Fusion navigation method, device and equipment and computer storage medium

Abstract

The invention discloses a fusion navigation method, device and equipment and a computer storage medium, and belongs to the technical field of fusion navigation, and the method comprises the following steps: obtaining continuous positioning data of a subject matter based on a plurality of inertial measurement units distributed in an array; acquiring positioning data of the subject matter based on a satellite navigation positioning unit; fusing the continuous positioning data and the positioning data through Kalman filtering, and determining navigation data of the subject matter according to the fused target positioning data; wherein in the data fusion process, the plurality of inertial measurement units share the same covariance matrix. A plurality of inertial measurement units distributed in an array are arranged to assist a satellite navigation positioning unit in correcting positioning data at a position with poor external signals, so that target positioning data with relatively high reliability is obtained, dependence on the external signals is reduced, and the reliability of navigation data of a subject matter is effectively improved; by sharing the same covariance matrix, the data processing amount is greatly reduced, and the navigation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of integrated navigation technology, and in particular, to an integrated navigation method, device, equipment and computer storage medium. Background Art

[0002] IMU (Inertial Measurement Unit) is the abbreviation of Inertial Measurement Unit. It is a small device containing multiple sensors, mainly used to measure and track the attitude, speed and direction of an object. These sensors usually include accelerometers and gyroscopes. Some advanced IMUs also include magnetometers or other auxiliary sensors. The accelerometer measures the linear acceleration of an object in three axial directions, while the gyroscope is used to measure the angular velocity of the object around each axis. By fusing the data of these sensors, the IMU can provide comprehensive information about the motion state of the object. That is to say, the IMU can obtain the motion data of the object.

[0003] The full name of GNSS is Global Navigation Satellite System. It generally refers to all satellite navigation systems. The main functions of GNSS are to provide services such as positioning, speed measurement and time service globally. By receiving and processing signals from multiple navigation satellites, GNSS can determine the three-dimensional position, speed and time information of the user.

[0004] In the prior art, GNSS is mostly used to locate an object and then realize navigation. However, GNSS is relatively dependent on external satellite signals. In underground areas with poor signals, it is difficult to ensure the positioning accuracy, resulting in inaccurate navigation data. Therefore, in the process of navigation in the prior art, there is a problem that the reliability of navigation data is low due to dependence on external signals. Summary of the Invention

[0005] In view of this, it is necessary to provide an integrated navigation method, device, equipment and computer storage medium to solve the problem that in the process of navigation in the prior art, the reliability of navigation data is low due to dependence on external signals.

[0006] To solve the above problems, in a first aspect, the present invention provides an integrated navigation method, including: Obtaining continuous positioning data of the target object based on a plurality of inertial measurement units distributed in an array; Obtaining the positioning data of the target object based on a satellite navigation positioning unit; Fusing the continuous positioning data and the positioning data through Kalman filtering, and determining the navigation data of the target object according to the fused target positioning data; Wherein, in the process of data fusion, the plurality of inertial measurement units share the same covariance matrix.

[0007] In some possible implementations, obtaining continuous positioning data of a target by multiple inertial measurement units based on an array distribution includes: Obtaining multiple positioning result data of the multiple inertial measurement units respectively; Filtering and fusing the multiple positioning result data to obtain the continuous positioning data of the target.

[0008] In some possible implementations, a method for determining that multiple inertial measurement units share the same covariance matrix includes: Obtaining the position data of the target by the same satellite navigation positioning unit; Controlling the multiple inertial measurement units to obtain high-frequency position data of the target based on a preset frequency; Taking the position data as a reference, fusing the high-frequency position data through Kalman filtering, and feeding back to determine the covariance matrix of each inertial measurement unit.

[0009] In some possible implementations, taking the position data as a reference, fusing the high-frequency position data through Kalman filtering, and feeding back to determine the covariance matrix of each inertial measurement unit includes: Performing time differentiation processing on the position data and the high-frequency position data to obtain position differential data and high-frequency position differential data; Constructing a system state vector of Kalman filtering, and determining a discrete-time system equation and a state transition matrix of the system state vector based on the high-frequency position differential data; Iteratively updating the discrete-time system equation and the state transition matrix according to the position differential data, and feedback-adjusting the system state vector according to the update result; Determining the measured covariance matrix corresponding to the iteratively updated high-frequency position differential data as the covariance matrix.

[0010] In some possible implementations, iteratively updating the discrete-time system equation and the state transition matrix according to the position differential data includes: Updating the discrete-time system equation and the state transition matrix corresponding to each inertial measurement unit according to the position differential data.

[0011] In some possible implementations, fusing the continuous positioning data and the positioning data through Kalman filtering, and determining the navigation data of the target according to the fused target positioning data further includes: When the positioning data is unavailable, obtaining joint data of an inertial navigation system and satellite navigation, and defining the joint data as the positioning data.

[0012] In some possible implementation manners, both the inertial measurement unit and the satellite navigation and positioning unit are arranged according to the loose coupling design principle.

[0013] In a second aspect, the present invention further provides a fusion navigation device, including: A continuous positioning data acquisition module, configured to acquire continuous positioning data of a target object based on a plurality of inertial measurement units distributed in an array; A positioning data acquisition module, configured to acquire positioning data of the target object based on the satellite navigation and positioning unit; A fusion navigation module, configured to fuse the continuous positioning data and the positioning data through Kalman filtering, and determine navigation data of the target object according to the fused target positioning data; Wherein, in the process of data fusion, a plurality of inertial measurement units share the same covariance matrix.

[0014] In a third aspect, the present invention further provides a navigation device, including a memory and a processor, wherein, The memory is configured to store a program; The processor is coupled to the memory and configured to execute the program stored in the memory to implement the steps in the fusion navigation method described above.

[0015] In a fourth aspect, the present invention further provides a computer storage medium, configured to store a computer-readable program or instruction, and when the program or instruction is executed by a processor, it can implement the steps in the fusion navigation method described above.

[0016] The beneficial effects of adopting the above embodiments are as follows: In a fusion navigation method provided by the present invention, the continuous positioning data and the positioning data are fused through Kalman filtering, and the satellite navigation and positioning unit is assisted to correct the positioning data at positions with poor external signals, so as to obtain target positioning data with relatively high reliability, reduce the dependence on external signals, and effectively improve the reliability of the navigation data of the target object; further, by sharing the same covariance matrix, the data processing amount during data fusion in the navigation process is greatly reduced, and the navigation efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flowchart of an embodiment of the fusion navigation method provided by the present invention; Figure 2 It is a schematic flowchart of an embodiment of determining that a plurality of inertial measurement units share the same covariance matrix provided by the present invention; Figure 3 It is a schematic flowchart of an embodiment of feeding back and determining the covariance matrix of each inertial measurement unit provided by the present invention; Figure 4 It is a structural block diagram of an embodiment of the fusion navigation device provided by the present invention; Figure 5 Structural block diagram of an embodiment of the navigation device provided by the present invention. Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0019] It should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the present invention illustrate the operations implemented according to some embodiments of the present invention. It should be understood that the operations in the flowchart may not be implemented in sequence, and steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present invention. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor systems and / or microcontroller systems.

[0020] The descriptions such as "first" and "second" involved in the embodiments of the present invention are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Therefore, the technical features defined with "first" and "second" may explicitly or implicitly include at least one such feature.

[0021] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0022] In order to solve the problem that in the process of path planning without a map for a vehicle in the prior art, there is a problem of path mutation due to the failure to consider time changes, the present invention provides a fusion navigation method, device, equipment and computer storage medium, which will be described in detail below.

[0023] As Figure 1 shown, Figure 1Schematic flowchart of an embodiment of the integrated navigation method provided by the present invention, including: S101: Obtain continuous positioning data of the target object based on a plurality of inertial measurement units distributed in an array; In some embodiments of the present invention, the target object may be any hardware entity in the fields of vehicles, drones, robots, and navigation and aviation, etc., without limitation here.

[0024] Continuous positioning data refers to positioning information with a continuous relationship in time, that is, the positioning information of the target object obtained according to a certain frequency.

[0025] S102: Obtain the positioning data of the target object based on the satellite navigation positioning unit; In some embodiments of the present invention, the positioning data refers to the positioning data of the target object obtained in real time by the satellite navigation positioning unit. It should be noted that since the reliability of the satellite navigation positioning unit is relatively high, the quantity of the positioning data is much smaller than that of the continuous positioning data.

[0026] S103: Fuse the continuous positioning data and the positioning data through Kalman filtering, and determine the navigation data of the target object according to the fused target positioning data; Among them, in the process of data fusion, a plurality of inertial measurement units share the same covariance matrix.

[0027] In some embodiments of the present invention, Kalman filtering is an algorithm that uses a linear system state equation to optimally estimate the system state through system input-output observation data.

[0028] Kalman filtering is an efficient recursive algorithm. It uses the state space description method and adopts a recursive form in the algorithm, which is applicable to linear, discrete, and finite-dimensional systems. It realizes the optimal estimation of the system state (in the sense of minimum mean square error) by combining the system model and the observed data of noise interference.

[0029] The core idea of Kalman filtering is an iterative process of two stages: prediction - update, which is applicable to linear Gaussian systems (that is, the system model and noise follow Gaussian distribution).

[0030] In this embodiment, the continuous positioning data and the positioning data are fused through Kalman filtering to assist the satellite navigation positioning unit in correcting the positioning data at positions with poor external signals, thereby obtaining target positioning data with relatively high reliability, reducing the dependence on external signals, and effectively improving the reliability of the navigation data of the target object; further, by sharing the same covariance matrix, the data processing volume during data fusion in the navigation process is greatly reduced, and the navigation efficiency is improved.

[0031] In some embodiments of the present invention, in S101, in order to obtain continuous positioning data of the target object based on multiple inertial measurement units distributed in an array, first, multiple positioning result data of the multiple inertial measurement units are obtained respectively; then, the multiple positioning result data are screened and fused to obtain the continuous positioning data of the target object.

[0032] In this embodiment, the inertial measurement unit may be a micro inertial measurement unit.

[0033] In some embodiments of the present invention, a similarity determination method may be used to determine whether there is invalid data in the multiple positioning result data, and other methods may also be used to judge the reliability of the multiple positioning result data, so as to ensure that each inertial measurement unit is in a normal operating state, thereby ensuring that the positioning result data obtained by each inertial measurement unit is reliable.

[0034] In some embodiments of the present invention, the multiple inertial measurement units distributed in an array are fixedly arranged relative to the target object, that is, the positional relationship between the multiple inertial measurement units distributed in an array and the target object is fixed and unchanged.

[0035] Specifically, the multiple inertial measurement units distributed in an array may be arranged inside the structure of the target object, and the specific connection method is not limited herein.

[0036] In some embodiments of the present invention, fusing the multiple positioning result data means performing weighted summation on the positioning result data of all reliability levels, finally obtaining the uniquely determined positioning data of the target object at any moment, and further obtaining the continuous positioning data.

[0037] It should be noted that due to the small volume and regular arrangement of the inertial measurement units, the differences between the multiple positioning result data are generally small.

[0038] In this embodiment, by screening and fusing the multiple positioning result data, the reliability and stability of the continuous positioning data of the target object can be ensured.

[0039] In some embodiments of the present invention, in S102, in order to reduce the coupling degree between the multiple inertial measurement units distributed in an array and the satellite navigation positioning unit, both the multiple inertial measurement units distributed in an array and the satellite navigation positioning unit are arranged according to the loose coupling design principle.

[0040] Specifically, when it is open-loop, it means that the reliability of the satellite navigation positioning unit is relatively low, and three independent navigation results (raw IMU, raw GNSS, and combined result) can be provided; when it is closed-loop, it means that the reliability of the satellite navigation positioning unit is relatively high, and two independent navigation results (raw GNSS, combined result) can be provided.

[0041] In some embodiments of the present invention, in S103, during the process of fusing continuous positioning data and positioning data through Kalman filtering and determining the navigation data of the target object based on the fused target positioning data, since Kalman filtering needs to dynamically adjust the covariance matrix of each inertial measurement unit during the calculation process, and the covariance matrix generally changes little, therefore, in order to reduce the data processing volume, a fixed covariance matrix can be set as the target covariance matrix of each inertial measurement unit, which can reduce the data processing volume during the data fusion process through Kalman filtering, as Figure 2 shown Figure 2 is a schematic flowchart of an embodiment for determining that multiple inertial measurement units share the same covariance matrix provided by the present invention, including: S201: Obtain the position data of the target object according to the same satellite navigation positioning unit; S202: Control multiple inertial measurement units to obtain the high-frequency position data of the target object based on a preset frequency; S203: Based on the position data as a reference, fuse the high-frequency position data through Kalman filtering, and feedback to determine the covariance matrix of each inertial measurement unit.

[0042] In this embodiment, by using the position data as a reference basis to fuse the high-frequency position data measured by each inertial measurement unit, the reliability of the data fusion result by Kalman filtering can be ensured; by obtaining the covariance matrix of each inertial measurement unit during the data fusion process and determining the covariance matrix through weighted summation, the covariance matrix is realized as the target covariance matrix of each inertial measurement unit; since reliable data is used during the data fusion process through Kalman filtering, the reliability of the data fusion process by Kalman filtering can be ensured, and thus the reliability of the covariance matrix of each inertial measurement unit can be ensured.

[0043] In some embodiments of the present invention, in S203, in order to fuse the high-frequency position data through Kalman filtering based on the position data as a reference and feedback to determine the covariance matrix of each inertial measurement unit, as Figure 3 shown Figure 3 is a schematic flowchart of an embodiment for feedback to determine the covariance matrix of each inertial measurement unit provided by the present invention, including: S301: Perform time differentiation processing on the position data and the high-frequency position data to obtain position differential data and high-frequency position differential data; S302: Construct the system state vector of Kalman filtering, and determine the discrete-time system equation and state transition matrix of the system state vector based on the high-frequency position differential data; S303: Iteratively update the discrete-time system equation and the state transition matrix according to the position differential data, and feedback and adjust the system state vector according to the update result; S304: Determine that the measured covariance matrix corresponding to the iteratively updated high-frequency position differential data is the covariance matrix.

[0044] In some embodiments of the present invention, in a specific application scenario, the IMU carrier coordinate system is defined as system, that is, the front-right-down system; the attitude or , velocity and position are calculated based on the navigation coordinate system ( system),

[0045]

[0046]

[0047]

[0048]

[0049] Among them, represents quaternion multiplication, represents the skew-symmetric matrix, system is the geodetic coordinate system , , correspond to the latitude, longitude and elevation of the earth respectively, is system relative to system's angular velocity projected on system's vector, represents the local gravity, and represent the radius of curvature of the meridian and the radius of curvature of the ellipsoid respectively, and , , represent the northward, eastward and vertical velocities respectively, , , , and represent , , , and differentials with respect to time.

[0050] Combined navigation is performed using the extended Kalman filter. When designing the extended Kalman filter, the system state vector includes the navigation state error and the IMU error, i.e.: The state error of inertial navigation

[0051] where are the position error, velocity error, and attitude error of inertial navigation respectively, and are the bias errors of the gyroscope and accelerometer respectively, and are the scale factor errors of the gyroscope and accelerometer respectively.

[0052] The above-mentioned bias errors and scale factor errors are modeled using a first-order Gaussian - Markov process. The discrete-time system equation is:[[]]

[0053] where The corresponding subscripts k and k - 1 represent the corresponding times, is the to state transition matrix, is the system noise.

[0054] In the prediction stage, according to the state transition matrix, the state and its covariance of the system are updated:[[]]

[0055]

[0056] where is the system state at is the predicted value of the system state, is the covariance of the system state at is the optimal estimated value of the covariance of the system state at is the covariance of the system state at is the covariance matrix of the system noise.

[0057] After the extended Kalman filter measurement update, the feedback error state is then cleared.

[0058] The observation equation can be written as:[[]]

[0059] where is the observation vector, ,[[]] is the GNSS position measurement error, modeled as white noise,[[]] , . is the lever arm of GNSS.

[0060] In summary, the update of GNSS position measurement is as follows:

[0061]

[0062]

[0063] where is the Kalman gain.

[0064] Finally, Feedback to the navigation state and IMU error, the corrected navigation state is used as the output of the system, and the corrected IMU error is compensated to the IMU measurement of the next cycle.

[0065] Furthermore, the IMUs within the IMU array share the same covariance matrix , design matrix , and Kalman gain .

[0066] In this embodiment, by sharing the same covariance matrix among all inertial measurement units, the data processing amount in the Kalman filtering process is greatly reduced, thereby improving the efficiency of data fusion. Since each inertial measurement unit is less affected by the outside, the accuracy of the fused positioning data will not cause a large deviation. Furthermore, on the basis of ensuring the availability of the data, the data processing efficiency is improved.

[0067] In some embodiments of the present invention, in S303, specifically, the discrete-time system equation and state transition matrix corresponding to each inertial measurement unit are updated according to the position differential data.

[0068] In some embodiments of the present invention, in order to improve the reliability of the covariance matrix, when the satellite navigation positioning unit is operating normally, data fusion is performed on multiple inertial measurement units distributed in an array at a preset frequency, and the corresponding covariance matrix is updated.

[0069] In some embodiments of the present invention, when the positioning data of the target is unavailable, that is, when the reliability of the positioning data cannot be guaranteed at the beginning, it is necessary to obtain the joint data of the inertial navigation system and satellite navigation, and define the joint data as the positioning data.

[0070] Inertial Navigation System (INS) is an autonomous navigation system that does not rely on external information and does not radiate energy to the outside. Its working environment includes not only air and ground, but also underwater. The inertial navigation system is based on Newton's laws of mechanics. By measuring the acceleration of the carrier in the inertial reference system, integrating it over time, and transforming it into the navigation coordinate system, it can obtain information such as speed, yaw angle and position in the navigation coordinate system.

[0071] In this embodiment, the reliability of positioning data is ensured by using an external inertial navigation system and satellite navigation, thereby improving subsequent navigation accuracy.

[0072] In this embodiment, continuous positioning data and positioning data are fused through Kalman filtering, and the auxiliary satellite navigation positioning unit corrects the positioning data at a location where the external signal is poor, thereby obtaining target positioning data with higher reliability, reducing dependence on external signals, and effectively improving the reliability of the navigation data of the target object; by all inertial measurement units sharing the same covariance matrix, the amount of data processing in the Kalman filtering process is greatly reduced, thereby improving the efficiency of data fusion; since each inertial measurement unit is less affected by external factors, the accuracy of the fused positioning data will not cause a large deviation, thereby improving the data processing efficiency while ensuring the availability of the data.

[0073] In order to better implement the fusion navigation method in the embodiment of the present invention, the embodiment of the present invention also provides a fusion navigation device, such as Figure 4 As shown, Figure 4 This is a structural block diagram of an embodiment of a fusion navigation device provided by the present invention. The fusion navigation device 400 includes: The continuous positioning data acquisition module 401 is used to acquire the continuous positioning data of the target object based on a plurality of inertial measurement units distributed in an array; The positioning data acquisition module 402 is used to acquire the positioning data of the target object based on the satellite navigation positioning unit; The fusion navigation module 403 is used to fuse the continuous positioning data and the positioning data through Kalman filtering, and determine the navigation data of the target object according to the fused target positioning data; In the process of data fusion, multiple inertial measurement units share the same covariance matrix.

[0074] The fusion navigation device 400 provided in the above embodiment can implement the technical solution described in the above fusion navigation method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above fusion navigation method embodiment, which will not be repeated here.

[0075] likeFigure 5 As shown, the present invention also correspondingly provides a navigation device 500. The navigation device 500 includes a processor 501, a memory 502, and a display 503. Figure 5 Only some components of the navigation device 500 are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented.

[0076] In some embodiments, the memory 502 can be an internal storage unit of the navigation device 500, such as the hard disk or memory of the navigation device 500. In some other embodiments, the memory 502 can also be an external storage device of the navigation device 500, such as a plug-in hard disk equipped on the navigation device 500, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0077] In some embodiments, the processor 501 can be a Central Processing Unit (CPU), a microprocessor, or other data processing chips, and is used to run the program code stored in the memory 502 or process data, such as the integrated navigation method in the present invention.

[0078] In some embodiments, the display 503 can be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. The display 503 is used to display information of the navigation device 500 and to display a visual user interface. The components 501 - 503 of the navigation device 500 communicate with each other through a system bus.

[0079] In some embodiments of the present invention, when the processor 501 executes the integrated navigation program in the memory 502, the following steps can be implemented: Obtain continuous positioning data of the target based on a plurality of inertial measurement units distributed in an array; Obtain positioning data of the target based on a satellite navigation positioning unit; Fuse the continuous positioning data and the positioning data through Kalman filtering, and determine the navigation data of the target based on the fused target positioning data; Wherein, during the data fusion process, the plurality of inertial measurement units share the same covariance matrix.

[0080] It should be understood that when the processor 501 executes the integrated navigation program in the memory 502, in addition to the above functions, other functions can also be implemented. For details, reference can be made to the description of the relevant method embodiments above.

[0081] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, the steps or functions in the fusion navigation method provided in the above method embodiments can be implemented.

[0082] Those skilled in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program. The computer program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a disk, an optical disc, a read-only memory or a random access memory, etc.

[0083] The above has introduced the fusion navigation method and device provided by the present invention in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A fusion navigation method, characterized in that: include: Acquire continuous positioning data of the target object based on multiple inertial measurement units distributed in an array; Acquiring positioning data of the target object based on a satellite navigation positioning unit; fusing the continuous positioning data and the positioning data through Kalman filtering, and determining the navigation data of the target object according to the fused target positioning data; In the process of data fusion, the multiple inertial measurement units share the same covariance matrix.

2. The fusion navigation method according to claim 1, characterized in that: The method of acquiring continuous positioning data of the target object based on multiple inertial measurement units distributed in an array includes: Respectively acquiring a plurality of positioning result data of a plurality of the inertial measurement units; The plurality of positioning result data are screened and merged to obtain the continuous positioning data of the target object.

3. The fusion navigation method according to claim 2, characterized in that: The method for determining that a plurality of said inertial measurement units share the same covariance matrix comprises: Acquiring the location data of the target object according to the same satellite navigation positioning unit; Controlling the plurality of inertial measurement units to acquire high-frequency position data of the target object based on a preset frequency; Based on the position data, the high-frequency position data is fused through Kalman filtering, and the covariance matrix of each inertial measurement unit is determined by feedback.

4. The fusion navigation method according to claim 3, characterized in that: The method of fusing the high-frequency position data by Kalman filtering based on the position data and feeding back to determine the covariance matrix of each inertial measurement unit includes: Performing time differentiation processing on the position data and the high-frequency position data to obtain position differential data and high-frequency position differential data; Constructing a system state vector of a Kalman filter, and determining a discrete time system equation and a state transfer matrix of the system state vector based on the high frequency position differential data; Iteratively updating the discrete time system equation and the state transfer matrix according to the position differential data, and adjusting the system state vector according to the feedback of the update result; The measured covariance matrix corresponding to the high-frequency position differential data after iterative updating is determined as the covariance matrix.

5. The fusion navigation method according to claim 4, characterized in that: The iterative updating of the discrete time system equation and the state transfer matrix according to the position differential data comprises: The discrete time system equation and the state transfer matrix corresponding to each of the inertial measurement units are updated according to the position differential data.

6. The fusion navigation method according to claim 1, characterized in that: The method of fusing the continuous positioning data and the positioning data by Kalman filtering, and determining the navigation data of the target object according to the fused target positioning data, further includes: When the positioning data is unavailable, joint data of an inertial navigation system and satellite navigation is acquired, and the joint data is defined as the positioning data.

7. The fusion navigation method according to claim 1, characterized in that: The inertial measurement unit and the satellite navigation positioning unit are both arranged using a loosely coupled design principle.

8. A fusion navigation device, characterized in that: include: A continuous positioning data acquisition module, used to acquire continuous positioning data of a target object based on a plurality of inertial measurement units distributed in an array; A positioning data acquisition module, used to acquire the positioning data of the target object based on a satellite navigation positioning unit; A fusion navigation module, used to fuse the continuous positioning data and the positioning data through Kalman filtering, and determine the navigation data of the target object according to the fused target positioning data; In the process of data fusion, the multiple inertial measurement units share the same covariance matrix.

9. A navigation device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the fusion navigation method described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the fusion navigation method described in any one of claims 1 to 7.

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