SINS and DDVL calibration method and device, and medium

Abstract

The invention relates to the technical field of inertial navigation, in particular to an SINS (Strapdown Inertial Navigation System) and DDVL calibration method, equipment and a medium, which can realize calibration of a top DVL and a bottom DVL at one time. According to the method, accurate calibration of the SINS and DVL systems is achieved by constructing the Kalman filtering model, the positioning accuracy of the systems can be improved, parameters such as installation errors and speed coefficients between devices can be effectively corrected, and therefore the performance of the whole navigation system is enhanced. The SINS and the DVL are combined, the Kalman filter is used for fusing the data of the two sensors, and the Kalman filter can effectively estimate and reduce measurement noise and uncertainty and deviation in the system, so that the positioning precision of the system can be remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of inertial navigation, and in particular to a calibration method, equipment and medium for SINS and DDVL. Background Art

[0002] Inertial navigation is the main navigation method for under-ice navigation in polar regions. Due to the convergence of the geographical meridians in the polar regions, there is a problem of error amplification in inertial navigation. In order to ensure the reliability of navigation results, DVL is usually used as an auxiliary device to improve the accuracy of inertial navigation. The measurement range of DVL is limited. Using two DVL devices to form a SINS and DDVL combined navigation system can ensure the reliability of navigation. The top DVL measures the speed towards the ice upward, and the bottom DVL measures the speed towards the bottom downward.

[0003] This installation method makes it difficult to calibrate the installation error angle between SINS and DDVL and the DVL scale coefficient error. Under normal lake test conditions, the DVL with the measurement direction upward can only measure the velocity relative to the water flow. Therefore, the two DVLs can only be calibrated under the ice, or the bottom DVL can be calibrated first, and then the top DVL can be flipped 180 degrees and calibrated again. The first method requires a harsh environment that is difficult to meet, and the second method requires multiple disassembly and assembly operations. Summary of the invention

[0004] In view of this, the present invention provides a SINS and DDVL calibration method, device and medium, which can realize the calibration of the top DVL and the bottom DVL at one time.

[0005] To achieve the above object, the present invention provides a SINS and DDVL calibration method, comprising the following steps:

[0006] S1: Construct the Kalman filter model for DVL calibration;

[0007] S2: The inertial navigation system is turned on and performs initial alignment until the alignment is completed;

[0008] S3: Make the experimental ship travel along a specific path, connect to GNSS for integrated navigation to calibrate the bottom DVL, and record the bottom DVL water velocity VWB i and top DVL water velocity VWT i , where i = 1, 2, ..., k, k represents the total number of sampling points;

[0009] S4: After the experimental ship completes the journey along a specific path, the bottom DVL scale factor dk is obtained od Filtering results of installation errors

[0010] S5: Calculate the ratio ddk of the velocity coefficients of the top DVL and the bottom DVL and the conversion matrix ddM between the top DVL and the bottom DVL;

[0011] S6: Calculate the installation error angle between the top DVL and the inertial navigation and scale factor dk odT , complete the calibration of the top DVL.

[0012] Among them, the state equation of the Kalman filter model is:

[0013] in, represents the differential of the state vector, X represents the state vector, and F is the intermediate matrix;

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Where: R M , R N are the principal curvature radii of the earth's meridian and meridian, V e ,V n ,V u are the east, north and celestial velocities calculated by the inertial navigation respectively, fx, fy and fz are the x-axis, y-axis and z-axis outputs of the table respectively, L is the latitude calculated by the inertial navigation, represents the direction cosine matrix of the inertial navigation solution, ω ie Represents the angular velocity of the Earth’s rotation, Vec X () represents the antisymmetric matrix of a vector.

[0027] The measurement equation of the Kalman filter model is:

[0028] Z=HX

[0029] Z=[δV e ,δV n ,δV u ,δL,δλ,δh,δL dr ,δλ dr ,δh dr ]

[0030]

[0031] δV e ,δV n ,δV u Indicates the difference between the inertial navigation velocity and the GNSS velocity, δL, δλ, δh indicates the difference between the inertial navigation solution position and the GNSS position, δL dr ,δλ dr ,δh dr Indicates the difference between the dead reckoning position and the GNSS position.

[0032] Among them, the ratio of the top DVL to the bottom DVL velocity coefficient ddk is:

[0033] Among them, the transformation matrix ddM between the top DVL and the bottom DVL is:

[0034] tM=∑VWB i *VWT i T

[0035]

[0036] ddM=UV T .

[0037] Among them, the installation error angle between the top DVL and the inertial navigation and scale factor dk odT Specifically:

[0038]

[0039]

[0040]

[0041] dk odT =dk od ·ddk.

[0042] The present invention also provides an electronic device, comprising a processor and a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement a SINS and DDVL calibration method described in the present invention.

[0043] The present invention also provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the SINS and DDVL calibration method described in the present invention.

[0044] Beneficial effects:

[0045] 1. In the method of the present invention, the accurate calibration of the SINS and DVL systems is achieved by constructing a Kalman filter model, which can not only improve the positioning accuracy of the system, but also effectively correct the installation error and speed coefficient between devices, thereby enhancing the performance of the entire navigation system.

[0046] 2. In the method of the present invention, by combining SINS with DVL and using Kalman filter to fuse the data of the two sensors, the positioning accuracy of the system can be significantly improved because the Kalman filter can effectively estimate and reduce measurement noise, as well as uncertainty and deviation within the system.

[0047] 3. In the method of the present invention, by comparing and analyzing the speed data of the top DVL and the bottom DVL, the actual motion state of the ship, including its speed and direction, can be determined more accurately, and the speed measurement is optimized. This optimization is very useful for application scenarios that require precise speed control, such as marine research, military operations, etc.

[0048] 4. In the method of the present invention, by calculating and correcting the installation error angle and scale coefficient between the top DVL and the inertial navigation, the measurement error caused by improper installation can be effectively reduced, ensuring that the various components work together more efficiently and accurately; by using GNSS data for auxiliary calibration, because the system has been fully trained, it can maintain high navigation accuracy even when the GNSS signal is poor or unavailable, and can operate stably in a variety of environments, improving the system's adaptability to different types of ships and navigation conditions, and has strong versatility and flexibility. By adjusting the parameter settings during the calibration process, it can easily adapt to different application scenario requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0051] The present invention provides a SINS and DDVL calibration method, which can realize the calibration of the top DVL and the bottom DVL at one time, comprising the following steps:

[0052] S1: Construct the Kalman filter model for DVL calibration;

[0053] The state equation of the Kalman filter model is:

[0054] in, represents the differential of the state vector, X represents the state vector, and F is the intermediate matrix;

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] Where: R M , R N are the principal curvature radii of the earth's meridian and meridian, V e ,V n ,V u are the east, north and celestial velocities calculated by the inertial navigation respectively, fx, fy and fz are the x-axis, y-axis and z-axis outputs of the table respectively, L is the latitude calculated by the inertial navigation, represents the direction cosine matrix of the inertial navigation solution, ω ie Represents the angular velocity of the Earth’s rotation, Vec X () represents the antisymmetric matrix of a vector.

[0068] The measurement equation of the Kalman filter model is:

[0069] Z=HX

[0070] Z=[δV e ,δV n ,δV u ,δL,δλ,δh,δL dr ,δλ dr ,δh dr ]

[0071]

[0072] δV e ,δV n ,δV u Indicates the difference between the inertial navigation velocity and the GNSS velocity, δL, δλ, δh indicates the difference between the inertial navigation solution position and the GNSS position, δL dr ,δλ dr ,δh dr Indicates the difference between the dead reckoning position and the GNSS position.

[0073] S2: The inertial navigation system is turned on and performs initial alignment until the alignment is completed;

[0074] S3: Make the experimental ship travel along a specific path, connect to GNSS for integrated navigation to calibrate the bottom DVL, and record the bottom DVL water velocity VWB i and top DVL water velocity VWT i , where i=1,2,…,k, and k represents the total number of sampling points.

[0075] S4: After the experimental ship completes the journey along a specific path, the bottom DVL scale factor dk can be obtained od Filtering results of installation errors

[0076] S5: Calculate the ratio ddk of the velocity coefficients of the top DVL and the bottom DVL and the conversion matrix ddM between the top DVL and the bottom DVL;

[0077]

[0078] tM=∑VWB i *VWT i T

[0079]

[0080] ddM=UV T

[0081] S6: Calculate the installation error angle between the top DVL and the inertial navigation and scale factor dk odT ;

[0082]

[0083]

[0084]

[0085] dk odT =dk od ·ddk

[0086] Complete the calibration of the top DVL.

[0087] In summary, the calibration method of SINS and DDVL of the present invention can significantly improve the overall performance of the navigation system, which is very valuable for application fields that require high-precision positioning and velocity measurement.

[0088] The embodiment of the present application also provides an electronic device, Figure 1 The structure of the electronic device provided by the embodiment of the present invention is shown. For example, the electronic device 10 may include a processor 11, a memory 12 and a transmission device 13. The processor is used to execute the calibration method of SINS and DDVL mentioned in the above embodiment, wherein the processor and the memory may be connected by a bus or other means, taking the connection by a bus as an example. The transmission device may be connected to the processor and the memory by wire or wirelessly. The memory, as a non-transient computer-readable storage medium, may be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the calibration method of SINS and DDVL in the embodiment of the present application. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory, that is, the calibration method of SINS and DDVL in the above method embodiment is realized. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The one or more modules are stored in the memory, and when executed by the processor, the calibration method of SINS and DDVL in the embodiment is executed.

[0089] As another aspect, the present application also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the device described in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into the device. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD ROM, or any other form of storage medium known in the technical field. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the calibration method of SINS and DDVL described in the present application.

[0090] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A calibration method for SINS and DDVL, characterized in that: The following steps are involved: S1: Construct the Kalman filter model for DVL calibration; S2: The inertial navigation system is turned on and performs initial alignment until the alignment is completed; S3: Make the experimental ship travel along a specific path, connect to GNSS for integrated navigation to calibrate the bottom DVL, and record the bottom DVL water velocity VWB i and top DVL water velocity VWT i , where i = 1, 2, ..., k, k represents the total number of sampling points; S4: After the experimental ship completes the journey along a specific path, the bottom DVL scale factor dk is obtained od Filtering results of installation errors S5: Calculate the ratio ddk of the velocity coefficients of the top DVL and the bottom DVL and the conversion matrix ddM between the top DVL and the bottom DVL; S6: Calculate the installation error angle between the top DVL and the inertial navigation and scale factor dk odT , complete the calibration of the top DVL.

2. The method according to claim 1, characterized in that The state equation of the Kalman filter model is: in, represents the differential of the state vector, X represents the state vector, and F is the intermediate matrix; Where: R M , R N are the principal curvature radii of the earth's meridian and meridian, V e ,V n ,V u are the east, north and celestial velocities calculated by the inertial navigation respectively, fx, fy and fz are the x-axis, y-axis and z-axis outputs of the table respectively, L is the latitude calculated by the inertial navigation, represents the direction cosine matrix of the inertial navigation solution, ω ie Represents the angular velocity of the Earth’s rotation, Vec X () represents the antisymmetric matrix of a vector. The measurement equation of the Kalman filter model is: Z=HX Z=[δV e ,δV n ,δV u ,δL,δλ,δh,δL dr ,sl dr ,dhh dr ] δV e ,δV n ,δV u Indicates the difference between the inertial navigation velocity and the GNSS velocity, δL, δλ, δh indicates the difference between the inertial navigation solution position and the GNSS position, δL dr ,δλ dr ,δh dr Indicates the difference between the dead reckoning position and the GNSS position.

3. The method according to claim 2, characterized in that , the ratio of the top DVL to the bottom DVL velocity coefficient ddk is:

4. The method according to claim 2 or 3, characterized in that , the transformation matrix ddM between the top DVL and the bottom DVL is: ddM=UV T 。 5. The method according to claim 4, characterized in that Installation error angle between top DVL and inertial navigation and scale factor dk odT Specifically: dk odT =dk od ·ddk.

6. An electronic device, characterized in that: The electronic device includes a processor and a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement a SINS and DDVL calibration method as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute a SINS and DDVL calibration method as described in any one of claims 1 to 5.

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