Integrated navigation optimization method, terminal equipment and storage medium

Abstract

The invention provides an integrated navigation optimization method, terminal equipment and a storage medium. The integrated navigation optimization method comprises the following steps: performing Kalman filtering and updating according to the current observed quantity of a target satellite to obtain an updated state vector variance matrix and an updated state vector; obtaining the geometric distance of the current epoch according to the updated state vector variance matrix and the updated state vector; obtaining the variance of the target satellite in the current epoch according to the geometric distance of the current epoch; and determining whether to reject the observed quantity of the target satellite according to the variance. According to the method, the variance is optimized, and the gross error is eliminated based on the optimized variance, so that the gross error caused by factors such as multiple paths and the like can be effectively eliminated, and the precision of tightly integrated navigation solution is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to a combined navigation optimization method, terminal equipment and storage medium. Background Art

[0002] The Global Navigation Satellite System (GNSS) has all-weather, global, fast, precise three-dimensional navigation and positioning capabilities. Currently, GNSS has been maturely applied in navigation, positioning, timing and other related fields. When the observation conditions are good, GNSS can work well alone, but in modern cities, the wild and other places, overpasses, tunnels, trees, etc. seriously block GNSS signals and cause a lot of signal multipath interference. At this time, GNSS does not have a good positioning effect, so a multi-satellite combined positioning system is needed for joint positioning.

[0003] Among them, the existing multi-system combination methods include: loose combination and tight combination. Compared with loose combination, the advantage of tight combination is that when the number of visible satellites is less than 4, the tight combination navigation system can still use limited information to perform navigation solutions, avoiding the combined navigation system from degenerating into a pure inertial navigation system. Therefore, it has stronger anti-interference ability and accuracy than loose combination, and has been more widely used in practical applications. In the tight combination navigation system, the observation quantity is the difference between the geometric distance and pseudorange of each satellite and the receiver. The variance of each satellite needs to be accurately determined to obtain accurate combined navigation results.

[0004] In the prior art, the tightly integrated navigation system usually sets a fixed variance or determines the variance of each satellite according to the satellite's altitude angle. In complex environments (for example, complex scenes such as urban canyons and boulevards), due to the existence of multipath and gross errors, the above variance determination methods are unreasonable, which seriously affects the accuracy of the tight combination. Summary of the invention

[0005] The embodiments of the present invention provide a combined navigation optimization method, a terminal device and a storage medium to solve the problem in the prior art that the variance is fixed or the variance is determined unreasonably according to the altitude angle, which affects the accuracy of the tight combination.

[0006] In a first aspect, an embodiment of the present invention provides a combined navigation optimization method, including: Perform Kalman filtering and updating according to the current observation quantity of the target satellite to obtain an updated state vector variance matrix and an updated state vector; According to the updated state vector variance matrix and the updated state vector, the geometric distance of the current epoch is obtained; According to the geometric distance of the current epoch, the variance of the target satellite in the current epoch is obtained; Determine whether to remove the observations of the target satellite based on the variance.

[0007] In a second aspect, an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the combined navigation optimization method provided in the first aspect or any possible implementation method of the first aspect are implemented.

[0008] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the combined navigation optimization method provided in the first aspect or any possible implementation method of the first aspect.

[0009] The embodiment of the present invention provides a combined navigation optimization method, a terminal device and a storage medium. The combined navigation optimization method comprises: performing Kalman filtering and updating according to the current observation amount of the target satellite to obtain an updated state vector variance matrix and an updated state vector; obtaining the geometric distance of the current epoch according to the updated state vector variance matrix and the updated state vector; obtaining the variance of the target satellite in the current epoch according to the geometric distance of the current epoch; and determining whether to remove the observation amount of the target satellite according to the variance. The present invention optimizes the variance, and removes the gross error based on the optimized variance, which can effectively remove the gross error caused by factors such as multipath, thereby effectively improving the accuracy of the tight combined navigation solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 is a flow chart of an implementation of a combined navigation optimization method provided by an embodiment of the present invention; Figure 2 is a structural diagram of a combined navigation optimization device provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0013] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] See also Figure 1 , which shows a flowchart of an implementation of a combined navigation optimization method provided by an embodiment of the present invention, and is described in detail as follows: The above combined navigation optimization method comprises: S101: Perform Kalman filtering and updating according to the current observation value of the target satellite to obtain an updated state vector variance matrix and an updated state vector; S102: Obtaining the geometric distance of the current epoch according to the updated state vector variance matrix and the updated state vector; S103: Obtaining the variance of the target satellite in the current epoch according to the geometric distance of the current epoch; S104: Determine whether to remove the observations of the target satellite according to the variance.

[0015] In the embodiment of the present invention, the geometric distance is calculated, and then the variance is calculated according to the geometric distance, and the gross error is eliminated based on the variance. Compared with the fixed variance or the variance determined according to the altitude angle of the satellite, the calculation of the variance is more reasonable, and the gross error caused by factors such as multipath can be effectively eliminated, thereby effectively improving the accuracy of the tightly combined navigation solution, which is more in line with the actual application requirements.

[0016] In a possible implementation, S101 may include: S1011: Establish a tight Kalman filter model and a Kalman filter noise matrix based on the current observations of the target satellite; The embodiment of the present invention establishes a tightly combined Kalman filter model and a Kalman filter noise matrix according to the current observation quantity of the target satellite.

[0017] In the embodiment of the present invention, the state vector is first selected according to the scenario of the navigation application. The more elements in the state vector, the more accurately the motion model can be reflected, and the more accurately the state vector can be estimated. At the same time, the selection of the state vector also depends on the level of the inertial navigation used; for example, for low-level MEMS (Micro-Electro-Mechanical System) inertial navigation, its proportional factor and cross-axis coupling error are large, and comprehensive considerations are required when selecting the state vector. However, as the number of elements in the state vector increases, the complexity of the calculation will also increase accordingly, so comprehensive considerations are required when selecting the state vector.

[0018] Specifically, in the embodiment of the present invention, the elements in the state vector may include: position error, velocity error, attitude error, gyro bias error, accelerometer bias error and lever arm error.

[0019]

[0020] in, , , are the position errors in the north, east and ground directions respectively; , , are the velocity errors in the north, east and ground directions respectively, , , They are roll, pitch and heading angle errors respectively; , , They are the zero bias errors of the gyroscope X-axis, Y-axis, and Z-axis respectively; , , They are the zero bias errors of the accelerometer on the X-axis, Y-axis, and Z-axis respectively. , , They are the errors of the lever arm effect in the X-axis, Y-axis and Z-axis directions of the carrier coordinate system.

[0021] In one possible implementation, the tightly combined Kalman filter model may include:

[0022]

[0023] in, The target satellite The state vector of the epoch period, For the The state transfer matrix after the discretization of epoch periods is: For the epoch period system noise driving array, For the The noise vector of the state of the epoch period, For the The measurement information matrix of epoch periods, For the The coefficient matrix of the measurement equation for the epoch period, For the The measurement noise term is the period of epochs.

[0024] First, the state transfer equation of the Kalman filter including the lever arm effect is established, and the formula is as follows:

[0025] Among them, based on the 18 state vector elements selected in the embodiment of the present invention, the state transfer matrix is ​​as follows:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] in, are the carrier's eastward velocity, northward velocity, and celestial velocity, respectively. is the angular velocity of the Earth’s rotation, M, N, h are the radius of the Earth’s meridian circle, the radius of the meridian circle, and the height of the carrier, respectively. is the carrier latitude, is the local normal gravity value; is the sampling interval, is the correlation time of the inertial navigation noise; They are the projections of the sensor output in the carrier coordinate system and the navigation coordinate system respectively; , , , , , , , , Pass parameters separately.

[0037] Secondly, the measurement equation of the Kalman filter including the lever arm effect is established, and the formula is as follows:

[0038] No. The coefficient matrix of the measurement equation for the epoch period The calculation formula is as follows:

[0039] in, is the lever arm value, is the direction cosine matrix from the carrier coordinate system to the navigation coordinate system, is the projection of the carrier's motion relative to the inertial system in the carrier coordinate system, is the projection of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, is the projection of the earth coordinate system relative to the inertial coordinate system in the navigation coordinate system; is the transformation matrix constructed by the earth's meridian and meridian.

[0040] The measurement noise term is selected as R matrix, and the formula is as follows:

[0041] in, They are respectively the eastward position variance value, northward position variance value, celestial position variance value, eastward velocity variance value, northward velocity variance value and celestial velocity variance value of the target satellite observation information.

[0042] In a possible implementation, the Kalman filter noise matrix may include: a Q matrix and a P matrix; The Q matrix is ​​the state quantity noise matrix, and the formula of the Q matrix can be:

[0043] The P matrix is ​​the state vector variance matrix, which reflects the initial uncertainty of the state vector. The formula of the P matrix can be:

[0044] in, is the random walk of the velocity of the inertial sensor, is the angular random walk of the inertial sensor, and is the output noise of the inertial sensor; They are respectively the eastward position variance value, northward position variance value, celestial position variance value, eastward velocity variance value, northward velocity variance value and celestial velocity variance value of the target satellite observation information; are the zero bias variance values ​​of the gyroscope x, y, and z axes, respectively. are the zero bias variance values ​​of the accelerometer x, y, and z axes, respectively. are the variance values ​​of the lever arms in the x, y, and z directions respectively; , , They are the roll, pitch and heading angle variance values ​​respectively.

[0045] S1012: Obtain a predicted state vector and a predicted state vector variance matrix according to the Kalman filter model and the Kalman filter noise matrix; The prediction formula of Kalman filter is as follows:

[0046] in, is the state transfer matrix.

[0047] S1013: Perform Kalman filter update according to the predicted state vector and the predicted state vector variance matrix to obtain an updated state vector variance matrix and an updated state vector.

[0048] The update formula of Kalman filter is as follows:

[0049] in, is the gain matrix, is the updated state vector, is the updated state vector variance matrix; is the identity matrix, For the The measurement information matrix of epoch periods, For the The coefficient matrix of the measurement equation for the epoch period.

[0050] During the navigation process, the Kalman filter is continuously updated to provide continuous and reliable navigation attitude information throughout the navigation process.

[0051] After the prediction is completed, each coordinate can be obtained, and then the geometric distance can be calculated according to the coordinates. Specifically, in a possible implementation, S102 may include: S1021: Obtaining the coordinates of the receiver and the coordinates of the target satellite according to the updated state vector variance matrix and the updated state vector; S1022: Obtain the geometric distance of the current epoch based on the coordinates of the receiver and the coordinates of the target satellite.

[0052] The specific calculation formula of geometric distance is as follows:

[0053] in, For the target satellite The geometric distance of the epoch period, , , Respectively The coordinates of the target satellite in epoch periods, , , Respectively The coordinates of the receiver for the epoch period.

[0054] In a possible implementation, S103 may include: S1031: Obtain the variance of the target satellite in the current epoch according to the geometric distance of the current epoch and in combination with the first formula; The first formula may include:

[0055] in, The target satellite The variance of the epoch period, The target satellite The geometric distance of the epoch period, For the target satellite The pseudorange of the epoch period, For the target satellite Ionospheric and tropospheric corrections for epoch periods.

[0056] In a possible implementation, S104 may include: S1041: Determine whether the variance is within a preset range; S1042: If not, remove the target satellite; S1043: If yes, the target satellite is not eliminated.

[0057] In the embodiment of the present invention, if the variance is outside the preset range, it means that the satellite data is unavailable, and the satellite data is removed. For any satellite in the system, steps S101 to S104 are executed to calculate the variance and determine whether it is within the preset range. If the variance of a satellite is not within the preset range, the satellite is removed, and the satellite data with variance within the preset range is retained, so as to avoid gross errors affecting the Kalman filter model, and effectively improve the accuracy of navigation solution.

[0058] It should be noted that the preset range can be set according to actual application requirements and is not specifically limited here.

[0059] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0060] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0061] Figure 2 The structure diagram of the combined navigation optimization device provided by the embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows: like Figure 2 As shown, the combined navigation optimization device includes: An updating module 21 is used to perform Kalman filtering and updating according to the current observation amount of the target satellite to obtain an updated state vector variance matrix and an updated state vector; A geometric distance calculation module 22, used to obtain the geometric distance of the current epoch according to the updated state vector variance matrix and the updated state vector; A variance calculation module 23 is used to obtain the variance of the target satellite in the current epoch according to the geometric distance of the current epoch; The gross error elimination module 24 is used to determine whether to eliminate the observation quantity of the target satellite according to the variance.

[0062] In a possible implementation, the variance calculation module 23 may be specifically used for: According to the geometric distance of the current epoch, the variance of the target satellite in the current epoch is obtained by combining the first formula; The first formula may include:

[0063] in, The target satellite The variance of the epoch period, The target satellite The geometric distance of the epoch period, For the target satellite The pseudorange of the epoch period, For the target satellite Ionospheric and tropospheric corrections for epoch periods.

[0064] In a possible implementation, the updating module 21 may include: A model building unit, used for building a tight Kalman filter model and a Kalman filter noise matrix according to the current observation quantity of the target satellite; A parameter prediction unit, used to predict a predicted state vector and a predicted state vector variance matrix according to a Kalman filter model and a Kalman filter noise matrix; The parameter updating unit is used to perform Kalman filter update according to the predicted state vector and the predicted state vector variance matrix to obtain an updated state vector variance matrix and an updated state vector.

[0065] In one possible implementation, the tightly combined Kalman filter model may include:

[0066]

[0067] in, The target satellite The state vector of the epoch period, For the The state transfer matrix after the discretization of epoch periods is: For the epoch period system noise driving array, For the The noise vector of the state of the epoch period, For the The measurement information matrix of epoch periods, For the The coefficient matrix of the measurement equation for the epoch period, For the The measurement noise term is the period of epochs.

[0068] In a possible implementation, the Kalman filter noise matrix may include: a Q matrix and a P matrix; The formula of Q matrix can be:

[0069] The formula of P matrix can be:

[0070] in, is the random walk of the velocity of the inertial sensor, is the angular random walk of the inertial sensor, and is the output noise of the inertial sensor; They are respectively the eastward position variance value, northward position variance value, celestial position variance value, eastward velocity variance value, northward velocity variance value and celestial velocity variance value of the target satellite observation information; are the zero bias variance values ​​of the gyroscope x, y, and z axes, respectively. are the zero bias variance values ​​of the accelerometer x, y, and z axes, respectively. are the variance values ​​of the lever arms in the x, y, and z directions respectively; , , They are the roll, pitch and heading angle variance values ​​respectively.

[0071] In a possible implementation, the geometric distance calculation module 22 may include: A coordinate output unit, used to obtain the coordinates of the receiver and the coordinates of the target satellite according to the updated state vector variance matrix and the updated state vector; The distance output unit is used to obtain the geometric distance of the current epoch based on the coordinates of the receiver and the coordinates of the target satellite.

[0072] In a possible implementation, the elements in the state vector may include: position error, velocity error, attitude error, gyro bias error, accelerometer bias error, and lever arm error.

[0073] In a possible implementation, the gross error elimination module 24 may include: A range determination unit, used to determine whether the variance is within a preset range; A first judgment unit, used for eliminating the target satellite if no; The second judgment unit is used for not eliminating the target satellite if the answer is yes.

[0074] Figure 3 Schematic diagram of a terminal device 3 provided in an embodiment of the present invention. Figure 3 As shown, the terminal device 3 of this embodiment includes: a processor 30 and a memory 31. The memory 31 is used to store a computer program 32, and the processor 30 is used to call and run the computer program 32 stored in the memory 31 to perform the steps in the above-mentioned various combined navigation optimization method embodiments, such as Figure 1 Alternatively, the processor 30 is used to call and run the computer program 32 stored in the memory 31 to implement the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 24 are shown.

[0075] Exemplarily, the computer program 32 may be divided into one or more modules / units, one or more modules / units are stored in the memory 31, and are executed by the processor 30 to implement the present invention. One or more modules / units may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into Figure 2 Modules / units 21 to 24 are shown.

[0076] The terminal device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of terminal device 3 and does not constitute a limitation on terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0077] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0078] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used to store computer programs and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0079] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0080] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0082] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0085] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media can include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A combined navigation optimization method, characterized in that: include: Perform Kalman filtering and updating according to the current observation quantity of the target satellite to obtain an updated state vector variance matrix and an updated state vector; Obtaining a geometric distance of a current epoch according to the updated state vector variance matrix and the updated state vector; Obtaining the variance of the target satellite at the current epoch according to the geometric distance of the current epoch; Determine whether to remove the observation quantity of the target satellite according to the variance.

2. The integrated navigation optimization method according to claim 1, characterized in that: The step of obtaining the variance of the target satellite at the current epoch according to the geometric distance at the current epoch includes: According to the geometric distance of the current epoch, the variance of the target satellite in the current epoch is obtained in combination with the first formula; The first formula includes: in, The target satellite The variance of the epoch period, The target satellite The geometric distance of the epoch period, The target satellite is The pseudorange of the epoch period, The target satellite is Ionospheric and tropospheric corrections for epoch periods.

3. The integrated navigation optimization method according to claim 1 or 2, characterized in that: The Kalman filtering and updating are performed according to the current observation amount of the target satellite to obtain the updated state vector variance matrix and the updated state vector, including: According to the current observations of the target satellite, a tight Kalman filter model and a Kalman filter noise matrix are established; According to the Kalman filter model and the Kalman filter noise matrix, a predicted state vector and a predicted state vector variance matrix are predicted; A Kalman filter is updated according to the predicted state vector and the predicted state vector variance matrix to obtain the updated state vector variance matrix and the updated state vector.

4. The integrated navigation optimization method according to claim 3, characterized in that: The tightly combined Kalman filter model includes: in, The target satellite The state vector of the epoch period, For the The state transfer matrix after the discretization of epoch periods is: For the epoch period system noise driving array, For the The noise vector of the state of the epoch period, For the The measurement information matrix of epoch periods, For the The coefficient matrix of the measurement equation for the epoch period, For the The measurement noise term is the period of epochs.

5. The integrated navigation optimization method according to claim 4, characterized in that: The Kalman filter noise matrix includes: a Q matrix and a P matrix; The formula of the Q matrix is: The formula of the P matrix is: in, is the random walk of the velocity of the inertial sensor, is the angular random walk of the inertial sensor, and is the output noise of the inertial sensor; They are respectively the eastward position variance value, northward position variance value, celestial position variance value, eastward velocity variance value, northward velocity variance value and celestial velocity variance value of the target satellite observation information; are the zero bias variance values ​​of the gyroscope x, y, and z axes, respectively. are the zero bias variance values ​​of the accelerometer x, y, and z axes, respectively. are the variance values ​​of the lever arms in the x, y, and z directions respectively; , , They are the roll, pitch and heading angle variance values ​​respectively.

6. The integrated navigation optimization method according to claim 3, characterized in that: The step of obtaining the geometric distance of the current epoch according to the updated state vector variance matrix and the updated state vector comprises: Obtaining the coordinates of the receiver and the coordinates of the target satellite according to the updated state vector variance matrix and the updated state vector; The geometric distance of the current epoch is obtained according to the coordinates of the receiver and the coordinates of the target satellite.

7. The integrated navigation optimization method according to claim 3, characterized in that: The elements in the state vector include: position error, velocity error, attitude error, gyro bias error, accelerometer bias error, and lever arm error.

8. The integrated navigation optimization method according to claim 1 or 2, characterized in that: The step of determining whether to remove the observation amount of the target satellite according to the variance includes: Determining whether the variance is within a preset range; If not, the target satellite is eliminated; If so, the target satellite is not eliminated.

9. A terminal device, characterized in that: It comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the steps of the combined navigation optimization method as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the combined navigation optimization method as described in any one of claims 1 to 8 are implemented.

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