An inertial navigation precise measurement method, device and storage medium in a dynamic environment

Abstract

The present application relates to the field of sensor data processing technology, and more specifically, to an inertial navigation precision measurement method, device, and storage medium in a dynamic environment. The method comprises: collecting inertial sensor data, filtering the inertial sensor data, and obtaining filtered inertial sensor data; constructing a gyroscope angular velocity error correction model, and correcting the angular velocity data in the filtered inertial sensor data to obtain corrected angular velocity data; constructing an acceleration error estimation model, and performing error estimation on the acceleration data in the filtered inertial sensor data, and correcting the acceleration data according to the error estimation result to obtain corrected acceleration data; and using the corrected angular velocity data and the corrected acceleration data to calculate the position update result and attitude update result of the object in a dynamic environment as the inertial navigation precision measurement result of the object. The present application improves the accuracy of inertial sensor data.

Description

Technical Field

[0001] The present application relates to the field of sensor data processing technology, and in particular to an inertial navigation precision measurement method, device, and storage medium in a dynamic environment. Background Art

[0002] The inertial navigation system infers the motion state of an object by measuring acceleration and angular velocity, and has the advantages of high frequency, high precision and autonomy.

[0003] However, in dynamic environments, especially those with complex motion and external interference, the accuracy of inertial navigation systems can be significantly affected. First, the motion of objects in dynamic environments is often nonlinear, and traditional linear filtering methods (such as Kalman filtering) struggle to effectively handle nonlinear motion. Second, external interference such as wind and vibration can affect sensor measurements, leading to accumulated errors. Furthermore, inertial measurement unit (IMU) sensors themselves are inherently noisy, and this impact on measurement results is particularly pronounced in highly dynamic environments.

[0004] In view of this, this application is hereby filed. Summary of the Invention

[0005] The purpose of this application is to provide a method, device and storage medium for accurate inertial navigation measurement in a dynamic environment, so as to adaptively correct the measurement error of inertial navigation and improve the accuracy of data.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for precise inertial navigation measurement in a dynamic environment, comprising:

[0008] Collecting inertial sensor data, and filtering the inertial sensor data to obtain filtered inertial sensor data, wherein the inertial sensor data includes: angular velocity data collected by a gyroscope and acceleration data collected by an accelerometer;

[0009] Constructing a gyroscope angular velocity error correction model to correct the angular velocity data in the filtered inertial sensor data to obtain corrected angular velocity data;

[0010] Constructing an acceleration error estimation model, performing error estimation on the acceleration data in the filtered inertial sensor data, and correcting the acceleration data according to the error estimation result to obtain corrected acceleration data;

[0011] The corrected angular velocity data and the corrected acceleration data are used to calculate the position update result and the attitude update result of the object in a dynamic environment as the inertial navigation precise measurement result of the object.

[0012] In a second aspect, the present application provides an electronic device, comprising:

[0013] at least one processor, and a memory communicatively coupled to the at least one processor;

[0014] The memory stores instructions that can be executed by at least one of the processors. The instructions are executed by at least one of the processors to enable the at least one processor to perform an inertial navigation precision measurement method in a dynamic environment.

[0015] In a third aspect, the present application provides a computer-readable storage medium, which stores a computer-executable program, and the computer-executable program is called by a processor to execute the steps of the inertial navigation precision measurement method in a dynamic environment.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] This application proposes a data correction method that combines frequency domain information. It uses a sliding filter weighted method to filter out noise information in inertial sensor data, divides the angular velocity data into multiple frequency domain representations, and iterates based on the frequency of the angular velocity data to extract the effective information of the angular velocity data in the frequency domain. The extracted effective information is integrated and the effective information of different Fourier points is fused to form the corrected angular velocity data, thereby removing frequency domain noise information in a dynamic environment.

[0018] At the same time, the present application proposes an error estimation method and an inertial navigation precise measurement method. By performing multi-scale decomposition and conversion processing on the acceleration data, the conversion result is used as the response of the acceleration data, and the information of the response change value is extracted to constitute the response gain of the acceleration data. The larger the response gain, the more complex the change of the acceleration data and the greater the sensor detection error. The response gain is converted into an error estimation result to realize the correction of the acceleration data. The corrected angular velocity data and acceleration data are used to calculate the position update result and attitude update result of the object in a dynamic environment, thereby realizing the inertial navigation precise measurement of the object. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 1 is a flow chart of a method for precise inertial navigation measurement in a dynamic environment provided by an embodiment of the present application;

[0021] Figure 2 It is a structural diagram of the electronic device provided in this application. DETAILED DESCRIPTION

[0022] The following description of exemplary embodiments of the present application is made in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] Example 1

[0024] An embodiment of the present application provides an inertial navigation precision measurement method in a dynamic environment, which is applicable to situations where an inertial sensor is used to measure the position and attitude of a dynamic object. The execution subject of the inertial navigation precision measurement method in a dynamic environment includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the inertial navigation precision measurement method in a dynamic environment can be executed by software or hardware installed in a terminal device or a server device, and the software can 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, etc.

[0025] See also Figure 1 , the method provided in this embodiment includes the following operations:

[0026] S110 : Collect inertial sensor data, and perform filtering on the inertial sensor data to obtain filtered inertial sensor data.

[0027] An inertial sensor is placed on the surface of an object to collect inertial sensor data during the object's motion. The inertial sensor data includes angular velocity data collected by a gyroscope and acceleration data collected by an accelerometer. The collected inertial sensor data is expressed as follows:

[0028]

[0029] in, represents the inertial sensor data sequence up to time N;

[0030] Represents the inertial sensor data collected at time n, Represents inertial sensor data respectively The acceleration data and angular velocity data in Indicates the acceleration of the object in the X-axis direction, Y-axis direction, and Z-axis direction collected at time n. Indicates the angular velocity of the object collected at time n in the X-axis direction, Y-axis direction, and Z-axis direction, respectively. Optionally, in an embodiment of the present invention, 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, the inertial sensor data has been converted to the ground coordinate system. The advantage of converting to the ground coordinate system is that the angular velocity data and acceleration data at different times are in the same standard coordinate system and will not change with the movement of the object.

[0031] The collected inertial sensor data is filtered to obtain filtered inertial sensor data. The main purpose of filtering sensor data is to remove noise and interference, improve data quality and reliability, and thus more accurately extract useful information. This embodiment does not limit the specific filtering algorithm.

[0032] In some optional implementations, a sliding window method is used to filter the acceleration data and angular velocity data in the inertial sensor data, including the following five steps:

[0033] Step 1: Build a sliding window; build a sliding window with a length of Len+1.

[0034] Step 2: Taking the inertial sensor data to be filtered as the center of the sliding window, a sliding filter sequence of the inertial sensor data is formed; the sliding filter sequence includes a plurality of sequence values;

[0035] Inertial sensor data The sliding filter sequence is :

[0036] ;

[0037] Step 3: Calculate the maximum and minimum values of the sliding filter sequence.

[0038] Sliding filter sequence The maximum value in , the minimum value is ; In the embodiment of the present application, if , it means , represents the L2 norm.

[0039] Step 4: Calculate the filter weight of each sequence value in the sliding filter sequence according to the maximum value and the minimum value;

[0040] Step 5: Perform weighted operation on each sequence value based on the filter weight, and use the weighted result as the filtered inertial sensor data.

[0041] Inertial sensor data The corresponding filtered inertial sensor data is :

[0042] ;

[0043] ;

[0044] in, Represents the sliding filter sequence mid-sequence value The filter weight of represents an exponential function with a natural constant as its base; represents a scale parameter; in the embodiment of the present invention, Set to 5.

[0045] S120: Construct a gyroscope angular velocity error correction model to correct the angular velocity data in the filtered inertial sensor data to obtain corrected angular velocity data.

[0046] The gyroscope 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 represents the angular velocity data in the frequency domain; the frequency calculation layer calculates the center frequency of the angular velocity data; and the iterative correction layer iteratively corrects the angular velocity data based on the frequency domain representation and the center frequency to obtain the corrected angular velocity data. The following details the correction process of the gyroscope angular velocity error correction model.

[0047] The angular velocity data is converted into a frequency domain representation result through the frequency domain representation layer, where the filtered inertial sensor data Medium angular velocity data The frequency domain representation result is :

[0048] ;

[0049] Wherein, j represents the imaginary unit; Indicates angular velocity data Frequency domain representation of the Fourier transform point number e.

[0050] The center frequency of the angular velocity data is calculated by the frequency calculation layer, where the angular velocity data The center frequency is :

[0051] ;

[0052] in, Indicates the time interval between adjacent moments.

[0053] The angular velocity data is iteratively corrected by combining the frequency domain representation result and the center frequency through the iterative correction layer to obtain the corrected angular velocity data. The corresponding correction result is Angular velocity data The iterative correction process is:

[0054] Set the cth iteration result of the frequency domain representation result to :

[0055] ;

[0056] in, Frequency domain representation The cth iteration result of , the initial value of c is 0, the maximum value is C, , ; n is the number of angular velocity data, which is also the number of acquisition moments.

[0057] Iterate the frequency domain representation in the frequency domain representation result, where The iterative formula is:

[0058] ;

[0059] in, express In the embodiment of the present invention, if c=0, then ;

[0060] Let c=c+1, return to the iterative step "iterate the frequency domain representation in the frequency domain representation result" until the maximum number of iterations is reached, and obtain the final iterative result of the frequency domain representation, where the frequency domain representation The final iteration result is .

[0061] The final iterative result represented in the frequency domain is used to form the angular velocity data Corresponding correction results :

[0062] ;

[0063] in, Represents the differential of the Fourier transform point e.

[0064] S130 , constructing an acceleration error estimation model, performing error estimation on the acceleration data in the filtered inertial sensor data, and correcting the acceleration data according to the error estimation result to obtain corrected acceleration data.

[0065] Optionally, an acceleration error estimation model is constructed; the acceleration error estimation model includes an acceleration transformation layer, a gain calculation layer, and an error estimation layer; the acceleration transformation layer is used to perform sequence decomposition and transformation processing on the acceleration data; the gain calculation layer is used to calculate the response gain of the acceleration data transformation result; and the error estimation layer is used to convert the response gain into an error estimation result. The following details the process of using the acceleration error estimation model to perform error estimation on filtered acceleration data and correct the filtered acceleration data.

[0066] First, the acceleration data in the filtered inertial sensor data is extracted. Then, the acceleration data is sequence decomposed and transformed through the acceleration transformation layer to form the acceleration data transformation result, where the filtered inertial sensor data Medium acceleration data The corresponding acceleration data transformation result is :

[0067] ;

[0068] in, represents the imaginary unit, Indicates acceleration data The decomposition transformation result at scale r is, Represents the sth filtered acceleration data, N is a natural number greater than 1, n Indicates the number of acceleration data, which is also the number of moments when acceleration data is collected.

[0069] The response gain of the acceleration data transformation result is calculated by the gain calculation layer, where the acceleration data transformation result The response gain is :

[0070] ;

[0071] in, express The response change value

[0072] The response gain is converted into an error estimation result through the error estimation layer, where the response gain The corresponding error estimation result is :

[0073] ;

[0074] in, represents the error control parameter; in the embodiment of the present invention, Set to 5.

[0075] Finally, the acceleration data is corrected according to the error estimation results, where the acceleration data The correction result is Acceleration data The correction formula is:

[0076] ;

[0077] in, Represents the acceleration data in the filtered inertial sensor data The correction result of Represents the mean value of acceleration data in all filtered inertial sensor data, Indicates the standard deviation of the acceleration data in all filtered inertial sensor data.

[0078] S140 , using the corrected angular velocity data and the corrected acceleration data to calculate the position update result and the attitude update result of the object in the dynamic environment as the inertial navigation precise measurement result of the object.

[0079] The corrected angular velocity data and acceleration data are used to calculate the updated posture result of the object in a dynamic environment. The calculation process of the updated posture result of the object at time n+1 is as follows:

[0080] S41, obtain the corrected angular velocity data from time 1 to time n, and the posture matrix of the object at the initial time ; The posture matrix is the product of the rotation matrices of the object rotating around the X axis, Y axis, and Z axis respectively.

[0081] S42, generating an angular velocity matrix based on the corrected angular velocity data, wherein the corrected angular velocity data The corresponding angular velocity matrix is :

[0082] ;

[0083] in, The corrected angular velocity data are shown in sequence The angular velocity of the object in the X-axis, Y-axis and Z-axis directions.

[0084] S43, iteratively update the posture matrix to obtain the posture matrix of the object at time n+1 :

[0085] ;

[0086] ;

[0087] in, represents the identity matrix; Represents the posture matrix of the object at time n; Indicates the time interval between adjacent moments.

[0088] S44, the posture matrix of the object at time n+1 As the attitude update result of the object at time n+1, the position update result of the object in the dynamic environment is calculated by combining the attitude update result and the corrected acceleration data.

[0089] The following describes in detail the process of calculating the updated position of an object in a dynamic environment by combining the attitude update results and the corrected acceleration data.

[0090] The calculation process of the object's position update result at time n+1 is as follows: Get the corrected acceleration data from time 1 to time n, the object's initial velocity and the initial position of the object In the embodiments of the present application, the speed and position of an object include the speed and position of the object in the X-axis, Y-axis, and Z-axis directions. Using the attitude update result, the corrected acceleration data at the same moment is converted to the navigation coordinate system. The navigation coordinate system refers to the coordinate system of the inertial sensor, with the X-axis representing the front of the sensor, the Y-axis representing the right side of the sensor, and the Z-axis representing the vertical direction of the sensor. The update conversion is performed based on the object's attitude matrix. By adjusting the acceleration and speed of the inertial sensor in the navigation coordinate system, the attitude and position of the object in a dynamic environment are indirectly updated.

[0091] The corrected acceleration data The conversion formula is:

[0092] ;

[0093] in, Represents the gravitational acceleration vector.

[0094] The velocity of the object is updated using the corrected acceleration data after coordinate transformation to obtain the velocity of the object from time 1 to time n+1, where the velocity of the object at time n+1 is :

[0095] ;

[0096] in, represents the differential operator.

[0097] According to the object's speed update result, the object's position update result at time n+1 is calculated :

[0098] ;

[0099] S45. Using the position update result and attitude update result of the object in the dynamic environment as the inertial navigation precise measurement result of the object.

[0100] Example 2

[0101] The present application also provides an electronic device. Figure 2 As shown, this embodiment provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor.

[0102] The memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to perform the above method. The at least one processor in the electronic device is capable of performing the above method, thereby having at least the same advantages as the above method.

[0103] Optionally, the electronic device also includes interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors can be used with multiple memories, and / or multiple buses can be used with multiple memories. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), with each device providing part of the necessary operations. Figure 2 A processor 301 is taken as an example.

[0104] Memory 302, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the inertial navigation precision measurement method in a dynamic environment described in the embodiments of this application. Processor 301 executes the software programs, instructions, and modules stored in memory 302 to execute various functional applications and data processing of the device, thereby implementing the aforementioned inertial navigation precision measurement method in a dynamic environment.

[0105] The memory 302 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data created based on the use of the terminal, etc. Furthermore, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include a memory remotely located relative to the processor 301, and these remote memories may be connected to the device via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0106] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means. Figure 2 The bus connection is taken as an example.

[0107] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), etc. 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 touch screen.

[0108] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0109] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for accurate inertial navigation measurement in a dynamic environment, characterized in that: include: Collecting inertial sensor data, and filtering the inertial sensor data to obtain filtered inertial sensor data, wherein the inertial sensor data includes: angular velocity data collected by a gyroscope and acceleration data collected by an accelerometer; Constructing a gyroscope angular velocity error correction model to correct the angular velocity data in the filtered inertial sensor data to obtain corrected angular velocity data, including: constructing a gyroscope angular velocity error correction model; wherein the gyroscope 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 frequency domain representation on the angular velocity data; the frequency calculation layer is used to calculate the center frequency of the angular velocity data; the iterative correction layer is used to iteratively correct the angular velocity data based on the frequency domain representation result and the center frequency to obtain the corrected angular velocity data; The angular velocity data is iteratively corrected in combination with the frequency domain representation result and the center frequency to obtain the corrected angular velocity data, including: setting the cth iteration result of the frequency domain representation to g′ n，c (e); g′ n，c =(g′ n，c (1),g′ n，c (2),...,g′ n，c (e),...,g′ n，c (n)); Among them, g′ n，c (e) is the frequency domain representation g′ n (e) The cth iteration result, the initial value of c is 0, the maximum value is C, g′ n，0 (e) = g′ n (e), g′ n，0 =g′ n , n is the number of angular velocity data; Iterate the frequency domain representation in the frequency domain representation result, where g′ n，c The iterative formula of (e) is: Among them, g′ n，c+1 (e) represents g′ n，c (e) Iteration result, f′ n is the center frequency; Let c = c + 1, return to the iterative step until the maximum number of iterations is reached, and obtain the final iterative result represented in the frequency domain, where the frequency domain represents g′ n The final iteration result of (e) is The final iterative result represented in the frequency domain is used to form the angular velocity data ω′ n Corresponding correction results Where de represents the differential of the Fourier transform point e; Constructing an acceleration error estimation model, performing error estimation on the acceleration data in the filtered inertial sensor data, and correcting the acceleration data according to the error estimation result to obtain corrected acceleration data; The corrected angular velocity data and the corrected acceleration data are used to calculate the position update result and the attitude update result of the object in a dynamic environment as the inertial navigation precise measurement result of the object.

2. The inertial navigation precise measurement method in a dynamic environment according to claim 1, characterized in that: Collect inertial sensor data, including: An inertial sensor is placed on the surface of an object to collect inertial sensor data of the object during its movement.

3. The inertial navigation precise measurement method in a dynamic environment according to claim 1, characterized in that: Filtering the inertial sensor data to obtain filtered inertial sensor data includes: Construct a sliding window; The inertial sensor data to be filtered is used as the center of the sliding window to form a sliding filter sequence of the inertial sensor data; the sliding filter sequence includes a plurality of sequence values; Calculating the maximum and minimum values of the sliding filter sequence; Calculate the filtering weight of each sequence value in the sliding filtering sequence according to the maximum value and the minimum value; A weighted operation is performed on each sequence value based on the filtering weight, and the weighted result is used as the filtered inertial sensor data.

4. The inertial navigation precise measurement method in a dynamic environment according to claim 1, characterized in that: Construct an acceleration error estimation model, including: 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 acceleration transformation layer is used to perform sequence decomposition and transformation processing on the acceleration data; the gain calculation layer is used to calculate the response gain of the acceleration data transformation result; and the error estimation layer is used to convert the response gain into an error estimation result.

5. The inertial navigation precise measurement method in a dynamic environment according to claim 4, characterized in that: Estimating the error of the acceleration data in the filtered inertial sensor data includes: Extracting acceleration data from filtered inertial sensor data; The acceleration data is sequentially decomposed and transformed by the acceleration transformation layer to form the acceleration data transformation result, wherein the filtered inertial sensor data u′ n Medium acceleration data a′ n The corresponding acceleration data transformation result is A′ n : A′ n ={A′ n (r)|r∈[1,N]} Where j represents the imaginary unit, A′ n (r) represents acceleration data a′ n The decomposition transformation result at scale r, a′ s Represents the sth filtered acceleration data, N is a natural number greater than 1, and n represents the number of acceleration data; The response gain of the acceleration data transformation result is calculated by the gain calculation layer; wherein the acceleration data transformation result A′ n The response gain is h n : in, Represents A′ n (r) the response change value; The response gain is converted into an error estimation result through the error estimation layer; wherein the response gain h n The corresponding error estimation result is H n : Where: τ represents the error control parameter.

6. The inertial navigation precise measurement method in a dynamic environment according to claim 5, characterized in that: Correct the acceleration data based on the error estimation results, including: Filtered acceleration data a′ n The correction formula is: in, Represents the acceleration data a′ in the filtered inertial sensor data n The corrected result, μ a Represents the mean value of acceleration data in all filtered inertial sensor data, σ a Indicates the standard deviation of the acceleration data in all filtered inertial sensor data.

7. An electronic device, characterized in that: The electronic device includes: at least one processor, and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the inertial navigation precision measurement method in a dynamic environment according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer executable program, which is called by a processor to execute the steps of the inertial navigation precision measurement method in a dynamic environment according to any one of claims 1 to 6.

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