[0005] (2) Historical data pollution: Kalman filtering continuously accumulates historical data, and when the mounting state suddenly changes, error estimation lags or even diverges.

[0006] (3) Insufficient real-time performance: Re-alignment needs to repeat the maneuver of the carrier, and cannot quickly respond to task changes.

[0007] At present, there is an urgent need for a new transfer alignment scheme that can perceive the mutation of the mounting state in real time, dynamically update parameters, and be computationally efficient, to solve the systematic defects of the prior art. SUMMARY

[0008] The present application provides a real-time dynamic parameter updating transfer alignment method, device and equipment, which solves the problems of static parameter assumption, historical data pollution and real-time deficiency in current transfer alignment technology.

[0009] The real-time dynamic parameter updating transfer alignment method provided by the present application comprises the following steps: Step S1: initialize the sliding window parameters: Set the sliding window start interval, including the maximum start interval and the minimum start interval; Initialize the installation error angle in the no-load state, and construct an extended Kalman filter state vector; Step S2: obtain the real-time detection result of the mounting state, and determine whether the mounting state has mutated; Step S3: according to the mounting state mutation judgment result, repeat the start of a new process to execute step S4 every sliding window start interval; If the current mounting state has not mutated, end the old process and start a new process to execute step S4 every maximum start interval; Otherwise, after an interval of the minimum start interval, end the old process and start a new process to execute step S4; Step S4: collect the measurement data of the main inertial navigation system and the sub-inertial navigation system, based on the initialized installation error angle in the no-load state and the extended Kalman filter state vector, use a matching algorithm to perform transfer alignment, and update the dynamic installation error angle in real time; Step S5: obtain the attitude error covariance trace tr(PδΘ) from the matching algorithm, and when the attitude error covariance trace tr(PδΘ) is less than a threshold η, terminate the alignment and output the alignment result.

[0010] Further, a preferred embodiment is provided, wherein in step S2, three detection methods of acceleration residual detection, vibration energy ratio detection and pressure threshold detection are combined to detect whether the mounting state has mutated: If the result obtained by using any one of the detection methods exceeds the given threshold, it is determined that the mounting state has mutated.

[0011] Further, a preferred embodiment is provided, wherein the matching algorithm in step S4 is a velocity + navigation matching algorithm: The system state is:

[0012] wherein, are east, north and sky velocity errors, respectively; are east, north and sky misalignment angles, respectively; are constant biases of x, y and z accelerometers, respectively; are constant drifts of x, y and z gyros, respectively.

[0013] Further, a preferred embodiment is provided, wherein the matching algorithm in step S4 is a velocity + angular rate matching algorithm: The system state is:

[0014] The velocity difference and angular rate difference between the master and slave INS are selected as the measurements:

[0015] wherein, denotes the velocity error between the master and slave INS; denotes the misalignment angle between the body coordinate system of the slave INS and the body coordinate system of the master INS, referred to as the measurement misalignment angle; denotes the installation error angle between the coordinate systems of the master and slave INS; denotes the time delay; is the flexure deformation angle; is the flexure deformation angle rate, being the derivative of is the angular rate error between the master and slave INS; is the observation matrix; is the observation noise.

[0016] Further, a preferred embodiment is provided, wherein the matching algorithm in step S4 is a velocity + attitude matching algorithm: The system state is:

[0017] wherein, is the static bar length.

[0018] Further, a preferred embodiment is provided, wherein the matching algorithm in step S4 is a velocity + angular rate integration matching algorithm: The calculation expression of the angular rate integration is defined as:

[0019] The angular rate integration is also included in the state quantity, and the above equation is derived to obtain: ​

[0020] The observation equation of the angular velocity integral matching is obtained as follows:

[0021] The system state is:

[0022] Wherein: is an integral period; is an angular velocity; is an angular velocity integral matching quantity; is an angular velocity measured by the sub-inertial navigation in the sub-inertial navigation coordinate system; is an angular velocity measured by the main inertial navigation in the main inertial navigation coordinate system; is a measurement error of the sub-inertial navigation.

[0023] The application further provides a transfer alignment device for real-time dynamic parameter updating, and the method comprises the following modules. Module S1: initialize the sliding window parameters: set the sliding window start interval, including the maximum start interval and the minimum start interval; initialize the installation error angle in the no-load state, and construct an extended Kalman filter state vector; Module S2: obtain the real-time detection result of the mounting state, and judge whether the mounting state has a mutation; Module S3: according to the mounting state mutation judgment result, a new process is repeatedly started every sliding window start interval to execute step S4; If the current mounting state has no mutation, then a new process is started every maximum start interval to execute module S4, and an old process is ended; Otherwise, after an interval of the minimum start interval, an old process is ended and a new process is started to execute module S4; Module S4: collect the measurement data of the main inertial navigation system and the sub-inertial navigation system, based on the initialized installation error angle in the no-load state and the extended Kalman filter state vector, a matching algorithm is used to execute transfer alignment, and the dynamic installation error angle is updated in real time; Module S5: obtain the attitude error covariance trace tr (PδΘ) from the matching algorithm, and when the attitude error covariance trace tr (PδΘ) is less than a threshold η, terminate the alignment and output the alignment result.

[0024] The application further provides a transfer alignment device for real-time dynamic parameter updating, comprising a processor and a memory, wherein the memory is used for storing executable instructions of the processor, and the processor is configured to execute the real-time dynamic parameter updating transfer alignment method according to any one of the above methods by executing the executable instructions.

[0025] The application further provides a computer storage medium, wherein the computer storage medium stores a computer program.

[0026] The application further provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the steps of the transfer alignment method for real-time dynamic parameter updating.

[0027] The application has the following beneficial effects: 1. The transfer alignment method for real-time dynamic parameter updating can monitor dynamic lever arm error mutation (including acceleration residual detection, vibration energy ratio detection and pressure threshold detection) in real time through a triple joint criterion, trigger a sliding window reset mechanism within 5 seconds when a mounting state mutates (such as a missile launch impact), and avoid the mounting error angle estimation lag problem caused by static parameter assumption.

[0028] 2. The transfer alignment method for real-time dynamic parameter updating can start a new process according to a preset quick start time (i.e., a minimum start interval) when mutation occurs, terminate the old process synchronously, block historical error data pollution, solve the divergence risk caused by the accumulation of outdated parameters in Kalman filtering, greatly improve the response speed, converge to a new steady state within 1 minute after the mounting mutation, and take more than 3 minutes by using a traditional method.

[0029] 3. The transfer alignment method for real-time dynamic parameter updating can realize high-precision estimation of mounting error angles (X / Y / Z axis precisions are 0.8558 angular minutes, 1.0051 angular minutes and 1.8459 angular minutes, respectively) in combined maneuvering conditions through a multi-error joint compensation strategy (a matching algorithm fuses dynamic lever arm compensation, gyro zero drift compensation and accelerometer zero offset compensation).

[0030] 4. The transfer alignment method for real-time dynamic parameter updating can reduce the state estimation data amount (only the latest time window parameters are reserved) through a sliding window mechanism, reduce the calculation load by 50%, be suitable for embedded systems, and meet the real-time requirements of the resource-limited scene of the airborne platform.

[0031] 5. The transfer alignment method for real-time dynamic parameter updating can significantly improve the navigation precision and stability of the subsystem (the horizontal misalignment angle estimation error is reduced by 41%-52% compared with the traditional method) by updating the mounting error angle estimation parameters in real time (a new alignment process is started every interval default time or when mutation occurs).

[0032] 6. The real-time dynamic parameter updating transfer alignment method of the present application, through the mutation judgment mechanism of the dynamic lever arm error (i.e. judging whether the mounting state is mutated) and the sliding window transfer alignment program starting mechanism (i.e. setting the sliding window starting interval), when the dynamic lever arm error is mutated, it can still be accurately and real-time transferred.

[0033] 7. The real-time dynamic parameter updating transfer alignment method of the present application, through the combination of three detection methods (including acceleration residual detection, vibration energy ratio detection, and pressure threshold detection), the lever arm error condition is judged from multiple dimensions to accurately judge whether the dynamic lever arm error is mutated; when all three detection methods are normal, a new process is started every sliding window default time (maximum starting interval) to use real-time dynamic parameters for transfer alignment, which can better estimate the installation error angle and improve the estimation accuracy; when one of the three detection methods detects a value exceeding the normal monitoring value, a new process is started according to the preset quick starting time (minimum starting interval) to handle the real-time parameter alignment after mutation, and the old process is ended to save computing resources; in summary, through the mutation judgment mechanism of the dynamic lever arm error and the sliding window transfer alignment program starting mechanism, the accuracy, real-time and effectiveness of the installation error angle estimation of the sub-inertial navigation system are improved, the system resource occupation of the old program is reduced, and the transfer alignment estimation lag caused by mutation is avoided, even the Kalman filter divergence situation.

[0034] The real-time dynamic parameter updating transfer alignment method, device and equipment of the present application are suitable for real-time navigation parameter transfer alignment of airborne missiles (weapons), unmanned aerial vehicles and other carriers in the dynamic change scene of the mounting state in high-speed maneuvering flight; through the sliding window mechanism, the installation error angle is estimated in real time to improve the navigation accuracy of the subsystem; it is applied to the technical field of high-precision positioning and attitude solution of high-speed maneuvering carrier sub-inertial navigation. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0036] Figure 1Figure 1 is a schematic diagram of a sliding window start-up algorithm (or sliding window transfer alignment program start-up mechanism) according to an embodiment of the present application. In the execution of processes 1 and 2, no mutation of the on-board state (mounting state) is detected, and a new process (thread) is started according to a preset default time interval (30 seconds, i.e. the maximum start-up interval). When process 3 is executed to the dot, a mutation of the on-board state is detected, and process 3 is ended and process 4 is started after a fast start-up time interval (5 seconds, i.e. the minimum start-up interval). Figure 2 Figure 2 is a schematic diagram of the basic flow of on-board missile transfer alignment according to an embodiment of the present application. Figure 3 Figure 3 is a schematic diagram of the structure of a transfer alignment device for real-time dynamic parameter updating according to an embodiment of the present application. Figure 4 Figure 4 is a schematic diagram of a matching algorithm according to an embodiment of the present application. Figure 5 Figure 5 is a comparison diagram of the transfer alignment accuracy of different matching algorithms (traditional transfer alignment methods) and the transfer alignment method for real-time dynamic parameter updating according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the technical solutions and advantages of the present application clearer, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. The various embodiments described below are only some preferred solutions of the present application, rather than all embodiments; the various embodiments described below are intended to explain the present application, and cannot be understood as limiting the present application; any reasonable combination of technical features defined in the various embodiments of the present application, and all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without making any creative efforts, all fall within the scope of protection of the present application.

[0038] Embodiment 1: a transfer alignment method for real-time dynamic parameter updating, the method comprising the following steps: Step S1: initialization of sliding window parameters: setting the sliding window start-up interval, including the maximum start-up interval and the minimum start-up interval; initializing the installation error angle in the empty load state, and constructing an extended Kalman filter state vector; Step S2: obtaining the real-time detection result of the mounting state, and determining whether a mutation of the mounting state occurs; Step S3: according to the mutation judgment result of the mounting state, repeatedly starting a new process to execute step S4 every sliding window start-up interval; if no mutation of the current mounting state occurs, ending the old process and starting a new process to execute step S4 every maximum start-up interval. Otherwise, after the interval of minimum start-up interval, end the old process and start a new process to execute step S4; Step S4: Collect the measurement data of the main inertial navigation system and the sub-inertial navigation system, based on the initialized installation error angle in the unloaded state and the extended Kalman filter state vector, use the matching algorithm to perform transfer alignment, and update the dynamic installation error angle in real time; Step S5: Obtain the attitude error covariance trace tr(PδΘ) from the matching algorithm, when the attitude error covariance trace tr(PδΘ) < threshold η, terminate the alignment and output the alignment result.

[0039] In this embodiment, the sliding window is a sliding window.

[0040] In this embodiment, the maximum start-up interval is in the range of 20 seconds to 2 minutes, and the typical value is 30 seconds.

[0041] In this embodiment, the minimum start-up interval is in the range of 1 second to 20 seconds, and the typical value is 5 seconds. The minimum start-up interval is less than the maximum start-up interval, and the typical value is that the minimum start-up interval is one sixth of the maximum start-up interval.

[0042] In this embodiment, the installation error angle in the unloaded state is initialized as: .

[0043] In this embodiment, the extended Kalman filter (EKF) state vector is constructed as:

[0044] wherein, is the dynamic installation error angle, and δΘ is the attitude misalignment angle.

[0045] In this embodiment, the mounting state is the mounting point load state, or the airborne state.

[0046] In this embodiment, if the current mounting state does not change suddenly, the old process is ended and the parameters of the old thread are retained every interval of the maximum start-up interval, and then a new process is started.

[0047] In this embodiment, the step S3 uses different sliding window start-up intervals to execute the transfer alignment process according to whether the mounting state changes suddenly, and this step is called a sliding window start-up algorithm, or a sliding window transfer alignment program start mechanism.

[0048] In this embodiment, in step S3, if it is detected that the mounting state changes suddenly while the transfer alignment is being executed according to the maximum start-up interval, the old process is ended and a new process is started to execute the transfer alignment after the interval of the minimum start-up interval.

[0049] It should be noted that only at the time of detecting the mutation, the new process needs to be started at the minimum start interval; during the execution of the first new process after detecting the mutation, if no new mutation is detected, the next new process is still started at the maximum start interval.

[0050] In the embodiment, the measurement data of the main inertial navigation system and the measurement data of the sub-inertial navigation system include: The measurement data of the main inertial navigation system: namely, the measured attitude data of the carrier, including the angular velocity ω of the carrier and the acceleration f of the carrier.

[0051] The measurement data of the sub-inertial navigation system: namely, the measured attitude data of the missile, including the angular velocity ω of the missile and the acceleration f of the missile.

[0052] In the embodiment, the measurement data further includes satellite time, flight height, flight speed, carrier attitude angle, etc.

[0053] In the embodiment, the threshold value .

[0054] In the embodiment, when the alignment ends, the alignment result is obtained: The alignment result can be displayed on the master control screen; or the alignment result can be saved in the form of a file to the hard disk.

[0055] In the embodiment, in the process of performing transfer alignment by using the matching algorithm: The matching algorithm automatically analyzes and compensates the data including various error sources (including bar arm error, time error, wing deflection error, etc.) according to the collected measurement data of the main inertial navigation system and the measurement data of the sub-inertial navigation system, and improves the alignment accuracy; the alignment accuracy is optimized by using Kalman filtering algorithm or the like, the transfer alignment parameters are updated, the transfer alignment task is re-performed, and the transfer alignment of real-time data is realized.

[0056] In the embodiment, the alignment result includes a dynamic installation error angle and an alignment parameter.

[0057] Embodiment 2: In the step S2, the three detection methods of acceleration residual detection, vibration energy ratio detection, and pressure threshold detection are combined to detect whether the mounting state has a mutation: If the result obtained by using any one of the detection methods exceeds the given threshold value, it is judged that the mounting state has a mutation.

[0058] In the embodiment, the acceleration residual detection: The velocity residual mutation detection (exceeding the threshold value to determine the state switching) of the sub-INS (namely, the sub-inertial navigation system) in the maneuvering process (namely, the carrier motion process).

[0059]

[0060] wherein, is an acceleration vector, is an acceleration residual threshold value; is a primary inertial navigation acceleration value; is a secondary inertial navigation acceleration value.

[0061] In this embodiment, the vibration energy ratio is detected: the vibration frequency spectrum characteristics (the low-frequency energy ratio increases when the missile is mounted) in the analysis window (i.e., the sliding window start interval) of the wing of the carrier aircraft used to mount the missile:

[0062]

[0063]

[0064]

[0065] The distribution of vibration energy is evaluated by a frequency domain method (Fourier transform); wherein, is the fast Fourier result of , the absolute value of represents the amplitude, which is used to analyze the frequency component strength of the signal; represents the low-frequency vibration energy, which quantifies the vibration strength in the low-frequency band; is a low-frequency cutoff frequency; represents the total vibration energy, which captures the total vibration strength in the entire analysis frequency band; is the maximum frequency in the frequency band; is the vibration capability ratio, which is used for comparison with the vibration capability ratio threshold value.

[0066] In this embodiment, the pressure threshold value is detected: The mounting point load state is determined by the external pressure sensor threshold value on the wing of the carrier aircraft.

[0067]

[0068] wherein, is a pressure sensor threshold value: is a mounting point pressure value collected by the pressure sensor.

[0069] In this embodiment, the sliding window is reset in real time by multi-source state perception, solving the problem of installation error angle jump caused by mounting mutation.

[0070] Embodiment 3: The matching algorithm in step S4 is a speed + navigation matching algorithm: The system state is:

[0071] where, are east, north and sky velocity errors, respectively; are east, north and sky misalignment angles, respectively; are constant biases of x, y and z accelerometers, respectively; are constant drifts of x, y and z gyros, respectively.

[0072] In this embodiment, the matched system states (or filter state quantities) are velocity, platform misalignment angles, constant errors of accelerometers and gyros.

[0073] In this embodiment, the x, y and z directions are only for accelerometers and gyros themselves, x is forward, y is left and z is up. This is consistent with the forward, left and up directions of the aircraft. When the accelerometers and gyros move with the aircraft, if the aircraft nose points east, the x direction is east, and if the aircraft nose points west, the x direction is west.

[0074] Embodiment 4: the matching algorithm in the step S4 is a velocity + angular velocity matching algorithm: The system states are:

[0075] The velocity difference and angular velocity difference between the primary and secondary inertial navigation systems are selected as the measurements:

[0076] where, denotes the velocity error of the primary and secondary inertial navigation systems; denotes the misalignment angle between the body coordinate system of the secondary inertial navigation computer and the body coordinate system of the primary inertial navigation, which is called the measurement misalignment angle; denotes the installation error angle between the coordinate systems of the primary and secondary inertial navigation systems; denotes the time delay; is the flexure deformation angle; is the flexure deformation angle rate, which is the derivative of ; denotes the angular velocity error of the primary and secondary inertial navigation systems; is the observation matrix; is the observation noise.

[0077] It should be noted that the system state at the next moment is obtained by the product of the state equation and the current system state: .

[0078] The velocity + angular velocity matching algorithm compared with the velocity + attitude matching algorithm: State equation are the same; The state quantities are not completely the same, i.e. the system states are different, so the parameters obtained in the Kalman filtering process are different, and the operation speed and the required observations are also different.

[0079] Embodiment 5: the matching algorithm in the step S4 is a velocity+attitude matching algorithm: The system state is:

[0080] wherein: represents the velocity error of the master and slave inertial navigation systems; represents the misalignment angle between the computer body coordinate system of the slave inertial navigation system and the body coordinate system of the master inertial navigation system, referred to as the measurement misalignment angle; represents the installation error angle between the coordinate systems of the master and slave inertial navigation systems; represents the time delay; is the flexure deformation angle; is the flexure deformation angle rate, which is the derivative of ; is the static rod length.

[0081] It should be noted that after initialization, there is a small misalignment angle between the slave inertial navigation (system) coordinate system and the master inertial navigation (system) coordinate system, which is the actual measured relative attitude error angle of the master and slave inertial navigation systems, and has , which is the installation error angle , the flexure deformation angle and the function of the gyro measurement error, so theoretically, the estimation of can be realized according to .

[0082] Considering that the relative velocity and relative attitude error caused by the gyro and accelerometer are very small due to the short time of transfer alignment, in order to reduce the system dimension and the calculation amount, these states are not considered in the Kalman filter, but the process noise in the relative velocity error and attitude error equation is increased to compensate for the unmodeled measurement error of the gyro and accelerometer. Therefore, in consideration of the aforementioned airborne transfer alignment error, the state quantity (system state) of the velocity+attitude matching algorithm is obtained:

[0083] Embodiment 6: the matching algorithm in the step S4 is a velocity+angular velocity integral matching algorithm: The calculation expression of the angular velocity integral is defined as:

[0084] Integrating the angular velocity and deriving the above equation, we have:

[0085] The observation equation for the angular velocity integral matching is:

[0086] The system state is:

[0087] Wherein: is the integral period; is the angular velocity; is the angular velocity integral matching quantity (integral matching observation value); is the angular velocity measured by the sub-inertial navigation in the sub-inertial navigation coordinate system; is the angular velocity measured by the main inertial navigation in the main inertial navigation coordinate system; is the installation error angle between the main and sub-inertial navigation coordinate systems; is the flexural deformation angle; is the flexural deformation angular rate; is the measurement error of the sub-inertial navigation; is the misalignment angle; is the velocity error of the main and sub-inertial navigation systems; is the time delay.

[0088] It should be noted that in the angular velocity matching transfer alignment, the angular velocity error caused by the high dynamic flexural deformation of the body is directly reflected in the measurement equation, and therefore affects the estimation performance of the angular velocity matching alignment filter. Therefore, to improve the estimation performance of the alignment Kalman filter, the high dynamic noise components caused by the carrier vibration and flexural effect in the velocity signal should be attenuated, and the integral velocity matching is a kind of alignment algorithm based on the above idea.

[0089] In this embodiment, since the integral operation is equivalent to the function of a low-pass filter, it can effectively attenuate the high dynamic noise components in the velocity signal. Similarly, since the angular velocity matching is sensitive to the body flexural deformation, the integral angular velocity matching can be used for transfer alignment.

[0090] It should be noted that the existing solutions attempt to improve the alignment accuracy through different technical routes, but still have significant limitations: (1) The Chinese patent document with publication number CN119469205A and publication date of 2025.02.18, entitled "Aerial missile secondary transfer alignment method and system, and electronic device" proposes a secondary transfer alignment method based on laser radar, which performs real-time monitoring and secondary correction through laser radar: The technical scheme is as follows: First alignment: the main inertial navigation system sends navigation parameters to the sub-inertial navigation system to complete initial transfer alignment; Real-time monitoring: the laser radar installed on the missile body captures point cloud data of the missile; Second correction: dynamically correct the position and attitude matrix of the sub-inertial navigation system based on the displacement of the center of mass and the change in attitude.

[0091] The technical defects are as follows: Dynamic compensation is missing: only relying on laser radar to measure the change in position and attitude, without establishing a dynamic lever error model, air turbulence at high speed causes point cloud distortion (such as sudden movement with acceleration > 0.02 m / s²), which cannot completely compensate for dynamic errors at high speed; Error source isolation: the inherent errors of inertial sensors such as gyro drift (typical value > 0.01° / h) and accelerometer zero offset (> 20μg) are not included in the joint estimation (compensation), and the inherent errors continue to accumulate, affecting the final accuracy; Real-time bottleneck: the real-time processing of light radar point cloud data (such as center of mass calculation and shape matching) is delayed, which cannot meet the second-level alignment requirement before missile launch; Insufficient error source coupling: without using multi-state filtering (such as joint estimation of lever error, sensor bias, and wing deformation), the error sources cannot be fully coupled.

[0092] (2) The Chinese patent document with publication number CN114462154A, published on February 28, 2025, titled "A transfer alignment method based on FBG + second-order Markov" proposes a FBG and second-order Markov joint modeling method, which uses FBG to measure the x-direction deformation angle of the wing with high precision, combines the second-order Markov model to compensate for small deformation in the y / z direction, and reduces errors through a dynamic lever model and a coupling angle decoupling mechanism; At the same time, multi-parameter matching (attitude + speed + angular velocity) is introduced to accelerate convergence, avoiding the limitations of single matching: The technical scheme is as follows: Deformation monitoring: FBG optical fiber sensor directly measures the x-direction deformation angle θ_x of the wing; Empirical compensation: the second-order Markov model estimates the small deformation angles θ_y and θ_z in the y / z direction; Multi-parameter matching: fusion of attitude + speed + angular velocity measurement to improve convergence speed.

[0093] The technical defects are as follows: Nonlinear failure: dynamic lever model Based on the small deformation assumption (such as θ < 5°), if the wing deforms greatly or moves at high speed (such as θ > 10° when maneuvering at high attack angle), nonlinear effects will occur, and the model may fail (model divergence).

[0094] Inherent error un-compensated: FBG temperature drift, gyro zero drift, etc. inherent error un-compensated, affecting the final accuracy.

[0095] In this embodiment, by establishing a dynamic lever arm error model and a consistency transfer alignment model (both models are embodied in the state quantity X and the measurement equation H of different matching algorithms), the technical problems existing in the above two patent documents are solved.

[0096] Embodiment 7: A transfer alignment device for real-time dynamic parameter updating, the method comprising the following modules: Module S1: Initialize the sliding window parameters: Set the sliding window start interval, including the maximum start interval and the minimum start interval; Initialize the installation error angle in the no-load state, and construct the extended Kalman filter state vector; Module S2: Obtain the real-time detection result of the mounting state, and judge whether the mounting state has mutated; Module S3: According to the mounting state mutation judgment result, repeat the new process to execute step S4 every interval sliding window start interval; If the current mounting state has not mutated, end the old process and start a new process to execute module S4 every maximum start interval; Otherwise, after the minimum start interval, end the old process and start a new process to execute module S4; Module S4: Collect the measurement data of the main inertial navigation system and the sub-inertial navigation system, based on the initialized installation error angle in the no-load state and the extended Kalman filter state vector, use the matching algorithm to perform transfer alignment, and update the dynamic installation error angle in real time; Module S5: Obtain the attitude error covariance trace tr(PδΘ) from the matching algorithm, and when the attitude error covariance trace tr(PδΘ) < threshold η, terminate the alignment and output the alignment result.

[0097] Embodiment 8: A transfer alignment device for real-time dynamic parameter updating, comprising: a processor and a memory, the memory being used to store executable instructions of the processor, and the processor being configured to execute the real-time dynamic parameter updating transfer alignment method described in any one of the above embodiments by executing the executable instructions.

[0098] In this embodiment, the hardware selection of the device is as follows: CPU (i.e. processor): i5, 12 cores, 4.4 GHz, 8 GB video memory; used to implement the alignment program running, including calculating the initial data required for missile alignment according to flight task parameters, completing the initial alignment calculation of the missile inertial navigation system, and supporting the import and analysis of various error sources including boom error, time error, wing flexure error, etc. Memory capacity (i.e. storage): 128 GB.

[0099] It also includes a hard disk, MCU, and a USB interface: Hard disk capacity: 1 TB, used to record input data, output results, and intermediate calculation results in the alignment process in real time, for example, saving the alignment results in the form of a file to the hard disk; MCU: STM32F103ZET6, used to control the data monitoring of the sensor; Number of USB interfaces: 4, used for data transmission between CPU and main inertial navigation system, sub-inertial navigation system, and MCU, receiving and outputting various transfer alignment related data, such as obtaining the synchronous angular velocity ω and acceleration f of the main / sub INS (inertial navigation system) through USB serial communication.

[0100] The CPU includes an interface for information transmission with the main control screen and human-computer interaction devices (such as mouse, keyboard), for example, displaying the alignment results through the main control screen.

[0101] In this embodiment, the device (also known as an airborne transfer alignment verification device): used to complete data acquisition, calculation, error import and analysis, and alignment result recording in the transfer alignment process; supports simulation and real measurement data input.

[0102] In this embodiment, the executable instructions (internal programs) of the device can be divided into the following modules: Input / output module: used to receive and output various transfer alignment related data; Airborne fire control flight task calculation module: calculates the initial data required for missile alignment according to flight task parameters; Airborne fire control missile alignment data calculation module: completes the initial alignment calculation of the missile inertial navigation system; Transfer alignment error analysis and import module: supports the import and analysis of various error sources including boom error, time error, wing flexure error, etc. Data acquisition and recording module: records input data, output results, and intermediate calculation results in the alignment process in real time.

[0103] In this embodiment, the basic process of airborne missile transfer alignment performed by the device is as follows: The main inertial navigation system can collect flight attitude, speed and position data of the carrier in real time and deliver them to the sub-inertial navigation system.

[0104] During the alignment process, the device is used to improve the alignment accuracy by analyzing and compensating error sources such as time delay, lever arm error, flexural deformation, etc.; the alignment accuracy is optimized by Kalman filtering algorithm, and finally the alignment result is output.

[0105] According to the set time interval (i.e. sliding window start interval), the above transfer alignment program (alignment process) is continuously started by sliding window method, so that the system can monitor the mounting condition in real time and update the transfer alignment accuracy result, and complete the online real-time updating function.

[0106] After each transfer alignment is completed, the data is saved in the hard disk of the device, which can be called and displayed on the main control screen. Among them, the alignment data can also be directly imported into the internal (CPU) for analysis through the USB interface of the device.

[0107] The transfer alignment data can also be obtained through the simulation function of the device.

[0108] Embodiment 9: A computer storage medium, the storage medium stores a computer program, when the computer program runs, executes the transfer alignment method of real-time dynamic parameter updating of any one of the above.

[0109] Embodiment 10: A computer program product, comprising computer programs / instructions, which are executed by a processor to implement the steps of the transfer alignment method of real-time dynamic parameter updating of any one of the above.

[0110] Embodiment 11: Verification of different transfer alignment algorithms based on simulation data: In the simulation, the three-axis flexural deformation related times in the airborne environment are set to 0.2s, 0.2s and 0.5s, the default time of the sliding window start program is set to 30s (i.e. maximum start interval), and the fast start time of the program after mutation is set to 5s (i.e. minimum start interval). The precision improvement of the real-time dynamic parameter updating high-precision transfer alignment system compared with the ordinary system is discussed.

[0111] (1) Simulation condition setting: ① The constant drift of the gyroscope is , the angular random walk is , the accelerometer zero offset is 20 g, and the speed random walk is set to 2 g / ; ② The initial value of the platform misalignment angle of the sub-inertial navigation system is set to ; ③ The deformation angle variance of the flexure deformation is set to , and the flexure deformation angle correlation time should be set to ; the static rod arm length is set to .

[0112] (2) Experimental precision comparison chart: As shown in the accompanying Figure 5 , it is the installation error estimation of the traditional transfer alignment method under different matching algorithms and the real-time dynamic parameter updating transfer alignment method (transfer alignment precision). The comparison results are shown in the following table: Table 1 Alignment error under speed matching (algorithm) (under traditional transfer alignment method)

[0113] Table 2 Alignment error under attitude matching (algorithm) (under traditional transfer alignment method)

[0114] Table 3 Alignment error under acceleration matching (algorithm) (under traditional transfer alignment method)

[0115] Table 4 Alignment error under real-time dynamic parameter updating transfer alignment method (called real-time combined matching)

[0116] Table 5 Alignment error RMS under different alignment methods

[0117] From the accompanying drawings and the above table data, compared with the transfer alignment under different matching algorithms under the traditional transfer alignment method, the real-time dynamic parameter updating transfer alignment method designed in this paper has faster convergence speed of estimation error, and in the estimation of installation error angle, the estimation error has better performance.

[0118] For example, by introducing wing deformation compensation (i.e. judging whether the mounting state has a sudden change, if there is a sudden change, starting a new process according to the minimum start interval) and multi-error joint compensation, using speed + angular velocity integral matching algorithm, the steady-state alignment error RMS is improved by combining the maneuverability to improve the observability of the state, and the estimation accuracy of the alignment error of X, Y, Z axes under the combined maneuverability 、 、 reaches 0.8558 arc minutes, 1.0051 arc minutes, and 1.8459 arc minutes, respectively, which is greatly improved compared with other matching methods and maneuvering methods.

[0119] The computer device or system provided by the embodiment has a general type of hardware device, which is not represented in the form of a diagram. The system includes a processor and a memory, where the processor and the memory can be connected through a bus or other means. The memory is a non-transitory computer readable storage medium, which can be used to store a non-transitory software program, a non-transitory computer executable program and a module, and corresponding program instructions / modules. The processor executes various function applications and data processing of the processor by running the non-transitory software program, instructions and modules stored in the memory, so as to implement the data space entity analysis data quality enhancement method in the above method embodiment.

[0120] The memory can include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required by a function. The data storage area can store data created by the processor and the like. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, an extranet, a mobile communication network, and a combination thereof.

[0121] One or more modules are stored in the memory, and when the processor executes, the method steps in the embodiment are executed. Thus, the inventive purpose of the present application can be achieved through the method and device and the process of the present application. The above computer device specific details can be understood by referring to the corresponding related description and effects in the embodiments, which will not be described here.

[0122] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid state disk (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0123] The technical solutions of the present application are described in further detail through several specific embodiments above, in order to highlight the advantages and benefits of the technical solutions provided by the present application. However, the above several specific embodiments are not used as a limitation to the present application, and any reasonable changes and improvements, reasonable combinations and equivalent replacements of the embodiments, etc. based on the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A transfer alignment method for real-time dynamic parameter updating, characterized in that: The method comprises the following steps: Step S1: Initialize sliding window parameters: Set the sliding window start interval, including the maximum start interval and the minimum start interval; Initialize the installation error angle in the no-load state and construct the extended Kalman filter state vector; Step S2: Obtain the real-time detection result of the mounting status and determine whether the mounting status has changed suddenly; Step S3: Based on the result of the mount status mutation judgment, a new process is repeatedly started at each sliding window startup interval to execute step S4; If the current mount state does not change suddenly, then every maximum startup interval, end the old process and start a new process to execute step S4; Otherwise, after the minimum start interval, the old process is terminated and a new process is started to execute step S4; Step S4: Collecting measurement data from the main inertial navigation system and the sub-inertial navigation system, performing transfer alignment using a matching algorithm based on the initialized no-load installation error angle and the extended Kalman filter state vector, and updating the dynamic installation error angle in real time; Step S5: Obtain the posture error covariance trace tr(PδΘ) from the matching algorithm. When the posture error covariance trace tr(PδΘ)<threshold η, terminate the alignment and output the alignment result.

2. The transfer alignment method for real-time dynamic parameter update according to claim 1, characterized in that: In step S2, three detection methods, namely, acceleration residual detection, vibration energy ratio detection, and pressure threshold detection, are combined to detect whether the mounting state has undergone a sudden change: If the result obtained by any of the detection methods exceeds a given threshold, it is determined that the mount status has changed suddenly.

3. The transfer alignment method for real-time dynamic parameter update according to claim 1, characterized in that: The matching algorithm in step S4 is a speed+navigation matching algorithm: The system status is: in, They are eastward velocity error, northward velocity error and celestial velocity error respectively; They are the east misalignment angle, north misalignment angle and celestial misalignment angle respectively; are the constant biases of the accelerometers in the x, y, and z directions respectively; are the constant drifts of the gyroscope in the x, y, and z directions respectively.

4. The transfer alignment method for real-time dynamic parameter update according to claim 1, characterized in that: The matching algorithm in step S4 is a velocity + angular velocity matching algorithm: The system status is: The velocity difference and angular velocity difference between the main and sub-INS are selected as the measurement quantities: in: Indicates the speed error of the main and sub inertial navigation systems; It represents the deflection angle between the computer coordinate system of the sub-INS and the main INS body coordinate system, which is called the measurement misalignment angle; Indicates the installation error angle between the main and sub-INS coordinate systems; Indicates time delay; is the flexural deformation angle; is the angular rate of deflection, The derivative of Angular velocity error of the main and sub-INS; is the observation matrix; is the observation noise.

5. The transfer alignment method for real-time dynamic parameter update according to claim 1, characterized in that: The matching algorithm in step S4 is a speed+posture matching algorithm: The system status is: in: is the static arm length.

6. The transfer alignment method for real-time dynamic parameter update according to claim 1, characterized in that: The matching algorithm in step S4 is a velocity + angular velocity integral matching algorithm: The calculation expression of angular velocity integral is defined as: The angular velocity integral is also included in the state quantity, and the derivative of the above formula is obtained: The observation equation for obtaining the angular velocity integral matching is: The system status is: ； in: is the integration period; is the angular velocity; is the angular velocity integral matching amount; is the angular velocity measured by the sub-INS in the sub-INS coordinate system; is the angular velocity measured by the master inertial navigation in the master inertial navigation coordinate system; is the sub-INS measurement error.

7. A transfer alignment device with real-time dynamic parameter update, characterized in that: The method includes the following modules: Module S1: Initialize sliding window parameters: Set the sliding window start interval, including the maximum start interval and the minimum start interval; Initialize the installation error angle in the no-load state and construct the extended Kalman filter state vector; Module S2: Obtains the real-time detection results of the mounting status and determines whether the mounting status has changed suddenly; Module S3: Based on the result of the mount status mutation judgment, a new process is repeatedly started at each sliding window startup interval to execute step S4; If the current mount state does not suddenly change, then every maximum startup interval, the old process is terminated and a new process is started to execute module S4; Otherwise, after the minimum start interval, the old process is terminated and a new process is started to execute module S4; Module S4: Collects measurement data from the main INS and sub-INS, performs transfer alignment using a matching algorithm based on the initialized no-load installation error angle and the extended Kalman filter state vector, and updates the dynamic installation error angle in real time. Module S5: Obtain the posture error covariance trace tr(PδΘ) from the matching algorithm. When the posture error covariance trace tr(PδΘ) < threshold η, terminate the alignment and output the alignment result.

8. Transfer alignment equipment with real-time dynamic parameter updates, including: A processor and a memory, characterized in that the memory is used to store executable instructions of the processor, and the processor is configured to execute the transfer alignment method for real-time dynamic parameter update according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer storage medium, characterized in that The storage medium stores a computer program, and when the computer program is run, the transfer alignment method for real-time dynamic parameter update according to any one of claims 1 to 6 is executed.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the real-time dynamic parameter update transfer alignment method according to any one of claims 1 to 6 are implemented.

Images