High-precision measurement method, apparatus, and recording medium for inertial navigation in dynamic environments

Abstract

This invention provides a high-precision measurement method, apparatus, and recording medium for inertial navigation in dynamic environments. [Solution] The present invention relates to the field of sensor data processing technology, and more specifically to a high-precision measurement method, apparatus and recording medium for inertial navigation in a dynamic environment. The method includes the steps of: acquiring inertial sensor data; performing a filter on the inertial sensor data to obtain filtered inertial sensor data; constructing a gyro angular velocity error correction model; correcting the angular velocity data in the filtered inertial sensor data to obtain corrected angular velocity data; constructing an acceleration error estimation model; estimating an error on the acceleration data in the filtered inertial sensor data; correcting the acceleration data based on the error estimation result to obtain corrected acceleration data; and using the corrected angular velocity data and corrected acceleration data to calculate the position update result and attitude update result when an object is in a dynamic environment, and obtaining the high-precision measurement result for inertial navigation of the object. The present invention improves the accuracy of inertial sensor data.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor data processing, and specifically to a high-precision measurement method, device, and recording medium for inertial navigation in a dynamic environment.

Background Art

[0002] An inertial navigation system (INS) estimates the motion state of an object by measuring acceleration and angular velocity, and has advantages such as high frequency, high precision, and autonomy.

[0003] However, in a dynamic environment, especially when there are complex motions and external interference, the accuracy of the inertial navigation system is significantly affected. The first problem is that the motion of an object in a dynamic environment is often non-linear, and it is difficult to effectively process the non-linear motion situation with conventional linear filter processing methods (such as Kalman filters). In addition, external interference factors such as wind and vibration affect the measurement results of the sensor, leading to error accumulation. Furthermore, there is noise in the inertial sensor (Inertial Measurement Unit, IMU) itself, and especially in a highly dynamic situation, the influence of noise on the measurement results becomes more significant.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention has been made in view of the above circumstances.

[0005] The object of the present invention is to provide a high-precision measurement method, device, and recording medium for inertial navigation in a dynamic environment, adaptively correct the measurement error of inertial navigation, and improve the accuracy of data.

Means for Solving the Problems

[0006] To achieve the above object, the present invention adopts the following technical solutions.

[0007] According to a first aspect, the present invention provides a high-precision measurement method for inertial navigation in a dynamic environment, the method comprising the following steps:

[0008] The steps include acquiring inertial sensor data, performing a filter on the inertial sensor data, and obtaining the filtered inertial sensor data. Here, the inertial sensor data includes angular velocity data acquired by a gyroscope and acceleration data acquired by an accelerometer.

[0009] The step of constructing a gyro angular velocity error correction model, correcting the angular velocity data in the inertial sensor data after the filtering process, and obtaining the corrected angular velocity data.

[0010] The steps include constructing an acceleration error estimation model, performing error estimation on the acceleration data in the filtered inertial sensor data, correcting the acceleration data based on the error estimation results, and obtaining corrected acceleration data.

[0011] Using the corrected angular velocity data and corrected acceleration data, the position update result and attitude update result for the object in a dynamic environment are calculated and obtained as the high-precision measurement result for the object's inertial navigation.

[0012] In a second aspect, the present invention provides an electronic device, which includes the following: At least one processor, and memory connected to at least one of the processors in communication.

[0013] The memory stores instructions that can be executed by at least one of the processors, and the execution of these instructions by at least one of the processors causes at least one of the processors to perform the high-precision measurement method for inertial navigation in a dynamic environment.

[0014] In a third aspect, the present invention provides a recording medium on which a computer executable program is stored, and the steps of the high-precision measurement method for inertial navigation in the dynamic environment are executed by calling the computer executable program by a processor. [Effects of the Invention]

[0015] Compared to the conventional technology, the beneficial effects of the present invention are as follows:

[0016] This invention proposes a data correction method that combines frequency domain information. It employs a weighting method using a sliding filter to filter out noise information in inertial sensor data, divides angular velocity data into multiple frequency domain representations, combines the center frequencies of the angular velocity data and performs iterations to extract effective information in the frequency domain of the angular velocity data, performs integration on the extracted effective information, fuses effective information with different Fourier point counts to construct corrected angular velocity data, and removes frequency domain noise information under dynamic conditions.

[0017] Simultaneously, the present invention proposes an error estimation method and a high-precision inertial navigation measurement method. By performing multiscale decomposition and transformation processing on acceleration data, the transformation result is used as the response of the acceleration data, and information on the response change value is extracted to construct the response gain of the acceleration data. Here, a larger response gain indicates that the changes in the acceleration data are more complex and that the existing sensor detection error is larger. Therefore, the response gain is converted into an error estimation result to correct the acceleration data. Then, using the corrected angular velocity data and acceleration data, the position update result and attitude update result of the object in a dynamic environment are calculated, thereby realizing high-precision inertial navigation measurement of the object. [Brief explanation of the drawing]

[0018] [Figure 1] This is a flowchart of a high-precision measurement method for inertial navigation in a dynamic environment according to an embodiment of the present invention. [Figure 2] This is a schematic diagram of the configuration of the electronic device according to the present invention. [Modes for carrying out the invention]

[0019] Illustrative embodiments of the present invention will be described below with reference to the drawings. Various details of the embodiments of the present invention are included to aid understanding, but these should be considered merely illustrative. Therefore, those skilled in the art should be aware that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of known functions and structures will be omitted in the following description. [Examples]

[0020] Embodiments of the present invention provide a high-precision measurement method for inertial navigation in a dynamic environment. This method is applicable to situations in which the position and attitude of a dynamic object are measured using an inertial sensor. The entity that performs the high-precision measurement method for inertial navigation in a dynamic environment includes, but is not limited to, at least one electronic device such as a server or terminal that can be configured to perform the method provided in the embodiments of the present invention. In other words, the high-precision measurement method for inertial navigation in a dynamic environment can be performed by software or hardware installed on a terminal or server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0021] Referring to Figure 1, the method provided in this embodiment includes the following steps:

[0022] S110, Inertial sensor data is acquired, and the inertial sensor data is filtered to obtain filtered inertial sensor data.

[0023] Install an inertial sensor on the surface of an object and obtain inertial sensor data during the movement process of the object. Here, the inertial sensor data includes angular velocity data obtained by a gyroscope and acceleration data obtained by an accelerometer, and the expression form of the obtained inertial sensor data is as follows.

[0024]

Number

[0025] Here, u(N) represents the inertial sensor data series up to time N. u n represents the inertial sensor data obtained at time n, and a n , ω n respectively represent the acceleration data and angular velocity data in the inertial sensor data u n . a n (x), a n (y), a n (z) respectively represent the accelerations of the object in the X-axis direction, Y-axis direction, and Z-axis direction obtained at time n, and ω n (x), ω n (y), ω n (z) respectively represent the angular velocities of the object in the X-axis direction, Y-axis direction, and Z-axis direction obtained at time n. Optionally, in this embodiment, the X-axis direction represents the east-west direction, the Y-axis direction represents the north-south direction, and the Z-axis direction represents the direction perpendicular to the ground. Here, it is assumed that the inertial sensor data has already been converted into the world coordinate system or the earth-fixed coordinate system. The advantage of converting to the world coordinate system is that the angular velocity data and acceleration data at different times are in the same reference coordinate system and do not change with the movement of the object.

[0026] Perform filtering processing on the obtained inertial sensor data to obtain the inertial sensor data after filtering. The main purpose of performing filtering processing on sensor data is to remove noise and interference, improve the quality and reliability of the data, and thereby extract useful information more accurately. This embodiment does not limit a specific filtering algorithm.

[0027] In some optional embodiments, a sliding window method is used to filter acceleration and angular velocity data in the inertial sensor data, respectively. This involves the following five steps.

[0028] Step 1: Construct a sliding window. Construct a sliding window with length Len+1.

[0029] Step 2: The inertial sensor data to be filtered is placed at the center of the sliding window, and a sliding filter sequence of the inertial sensor data is constructed. The sliding filter sequence includes multiple sequence values.

[0030] Inertial sensor data u n The sliding filter sequence is L n Let's assume that.

[0031]

number

[0032] Step 3: The maximum and minimum values ​​of the sliding filter sequence are calculated and obtained. Sliding filter series L n The maximum value in Max n , the minimum value Min n In the embodiments of the present invention,

number

number

[0033] Step 4: Based on the maximum and minimum values, the filter weights of each series value in the sliding filter series are calculated and obtained.

[0034] Step 5: A weighting calculation is performed on each series value based on the filter weights, and the weighted result is used as the filtered inertial sensor data.

[0035] Inertial sensor data u n The filtered inertial sensor data corresponding to u' n Let's assume that.

[0036]

number

[0037]

number

[0038] Here, w n (l) is the sliding filter series L n The sequence value u n+l The filter weights are represented by exp(·), exp(·) represents an exponential function with base Napier's number, and σ represents the scale parameter. In the embodiment of this invention, σ is set to 5.

[0039] S120 constructs a gyro angular velocity error correction model, applies correction to the angular velocity data in the filtered inertial sensor data, and obtains the corrected angular velocity data.

[0040] The gyro angular velocity error correction model includes a frequency domain representation layer, a frequency calculation layer, an iterative correction layer, and an output layer. Here, the frequency domain representation layer is used to perform a frequency domain representation on the angular velocity data. The frequency calculation layer is used to calculate and obtain the center frequency of the angular velocity data. The iterative correction layer is used to perform iterative correction on the angular velocity data by combining the frequency domain representation result and the center frequency to obtain the corrected angular velocity data. The correction process of the gyro angular velocity error correction model will be described in detail below.

[0041] The frequency domain representation layer converts the angular velocity data into a frequency domain representation result. Here, the filtered inertial sensor data u' n Angular velocity data ω' n g' is the frequency domain representation result of n Let's assume that.

[0042]

number

[0043] Here, j represents the imaginary unit, and g' n (e) is angular velocity data ω' n This represents the frequency domain representation of the Fourier transform number e.

[0044] The center frequency of the angular velocity data is calculated and obtained using a frequency calculation layer. Here, the angular velocity data ω' n The center frequency is f' n Let's assume that.

[0045]

number

[0046] Here, Δ represents the time interval between adjacent time points.

[0047] The iterative correction layer combines the frequency domain representation result and the center frequency to perform iterative correction on the angular velocity data, thereby obtaining the corrected angular velocity data. Here, the angular velocity data ω' n The correction result corresponding to ω * n Let's assume the angular velocity data is ω'. n The iterative correction flow is as follows:

[0048] g' is the result of the c-th iteration of the frequency domain representation. n,c Set to (e).

[0049]

number

[0050] Here, g' n,c (e) is the frequency domain representation g' n This represents the result of the c-th iteration of (e). The initial value of c is 0, and the maximum value is C, and g' n,0 (e) = g' n (e), g' n,0 =g' n Here, n is the number of angular velocity data points, and also the number of acquisition times.

[0051] The frequency domain representation of the frequency domain representation result is iterated over. Here, g' n,c The iteration formula for (e) is as follows:

[0052]

number

[0053] Here, g' n,c+1 (e) is g' n,c (e) represents the iterative result. In the embodiment of the present invention, when c=0, g' n,c-1 Let (e) = 0. Let c = c + 1, and return to the iteration step "Iterate over the frequency domain representation in the frequency domain representation result" until the maximum number of iterations is reached, and obtain the final iteration result of the frequency domain representation. Here, the frequency domain representation g' n(e) The final iteration result is g * n (e)

[0054] Using the final iteration result of the frequency domain representation, angular velocity data ω' n The corresponding correction result ω * n It constitutes.

[0055]

number

[0056] Here, de represents the derivative of the Fourier transform point number e.

[0057] S130, an acceleration error estimation model is constructed, and an error estimation is performed on the acceleration data in the inertial sensor data after the filtering process. Based on the error estimation result, the acceleration data is corrected to obtain the corrected acceleration data.

[0058] An acceleration error estimation model is optionally constructed. Here, the acceleration error estimation model includes an acceleration conversion layer, a gain calculation layer, and an error estimation layer. The acceleration conversion layer is used to perform sequence decomposition and transformation processing on acceleration data. The gain calculation layer is used to calculate and obtain the response gain of the acceleration data conversion result. The error estimation layer is used to convert the response gain into an error estimation result. The process of performing error estimation on filtered acceleration data using the acceleration error estimation model and correcting the filtered acceleration data will be described in detail below.

[0059] First, acceleration data is extracted from the filtered inertial sensor data. Next, the acceleration conversion layer performs sequence decomposition and conversion processing on the acceleration data to construct the acceleration data conversion result. Here, the filtered inertial sensor data u' n Acceleration data a' in n A' is the result of the acceleration data conversion corresponding to A'. n Let's assume that.

number

[0060]

number

[0061] Here, j represents the imaginary unit, and A' n (r) is acceleration data a' n This represents the decomposition transformation result at scale r, and a' s represents the acceleration data after the s-th filtering process, where N is a natural number greater than 1, and n is both the number of acceleration data points and the number of time points at which the acceleration data was acquired.

[0062] The response gain of the acceleration data conversion result is calculated and obtained by the gain calculation layer. Here, the acceleration data conversion result A' n The response gain of h n Let's assume that.

[0063]

number

[0064] Here,

number

[0065] The error estimation layer converts the response gain into an error estimation result. Here, the response gain h n The error estimation result corresponding to H n Let's assume that.

[0066]

number

[0067] Here, τ represents the error control parameter. In this embodiment of the present invention, τ is set to 5.

[0068] Finally, the acceleration data is corrected based on the error estimation results. Here, the acceleration data a' n The correction result of a * n Let's assume that acceleration data a' n The correction formula is as follows:

[0069]

number

[0070] Here, a * n This is the acceleration data a' in the filtered inertial sensor data. n This represents the correction result of μ a σ represents the average value of acceleration data in all filtered inertial sensor data, a This represents the standard deviation of acceleration data in the inertial sensor data after all filtering.

[0071] S140. Using the corrected angular velocity data and corrected acceleration data, the position update result and attitude update result for the object in a dynamic environment are calculated and used as the high-precision measurement result for the object's inertial navigation.

[0072] The corrected angular velocity and acceleration data are used to calculate and obtain the attitude update result for an object in a dynamic environment. The calculation flow for the attitude update result of the object at time n+1 is as follows.

[0073] In S41, the corrected angular velocity data from time 1 to time n, and the attitude matrix E0 of the object at its initial time are obtained. Here, the attitude matrix is ​​the product of rotation matrices representing the rotations of the object around the X, Y, and Z axes, respectively.

[0074] S42, An angular velocity matrix is ​​generated based on the corrected angular velocity data. Here, the ω corresponding to the corrected angular velocity data* n The angular velocity matrix is ​​W n Let's assume that.

[0075]

number

[0076] Here, ω * n (x), ω * n (y), ω * n (z) represents the angular velocity of the object in the X, Y, and Z directions in the corrected angular velocity data, respectively.

[0077] S43. The attitude matrix is ​​iteratively updated, and the attitude matrix E at time n+1 of the object is obtained. n+1 To obtain.

[0078]

number

[0079]

number

[0080] Here, I represents the identity matrix, and E n Δ represents the attitude matrix of the object at time n, and Δ represents the time interval between adjacent time points.

[0081] S44, Attitude matrix E of the object at time n+1 n+1 The object's attitude update result at time n+1 is used, and the attitude update result and corrected acceleration data are combined to calculate and obtain the position update result when the object is in a dynamic environment.

[0082] The following describes in detail the process of calculating and obtaining the position update result when an object is in a dynamic environment by combining the attitude update result and corrected acceleration data.

[0083] The calculation flow for the object's position update result at time n+1 is as follows: Corrected acceleration data, the object's initial velocity v0, and the object's initial position Loc0 are obtained from time 1 to time n. In the embodiment of the present invention, the object's velocity and position both include the velocity and position in the X-axis, Y-axis, and Z-axis directions. Using the attitude update result, the corrected acceleration data at the same time is converted to the navigation coordinate system. The navigation coordinate system refers to the coordinate system of the inertial sensor, where the X-axis is forward of the sensor, the Y-axis is to the right of the sensor, and the Z-axis is vertically upward of the sensor. Based on the object's attitude matrix, an update transformation is performed, and by adjusting the acceleration and velocity in the inertial sensor's navigation coordinate system, the object's attitude and position in a dynamic environment are indirectly updated.

[0084] Here, corrected acceleration data a * n The conversion formula is as follows:

number

[0085] Here, g represents the gravitational acceleration vector. The velocity of the object is updated using the corrected acceleration data after coordinate transformation, and the velocity of the object from time 1 to time n+1 is obtained. Here, the velocity of the object at time n+1 is v n Let's assume that.

[0086]

number

[0087] Here, df represents the differential operator. Based on the object's velocity update result, the object's position update result at time n+1 is Loc n+1 This is obtained by calculation.

[0088]

number

[0089] S45. The position update results and attitude update results when the object is in a dynamic environment are taken as the high-precision measurement results of the object's inertial navigation. [Examples]

[0090] Embodiments of the present invention further provide electronic devices. As shown in Figure 2, these embodiments provide electronic devices that include at least one processor and at least one memory that is communicated with the processor.

[0091] The memory stores instructions that can be executed by at least one of the processors, and the execution of these instructions by at least one of the processors causes at least one of the processors to perform the above method. Since at least one processor in the electronic device is capable of performing the above method, it has at least the same advantages as the above method.

[0092] Optionally, the electronic device further includes interfaces for connecting each component, including high-speed and low-speed interfaces. Each component is interconnected using different buses and may be mounted on a common motherboard or in other ways as needed. The processor can process instructions executed within the electronic device, including instructions for displaying graphical user interface (GUI) information in or on memory and on external input / output devices (e.g., display devices coupled to the interface). In other embodiments, if necessary, multiple processors may be used with multiple memories, and / or multiple buses may be used with multiple memories. Similarly, multiple electronic devices may be connected (e.g., as a server array, a group of blade servers, or a multiprocessor system), with each device providing some of the necessary operations. Figure 2 shows a single processor 301 as an example.

[0093] Memory 302, as a type of computer-readable recording medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the high-precision measurement method of inertial navigation in a dynamic environment in the embodiment of the present invention. The processor 301 executes various functional applications and data processing of the device by executing the software programs, instructions, and modules stored in memory 302. That is, it realizes the high-precision measurement method of inertial navigation in a dynamic environment described above.

[0094] The memory 302 may mainly include a program storage area and a data storage area, where the program storage area can store the operating system and application programs necessary for at least one function, and the data storage area can store data created according to the terminal's usage. Furthermore, the memory 302 may include high-speed random access memory and may further include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the memory 302 may further include memory located remotely from the processor 301, and these remote memories may be connected to the device via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0095] The electronic device may further include an input device 303 and an output device 304. The processor 301, memory 302, input device 303, and output device 304 can be connected by a bus or other means, with Figure 2 showing a bus connection as an example.

[0096] The input device 303 can receive input numerical or character information, and the output device 304 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touchscreen.

[0097] It should be understood that the steps can be reordered, added, or deleted using the various forms of flows described above. For example, each step described in this invention may be performed in parallel, sequentially, or in a different order. This specification does not limit this to as long as the desired results of the technical solutions disclosed herein can be achieved.

[0098] The specific embodiments described above do not limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications, combinations, partial combinations, and substitutions can be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A high-precision measurement method for inertial navigation in a dynamic environment, The process includes acquiring inertial sensor data, performing a filter on the inertial sensor data, and obtaining the filtered inertial sensor data, wherein the inertial sensor data includes angular velocity data acquired by a gyroscope and acceleration data acquired by an accelerometer. Constructing a gyro angular velocity error correction model, correcting the angular velocity data in the filtered inertial sensor data, and obtaining corrected angular velocity data includes constructing a gyro angular velocity error correction model, where the gyro angular velocity error correction model includes a frequency domain representation layer, a frequency calculation layer, an iterative correction layer, and an output layer, the frequency domain representation layer is used to perform a frequency domain representation on the angular velocity data, the frequency calculation layer is used to calculate and obtain the center frequency of the angular velocity data, and the iterative correction layer is used to perform iterative correction on the angular velocity data by combining the frequency domain representation result and the center frequency, and to obtain corrected angular velocity data. Here, the frequency domain representation result and the center frequency are combined and iteratively corrected for the angular velocity data to obtain the corrected angular velocity data, which is done by taking the c-th iteration result of the frequency domain representation as g' n,c (e) including setting [Math 1] Here, g' n,c (e) is the frequency domain representation g' n This is the result of the c-th iteration of (e), where the initial value of c is 0 and the maximum value is C. [Math 2] And n is the number of angular velocity data points, The frequency domain representation in the frequency domain representation result is iterated over, and here g' n,c (e) The iterative expression is, [Math 3] And, Here, g' n,c+1 (e) is g' n,c (e) represents the iterative result, and f' n This is the center frequency, Set \(c = c + 1\), return to the iterative step until the maximum number of iterations is reached, obtain the final iteration result of the frequency-domain representation, where the final iteration result of the frequency-domain representation \(g'\) n The final iteration result of (e) is \(g\) * n (e), and Using the final iteration result of the frequency domain representation, angular velocity data ω' n The corresponding correction result ω * n Constitute, [Math 4] Here, de represents the derivative of the Fourier transform point number e, An acceleration error estimation model is constructed, and an error estimation is performed on the acceleration data in the filtered inertial sensor data. Based on the error estimation results, the acceleration data is corrected, and the corrected acceleration data is obtained. A method for measuring high-precision inertial navigation in a dynamic environment, characterized by using the corrected angular velocity data and corrected acceleration data to calculate the position update result and attitude update result when the object is in a dynamic environment, and using these as the high-precision measurement result of the object's inertial navigation.

2. Acquiring inertial sensor data is A high-precision measurement method for inertial navigation in a dynamic environment according to claim 1, comprising installing an inertial sensor on the surface of an object and acquiring inertial sensor data during the motion process of the object.

3. Performing a filter on the aforementioned inertial sensor data to obtain the filtered inertial sensor data is, Create a sliding window, The inertial sensor data to be filtered is set as the center of the sliding window, a sliding filter sequence of the inertial sensor data is constructed, and the sliding filter sequence includes multiple sequence values. The maximum and minimum values ​​of the sliding filter sequence are calculated and obtained, Based on the aforementioned maximum and minimum values, the filter weights of each series value in the sliding filter series are calculated and obtained. A high-precision measurement method for inertial navigation in a dynamic environment according to claim 1, comprising performing a weighting calculation on each series value based on the filter weights, and using the weighting results as filtered inertial sensor data.

4. Building an acceleration error estimation model is This includes constructing an acceleration error estimation model, wherein the acceleration error estimation model includes an acceleration conversion layer, a gain calculation layer, and an error estimation layer. The high-precision measurement method for inertial navigation in a dynamic environment according to claim 1, wherein the acceleration conversion layer is used to perform sequence decomposition and conversion processing on acceleration data, the gain calculation layer is used to calculate and obtain the response gain of the acceleration data conversion result, and the error estimation layer is used to convert the response gain into an error estimation result.

5. Performing error estimation on acceleration data in the inertial sensor data after the filtering process is, Extract acceleration data from the filtered inertial sensor data. The acceleration conversion layer performs sequence decomposition and conversion processing on the acceleration data to construct the acceleration data conversion result, where the inertial sensor data u' after filtering is obtained. ｎ Acceleration data a' in ｎ The corresponding acceleration data conversion result is A' ｎ And, [Math 5] [Math 6] Here, j represents the imaginary unit, and A' ｎ (r) is acceleration data a' ｎ This represents the decomposition transformation result at scale r, and a' s represents the acceleration data after the s-th filtering process, where N is a natural number greater than 1, and n is the number of acceleration data points. The response gain of the acceleration data conversion result is calculated and obtained by the gain calculation layer, where the acceleration data conversion result A' ｎ Response gain h n teeth, [Number 7] And here, [Number 8] is A' ｎ (r) is the response change value, The error estimation layer converts the response gain into an error estimation result, where the response gain h n Corresponding error estimation result H n teeth, [Number 9] And, The method for measuring high-precision inertial navigation in a dynamic environment according to claim 4, wherein τ represents an error control parameter.

6. Applying corrections to acceleration data based on error estimation results is, Filtered acceleration data a' ｎ The correction formula is, [Number 10] This includes the fact that, here, a * n This is the acceleration data a' in the filtered inertial sensor data. ｎ This represents the correction result of μ a σ represents the average value of acceleration data in all filtered inertial sensor data, a The method for measuring high-precision inertial navigation in a dynamic environment according to claim 5, wherein represents the standard deviation of acceleration data in the inertial sensor data after all filtering.

7. An electronic device, wherein the electronic device includes at least one processor and at least one memory that is communicated with the processor, The electronic device is characterized in that the memory stores instructions that can be executed by at least one of the processors, and by executing the instructions by at least one of the processors, at least one of the processors is made to perform the high-precision measurement method for inertial navigation in a dynamic environment described in any one of claims 1 to 6.

8. A computer-readable recording medium storing a computer-executable program, characterized in that the computer-executable program is invoked by a processor to perform the steps of the high-precision measurement method for inertial navigation in a dynamic environment described in any one of claims 1 to 6.

Images