Inertial navigation attitude calibration method and device, electronic equipment, storage medium and program

Abstract

Embodiments of the present application disclose a kind of attitude calibration method, device, electronic equipment, storage medium and program of inertial navigation, wherein, method includes: in the case where determining the current state of inertial navigation bearing surface is relative static state, obtain the multiple north-seeking results of inertial navigation on the inertial navigation bearing surface in current north-seeking cycle;The intermediate optimal solution of azimuth is iteratively calculated using Kalman filtering algorithm to the multiple north-seeking results of the inertial navigation;According to the intermediate optimal solution of azimuth corresponding to each north-seeking result, the target optimal solution of azimuth in the current north-seeking cycle is calculated;According to the target optimal solution of azimuth in the current north-seeking cycle, the attitude of the inertial navigation in the current north-seeking cycle is calibrated.The technical scheme of the embodiment of the present application can improve the precision and reliability of inertial navigation attitude calibration.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of inertial navigation, in particular to a method and device for calibrating the attitude of an inertial navigation system, an electronic device, a storage medium and a program. BACKGROUND

[0002] Strap-down inertial navigation is a self-contained navigation technology that directly fixes inertial measurement elements (gyroscopes and accelerometers) to a carrier, and calculates real-time attitude, velocity and position parameters through a computer.

[0003] For long-time attitude maintenance of a strap-down inertial navigation device, on the one hand, since the gyroscopes and accelerometers have various errors, the errors will accumulate and diverge over time during the process of integrating angular velocity and acceleration, resulting in a sharp decline in navigation accuracy. On the other hand, the initial alignment error will also cause huge position and velocity errors after long-time navigation. In order to suppress error accumulation, the conventional technical solution is to use GPS (Global Positioning System) or Beidou combined navigation to provide reference navigation data for calibrating the attitude of the inertial navigation system. The inertial navigation system calibrates its own navigation data using the reference navigation data.

[0004] The inventors found in the process of implementing the present application that the prior art has the following defects: the motion state of the bearing surface on which the inertial navigation system is installed has certain particularity, and it will sometimes remain upright and stationary for a long time relative to the inertial navigation device, which is not conducive to receiving GPS / Beidou data signals, and at this time, abnormal phenomena such as GPS / Beidou loss of lock often occur, which seriously affects the navigation accuracy of the inertial navigation system and further affects the normal operation of the inertial navigation device. SUMMARY

[0005] Embodiments of the present application provide a method and device for calibrating the attitude of an inertial navigation system, an electronic device, a storage medium and a program, which can improve the accuracy and reliability of the attitude calibration of the inertial navigation system.

[0006] According to an aspect of the present application, a method for calibrating the attitude of an inertial navigation system is provided, comprising:

[0007] In a case where it is determined that the current state of the bearing surface of the inertial navigation system is a relative static state, a plurality of north-seeking results of the inertial navigation system on the bearing surface in a current north-seeking cycle are obtained;

[0008] The plurality of north-seeking results of the inertial navigation system are iteratively calculated for an intermediate optimal solution of the azimuth angle using a Kalman filtering algorithm;

[0009] A target optimal solution of the azimuth angle in the current north-seeking cycle is calculated according to the intermediate optimal solution of the azimuth angle corresponding to each of the north-seeking results.

[0010] The inertial navigation attitude in the current north-seeking period is calibrated according to the target optimal solution of the azimuth angle of the current north-seeking period.

[0011] According to another aspect of the present application, there is provided an inertial navigation attitude calibration device, comprising:

[0012] A north-seeking result acquisition module is configured to acquire a plurality of north-seeking results of the inertial navigation in a current north-seeking period on an inertial navigation bearing surface, in a case where a current state of the inertial navigation bearing surface is determined to be a relative static state.

[0013] An intermediate optimal solution calculation module is configured to calculate an intermediate optimal solution of an azimuth angle by using a Kalman filtering algorithm to iteratively calculate the azimuth angle based on the plurality of north-seeking results of the inertial navigation.

[0014] A target optimal solution calculation module is configured to calculate a target optimal solution of the azimuth angle of the current north-seeking period based on the intermediate optimal solution of the azimuth angle corresponding to each of the north-seeking results.

[0015] An inertial navigation attitude calibration module is configured to calibrate the attitude of the inertial navigation in the current north-seeking period according to the target optimal solution of the azimuth angle of the current north-seeking period.

[0016] According to another aspect of the present application, there is provided an electronic device, comprising:

[0017] at least one processor; and

[0018] a memory connected to the at least one processor in communication; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the inertial navigation attitude calibration method according to any one of the embodiments of the present application.

[0020] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to implement the inertial navigation attitude calibration method according to any one of the embodiments of the present application when executed by the processor.

[0021] According to another aspect of the present application, there is also provided a computer program product comprising a computer program for implementing the inertial navigation attitude calibration method according to any one of the embodiments of the present application when executed by a processor.

[0022] In the case that the current state of the inertial navigation support surface is determined as a relatively static state, the embodiment of the application acquires multiple north-seeking results of the inertial navigation on the inertial navigation support surface in a current north-seeking cycle, and iteratively calculates an intermediate optimal solution of the azimuth angle by using a Kalman filtering algorithm on the multiple north-seeking results of the inertial navigation, further calculates a target optimal solution of the azimuth angle in the current north-seeking cycle according to the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result, and finally calibrates the attitude of the inertial navigation in the current north-seeking cycle according to the target optimal solution of the azimuth angle in the current north-seeking cycle. Since the optimal solution of the azimuth angle calculated by comprehensively utilizing multiple north-seeking results is used to calculate the final azimuth angle solution, the solution accuracy of the azimuth angle is effectively improved, and therefore the attitude calibration accuracy of the inertial navigation based on the azimuth angle can be further improved, and the problems of low accuracy and low reliability existing in the attitude calibration of the inertial navigation based on other navigation data can be solved, and the accuracy and reliability of the attitude calibration of the inertial navigation can be improved.

[0023] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating any inventive labor.

[0025] Figure 1 is a flow chart of an attitude calibration method of an inertial navigation provided by an embodiment of the application;

[0026] Figure 2 is a flow chart of an attitude calibration method of an inertial navigation provided by an embodiment of the application;

[0027] Figure 3 is a flow chart of an attitude calibration method of an inertial navigation provided by an embodiment of the application;

[0028] Figure 4 is a structural schematic diagram of a radar array of a vehicle-mounted radar provided by an embodiment of the application;

[0029] Figure 5 is a flow chart of determining the current state of the radar array according to the attitude angle change value provided by an embodiment of the application;

[0030] Figure 6 is a flow chart of determining the current state of the radar array according to the output value of the inertial element provided by an embodiment of the application;

[0031] Figure 7 is a flow chart for determining the current state of the radar array according to the inertial navigation output speed value provided by the second embodiment of the present application;

[0032] Figure 8 is a flow chart for determining the current state of the radar array according to the incremental output value of the odometer provided by the second embodiment of the present application;

[0033] Figure 9 is a schematic diagram of an inertial navigation attitude calibration device provided by the third embodiment of the present application;

[0034] Figure 10 is a structural schematic diagram of an electronic device provided by the fourth embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the personnel in the art without creative labor should belong to the scope of protection of the present application.

[0036] It should be noted that the terms "first", "second", "third", and "fourth" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] Embodiment One

[0038] Figure 1is a flowchart of a method for attitude calibration of inertial navigation provided by an embodiment of the present application. The embodiment can be applicable to the case of attitude calibration of inertial navigation according to the optimal solution of the azimuth angles corresponding to a plurality of north-seeking results of the inertial navigation. The method can be executed by an attitude calibration device for inertial navigation. The device can be implemented in software and / or hardware, and can be generally integrated in an electronic device. The electronic device can be a terminal device or a server device, as long as it can execute the method for attitude calibration of inertial navigation. The embodiment of the present application does not limit the specific type of the electronic device. Accordingly, as shown in Figure 1 the method includes the following operations:

[0039] S110, in a case where it is determined that the current state of the inertial navigation support surface is a relatively static state, obtaining a plurality of north-seeking results of the inertial navigation in a current north-seeking period on the inertial navigation support surface.

[0040] The inertial navigation support surface can be a support surface on which an inertial navigation device (hereinafter referred to as inertial navigation) is installed in a device that uses the inertial navigation device for navigation. The current north-seeking period can be a period of time in which the inertial navigation is currently located and can perform a north-seeking operation.

[0041] It can be understood that when the support surface on which the inertial navigation is installed is in a dynamic motion state relative to the device on which the inertial navigation is installed, the inertial navigation is not suitable for attitude calibration, and needs to continuously provide navigation function. Therefore, only when the support surface on which the inertial navigation is installed is in a static state relative to the device on which the inertial navigation is installed, the inertial navigation can be calibrated during this period. The relatively static state of the inertial navigation support surface means that the support surface on which the inertial navigation is installed is in a static state relative to the device on which the inertial navigation is installed. If it is determined that the current state of the inertial navigation support surface is a relatively static state, it indicates that the attitude of the inertial navigation can be calibrated automatically.

[0042] The single north-seeking time of the inertial navigation can be configured as needed, for example, the time length of a north-seeking operation can be 5 minutes. The period during which the current state of the inertial navigation support surface is a relatively static state can be referred to as a north-seeking period. The cycle length of the north-seeking period is dynamically changed, and can be determined according to the duration of the relatively static state of the inertial navigation support surface. At least one attitude calibration process can be performed on the inertial navigation in each north-seeking period. Accordingly, each north-seeking period can be used as a current north-seeking period to execute the method for attitude calibration of inertial navigation provided by the embodiment of the present application. It can be understood that the duration during which the current state of the inertial navigation support surface is a relatively static state can also be less than the time length of a north-seeking operation.

[0043] During the current north-seeking cycle of the inertial navigation system (INS), the INS can enter the periodic automatic static north-seeking mode to perform at least one north-seeking operation. If, during the INS north-seeking period, the INS bearing surface switches from a relatively static state to a relatively dynamic state, the north-seeking operation is canceled, and the system switches to pure inertial navigation mode. Once the INS bearing surface is determined to have returned to a relatively static state, the system re-enters the periodic automatic static north-seeking mode.

[0044] Inertial navigation systems (INS) can obtain multiple north-finding results through multiple static north-finding cycles within the current north-finding period. Each north-finding result can include relevant information such as roll, pitch, and azimuth (also known as heading). Among these, the roll and pitch angles included in the north-finding results have sufficiently high north-finding accuracy and can be directly used to correct system values. However, the azimuth angle is somewhat unstable due to the inherent accuracy limitations of north-finding, so it is crucial to focus on calculating the optimal value of the azimuth angle.

[0045] S120. The Kalman filter algorithm is used to iteratively calculate the intermediate optimal solution of the azimuth angle from the multiple north-finding results of the inertial navigation system.

[0046] Among them, the intermediate optimal solution can be the optimal solution of the azimuth angle calculated for each north-finding result.

[0047] For each north-finding result from the inertial navigation system, the optimal azimuth angle can be calculated iteratively using the Kalman filter algorithm, serving as an intermediate optimal azimuth angle solution among the north-finding results. Since one north-finding cycle includes multiple north-finding results, multiple intermediate optimal azimuth angle solutions can be calculated within one north-finding cycle.

[0048] S130. Calculate the target optimal solution of the azimuth angle for the current north-finding cycle based on the intermediate optimal solution of the azimuth angle corresponding to each north-finding result.

[0049] Among them, the target optimal solution can be the unique optimal solution for the azimuth angle calculated by the inertial navigation system in the current north-seeking cycle.

[0050] Correspondingly, after obtaining the intermediate optimal solution of the azimuth angle corresponding to each north-finding result, the accuracy of the intermediate optimal solution of the azimuth angle corresponding to each north-finding result can be analyzed, and the intermediate optimal solution of the azimuth angle can be solved simultaneously based on the accuracy of the intermediate optimal solution of the azimuth angle corresponding to each north-finding result to obtain the unique target optimal solution of the azimuth angle for the current north-finding cycle.

[0051] For example, the intermediate optimal solution corresponding to the azimuth angle with the highest accuracy can be selected as the target optimal solution of the azimuth angle in the current north-seeking period according to the accuracy of the intermediate optimal solution corresponding to each north-seeking result. Alternatively, the target optimal solution of the azimuth angle in the current north-seeking period can also be obtained by weighting calculation according to the corresponding accuracy and setting a corresponding weight for part or all of the intermediate optimal solutions of the azimuth angle.

[0052] S140, calibrating the attitude of the inertial navigation system in the current north-seeking period according to the target optimal solution of the azimuth angle in the current north-seeking period.

[0053] After the target optimal solution of the azimuth angle in the current north-seeking period is obtained, the current roll angle and the current pitch angle in the inertial navigation system attitude can be directly updated by using the roll angle and the pitch angle in the north-seeking result, and the current azimuth angle in the inertial navigation system attitude can be updated by using the target optimal solution of the azimuth angle in the current north-seeking period. After the current roll angle, the current pitch angle and the current azimuth angle of the inertial navigation system are updated, a new set of Euler angles is formed and the current attitude of the inertial navigation system is updated.

[0054] In the case where the current state of the inertial navigation system bearing surface is determined to be a relatively static state, the plurality of north-seeking results of the inertial navigation system on the inertial navigation system bearing surface in the current north-seeking period are obtained, the intermediate optimal solutions of the azimuth angle are iteratively calculated by using the Kalman filtering algorithm for the plurality of north-seeking results of the inertial navigation system, the target optimal solution of the azimuth angle in the current north-seeking period is further calculated according to the intermediate optimal solutions of the azimuth angle corresponding to each north-seeking result, and finally the attitude of the inertial navigation system in the current north-seeking period is calibrated according to the target optimal solution of the azimuth angle in the current north-seeking period. Since the optimal solution of the azimuth angle calculated by the above technical solution is obtained by simultaneously optimizing the plurality of north-seeking results, the accuracy of the azimuth angle calculation is effectively improved, and therefore the attitude calibration accuracy of the inertial navigation system based on the azimuth angle can be further improved, and the problems of low accuracy and low reliability in the existing attitude calibration of the inertial navigation system based on other navigation data can be solved, and the accuracy and reliability of the attitude calibration of the inertial navigation system can be improved.

[0055] Embodiment Two

[0056] Figure 2 is a flowchart of a method for calibrating the attitude of an inertial navigation system according to an embodiment of the present application, Figure 3 is a flowchart of a method for calibrating the attitude of an inertial navigation system according to an embodiment of the present application, and the above embodiment is further specified in this embodiment. In this embodiment, a plurality of specific and optional implementation manners for determining that the current state of the inertial navigation system bearing surface is a relatively static state, iteratively calculating the intermediate optimal solutions of the azimuth angle and calculating the target optimal solution of the azimuth angle in the current north-seeking period are given. Accordingly, as shown in Figure 2 and Figure 3 the method of the present embodiment can comprise:

[0057] S210, acquire multi-dimensional correlation determination data for state determination of the inertial navigation, and determine that the current state of the inertial navigation support surface is a relative static state if it is determined that all the multi-dimensional correlation determination data meet corresponding relative static state conditions.

[0058] The multi-dimensional correlation determination data can be multi-dimensional relevant data for determining the current state of the inertial navigation.

[0059] In the embodiments of the present application, in order to improve the determination accuracy of the current state of the inertial navigation support surface, the current state of the inertial navigation can be determined from a multi-dimensional perspective. Specifically, multi-dimensional correlation determination data for state determination of the inertial navigation can be acquired, and it can be determined whether each dimension of the correlation determination data meets the relative static state condition. Only if all the multi-dimensional correlation determination data meet the corresponding relative static state condition, it can be determined that the current state of the inertial navigation support surface is a relative static state. If at least one dimension of the multi-dimensional correlation determination data does not meet the corresponding relative static state condition, it can be determined that the current state of the inertial navigation support surface is a relative dynamic state. The relative dynamic state of the inertial navigation support surface means that the support surface on which the inertial navigation is installed is in a state of motion relative to the device on which the inertial navigation is installed.

[0060] In an optional embodiment of the present application, the multi-dimensional correlation determination data can include a change value of an attitude angle of the inertial navigation in adjacent time windows, an output value of an inertial element in the inertial navigation in a set direction, a speed value of a set direction output by the inertial navigation, and an incremental output value of a mileage sensor; and the determination that all the multi-dimensional correlation determination data meet corresponding relative static state conditions can include: determining that the change value of the attitude angle of the inertial navigation in the adjacent time windows meets a first relative static state condition if it is determined that the change value of the attitude angle of the inertial navigation in the adjacent time windows is less than a first set threshold value; determining that the output value of the inertial element in the set direction meets a second relative static state condition if it is determined that the output value of the inertial element in the set direction is less than a second set threshold value; determining that the speed value of the set direction output by the inertial navigation meets a third relative static state condition if it is determined that the speed value of the set direction output by the inertial navigation is less than a third set threshold value; and determining that the incremental output value of the mileage sensor meets a fourth relative static state condition if it is determined that the incremental output value of the mileage sensor is a fourth set threshold value.

[0061] The setting direction, the first setting threshold, the second setting threshold, the third setting threshold and the fourth setting threshold can be configured according to actual requirements, and the embodiment of the application does not limit the above specific values. The first relative static state condition can be a judgment condition for judging the current state of the inertial navigation support surface, which is set for the attitude angle change value of the inertial navigation in adjacent time windows. The second relative static state condition can be a judgment condition for judging the current state of the inertial navigation support surface, which is set for the output value of the inertial element in the setting direction. The third relative static state condition can be a judgment condition for judging the current state of the inertial navigation support surface, which is set for the speed value of the setting direction output by the inertial navigation. The fourth relative static state condition can be a judgment condition for judging the current state of the inertial navigation support surface, which is set for the incremental output value of the odometer sensor.

[0062] In a specific application scenario, a vehicle-mounted radar is taken as an example to be described in detail. Figure 4 is a structural schematic diagram of a radar array of a vehicle-mounted radar provided by the second embodiment of the application. As shown in the figure, Figure 4 In the vehicle-mounted radar, the inertial navigation can be installed on the radar array, and in this application scenario, the radar array can be used as the inertial navigation support surface. When the radar array is in a vertical state or a prostrate state and does not move, it can be determined that the current state of the radar array as the inertial navigation support surface is a relative static state. When the radar array moves from the vertical state to the prostrate state, it can be determined that the current state of the radar array as the inertial navigation support surface is a relative dynamic state. Due to the particularity of the radar array, it will remain in the vertical state for a long time and thus is not conducive to receiving GPS / Beidou data signals, and abnormal phenomena such as GPS / Beidou loss of lock often occur, which cannot use the GPS / Beidou data signals as reference navigation data for attitude calibration, thereby affecting the normal work of the radar.

[0063] To solve the technical problem, when the inertial navigation on the radar array is powered on and starts to align, the alignment is completed and enters a navigation state, and the self-determination process of the relative static state of the radar array is started. In the self-determination process of the relative static state of the radar array, in the initial state, the radar array of the radar vehicle is vertical. After the inertial navigation is powered on and starts to align, the radar array remains stationary for about 5 minutes, the inertial navigation is completed and enters a pure inertial stage, and the initial attitude angle of the radar array is obtained. Subsequently, the radar array dynamic and static self-determination process is entered. In the determination process, the attitude angle change value of the inertial navigation in adjacent time windows, the output value of the inertial element in the setting direction in the inertial navigation, the speed value of the setting direction output by the inertial navigation and the incremental output value of the odometer sensor and other multi-dimensional associated determination data can be obtained, and whether each dimension of the associated determination data satisfies the corresponding relative static state condition is judged.

[0064] Figure 5is a flowchart provided by the second embodiment of the present application for determining the current state of the radar array according to the attitude angle change value. In a specific example, as shown in Figure 5 the attitude angle change value of the inertial navigation in adjacent time windows can be used to determine whether the radar array satisfies the first static criterion. Specifically, 4s can be taken as a static determination period, and 2s can be taken as a window. The first 2s is taken as the first time window, and the average value of the attitude angles in the window is recorded as att_1[3]. The second 2s is taken as the second time window, and the average value of the attitude angles in the window is recorded as att_2[3]. Each attitude angle can include three elements, such as roll angle, pitch angle, and azimuth angle. Further, the absolute value of the difference between the average values of the corresponding elements of the two attitude angles is calculated to obtain att_d[3]. If the change value of the three elements of the attitude angle of the inertial navigation in adjacent time windows, i.e., the absolute value of the average difference, is less than the first set threshold A, it is determined that the change value of the attitude angle of the inertial navigation in adjacent time windows satisfies the first relative static state condition, i.e., it is determined that the current state of the radar array satisfies the first static criterion of the radar array. Otherwise, it is determined that the current state of the radar array satisfies the first dynamic criterion of the radar array.

[0065] Figure 6 is a flowchart provided by the second embodiment of the present application for determining the current state of the radar array according to the output value of the inertial element. In a specific example, as shown in Figure 6 the output value of the inertial element in a set direction in the inertial navigation can be used to determine whether the radar array satisfies the second static criterion. Specifically, the output values of the inertial element in the east direction, the north direction, and the sky direction in the inertial navigation can be obtained in real time. If the angular velocity or acceleration values output by the inertial element in the three directions are all less than the second set threshold B, it is determined that the output value of the inertial element in the set direction satisfies the second relative static state condition, i.e., it is determined that the current state of the radar array satisfies the second static criterion of the radar array. Otherwise, it is determined that the current state of the radar array satisfies the second dynamic criterion of the radar array.

[0066] Figure 7 is a flowchart provided by the second embodiment of the present application for determining the current state of the radar array according to the speed value output by the inertial navigation. In a specific example, as shown in Figure 7 the speed value output by the inertial navigation in a set direction can be used to determine whether the radar array satisfies the third static criterion. Specifically, the speed output values of the inertial navigation in the east direction, the north direction, and the sky direction can be obtained in real time. If the speed values output by the inertial navigation in the three directions are all less than the third set threshold C, i.e., the east speed, the north speed, and the sky speed are all less than the third set threshold C, it is determined that the speed value output by the inertial navigation in the set direction satisfies the third relative static state condition, i.e., it is determined that the current state of the radar array satisfies the third static criterion of the radar array. Otherwise, it is determined that the current state of the radar array satisfies the third dynamic criterion of the radar array.

[0067] Figure 8 is a flowchart provided by the second embodiment of the present application for determining the current state of the radar array according to the incremental output value of the odometer sensor. In a specific example, as shown in FIG. 4, it can be determined whether the radar array satisfies the fourth static criterion according to the incremental output value of the odometer sensor. Specifically, the incremental output value of the odometer sensor can be acquired in real time, and if the incremental output value of the odometer sensor is the fourth set threshold value 0, it is determined that the incremental output value of the odometer sensor satisfies the fourth relative static state condition, i.e., it is determined that the current state of the radar array satisfies the fourth static criterion of the radar array, otherwise it is determined that the current state of the radar array satisfies the fourth dynamic criterion of the radar array. Figure 8

[0068] Correspondingly, by comprehensively considering the above four criteria, if the current state of the radar array satisfies the above four static criteria, it is comprehensively determined that the radar array is in a relative static state; otherwise, if the current state of the radar array satisfies any one of the dynamic criteria, it is comprehensively determined that the radar array is in a relative dynamic state.

[0069] S220, determining a current north-seeking azimuth angle in the current north-seeking result according to a north-seeking sequence of each of the north-seeking results, and updating a mean square error corresponding to the current azimuth angle according to a mean square error of a Kalman filtering algorithm of a last north-seeking result.

[0070] S230, performing a Kalman filtering algorithm according to the current azimuth angle, the current north-seeking azimuth angle and the mean square error corresponding to the current azimuth angle to obtain a current intermediate optimal solution of the current azimuth angle.

[0071] The current azimuth angle can be an azimuth angle included in a current attitude of the inertial navigation device, and the current north-seeking azimuth angle can be an azimuth angle included in a current north-seeking result of the inertial navigation device.

[0072] If it is determined that the current state of the inertial navigation device bearing surface is a relative static state, the current position of the inertial navigation device is taken as an input to enter a periodic automatic static north-seeking working mode of the inertial navigation device. The time of each north-seeking operation in each north-seeking period can be configured as needed, such as 5 minutes. After the current north-seeking is completed, the attitude angle in the north-seeking result is saved, and the next north-seeking operation is continued. In order to further improve the accuracy of attitude calibration, a one-dimensional Kalman filtering algorithm can be used to start an iterative calculation process of the azimuth angle after each north-seeking of the inertial navigation device is completed, so as to obtain an optimal solution of the azimuth angle in each north-seeking result as an intermediate optimal solution of the azimuth angle.

[0073] ​Accordingly, the current north-seeking azimuth in the current north-seeking result is determined in turn according to the north-seeking sequence of the north-seeking results, the mean square error corresponding to the current azimuth is updated by using the mean square error of the Kalman filtering algorithm calculated in the last north-seeking result, and the Kalman filtering algorithm is executed according to the current azimuth, the current north-seeking azimuth and the mean square error corresponding to the current azimuth, so that the current intermediate optimal solution of the current azimuth is obtained.

[0074] For example, the calculation process of the Kalman filtering algorithm is as follows:

[0075] First, the current azimuth is determined:

[0076]

[0077] Further, the mean square error corresponding to the current north-seeking result is calculated:

[0078]

[0079] The filtering gain is calculated:

[0080]

[0081] The filtering estimation is executed according to the current azimuth and the current north-seeking azimuth:

[0082]

[0083] The mean square error is updated:

[0084]

[0085] Wherein, Hp_pre and Hp_k are the current azimuth, P_k is the current mean square error, P is the mean square error updated by the current Kalman filtering algorithm, Nav_count is the navigation time length count, FREQ is the count frequency, which can be 200Hz, Nav_Nodamp is the pure inertial azimuth keeping accuracy, which can be DEG2RAD is the degree to radian conversion coefficient, K_k is the filtering gain, R is the north-seeking mean square error, which can be 0.5mil, Hp_cur is the filtering estimation, Hp_NorthSeek is the azimuth result of the current north-seeking, i.e. the current north-seeking azimuth, and Hp_cur can be used as the current intermediate optimal solution of the current azimuth calculated according to the current north-seeking result.

[0086] Each north-seeking result is based on the mean square error P updated by the Kalman filtering algorithm of the last north-seeking result, and the Kalman filtering algorithm is iteratively executed in combination with the current north-seeking azimuth and the related parameters of the inertial navigation, so that the intermediate optimal solution of the azimuth corresponding to each north-seeking result is obtained.

[0087] S240, obtain azimuth P matrix values corresponding to the azimuth of each north-seeking result, and generate a weighting coefficient matrix of the intermediate optimal solution of the azimuth corresponding to each north-seeking result according to the azimuth P matrix values corresponding to the azimuth of each north-seeking result.

[0088] The azimuth P matrix value can be a parameter value of the north-seeking result P matrix related to the azimuth. The weighting coefficient matrix can be a matrix composed of weight coefficients determined for the intermediate optimal solution of each azimuth.

[0089] The "P matrix" mentioned in the process of inertial navigation north-seeking generally refers to the error covariance matrix of state estimation. The P matrix describes the uncertainty of the current state estimation value of the Kalman filter. The larger the value in the P matrix, the more uncertain and less reliable the state estimation. As a covariance matrix, the P matrix not only contains the variance of the estimation error of each state variable itself (the elements on the diagonal line), but also contains the covariance between the estimation errors of different state variables (the elements off the diagonal line). In the application scenario of inertial navigation north-seeking, one dimension of the matrix elements of the P matrix is the element corresponding to the azimuth, which can reflect the accuracy of the azimuth estimation calculation and can be referred to as the azimuth P matrix value.

[0090] After obtaining the intermediate optimal solution of the azimuth corresponding to each north-seeking result, the accuracy of the intermediate optimal solution of the azimuth corresponding to each north-seeking result needs to be preliminarily evaluated, so as to select some or all of the intermediate optimal solutions of the azimuth to calculate the target optimal solution of the azimuth of the current north-seeking period. When evaluating the accuracy of the intermediate optimal solution of the azimuth, the azimuth P matrix value corresponding to the azimuth of each north-seeking result can be obtained, and the accuracy of the intermediate optimal solution of the azimuth can be evaluated through the azimuth P matrix value. The smaller the azimuth P matrix value, the better the convergence effect of the Kalman filter during north-seeking, the higher the accuracy of the north-seeking result, and the higher the accuracy of the intermediate optimal solution of the azimuth. Therefore, the weighting coefficient matrix of the intermediate optimal solution of the azimuth corresponding to each north-seeking result can be generated according to the azimuth P matrix value corresponding to the azimuth of each north-seeking result. The larger the azimuth P matrix value corresponding to the azimuth of the north-seeking result, the smaller the weighting coefficient of the intermediate optimal solution of the azimuth corresponding to the north-seeking result. The smaller the azimuth P matrix value corresponding to the azimuth of the north-seeking result, the larger the weighting coefficient of the intermediate optimal solution of the azimuth corresponding to the north-seeking result. The weighting coefficients of the intermediate optimal solutions of each azimuth constitute the weighting coefficient matrix of the intermediate optimal solution of the azimuth. The larger the weighting coefficient in the weighting coefficient matrix, the higher the accuracy of the intermediate optimal solution of the corresponding azimuth.

[0091] S250, according to the weighting coefficient matrix of the intermediate optimal solution of the azimuth corresponding to each north-seeking result, the intermediate optimal solution of the azimuth corresponding to each north-seeking result is weighted and calculated to obtain the target optimal solution of the azimuth of the current north-seeking period.

[0092] For example, it is assumed that an array Hp[n] is used to store the intermediate optimal solution Hp_cur of the azimuth angle corresponding to all the north-seeking results in the current north-seeking cycle, and an array Hp_P[n] is used to latch the azimuth P matrix value P_cur of the azimuth angle corresponding to the Kalman filter at the moment when each north-seeking ends. Hp_cur and P_cur are the latest elements, which are respectively latched into the array Hp[n] and the array Hp_P[n]. The elements in the array Hp[n] and the array Hp_P[n] correspond to each other, and according to the size of the P_cur element in the array Hp_P[n], the current moment of calculating the intermediate optimal solution of the azimuth angle is assigned to the corresponding intermediate optimal solution element in the array Hp[n] with a weighted coefficient Hp_Kcl[n]. The smaller the element in the array Hp_P[n] is, the better the convergence effect of the Kalman filter during the north-seeking is, the higher the accuracy of the north-seeking result is, the larger the set value of the corresponding weighted coefficient is, and the weighted coefficient satisfies . Then, the target optimal solution Hp_opt of the azimuth angle in the current north-seeking cycle can be calculated based on the following formula:

[0093]

[0094] In an optional embodiment of the present application, the generation of the weighted coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result based on the azimuth P matrix value of the azimuth angle corresponding to each north-seeking result can include: generating the weighted coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result based on the following formula:

[0095]

[0096]

[0097] wherein, represents the weighted coefficient of the intermediate optimal solution of the azimuth angle corresponding to the kth north-seeking result, represents the azimuth P matrix value of the azimuth angle corresponding to the kth north-seeking result, represents the azimuth P matrix value of the azimuth angle corresponding to the ith north-seeking result.

[0098] S260, calibrating the attitude of the inertial navigation system in the current north-seeking cycle according to the target optimal solution of the azimuth angle in the current north-seeking cycle.

[0099] The technical scheme is characterized in that during the period when the inertial navigation bearing surface is in a relatively static state, the Kalman filtering algorithm is used to iteratively calculate the intermediate optimal solution of the azimuth angle based on multiple north-seeking results of the inertial navigation, a weighting coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result is generated according to the azimuth P matrix value of the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result, and then the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result is calculated by weighting according to the weighting coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result, so as to obtain the target optimal solution of the azimuth angle in the current north-seeking period. The whole inertial navigation attitude calibration process is independent of external reference navigation information, the azimuth angle calculation accuracy is effectively improved, the accuracy and reliability of the inertial navigation attitude calibration are improved, and the long-time attitude accuracy of the strapdown inertial navigation equipment can be maintained.

[0100] It should be noted that the related information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present disclosure are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data comply with relevant laws, regulations and standards in the relevant region.

[0101] It should be noted that any arrangement and combination of the technical features in the above embodiments also belong to the protection scope of the present application.

[0102] Embodiment three

[0103] Figure 9 is a schematic diagram of an inertial navigation attitude calibration device provided by the third embodiment of the present application, as shown in Figure 9 The device comprises a north-seeking result acquisition module 910, an intermediate optimal solution calculation module 920, a target optimal solution calculation module 930 and an inertial navigation attitude calibration module 940, wherein:

[0104] The north-seeking result acquisition module 910 is configured to acquire multiple north-seeking results of the inertial navigation on the inertial navigation bearing surface in the current north-seeking period when it is determined that the current state of the inertial navigation bearing surface is a relatively static state.

[0105] The intermediate optimal solution calculation module 920 is configured to iteratively calculate the intermediate optimal solution of the azimuth angle based on the multiple north-seeking results of the inertial navigation by using the Kalman filtering algorithm.

[0106] The target optimal solution calculation module 930 is configured to calculate the target optimal solution of the azimuth angle in the current north-seeking period according to the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result.

[0107] The inertial navigation attitude calibration module 940 is configured to calibrate the attitude of the inertial navigation in the current north-seeking period according to the target optimal solution of the azimuth angle in the current north-seeking period.

[0108] In the case that the current state of the inertial navigation support surface is determined to be a relatively static state, the embodiment of the present application acquires multiple north-seeking results of the inertial navigation in the current north-seeking cycle on the inertial navigation support surface, and iteratively calculates an intermediate optimal solution of the azimuth angle by using a Kalman filtering algorithm on the multiple north-seeking results of the inertial navigation, further calculates a target optimal solution of the azimuth angle in the current north-seeking cycle according to the intermediate optimal solution of the azimuth angle corresponding to each north-seeking result, and finally calibrates the attitude of the inertial navigation in the current north-seeking cycle according to the target optimal solution of the azimuth angle in the current north-seeking cycle. Since the above technical solution comprehensively utilizes the optimal solutions of the azimuth angle calculated by using multiple north-seeking results to obtain the final azimuth angle calculation result by simultaneous optimization, the calculation accuracy of the azimuth angle is effectively improved, and therefore the attitude calibration accuracy of the inertial navigation based on the azimuth angle can be further improved, and the problems of low accuracy and low reliability existing in the attitude calibration of the inertial navigation based on other navigation data can be solved, and the accuracy and reliability of the attitude calibration of the inertial navigation can be improved.

[0109] Optionally, the north-seeking result acquisition module 910 is further configured to acquire multi-dimensional correlation determination data for state determination of the inertial navigation, and determine that the current state of the inertial navigation support surface is a relatively static state in the case that the multi-dimensional correlation determination data all meet corresponding relatively static state conditions.

[0110] Optionally, the multi-dimensional correlation determination data includes an attitude angle change value of the inertial navigation in adjacent time windows, an output value of an inertial element in the inertial navigation in a set direction, a speed value of the inertial navigation output in the set direction, and an incremental output value of a mileage sensor, and the north-seeking result acquisition module 910 is further configured to determine that the attitude angle change value of the inertial navigation in the adjacent time windows meets a first relatively static state condition in the case that the attitude angle change value of the inertial navigation in the adjacent time windows is less than a first set threshold, determine that the output value of the inertial element in the set direction meets a second relatively static state condition in the case that the output value of the inertial element in the set direction is less than a second set threshold, determine that the speed value of the inertial navigation output in the set direction meets a third relatively static state condition in the case that the speed value of the inertial navigation output in the set direction is less than a third set threshold, and determine that the incremental output value of the mileage sensor meets a fourth relatively static state condition in the case that the incremental output value of the mileage sensor is a fourth set threshold.

[0111] Optionally, the intermediate optimal solution calculation module 920 is further configured to determine a current north-seeking azimuth angle in the current north-seeking result according to a north-seeking sequence of each north-seeking result, update a mean square error corresponding to the current azimuth angle according to a mean square error of a Kalman filtering algorithm of the last north-seeking result, and perform the Kalman filtering algorithm according to the current azimuth angle, the current north-seeking azimuth angle and the mean square error corresponding to the current azimuth angle to obtain a current intermediate optimal solution of the current azimuth angle.

[0112] Optionally, the target optimal solution calculation module 930 is further configured to: obtain azimuth P-matrix values of the azimuths corresponding to the north-seeking results; generate a weighting coefficient matrix of the intermediate optimal solutions of the azimuths corresponding to the north-seeking results according to the azimuth P-matrix values of the azimuths corresponding to the north-seeking results; and perform weighted calculation on the intermediate optimal solutions of the azimuths corresponding to the north-seeking results according to the weighting coefficient matrix of the intermediate optimal solutions of the azimuths corresponding to the north-seeking results, to obtain the target optimal solution of the azimuth of the current north-seeking cycle.

[0113] Optionally, the target optimal solution calculation module 930 is further configured to generate the weighting coefficient matrix of the intermediate optimal solutions of the azimuths corresponding to the north-seeking results based on the following formula:

[0114]

[0115]

[0116] wherein, indicates the weighting coefficient of the intermediate optimal solution of the azimuth corresponding to the kth north-seeking result, indicates the azimuth P-matrix value of the azimuth corresponding to the kth north-seeking result, indicates the azimuth P-matrix value of the azimuth corresponding to the ith north-seeking result.

[0117] The above-mentioned inertial navigation attitude calibration device can execute the inertial navigation attitude calibration method provided by any embodiment of the present application, has the function modules and beneficial effects corresponding to the execution method. The technical details not described in detail in the present embodiment can be referred to the inertial navigation attitude calibration method provided by any embodiment of the present application.

[0118] Since the above-mentioned inertial navigation attitude calibration device can execute the inertial navigation attitude calibration method in the embodiments of the present application, based on the inertial navigation attitude calibration method described in the embodiments of the present application, those skilled in the art can understand the specific implementation of the inertial navigation attitude calibration device of the present embodiment and its various forms, so the inertial navigation attitude calibration device how to realize the inertial navigation attitude calibration method in the embodiments of the present application will not be introduced in detail. As long as the device used to implement the inertial navigation attitude calibration method in the embodiments of the present application is implemented by those skilled in the art, it belongs to the scope of the present application.

[0119] Embodiment Four

[0120] Figure 10A structural diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0121] As shown in Figure 10 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected in communication with the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0122] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0123] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the attitude calibration method of inertial navigation.

[0124] Optionally, the attitude calibration method of the inertial navigation system can comprise: in a case where the current state of the inertial navigation system carrier surface is determined to be a relatively static state, acquiring a plurality of north-seeking results of the inertial navigation system on the inertial navigation system carrier surface in a current north-seeking cycle; iteratively calculating an intermediate optimal solution of the azimuth angle by using a Kalman filtering algorithm on the plurality of north-seeking results of the inertial navigation system; calculating a target optimal solution of the azimuth angle of the current north-seeking cycle according to the intermediate optimal solution of the azimuth angle corresponding to each of the north-seeking results; and calibrating the attitude of the inertial navigation system in the current north-seeking cycle according to the target optimal solution of the azimuth angle of the current north-seeking cycle.

[0125] In some embodiments, the attitude calibration method of the inertial navigation system can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the attitude calibration method of the inertial navigation system described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the attitude calibration method of the inertial navigation system by any other suitable means, such as by means of firmware.

[0126] The various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0127] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0128] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0130] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0131] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0132] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which are not limited herein.

[0133] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An attitude calibration method for inertial navigation systems, characterized in that, include: Given that the current state of the inertial navigation bearing surface is determined to be a relatively static state, multiple north-seeking results of the inertial navigation on the inertial navigation bearing surface during the current north-seeking cycle are obtained; The intermediate optimal solution of the azimuth angle is calculated iteratively using the Kalman filter algorithm on multiple north-finding results of the inertial navigation system. The target optimal solution of the azimuth angle for the current north-finding cycle is calculated based on the intermediate optimal solution of the azimuth angle corresponding to each of the north-finding results. The attitude of the inertial navigation system is calibrated in the current north-seeking cycle based on the target optimal solution of the azimuth angle in the current north-seeking cycle. The step of calculating the target optimal solution of the azimuth angle for the current north-finding cycle based on the intermediate optimal solution of the azimuth angle corresponding to each of the north-finding results includes: Obtain the azimuth P matrix value corresponding to each of the north-finding results; Based on the azimuth P matrix values ​​of the azimuth angles corresponding to each north-finding result, a weighted coefficient matrix of the intermediate optimal solution of the azimuth angles corresponding to each north-finding result is generated. The intermediate optimal solutions of the azimuth angles corresponding to each north-finding result are weighted and calculated based on the weighted coefficient matrix of the intermediate optimal solutions of the azimuth angles corresponding to each north-finding result, so as to obtain the target optimal solution of the azimuth angle for the current north-finding cycle.

2. The method according to claim 1, characterized in that, The determination that the current state of the inertial navigation bearing surface is a relatively static state includes: Obtain multidimensional correlation determination data for state determination of the inertial navigation system; If all the multidimensional correlation judgment data meet the corresponding relative static state conditions, the current state of the inertial navigation bearing surface is determined to be a relative static state.

3. The method according to claim 2, characterized in that, The multidimensional correlation determination data includes the attitude angle change value of the inertial navigation system in adjacent time windows, the output value of the inertial element in the inertial navigation system in the set direction, the velocity value output by the inertial navigation system in the set direction, and the incremental output value of the odometer. The step of determining that all the multidimensional correlation judgment data satisfy the corresponding relative static state conditions includes: If it is determined that the attitude angle change value of the inertial navigation system in an adjacent time window is less than a first set threshold, it is determined that the attitude angle change value of the inertial navigation system in an adjacent time window satisfies a first relative static state condition. If it is determined that the output value of the inertial element in the inertial navigation system in the set direction is less than the second set threshold, then it is determined that the output value of the inertial element in the set direction satisfies the second relative static state condition. If the velocity value of the inertial navigation output in the set direction is less than the third set threshold, then the velocity value of the inertial navigation output in the set direction satisfies the third relative static state condition. If the incremental output value of the odometer sensor is determined to be a fourth preset threshold, then the incremental output value of the odometer sensor is determined to satisfy a fourth relative static state condition.

4. The method according to claim 1, characterized in that, The intermediate optimal solution for the azimuth angle is calculated iteratively using the Kalman filter algorithm from multiple north-finding results of the inertial navigation system, including: The current north azimuth angle in the current north-finding result is determined according to the north-finding order of each of the aforementioned north-finding results; Update the mean square error of the current azimuth angle based on the mean square error of the Kalman filter algorithm of the previous north-finding result; The Kalman filter algorithm is executed based on the current azimuth angle, the current north-finding azimuth angle, and the mean square error corresponding to the current azimuth angle to obtain the current intermediate optimal solution for the current azimuth angle.

5. The method according to claim 1, characterized in that, The step of generating the weighted coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each of the north-finding results based on the azimuth angle P matrix values ​​of each of the north-finding results includes: The weighted coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each of the north-finding results is generated based on the following formula: in, This represents the weighting coefficient of the intermediate optimal solution for the azimuth angle corresponding to the k-th north-finding result. This represents the azimuth matrix value P corresponding to the k-th north-finding result. Let P represent the azimuth matrix value corresponding to the azimuth angle of the i-th north-finding result.

6. An attitude calibration device for inertial navigation, characterized in that, include: The north-finding result acquisition module is used to acquire multiple north-finding results of the inertial navigation system on the inertial navigation bearing surface during the current north-finding cycle, when the current state of the inertial navigation bearing surface is determined to be a relatively static state. The intermediate optimal solution calculation module is used to iteratively calculate the intermediate optimal solution of the azimuth angle from multiple north-finding results of the inertial navigation system using the Kalman filter algorithm. The target optimal solution calculation module is used to calculate the target optimal solution of the azimuth angle of the current north-seeking cycle based on the intermediate optimal solution of the azimuth angle corresponding to each of the north-seeking results. An inertial navigation attitude calibration module is used to calibrate the attitude of the inertial navigation system in the current north-seeking cycle based on the target optimal solution of the azimuth angle in the current north-seeking cycle. The target optimal solution calculation module is further configured to: obtain the azimuth P matrix value of the azimuth angle corresponding to each of the north-finding results; generate a weighted coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each of the north-finding results based on the azimuth P matrix value of the azimuth angle corresponding to each of the north-finding results; and perform weighted calculation on the intermediate optimal solution of the azimuth angle corresponding to each of the north-finding results based on the weighted coefficient matrix of the intermediate optimal solution of the azimuth angle corresponding to each of the north-finding results to obtain the target optimal solution of the azimuth angle of the current north-finding cycle.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the attitude calibration method of the inertial navigation system as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to perform attitude calibration of the inertial navigation system as described in any one of claims 1-5 when executed.

9. A computer program product, characterized in that, Includes a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the attitude calibration of the inertial navigation system as described in any one of claims 1-5.

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