Method for measuring damped velocity based on additional accelerometer for inertial navigation system

Abstract

The application discloses a kind of methods for measuring damping velocity based on additional accelerometer of inertial navigation system, it is related to the technical field of inertial navigation system application.The method comprises: obtaining the first road Kalman filter rough alignment state variable of inertial navigation system, and first road Kalman filter fine alignment state data;According to the first road Kalman filter rough alignment state variable, and first road Kalman filter fine alignment state data, calculate the first road navigation solution correction parameter;According to the first road navigation solution correction parameter, determine current orientation cosine matrix, and obtain the acceleration measurement value of accelerometer output;In the second road navigation solution loop, according to the acceleration measurement value, the current orientation cosine matrix and accelerometer component speed measurement equation, calculate the damping velocity of ship.The technical scheme of the embodiment of the application can accurately calculate the damping velocity of ship under the premise of low hardware cost, and provide data basis for ship navigation.

Description

Technical Field

[0001] This invention relates to the field of inertial navigation system application technology, and in particular to a method for measuring damped velocity in an inertial navigation system based on an additional accelerometer. Background Technology

[0002] Due to the influence of error sources such as gyroscopes and accelerometers, the navigation error of the inertial navigation system accumulates and oscillates over time, generally requiring external velocity information to dampen these oscillation errors. For some underwater vehicles, due to limitations in structure, size, weight, or cost, it is impossible to install underwater acoustic velocimetry equipment to obtain external velocity information. For applications where underwater acoustic velocimetry can be installed, dedicated space needs to be reserved on the carrier. Furthermore, underwater acoustic velocimetry equipment is expensive, and its installation and calibration are quite complex. Summary of the Invention

[0003] This invention provides a method for measuring damped velocity in an inertial navigation system based on an additional accelerometer, in order to solve the problem of high hardware cost in calculating damped velocity based on existing traditional acoustic velocimetry technology.

[0004] According to one aspect of the present invention, a method for measuring damped velocity in an inertial navigation system based on an additional accelerometer is provided, comprising:

[0005] Acquire the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system;

[0006] Based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data, calculate the first navigation solution correction parameters;

[0007] Based on the correction parameters calculated from the first navigation path, the current azimuth cosine matrix is ​​determined, and the acceleration measurement value output by the accelerometer is obtained; wherein, the accelerometer is fixedly connected to the inertial navigation system;

[0008] In the second navigation solution loop, the ship's damped speed is calculated based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation.

[0009] According to another aspect of the present invention, an apparatus for measuring damped velocity in an inertial navigation system based on an additional accelerometer is provided, comprising:

[0010] The data acquisition module is used to acquire the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system.

[0011] The correction parameter calculation module is used to calculate the first navigation solution correction parameters based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data.

[0012] The intermediate data acquisition module is used to determine the current azimuth cosine matrix based on the correction parameters calculated by the first navigation path, and to acquire the acceleration measurement value output by the accelerometer; wherein, the accelerometer is fixedly connected to the inertial navigation system;

[0013] The damping speed calculation module is used in the second navigation solution loop to calculate the ship's damping speed based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation.

[0014] According to another aspect of the present invention, an inertial navigation system is provided, the inertial navigation system comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for measuring damped velocity based on an additional accelerometer in an inertial navigation system according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for measuring damped velocity based on an additional accelerometer in an inertial navigation system according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the method for measuring damped velocity based on an additional accelerometer in an inertial navigation system according to any embodiment of the present invention.

[0020] The technical solution of this invention obtains the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system. Based on these data, the first navigation solution correction parameters are calculated. Then, based on these parameters, the current azimuth cosine matrix is ​​determined, and the acceleration measurement value output by the accelerometer is obtained. In the second navigation solution loop, the ship's damping speed is calculated based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation. This solution eliminates the need to reserve space on the carrier for installing underwater acoustic velocimetry equipment. It directly connects the accelerometer component to the inertial navigation system (INS), and through the additional accelerometer component and the INS strapdown mathematical calculation platform, forms a calculation loop to obtain the damped velocity. This enables self-damping of the INS, reducing navigation parameter divergence caused by accumulated integral errors and preventing oscillation amplitude from increasing over time. It solves the problem of high hardware costs associated with calculating damped velocity using existing traditional acoustic velocimetry techniques, enabling accurate calculation of the ship's damped velocity with low hardware costs, thus providing a data foundation for ship navigation.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a method for measuring damped velocity in an inertial navigation system based on an additional accelerometer, provided in Embodiment 1 of the present invention.

[0024] Figure 2 This is a timing diagram for calculating the damping speed of a ship, provided in Embodiment 1 of the present invention.

[0025] Figure 3 This is a flowchart of a method for measuring damped velocity in an inertial navigation system based on an additional accelerometer, provided in Embodiment 2 of the present invention.

[0026] Figure 4 This is a schematic diagram of a device for measuring damped velocity in an inertial navigation system based on an additional accelerometer, provided in Embodiment 3 of the present invention.

[0027] Figure 5A schematic diagram of an inertial navigation system that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a method for measuring damped velocity in an inertial navigation system based on an additional accelerometer, provided in Embodiment 1 of the present invention. This embodiment is applicable to situations requiring low hardware cost for calculating ship damped velocity. The method can be executed by a device for measuring damped velocity in an inertial navigation system based on an additional accelerometer. This device can be implemented in hardware and / or software and can be configured within the inertial navigation system. Figure 1 As shown, the method includes:

[0032] Step 110: Obtain the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system.

[0033] The inertial navigation system (INS) can perform two parallel processing paths on the same navigation signal source, namely, a first navigation solution path and a second navigation solution path. The first Kalman filter coarse alignment state variable can be the state variable estimated by the first navigation solution path using the Kalman filter algorithm during coarse alignment. The first Kalman filter fine alignment state data can be the real-time state data calculated by the first navigation solution path using the Kalman filter algorithm during fine alignment.

[0034] For example, the coarse alignment state variable of the first Kalman filter can be represented as: ,in, , , These represent the alignment attitude estimation errors for the east, north, and sky directions, respectively, in the coarse alignment state variables of the first Kalman filter. , , These represent the alignment velocity estimation errors for the east, north, and sky directions in the coarse alignment state variables of the first Kalman filter. , , These represent the alignment latitude, longitude, and altitude estimation errors in the coarse alignment state variables of the first Kalman filter. , , These represent the alignment eastward, northward, and upward gyroscope drift estimation errors in the coarse alignment state variables of the first Kalman filter. , , These represent the zero-bias estimation errors of the alignment eastward, northward, and upward accelerometers in the coarse alignment state variables of the first Kalman filter.

[0035] In this embodiment of the invention, after the navigation system is powered on and completes self-test initialization, initial position work, and coarse alignment, the first Kalman filter coarse alignment state variables are estimated based on the first navigation solution path of the inertial navigation system. After the navigation system completes fine alignment, the first Kalman filter fine alignment state data are solved based on the first navigation solution path of the inertial navigation system.

[0036] Step 120: Calculate the first navigation solution correction parameters based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data.

[0037] Among them, the first navigation solution correction parameter can be the data after correcting the first Kalman filter fine alignment state data based on the first Kalman filter coarse alignment state variable.

[0038] In this embodiment of the invention, the estimation error in the coarse alignment state variable of the first Kalman filter can be used to correct the fine alignment state data of the first Kalman filter to obtain the first navigation solution correction parameters.

[0039] Step 130: Determine the current azimuth cosine matrix based on the first navigation solution correction parameters, and obtain the acceleration measurement value output by the accelerometer.

[0040] The accelerometer is directly and fixedly connected to the inertial navigation system, eliminating the need for dedicated space on the vehicle and simplifying installation and calibration. The current positioning cosine matrix can be a rotation matrix calculated based on the current first-path navigation solution correction parameters. The acceleration measurement value can be the output value of the accelerometer fixedly connected to the inertial navigation system.

[0041] In this embodiment of the invention, the rotation matrix from the carrier system to the navigation system, i.e. the current azimuth cosine matrix, can be calculated based on the current first-path navigation solution correction parameters, and the acceleration measurement value output by the accelerometer can be obtained.

[0042] Step 140: In the second navigation solution loop, calculate the ship's damped speed based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation.

[0043] The accelerometer component's velocity measurement equation can be the accelerometer's specific force equation. Ship damping velocity can be a physical quantity that indicates a linear decrease in speed over time due to resistance proportional to the speed experienced during vibration or motion.

[0044] In this embodiment of the invention, the second navigation calculation loop can calculate the value of the acceleration measurement in the navigation system based on the acceleration measurement value and the current azimuth cosine matrix. Then, based on the calculated value of the acceleration measurement in the navigation system and the accelerometer component velocity equation, the ship's damping speed can be calculated according to Newton's second law.

[0045] Figure 2 This is a timing diagram for calculating the damping speed of a ship, provided in Embodiment 1 of the present invention. Figure 2 As shown, time T0 represents the power-on time of the inertial navigation system. Initial information is loaded and the system is initialized between time T0 and time T1. Time T1 represents the time when the system self-test initialization is completed. The alignment command is sent at time T2. After time T2, the inertial navigation system enters the coarse alignment state. Coarse alignment is completed at time T3 and it automatically enters the fine alignment state. After time T4, the ship's damping speed is calculated based on the first navigation solution path and the second navigation solution path.

[0046] The technical solution of this invention obtains the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system. Based on these data, the first navigation solution correction parameters are calculated. Then, based on these parameters, the current azimuth cosine matrix is ​​determined, and the acceleration measurement value output by the accelerometer is obtained. In the second navigation solution loop, the ship's damping speed is calculated based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation. This solution eliminates the need to reserve space on the carrier for installing underwater acoustic velocimetry equipment. It directly connects the accelerometer component to the inertial navigation system (INS), and through the additional accelerometer component and the INS strapdown mathematical calculation platform, forms a calculation loop to obtain the damped velocity. This enables self-damping of the INS, reducing navigation parameter divergence caused by accumulated integral errors and preventing oscillation amplitude from increasing over time. It solves the problem of high hardware costs associated with calculating damped velocity using existing traditional acoustic velocimetry techniques, enabling accurate calculation of the ship's damped velocity with low hardware costs, thus providing a data foundation for ship navigation.

[0047] Example 2

[0048] Figure 3 This is a flowchart of a method for measuring damped velocity in an inertial navigation system based on an additional accelerometer, provided in Embodiment 2 of the present invention. This embodiment is a specific embodiment based on the above embodiment, providing a specific optional implementation method for calculating the first-path navigation solution correction parameters based on the first-path Kalman filter coarse alignment state variables and the first-path Kalman filter fine alignment state data. This includes: correcting the ship's attitude parameters and velocity parameters based on the first-path Kalman filter coarse alignment state variables and the first-path Kalman filter fine alignment state data to obtain the first-path navigation solution correction parameters. Figure 3 As shown, the method includes:

[0049] Step 210: Obtain the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system.

[0050] Step 220: Based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data, correct the ship attitude parameters and speed parameters to obtain the first navigation solution correction parameters.

[0051] The first Kalman filter coarse alignment state variables may include attitude estimation error and velocity estimation error; the first Kalman filter fine alignment state data may include the ship's current attitude and the ship's current velocity.

[0052] In this embodiment of the invention, the ship attitude parameters and velocity parameters in the first Kalman filter fine alignment state data can be corrected based on the attitude estimation error and velocity estimation error in the first Kalman filter coarse alignment state variables, respectively, to obtain the first navigation solution correction parameters.

[0053] For example, taking the attitude estimation error and velocity estimation error in the first Kalman filter coarse alignment state variables from the aforementioned example as examples, we will continue the explanation. Assume that the ship attitude parameters and velocity parameters in the first Kalman filter fine alignment state data are respectively... , , , , , The first navigation solution correction parameters are: , , , , , .in, , , These are the real-time eastward, northward, and celestial attitudes calculated by the first navigation path. , , These are the real-time eastward, northward, and azimuth velocities calculated by the first navigation system. The corrected real-time eastward, northward, and azimuth attitudes are recorded separately. , , The corrected real-time eastward, northward, and celestial velocities are recorded separately. , , .

[0054] Step 230: Determine the current azimuth cosine matrix based on the first navigation solution correction parameters, and obtain the acceleration measurement value output by the accelerometer.

[0055] For example, continuing from the previous example, the current azimuth cosine matrix can be represented as: ;

[0056] in, ; ; ; ; ; ; ; ; .

[0057] Step 240: In the second navigation solution loop, calculate the ship's damped speed based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation.

[0058] In an optional embodiment of the present invention, in the second navigation solution loop, calculating the ship's damped speed based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation may include: in the second navigation solution loop, calculating the current coordinate transformation acceleration based on the acceleration measurement value and the current azimuth cosine matrix; integrating the accelerometer component velocity equation according to the speed update cycle to obtain the target integral value; and summing the current coordinate transformation acceleration with the target integral value to obtain the ship's damped speed.

[0059] Here, the current coordinate transformation acceleration can be the current acceleration measurement value in the navigation system. The velocity update cycle can be a pre-set cycle for the inertial navigation system to update the ship's damped velocity. The target integral value can be the integral result of the accelerometer component's velocity measurement equation under the current velocity update cycle.

[0060] In this embodiment of the invention, based on the second navigation solution loop, the product of the acceleration measurement value and the current azimuth cosine matrix can be calculated to obtain the current coordinate transformation acceleration. Then, according to the speed update cycle, the velocity measurement equation of the accelerometer component is integrated to obtain the target integral value. Finally, the current coordinate transformation acceleration and the target integral value are summed to obtain the ship damping speed.

[0061] In an optional embodiment of the present invention, the accelerometer assembly velocity measurement equation includes: ,in, This represents the differential speed of the second autonomous navigation path; Indicates the acceleration during the current coordinate transformation; Indicates the angular velocity of rotation in the navigation system; Indicates the angular velocity of the navigation system; Indicates the acceleration due to gravity in the navigation system; This indicates the speed of the second autonomous navigation route.

[0062] The second-path autonomous navigation velocity differential can be understood as the rate of change of the ground velocity vector (the velocity vector of the vehicle relative to the Earth) observed in the navigation coordinate system. The second-path autonomous navigation velocity can be understood as the ground velocity vector observed in the navigation coordinate system. The rotational angular velocity in the navigation system can include the rotational angular velocities in the east, north, and celestial directions. The vehicle angular velocity in the navigation system can include the vehicle angular velocities in the east, north, and celestial directions.

[0063] In an optional embodiment of the present invention, integrating the velocity measurement equation of the accelerometer component according to the velocity update cycle to obtain a target integral value may include obtaining the target integral value based on the following formula: ,in, Indicates the current time; This indicates the next velocity update time after the current time, and T represents the velocity update period. This indicates the measured acceleration value; Indicates the angular velocity of rotation in the navigation system; Indicates the angular velocity of the navigation system; Indicates the acceleration due to gravity in the navigation system; Indicates the speed of the second autonomous navigation path; This represents the current azimuth cosine matrix.

[0064] In an optional embodiment of the present invention, after calculating the ship's damped speed based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation in the second navigation calculation loop, the method may further include: transmitting the ship's damped speed to the first navigation calculation loop; and calculating the ship's driving parameters based on the ship's damped speed in the first navigation calculation loop.

[0065] Among them, ship navigation parameters can be understood as ship navigation parameters (such as heading and speed) provided by the navigation system.

[0066] In this embodiment of the invention, the ship's damping speed calculated by the second navigation calculation loop can be transmitted to the first navigation calculation loop, and then the ship's driving parameters can be calculated in the first navigation calculation loop based on the ship's damping speed and the built-in algorithm in the navigation system.

[0067] In an optional embodiment of the present invention, after calculating the ship's driving parameters based on the ship's damping speed, the method may further include: reacquiring and updating the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system according to the speed update cycle; and returning to perform the operation of calculating the first navigation solution correction parameters based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data.

[0068] In this embodiment of the invention, after the ship's damping speed has undergone one speed update cycle, the first Kalman filter coarse alignment state variable and the first Kalman filter fine alignment state data of the inertial navigation system are reacquired and updated through the first navigation solution loop. Then, the operation of calculating the first navigation solution correction parameters based on the first Kalman filter coarse alignment state variable and the first Kalman filter fine alignment state data is returned to be executed until the navigation system is shut down.

[0069] The technical solution of this invention obtains the first-path Kalman filter coarse alignment state variables and the first-path Kalman filter fine alignment state data of the inertial navigation system. Based on these data, the ship's attitude parameters and velocity parameters are corrected to obtain the first-path navigation solution correction parameters. Then, based on these parameters, the current azimuth cosine matrix is ​​determined, and the acceleration measurement value output by the accelerometer is obtained. In the second navigation solution loop, the ship's damped velocity is calculated based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation. This solution eliminates the need to reserve space on the carrier for installing underwater acoustic velocimetry equipment. It directly connects the accelerometer component to the inertial navigation system (INS), and through the additional accelerometer component and the INS strapdown mathematical calculation platform, forms a calculation loop to obtain the damped velocity. This enables self-damping of the INS, reducing navigation parameter divergence caused by accumulated integral errors and preventing oscillation amplitude from increasing over time. It solves the problem of high hardware costs associated with calculating damped velocity using existing traditional acoustic velocimetry techniques, enabling accurate calculation of the ship's damped velocity with low hardware costs, thus providing a data foundation for ship navigation.

[0070] Example 3

[0071] Figure 4 This is a schematic diagram of a device for measuring damped velocity in an inertial navigation system based on an additional accelerometer, provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes:

[0072] The data acquisition module 310 is used to acquire the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data of the inertial navigation system.

[0073] The correction parameter calculation module 320 is used to calculate the first navigation solution correction parameters based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data.

[0074] The intermediate data acquisition module 330 is used to determine the current azimuth cosine matrix based on the correction parameters calculated by the first navigation path, and to acquire the acceleration measurement value output by the accelerometer; wherein, the accelerometer is fixedly connected to the inertial navigation system;

[0075] The damping speed calculation module 340 is used in the second navigation solution loop to calculate the ship's damping speed based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation.

[0076] The technical solution of this invention obtains the first-path Kalman filter coarse alignment state variables and the first-path Kalman filter fine alignment state data of the inertial navigation system. Based on these data, it calculates the first-path navigation solution correction parameters, determines the current azimuth cosine matrix, and obtains the acceleration measurement value output by the accelerometer. In the second-path navigation solution loop, it calculates the ship's damping speed based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation. This solution eliminates the need to reserve space on the carrier for installing underwater acoustic velocimetry equipment. It directly connects the accelerometer component to the inertial navigation system (INS), and through the additional accelerometer component and the INS strapdown mathematical calculation platform, forms a calculation loop to obtain the damped velocity. This enables self-damping of the INS, reducing navigation parameter divergence caused by accumulated integral errors and preventing oscillation amplitude from increasing over time. It solves the problem of high hardware costs associated with calculating damped velocity using existing traditional acoustic velocimetry techniques, enabling accurate calculation of the ship's damped velocity with low hardware costs, thus providing a data foundation for ship navigation.

[0077] Optionally, the correction parameter calculation module 320 is used to correct the ship's attitude parameters and speed parameters based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data to obtain the first navigation solution correction parameters; wherein, the first Kalman filter coarse alignment state variables include attitude estimation error and speed estimation error; the first Kalman filter fine alignment state data includes the ship's current attitude and the ship's current speed.

[0078] Optionally, the damping speed calculation module 340 is used in the second navigation solution loop to calculate the current coordinate transformation acceleration based on the acceleration measurement value and the current azimuth cosine matrix; to integrate the velocity equation of the accelerometer component according to the speed update cycle to obtain the target integral value; and to sum the current coordinate transformation acceleration with the target integral value to obtain the ship damping speed.

[0079] Optionally, the inertial navigation system, based on the device for measuring damped velocity using an additional accelerometer, further includes a ship driving parameter calculation module for transmitting the ship's damped velocity to a first navigation calculation loop; and in the first navigation calculation loop, calculating the ship's driving parameters based on the ship's damped velocity.

[0080] Optionally, the device for measuring damped velocity based on an additional accelerometer in the inertial navigation system further includes a data iteration update module, used to reacquire and update the first Kalman filter coarse alignment state variable and the first Kalman filter fine alignment state data of the inertial navigation system according to the velocity update cycle; and return to perform the operation of calculating the first navigation solution correction parameters based on the first Kalman filter coarse alignment state variable and the first Kalman filter fine alignment state data.

[0081] Optional, accelerometer assembly velocities equations, including: ;in, This represents the differential speed of the second autonomous navigation path; Indicates the acceleration during the current coordinate transformation; Indicates the angular velocity of rotation in the navigation system; Indicates the angular velocity of the navigation system; Indicates the acceleration due to gravity in the navigation system; This indicates the speed of the second autonomous navigation route.

[0082] Optionally, the damped velocity calculation module 340 includes an integration unit for obtaining the target integral value based on the following formula: ;in, Indicates the current time; This indicates the next velocity update time after the current time, and T represents the velocity update period. This indicates the measured acceleration value; Indicates the angular velocity of rotation in the navigation system; Indicates the angular velocity of the navigation system; Indicates the acceleration due to gravity in the navigation system; Indicates the speed of the second autonomous navigation path; This represents the current azimuth cosine matrix.

[0083] The inertial navigation system based on the additional accelerometer for measuring damped velocity provided in the embodiments of the present invention can execute the inertial navigation system based on the additional accelerometer for measuring damped velocity provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0084] Example 4

[0085] Figure 5A schematic diagram of an inertial navigation system that can be used to implement embodiments of the present invention is shown. The inertial navigation system is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The inertial navigation system can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0086] like Figure 5 As shown, the inertial navigation system 10 includes at least one processor 11 and a memory, such as ROM 12 or RAM 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. The RAM 13 can also store various programs and data required for the operation of the inertial navigation system 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An I / O interface 15 is also connected to the bus 14. The ROM 12 is a read-only memory, the RAM 13 is a random access memory, and the I / O interface 15 is an input / output interface.

[0087] Multiple components in the inertial navigation system 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the inertial navigation system 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method of measuring damped velocity based on an additional accelerometer in an inertial navigation system.

[0089] In some embodiments, the method for measuring damped velocity using an inertial navigation system based on an additional accelerometer can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the inertial navigation system 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for measuring damped velocity using an additional accelerometer based on the inertial navigation system described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for measuring damped velocity using an additional accelerometer based on the inertial navigation system by any other suitable means (e.g., by means of firmware).

[0090] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0093] To provide interaction with the user, the systems and techniques described herein can be implemented on an inertial navigation system having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the inertial navigation system. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0094] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0095] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0096] This application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the method for measuring damped velocity of an inertial navigation system based on an additional accelerometer, as provided in any embodiment of this application. This program product and the method for measuring damped velocity of an inertial navigation system based on an additional accelerometer disclosed in the embodiments of this application belong to the same inventive concept, and therefore will not be described in detail here.

[0097] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0098] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for measuring damped velocity in an inertial navigation system based on an additional accelerometer, characterized in that, include: The first Kalman filter coarse alignment state variable and the first Kalman filter fine alignment state data of the inertial navigation system are obtained; wherein, the inertial navigation system performs two parallel processing paths for the same navigation signal source, namely, the first navigation solution path and the second navigation solution path. Based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data, calculate the first navigation solution correction parameters; Based on the first navigation solution correction parameters, the current azimuth cosine matrix is ​​determined, and the acceleration measurement value output by the accelerometer is obtained; wherein, the accelerometer is fixedly connected to the inertial navigation system; In the second navigation calculation loop, the ship's damped speed is calculated based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer component velocity equation. In the second navigation calculation loop, the ship's damped speed is calculated based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer assembly velocity equation, including: In the second navigation solution loop, the current coordinate transformation acceleration is calculated based on the acceleration measurement value and the current azimuth cosine matrix; According to the velocity update cycle, the velocity measurement equation of the accelerometer component is integrated to obtain the target integral value; The ship's damping speed is obtained by summing the current coordinate transformation acceleration with the target integral value.

2. The method according to claim 1, characterized in that, Based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data, the first navigation solution correction parameters are calculated, including: Based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data, the ship attitude parameters and speed parameters are corrected to obtain the first navigation solution correction parameters. The first Kalman filter coarse alignment state variables include attitude estimation error and velocity estimation error; the first Kalman filter fine alignment state data includes the ship's current attitude and the ship's current velocity.

3. The method according to claim 1, characterized in that, In the second navigation calculation loop, after calculating the ship's damped velocity based on the acceleration measurement value, the current azimuth cosine matrix, and the accelerometer assembly velocity equation, the following steps are also included: The ship's damped speed is transmitted to the first navigation calculation loop; In the first navigation calculation loop, the ship's driving parameters are calculated based on the ship's damping speed.

4. The method according to claim 3, characterized in that, After calculating the ship's driving parameters based on the ship's damping speed, the process also includes: According to the velocity update cycle, the first Kalman filter coarse alignment state variable and the first Kalman filter fine alignment state data of the inertial navigation system are reacquired and updated. Return to the operation of calculating the first navigation solution correction parameters based on the first Kalman filter coarse alignment state variables and the first Kalman filter fine alignment state data.

5. The method according to claim 1, characterized in that, The accelerometer assembly velocity measurement equations include: ； in, This represents the differential speed of the second autonomous navigation path; Indicates the acceleration during the current coordinate transformation; Indicates the angular velocity of rotation in the navigation system; Indicates the angular velocity of the navigation system; Indicates the acceleration due to gravity in the navigation system; This indicates the speed of the second autonomous navigation route.

6. The method according to claim 5, characterized in that, According to the velocity update cycle, the velocity measurement equation of the accelerometer component is integrated to obtain the target integral value, including: The target integral value is obtained based on the following formula: ； in, Indicates the current moment; This indicates the next velocity update time after the current time, and T represents the velocity update period. This indicates the measured acceleration value; This represents the current azimuth cosine matrix.

7. An inertial navigation system, characterized in that, The inertial navigation system includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the method of measuring damped velocity based on an additional accelerometer using the inertial navigation system according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for measuring damped velocity based on an additional accelerometer using the inertial navigation system as described in any one of claims 1-6.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements a method for measuring damped velocity based on an additional accelerometer in an inertial navigation system according to any one of claims 1-6.

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