Unmanned aerial vehicle combined navigation coarse alignment method, unmanned aerial vehicle and storage medium
By constructing an optimized alignment model and error-state Kalman filtering, the initial alignment problem of a small UAV integrated navigation system under strong magnetic interference was solved, achieving fast and accurate heading calculation and reducing computational burden and error.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHUHAI LANZHONG INNOVATION TECHNOLOGY CO LTD
- Filing Date
- 2026-03-30
- Publication Date
- 2026-06-19
AI Technical Summary
In environments with strong magnetic interference, small UAVs cannot effectively perform initial alignment of the integrated navigation system. Existing methods consume a huge amount of computing power and lack sufficient accuracy, which can easily lead to errors during flight.
An optimized alignment model is constructed based on IMU velocity increment and GNSS velocity. The initial alignment attitude is solved by nonlinear least squares method, and roll and pitch angles are estimated by error state Kalman filtering, which reduces the computational dimension and computing power and avoids the influence of magnetometer interference.
Achieving rapid and accurate integrated navigation initialization under strong magnetic interference, the alignment process is efficient and simple, reducing computational overhead and errors, and improving the stability and accuracy of heading calculation.
Smart Images
Figure CN122237640A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a coarse alignment method for UAV integrated navigation, a UAV using the coarse alignment method for UAV integrated navigation, and a computer-readable storage medium using the coarse alignment method for UAV integrated navigation. Background Technology
[0002] Before outputting navigation information, the integrated navigation unit of an unmanned aerial vehicle (UAV) must undergo initial alignment. Small UAVs typically use the magnetic heading from a magnetometer to assign initial heading angles to their low-cost integrated navigation systems, achieving a static initialization coarse alignment process. However, in the presence of significant magnetic interference, the error and noise of the magnetic heading increase sharply, making this method unable to obtain a sufficiently accurate initial heading. This results in large errors in the UAV's horizontal information and a tendency for "panning" (or "panning") during flight. To achieve initial alignment in environments with strong magnetic interference, medium and large UAVs often employ dual-antenna GNSS (Global Navigation Satellite System) equipment, utilizing the GNSS dual-antenna heading information for initial alignment of the integrated navigation system. However, when small UAVs cannot be equipped with dual-antenna equipment, how to align the integrated navigation system becomes a significant challenge in the UAV field.
[0003] The existing open-source flight controller PX4 uses the following method: It simultaneously runs n integrated navigation systems, subdividing the 0-360° heading angle into n equal parts, and uses these n heading angles to initialize the n integrated navigation systems. When the UAV's horizontal speed exceeds a threshold, it performs a weighted average of the heading angles output by the n integrated navigation systems to obtain the final heading angle. This method can achieve dynamic initial alignment under strong magnetic interference, but it requires running n integrated navigation systems simultaneously, resulting in huge computational consumption.
[0004] Therefore, a more optimized coarse alignment method for UAV integrated navigation needs to be considered. Summary of the Invention
[0005] The primary objective of this invention is to provide a coarse alignment method for UAV integrated navigation that enables initial alignment of integrated navigation in environments with strong magnetic interference, while reducing computational dimensionality and computational power and ensuring alignment accuracy.
[0006] The second objective of this invention is to provide a UAV that can achieve initial alignment of integrated navigation in environments with strong magnetic interference, while reducing computational dimensions and computing power and ensuring alignment accuracy.
[0007] A third objective of this invention is to provide a computer-readable storage medium that enables initial alignment of integrated navigation in environments with strong magnetic interference, while reducing computational complexity and power and ensuring alignment accuracy.
[0008] To achieve the aforementioned first objective, the coarse alignment method for UAV integrated navigation provided by the present invention includes: acquiring local geomagnetic measurement values when the UAV is stationary; determining whether the magnetometer is subjected to strong magnetic interference based on the local geomagnetic measurement values; if so, taking off the UAV in a preset mode; acquiring the velocity increment of the IMU between two adjacent frames of the global satellite navigation system and the velocity of the global satellite navigation system in the navigation coordinate system when the speed of the UAV reaches a preset speed threshold; estimating the initial heading angle using an optimized alignment method; and estimating the final roll angle and final pitch angle using an error state Kalman filter.
[0009] As can be seen from the above scheme, in the coarse alignment method of the UAV integrated navigation of the present invention, when the magnetometer is subjected to strong magnetic interference, the optimized alignment method of the platform inertial navigation is applied to the low-cost strapdown inertial navigation system. This enables the UAV integrated navigation system to converge quickly under large misalignment angles, rather than performing optimization operations directly in the integrated navigation system, thus reducing the computational dimensionality and computing power. Simultaneously, the roll and pitch angles are estimated using error state Kalman filtering, suppressing the roll and pitch angle drift of the low-cost MEMS IMU during the optimization alignment method's calculation process. Furthermore, error state Kalman filtering does not perform heading estimation, reducing the computational dimensionality of error state Kalman filtering while also reducing the coupling between the optimized alignment method and the error state Kalman filtering. This reduces mutual interference between the heading estimation of the optimized alignment method and the error state Kalman filtering, accelerating the convergence speed and improving alignment accuracy.
[0010] In a further scheme, the step of estimating the initial heading angle using the optimized alignment method includes: constructing an optimized alignment model using the velocity increment of the IMU between two adjacent frames of the Global Navigation Satellite System (GNSS) and the velocity of the GNSS in the navigation coordinate system. ,in: ; ; For the IMU velocity increment observation equation, The gyroscope from the moment it was turned on until the current moment. Integral attitude, This is the accelerometer value; For GNSS velocity observation equations, and They represent and GNSS velocity at any given time The local gravitational acceleration is represented; the initial alignment attitude is obtained by solving the alignment model using the nonlinear least squares method. Thus, the initial heading angle is obtained.
[0011] Therefore, it can be seen that by constructing an optimized alignment model based on IMU velocity increment and GNSS velocity, and solving the initial alignment attitude using the nonlinear least squares method to obtain the initial heading angle, the method does not rely on magnetometer information, thus avoiding the influence of geomagnetic interference on heading calculation and ensuring that a reliable initial heading can still be obtained under strong magnetic interference environment. At the same time, the optimized alignment model directly integrates IMU and GNSS observation information for overall attitude solution, making full use of dynamic sensor observation information to achieve rapid convergence, thereby quickly realizing the heading initialization of UAV integrated navigation.
[0012] In a further scheme, the steps for estimating the final roll and pitch angles using error-state Kalman filtering include: selecting roll drift error and pitch drift error as filter state variables. , For roll angle error, For pitch angle error; establish the error state Kalman filter equation system: , , where the state transition matrix , Antisymmetric matrix constructed for gyroscope angular rate T is the sampling time interval of the IMU, and I is the identity matrix. This is the process noise driving matrix. Let be the system process noise vector at time k+1. Let be the measurement noise covariance matrix of the error state Kalman filter at time k+1. The optimal estimates of roll drift and pitch drift errors are obtained through iterative updates using error state Kalman filtering. The roll drift error and pitch drift error are corrected in the IMU observation equation using the following formula: ,in, ; the revised Substitute the values into the optimization alignment model to solve for the final roll angle and final pitch angle.
[0013] Therefore, by constructing an error-state Kalman filter specifically for roll and pitch drift errors, it is possible to accurately estimate and compensate for attitude drift generated by low-cost MEMS IMUs during alignment, effectively suppressing roll and pitch drift and improving attitude calculation stability. Furthermore, the error-state Kalman filter directly reuses observation information from the optimized alignment model, eliminating the need for additional sensor data and thus improving computational efficiency.
[0014] In a further proposed solution, after determining whether the magnetometer is subject to strong magnetic interference based on local geomagnetic measurements, the solution also includes: if the magnetometer is not subject to strong magnetic interference, then the magnetic heading calculated by the magnetometer is used as the coarse alignment heading angle for the UAV integrated navigation.
[0015] Therefore, when the magnetometer is not subject to strong magnetic interference, the magnetic heading of the magnetometer can be directly used as the coarse alignment heading angle of the integrated navigation. Static heading initialization can be completed without the need for UAV take-off maneuvers. The alignment process is fast, efficient, and easy to operate, making full use of the static heading information of the magnetometer and reducing the flight maneuvers and computational overhead required for dynamic alignment.
[0016] In a further proposed solution, the steps for obtaining local geomagnetic measurements include: using the accelerometer in the UAV to obtain the initial roll angle and initial pitch angle at the moment of alignment; converting the roll angle and pitch angle at the moment of alignment into a rotation matrix; obtaining the raw geomagnetic measurements from the magnetometer; and calculating the local geomagnetic measurements based on the raw geomagnetic measurements and the rotation matrix.
[0017] In a further proposed scheme, local geomagnetic measurements are obtained using the following formula: ,in, These are the original geomagnetic measurements. It is a rotation matrix.
[0018] Therefore, by calculating the initial roll and pitch angles using the accelerometer and constructing a rotation matrix, the original measurements from the magnetometer can be transformed into coordinates. This allows for accurate conversion of geomagnetic observations from the UAV system to the navigation coordinate system, eliminating the influence of the UAV's attitude on geomagnetic measurements and obtaining measurements that truly reflect the local geomagnetic field.
[0019] In a further proposed scheme, if the absolute value of the difference between the local geomagnetic measurement value and the local geomagnetic theoretical value is greater than a preset threshold, then the magnetometer is confirmed to be subjected to strong magnetic interference.
[0020] Therefore, it can be seen that by comparing the difference between the local geomagnetic measurement value and the local geomagnetic theoretical value, and judging whether the absolute value of the difference exceeds the preset threshold, it is possible to determine whether the magnetometer is affected by strong magnetic interference. This method involves a small amount of computation and is easy to implement in engineering.
[0021] In a further proposed approach, the theoretical value of the local geomagnetic field is obtained using the global geomagnetic field model.
[0022] Therefore, by using the world geomagnetic field model to obtain local geomagnetic theoretical values, we can accurately obtain real geomagnetic field reference data at the corresponding latitude and longitude location. There is no need to set up a geomagnetic benchmark on-site or add any additional hardware equipment. Only the latitude and longitude information of GNSS positioning is needed to quickly provide a comparison benchmark for geomagnetic interference detection.
[0023] To achieve the second objective of the present invention, the present invention provides a drone including a processor and a memory, the memory storing a computer program, which, when executed by the processor, implements the steps of the above-described coarse alignment method for drone integrated navigation.
[0024] To achieve the third objective of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a controller, implements the steps of the coarse alignment method for unmanned aerial vehicle integrated navigation described above. Attached Figure Description
[0025] Figure 1 This is a flowchart of an embodiment of the coarse alignment method for UAV integrated navigation of the present invention.
[0026] Figure 2 This is a flowchart of the step of obtaining local geomagnetic measurement values in an embodiment of the coarse alignment method for UAV integrated navigation of the present invention.
[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments. Detailed Implementation
[0028] Example of a coarse alignment method for UAV integrated navigation: The coarse alignment method for UAV integrated navigation of the present invention is applied in an application program within the UAV for coarse alignment of the UAV integrated navigation. The UAV is equipped with an IMU (Inertial Measurement Unit) module and a magnetometer. The IMU module includes a gyroscope and an accelerometer. The system structure of the UAV is well-known to those skilled in the art and will not be described in detail here.
[0029] In this embodiment, the coarse alignment method for UAV integrated navigation first executes step S1, acquiring local geomagnetic measurements while the UAV is stationary. To ensure the accuracy of these measurements, the local geomagnetic field needs to be measured while the UAV is stationary. In this embodiment, after the UAV is stationary, the local geomagnetic measurements are only acquired when the Horizontal Dilution of Precision (HDOP) of the GNSS (Global Navigation Satellite System) within the UAV falls below a preset precision threshold, indicating that the GNSS data has entered a healthy and stable state. HDOP characterizes the horizontal positioning accuracy of GNSS; only when HDOP is below the threshold does it indicate stable GNSS horizontal positioning and small latitude and longitude errors, resulting in higher accuracy of the measured local geomagnetic values.
[0030] In this embodiment, see Figure 2When acquiring local geomagnetic measurements, step S11 is executed first to obtain the initial roll angle and initial pitch angle at the alignment start time using the accelerometer in the UAV. The initial roll angle and initial pitch angle at the alignment start time are obtained by the following formulas: , ,in, , , Indicates the xyz axis components of the accelerometer. The initial pitch angle, This is the starting roll angle.
[0031] After obtaining the initial roll and pitch angles at the alignment start time, step S12 is executed to convert the roll and pitch angles at the alignment start time into rotation matrices. The rotation matrices converted from the roll and pitch angles at the alignment start time are as follows: .
[0032] After obtaining the rotation matrix, step S13 is executed to acquire the raw geomagnetic measurement values from the magnetometer. The local geomagnetic measurement values are then calculated based on the raw geomagnetic measurement values and the rotation matrix. The raw geomagnetic measurement values from the magnetometer can be obtained by reading data collected by the geomagnetic sensor. The local geomagnetic measurement values are obtained using the following formula: ,in, These are the original geomagnetic measurements. The rotation matrix is used to calculate the initial roll and pitch angles using the accelerometer and construct the rotation matrix. This allows for coordinate transformation of the original magnetometer measurements, accurately converting geomagnetic observations from the UAV system to the navigation coordinate system. This eliminates the influence of the UAV's attitude on geomagnetic measurements and yields measurements that truly reflect the local geomagnetic field.
[0033] After obtaining the local geomagnetic measurement value, step S2 is executed to determine whether the magnetometer is confirmed to be subject to strong magnetic interference based on the local geomagnetic measurement value. In this embodiment, if the absolute value of the difference between the local geomagnetic measurement value and the local theoretical geomagnetic value is greater than a preset threshold, it is confirmed that the magnetometer is subject to strong magnetic interference. The preset threshold can be pre-set based on experimental data. By comparing the difference between the local geomagnetic measurement value and the local theoretical geomagnetic value, and determining whether the absolute value of the difference exceeds the preset threshold, the magnetometer is determined to be subject to strong magnetic interference. This method involves minimal computation and is easy to implement in engineering.
[0034] In this embodiment, the theoretical value of the local geomagnetic field is obtained through the World Geomagnetic Magnetic Model (WMM). Using the well-known WMM, and inputting GNSS latitude and longitude information, the theoretical value of the local geomagnetic field can be output. By using the WMM to obtain the theoretical value of the local geomagnetic field, accurate reference data of the real geomagnetic field at the corresponding latitude and longitude location can be obtained. There is no need for on-site geomagnetic benchmark setting or additional hardware equipment; only the latitude and longitude information from GNSS positioning is required to quickly provide a comparison benchmark for geomagnetic interference detection.
[0035] If the magnetometer is not subjected to strong magnetic interference, proceed to step S3, using the magnetic heading calculated by the magnetometer as the coarse alignment heading angle for the UAV integrated navigation. When the magnetometer is not subjected to strong magnetic interference, the magnetic heading is directly used as the coarse alignment heading angle for integrated navigation. Static heading initialization can be completed without UAV takeoff maneuvers, making the alignment process fast, efficient, and easy to operate. It fully utilizes the static heading information from the magnetometer, reducing the flight maneuvers and computational overhead required for dynamic alignment. When using the magnetic heading calculated by the magnetometer as the coarse alignment heading angle for the UAV integrated navigation, the initial roll angle and initial pitch angle obtained from the accelerometer at the start of alignment are also used as the roll angle and pitch angle for coarse alignment.
[0036] If the magnetometer is subjected to strong magnetic interference, step S4 is executed, causing the UAV to take off in a preset mode. When the UAV's speed reaches a preset speed threshold, the velocity increment of the IMU between two adjacent frames of the UAV's Global Navigation Satellite System (GNSS) and the speed of the GNSS in the navigation coordinate system are acquired. The initial heading angle is estimated using the Optimization-Based Alignment (OBA) method, and the final roll and pitch angles are estimated using error state Kalman filtering. The preset mode can be selected as needed; for example, it can be an attitude mode or an altitude hold mode, allowing for S-curve flight, or flight in all directions. The preset speed threshold can be preset based on experimental data. At low speeds, GNSS velocity measurement has high noise and low resolution, making it prone to speed jumps and zero-speed drift. Only when the UAV's speed reaches the preset threshold can the GNSS navigation coordinate system velocity observation accuracy be high and the data stable, providing a reliable GNSS velocity difference reference and avoiding the contamination of heading calculation by low-speed velocity measurement errors.
[0037] In this embodiment, the step of estimating the initial heading angle using the optimized alignment method includes: constructing an optimized alignment model using the velocity increment of the IMU between two adjacent frames of the Global Navigation Satellite System (GNSS) and the velocity of the GNSS in the navigation coordinate system. ;in: ; ; For the IMU velocity increment observation equation, The gyroscope from the moment it was turned on until the current moment. Integral attitude, This is the accelerometer value; For GNSS velocity observation equations, and They represent and GNSS velocity at any given time The local gravitational acceleration is represented; the initial alignment attitude is obtained by solving the alignment model using the nonlinear least squares method. Initial alignment posture This includes roll angle, pitch angle, and heading angle information, allowing the initial heading angle for coarse alignment to be obtained from the initial alignment attitude. The nonlinear least squares method is a well-known technique to those skilled in the art and will not be elaborated upon here. An optimized alignment model is constructed based on IMU velocity increments and GNSS velocity, and the initial heading angle is obtained by solving the initial alignment attitude using the nonlinear least squares method. This eliminates the need for magnetometer information, avoiding the impact of geomagnetic interference on heading calculation and ensuring reliable initial heading even in environments with strong magnetic interference. Furthermore, the optimized alignment model directly integrates IMU and GNSS observation information for overall attitude solution, fully utilizing dynamic sensor observation information to achieve rapid convergence and thus quickly initialize the heading for UAV integrated navigation.
[0038] In this embodiment, the steps of estimating the final roll angle and the final pitch angle using error state Kalman filtering include: Roll drift error and pitch drift error are selected as the filter state variables: , For roll angle error, This refers to the pitch angle error; Establish the error state Kalman filter equation system: , , Wherein, the state transition matrix , Antisymmetric matrix constructed for gyroscope angular rate T is the sampling time interval of the IMU, and I is the identity matrix. This is the process noise driving matrix. Let be the system process noise vector at time k+1. Let be the measurement noise covariance matrix of the error state Kalman filter at time k+1. ; The optimal estimates of roll drift error and pitch drift error are obtained through iterative updates using error state Kalman filtering. ; The roll drift error and pitch drift error are corrected in the IMU observation equation using the following formula: ,in, ; The revised Substitute the values into the optimization alignment model to solve for the final roll angle and final pitch angle.
[0039] In this embodiment, the iterative update of the error state Kalman filter is a well-known technique to those skilled in the art and will not be described in detail here. The corrected... When substituting into the optimization alignment model for solution, Substituting the IMU velocity increment observation equation into the optimized alignment model, the initial alignment attitude can be obtained. Due to the initial alignment posture This includes roll and pitch angle information, which allows for the determination of the initial alignment attitude. The roll and pitch angles in the model are used as the final roll and pitch angles. By constructing an error-state Kalman filter specifically for roll and pitch drift errors, the attitude drift generated by the low-cost MEMS IMU during alignment can be accurately estimated and compensated, effectively suppressing roll and pitch drift and improving the stability of attitude calculation. Simultaneously, the error-state Kalman filter directly reuses the observation information in the optimized alignment model, eliminating the need for additional sensor data and improving computational efficiency.
[0040] As described above, in the coarse alignment method of UAV integrated navigation of the present invention, when the magnetometer is subjected to strong magnetic interference, the optimized alignment method of the platform inertial navigation is applied to the low-cost strapdown inertial navigation system. This enables the UAV integrated navigation system to converge quickly under large misalignment angles, rather than performing optimization operations directly in the integrated navigation system, thus reducing the computational dimensionality and computational power. Simultaneously, the roll and pitch angles are estimated using error state Kalman filtering, suppressing the roll and pitch angle drift of the low-cost MEMS IMU during the optimization alignment method's calculation process. Furthermore, error state Kalman filtering does not perform heading estimation, reducing the computational dimensionality of error state Kalman filtering while also reducing the coupling between the optimized alignment method and the error state Kalman filtering. This reduces mutual interference between the heading estimation of the optimized alignment method and the error state Kalman filtering, accelerating the convergence speed and improving alignment accuracy.
[0041] Example of a drone: The drone in this embodiment includes a controller, which executes a computer program to implement the steps in the above-described embodiment of the coarse alignment method for drone integrated navigation.
[0042] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory and executed by a controller to perform the present invention. One or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the drone.
[0043] The drone may include, but is not limited to, controllers and memory. Those skilled in the art will understand that the drone may include more or fewer components, or combinations of certain components, or different components; for example, the drone may also include input / output devices, network access devices, buses, etc.
[0044] For example, the controller can be a Central Processing Unit (CPU), or other general-purpose controllers, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose controller can be a microcontroller or any conventional controller. The controller is the control center of the UAV, connecting all parts of the UAV through various interfaces and lines.
[0045] The memory can be used to store computer programs and / or modules. The controller implements various functions of the UAV by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. For example, the memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound reception function, sound-to-text function, etc.), etc.; the data storage area may store data created based on the use of the phone (such as audio data, text data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, SmartMediaCard (SMC), Secure Digital (SD) card, Flash Card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0046] Examples of computer-readable storage media: If the modules integrated into the UAV in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the coarse alignment method for UAV integrated navigation can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a controller, it can implement the steps of the above embodiments of the coarse alignment method for UAV integrated navigation. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The storage medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.
[0047] It should be noted that the above are only preferred embodiments of the present invention, but the design concept of the invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept also fall within the protection scope of the present invention.
Claims
1. A coarse alignment method for unmanned aerial vehicle (UAV) integrated navigation, characterized in that: include: Acquire local geomagnetic measurements while the drone is stationary; Based on the local geomagnetic measurement value, it is determined whether the magnetometer is subject to strong magnetic interference. If so, the UAV takes off in a preset mode. When the speed of the UAV reaches a preset speed threshold, the velocity increment of the IMU between two adjacent frames of the UAV's global satellite navigation system and the speed of the global satellite navigation system in the navigation coordinate system are obtained. The initial heading angle is estimated using an optimized alignment method, and the final roll angle and final pitch angle are estimated using an error state Kalman filter.
2. The coarse alignment method for UAV integrated navigation according to claim 1, characterized in that: The steps for estimating the initial heading angle using the optimized alignment method include: An optimized alignment model is constructed using the velocity increment of the IMU between two adjacent frames of the global navigation satellite system and the velocity of the global navigation satellite system in the navigation coordinate system. ， in: ； ； For the IMU velocity increment observation equation, The gyroscope from the moment it was turned on until the current moment. Integral attitude, This is the accelerometer value; For GNSS velocity observation equations, and They represent and GNSS velocity at any given time This indicates the local gravitational acceleration; The optimized alignment model is solved using the nonlinear least squares method to obtain the initial alignment posture. Thus, the initial heading angle is obtained.
3. The coarse alignment method for UAV integrated navigation according to claim 2, characterized in that: The steps for estimating the final roll and pitch angles using error-state Kalman filtering include: Roll drift error and pitch drift error are selected as the filter state variables: , The roll angle error is... The pitch angle error is mentioned above; Establish the error state Kalman filter equation system: ， ， Wherein, the state transition matrix , Antisymmetric matrix constructed for gyroscope angular rate T is the sampling time interval of the IMU, and I is the identity matrix. This is the process noise driving matrix. Let be the system process noise vector at time k+1. Let be the measurement noise covariance matrix of the error state Kalman filter at time k+1. ; The optimal estimates of roll drift error and pitch drift error are obtained through iterative updates using error state Kalman filtering. ; The roll drift error and pitch drift error are corrected in the IMU observation equation using the following formula: ,in, ; The revised Substitute the values into the optimized alignment model to obtain the final roll angle and the final pitch angle.
4. The coarse alignment method for UAV integrated navigation according to any one of claims 1 to 3, characterized in that: After determining whether the magnetometer is subject to strong magnetic interference based on the local geomagnetic measurement values, the method further includes: If the magnetometer is not subjected to strong magnetic interference, the magnetic heading calculated by the magnetometer is used as the coarse alignment heading angle for the UAV integrated navigation.
5. The coarse alignment method for UAV integrated navigation according to any one of claims 1 to 3, characterized in that: The steps to obtain local geomagnetic measurements include: The initial roll angle and initial pitch angle at the moment of alignment are obtained using the accelerometer in the UAV. Convert the roll and pitch angles at the initial alignment moment into rotation matrices; The original geomagnetic measurement value of the magnetometer is obtained, and the local geomagnetic measurement value is calculated based on the original geomagnetic measurement value and the rotation matrix.
6. The coarse alignment method for UAV integrated navigation according to any one of claims 1 to 3, characterized in that: The local geomagnetic measurement value is obtained by the following formula: ， in, The original geomagnetic measurement value, Let be the rotation matrix.
7. The coarse alignment method for UAV integrated navigation according to any one of claims 1 to 3, characterized in that: When the absolute value of the difference between the local geomagnetic measurement value and the local geomagnetic theoretical value is greater than a preset threshold, it is confirmed that the magnetometer is subjected to strong magnetic interference.
8. The coarse alignment method for UAV integrated navigation according to claim 7, characterized in that: The theoretical value of the local geomagnetic field was obtained through the world geomagnetic field model.
9. A drone, comprising a processor and a memory, characterized in that: The memory stores a computer program that, when executed by the processor, implements the steps of the coarse alignment method for unmanned aerial vehicle integrated navigation as described in any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the controller, it implements the steps of the coarse alignment method for UAV integrated navigation as described in any one of claims 1 to 8.