CN114689901B - Accelerometer field calibration method and device - Google Patents
Accelerometer field calibration method and device Download PDFInfo
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- CN114689901B CN114689901B CN202210100948.9A CN202210100948A CN114689901B CN 114689901 B CN114689901 B CN 114689901B CN 202210100948 A CN202210100948 A CN 202210100948A CN 114689901 B CN114689901 B CN 114689901B
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Abstract
The application provides an accelerometer field calibration method and device, wherein the method comprises the following steps: detecting whether the unmanned aerial vehicle is in a static state after rotation currently according to a preset multi-criterion zero-speed interval detection mode, and if so, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle; based on the initial values of the error parameter sets, calibrating each error of the inertial sensor corresponding to the error parameter sets, and automatically judging whether the corresponding calibration result is in a finished state. The application can effectively improve the efficiency and the accuracy of the zero-speed interval detection of the accelerometer, further can effectively improve the efficiency and the accuracy of the on-site calibration error parameters of the accelerometer, and can effectively improve the automation degree and the reliability of the on-site calibration error parameters of the accelerometer.
Description
Technical Field
The application relates to the technical field of MEMS acceleration calibration, in particular to an accelerometer field calibration method and device.
Background
With the development of Micro-Electro-Mechanical Systems (MEMS), the low-cost MEMS inertial sensor IMU has advantages of small volume, low power consumption, light weight, etc., so that it is gradually developed in the positioning technology. The main errors of the inertial sensor IMU with low cost comprise systematic errors and random errors, wherein the systematic errors mainly comprise installation errors, scaling factors, zero offset errors and the like, so that how to accurately and quickly mark various error parameters of the inertial sensor IMU becomes an important research direction.
The on-site calibration method of the accelerometer needs to collect static acceleration values under a plurality of different static postures, and the collection mode determines the calibration time of the accelerometer. The current acceleration data acquisition mode can be divided into two methods based on manual detection and zero-speed interval detection. The method has the problems of complicated operation, long acquisition time and the like based on a manual mode, and cannot be applied to accelerometer calibration of multiple unmanned aerial vehicles; based on the zero-speed interval detection method, dynamic and static interval data can be automatically distinguished, and the method has the advantage of high efficiency. However, the existing zero-speed interval detection methods have the problems of poor accuracy, low efficiency and the like, so that the existing methods for calibrating error parameters of the inertial sensor IMU on site by using the accelerometer also have the problems of poor accuracy, low efficiency and the like.
Disclosure of Invention
In view of this, embodiments of the present application provide methods and apparatus for accelerometer field calibration that obviate or ameliorate one or more of the disadvantages of the prior art.
One aspect of the application provides an accelerometer field calibration method, comprising:
detecting whether the unmanned aerial vehicle is in a static state after rotation currently according to a preset multi-criterion zero-speed interval detection mode, and if so, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle;
And calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial value of the error parameter set, and automatically judging whether the corresponding calibration result is in a finished state.
In some embodiments of the present application, the detecting, according to a preset multi-criterion zero-speed interval detection manner, whether the unmanned aerial vehicle is currently in a static state after rotation, if so, obtaining a parameter initial value of an error parameter set to be calibrated currently by an accelerometer of the unmanned aerial vehicle includes:
acquiring a current zero-speed interval detection result of the unmanned aerial vehicle according to the current acceleration differential module value, the angular velocity differential module value and the acceleration variance of the unmanned aerial vehicle;
screening burr data in the zero-speed interval detection result based on a finite state machine to obtain a corresponding target detection result;
and if the target detection result shows that the unmanned aerial vehicle is in a static state after rotation, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle.
In some embodiments of the present application, the obtaining the current zero-speed interval detection result of the unmanned aerial vehicle according to the current acceleration differential mode value, the angular velocity differential mode value and the acceleration variance of the unmanned aerial vehicle includes:
Acquiring a current acceleration differential module value and an angular velocity differential module value of the unmanned aerial vehicle;
judging whether the acceleration differential module value and the angular velocity differential module value are smaller than respective corresponding threshold values, if yes, acquiring the current acceleration variance of the unmanned aerial vehicle;
and judging whether the acceleration variance is smaller than a corresponding threshold value, and if so, acquiring a current zero-speed interval detection result of the unmanned aerial vehicle.
In some embodiments of the present application, the error parameter set includes: and the unmanned aerial vehicle comprises a non-orthogonal axis error rotation angle, zero point offset and scale parameters of an inertial sensor.
In some embodiments of the present application, before detecting whether the unmanned aerial vehicle is currently in a static state after rotation according to the preset multi-criterion zero-speed interval detection mode, if so, acquiring a parameter initial value of an error parameter set to be calibrated currently by an accelerometer of the unmanned aerial vehicle, further includes:
determining the local gravity acceleration of the unmanned aerial vehicle according to the local latitude value and the local altitude value of the unmanned aerial vehicle;
establishing an error model of an accelerometer of the unmanned aerial vehicle;
correspondingly, the calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial parameter value of the error parameter set includes:
Setting an initial value of a non-orthogonal axis error rotation angle of an inertial sensor of the unmanned aerial vehicle to 0;
simplifying the error model of the accelerometer, and generating an initial value of zero offset of the inertial sensor based on some simplifying results;
generating an initial value of a scale parameter of the inertial sensor according to the local gravitational acceleration;
generating a parameter initial value of an error parameter set based on the initial value of the non-orthogonal axis error rotation angle, the initial value of the zero point offset and the initial value of the scale parameter;
and calibrating the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter respectively by adopting the parameter initial value of the error parameter set.
In some embodiments of the present application, the calibrating the non-orthogonal axis error rotation angle, the zero offset, and the scale parameter with the parameter initial values of the error parameter set includes:
acquiring a nonlinear least square regression fit optimization function of the error parameter set;
optimizing the nonlinear least square regression fit optimization function based on a reliability domain Dogleg algorithm, and iterating the optimized nonlinear least square regression fit optimization function based on the initial parameter value of the error parameter set to obtain the calibration results corresponding to the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter.
In some embodiments of the present application, the automatically determining whether the corresponding calibration result is in a completed state includes:
based on a preset calibration precision factor, whether the calibration result corresponding to each error of the inertial sensor is in a finished state or not is automatically judged.
Another aspect of the application provides an accelerometer field calibration device comprising:
the zero-speed interval detection module is used for detecting whether the unmanned aerial vehicle is in a static state after rotation at present according to a preset multi-criterion zero-speed interval detection mode, and if so, acquiring a parameter initial value of an error parameter set to be calibrated at present of an accelerometer of the unmanned aerial vehicle;
and the error calibration and result judgment module is used for respectively calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial parameter value of the error parameter set and automatically judging whether the corresponding calibration result is in a finished state.
Another aspect of the application provides an electronic device comprising a memory, a computer program comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the accelerometer field calibration method when executing the computer program.
Another aspect of the application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the accelerometer field calibration method.
According to the method for calibrating the accelerometer on site, whether the unmanned aerial vehicle is in a static state after rotation at present is detected according to a preset multi-criterion zero-speed interval detection mode, so that accurate and quick judgment of the zero-speed interval detection of the accelerometer can be realized, the efficiency and the accuracy of the zero-speed interval detection of the accelerometer can be effectively improved, and further, the efficiency and the accuracy of the accelerometer on site calibration error parameters can be effectively improved; and because the modes of machine learning and the like are not needed, complex operation is not needed, and a large amount of data training is not needed, the efficiency of the on-site calibration error parameters of the accelerometer can be further improved; by automatically judging whether the error calibration result of the inertial sensor is in a completed state or not, the problems of low calibration efficiency and long calibration time caused by manual judgment of the calibration completion state can be effectively solved, so that the efficiency of the on-site calibration error parameters of the accelerometer can be further improved, and the automation degree and the reliability of the on-site calibration error parameters of the accelerometer can be effectively improved.
Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the above-described specific ones, and that the above and other objects that can be achieved with the present application will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and together with the description serve to explain the application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. Corresponding parts in the drawings may be exaggerated, i.e. made larger relative to other parts in an exemplary device actually manufactured according to the present application, for convenience in showing and describing some parts of the present application. In the drawings:
FIG. 1 is a general flow chart of an accelerometer field calibration method according to an embodiment of the application.
FIG. 2 is a flow chart of an accelerometer field calibration method according to an embodiment of the application.
FIG. 3 is a schematic view of an accelerometer field calibration apparatus according to another embodiment of the application.
Fig. 4 is a schematic diagram of an overall calibration flow of an accelerometer field calibration method based on a finite state machine and a precision factor according to an application example of the present application.
Fig. 5 is a schematic flowchart of an algorithm for zero-speed interval detection based on multiple criteria provided by an application example of the present application.
Fig. 6 is a schematic diagram of a burr phenomenon in multi-criterion zero-speed interval detection provided by the application example of the present application.
Fig. 7 is a schematic diagram of a state transition of zero-speed interval detection of a finite state machine based on multiple criteria provided by an application example of the present application.
Detailed Description
The present application will be described in further detail with reference to the following embodiments and the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent. The exemplary embodiments of the present application and the descriptions thereof are used herein to explain the present application, but are not intended to limit the application.
It should be noted here that, in order to avoid obscuring the present application due to unnecessary details, only structures and/or processing steps closely related to the solution according to the present application are shown in the drawings, while other details not greatly related to the present application are omitted.
It should be emphasized that the term "comprises/comprising" when used herein is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
It is also noted herein that the term "coupled" may refer to not only a direct connection, but also an indirect connection in which an intermediate is present, unless otherwise specified.
Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
One of the field calibration modes of the accelerometer can consider that a least square fitting method is adopted, and various error coefficients of the inertial sensor IMU are obtained by solving the inertial data output by the system through multiple overturning and applying the least square fitting method. However, the inertial data acquisition time is long, the time consumption is high, the operation is complex, errors are easily caused by human factors, and the calibration precision is influenced.
The second field calibration mode of the accelerometer can be considered to adopt a deep learning method, measurement output information of the MEMS accelerometer is taken as input, error compensation is carried out by using a deep learning algorithm, and key error parameters of MEMS inertial navigation can be predicted. However, the method of deep learning has large calculated amount, complex network, large requirement on data amount, and the quality of the deep learning network can influence the calibration result.
That is, the existing error detection methods of the inertial sensor IMU have the problems of poor accuracy, low efficiency and the like.
Based on the problems of low precision of calibration parameters, complex detection process, low detection efficiency and the like caused by the fact that the existing accelerometer field calibration method is poor in detection precision, the accelerometer field calibration device used for executing the accelerometer field calibration method, electronic equipment serving as an entity of the accelerometer field calibration device and a storage medium are respectively provided, wherein the accelerometer field calibration method detects whether an unmanned aerial vehicle is in a static state after rotation or not according to a preset multi-criterion zero-speed interval detection mode, accurate and rapid judgment of zero-speed interval detection of an accelerometer can be achieved, efficiency and precision of zero-speed interval detection of the accelerometer can be effectively improved, and efficiency and precision of accelerometer field calibration error parameters can be effectively improved; and because the modes of machine learning and the like are not needed, complex operation is not needed, and a large amount of data training is not needed, the efficiency of the on-site calibration error parameters of the accelerometer can be further improved; by automatically judging whether the error calibration result of the inertial sensor is in a completed state or not, the problems of low calibration efficiency and long calibration time caused by manual judgment of the calibration completion state can be effectively solved, so that the efficiency of the on-site calibration error parameters of the accelerometer can be further improved, and the automation degree and the reliability of the on-site calibration error parameters of the accelerometer can be effectively improved.
In one or more embodiments of the application, an IMU refers to an inertial measurement unit or inertial sensor.
Based on the above, the present application further provides an accelerometer field calibration device for implementing the accelerometer field calibration method provided in one or more embodiments of the present application, where the accelerometer field calibration device may be a server, and the accelerometer field calibration device may be connected to the unmanned aerial vehicle and the control station by itself or through a third party server or the like in a communication manner, so as to obtain various sensing data corresponding to the unmanned aerial vehicle, execute the accelerometer field calibration method mentioned in the embodiments of the present application according to these data, and after obtaining a final calibration result, send the calibration result to a control station or a client device held by an operation and maintenance person for displaying, so that the operation and maintenance person can learn and analyze the calibration result in time.
In addition, the part of the accelerometer field calibration device for performing accelerometer field calibration can be executed in the server as described above, and in another practical application case, all operations can be completed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor for specific processing of accelerometer field calibration.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Any suitable network protocol may be used between the server and the client device, including those not yet developed on the filing date of the present application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol may also include, for example, RPC protocol (Remote Procedure Call Protocol ), REST protocol (Representational State Transfer, representational state transfer protocol), etc. used above the above-described protocol.
The following embodiments and application examples are described in detail.
In order to solve the problems of poor accuracy, low efficiency and the like existing in the existing zero-speed interval detection mode, and therefore the problems of poor accuracy, low efficiency and the like existing in the existing accelerometer field calibration inertial sensor IMU mode, the application provides an embodiment of an accelerometer field calibration method, referring to fig. 1, the accelerometer field calibration method executed based on the accelerometer field calibration device specifically comprises the following contents:
step 100: and detecting whether the unmanned aerial vehicle is in a static state after rotation currently according to a preset multi-criterion zero-speed interval detection mode, and if so, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle.
In step 100, if it is detected that the unmanned aerial vehicle is not in the post-rotation stationary state at present according to the preset multi-criterion zero-speed interval detection mode, the subsequent execution is not performed, and step 100 may be re-executed again after a preset time interval until it is determined that the unmanned aerial vehicle is in the post-rotation stationary state at present.
It will be appreciated that the reference to the post-rotation rest state in step 100 refers to the transition of the drone from the rotated state to the rest state, for distinguishing between initial rest states in which the drone is not operating.
In addition, the multi-criterion zero-speed interval detection mode refers to a mode of adopting various detection standards or criteria to detect whether the unmanned aerial vehicle is currently in a zero-speed interval (namely, a static state after rotation).
In one or more embodiments of the present application, the error parameter set includes a plurality of error parameters of the inertial sensor, and it is understood that the initial parameter value of the error parameter set refers to an initial value corresponding to each error parameter in the error parameter set.
Step 200: and calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial value of the error parameter set, and automatically judging whether the corresponding calibration result is in a finished state.
As can be seen from the above description, according to the accelerometer field calibration method provided by the embodiment of the present application, by detecting whether the unmanned aerial vehicle is currently in a static state after rotation according to a preset multi-criterion zero-speed interval detection mode, accurate and rapid discrimination of the accelerometer zero-speed interval detection can be realized, the efficiency and accuracy of the accelerometer zero-speed interval detection can be effectively improved, and further the efficiency and accuracy of the accelerometer field calibration error parameters can be effectively improved; and because the modes of machine learning and the like are not needed, complex operation is not needed, and a large amount of data training is not needed, the efficiency of the on-site calibration error parameters of the accelerometer can be further improved; by automatically judging whether the error calibration result of the inertial sensor is in a completed state or not, the problems of low calibration efficiency and long calibration time caused by manual judgment of the calibration completion state can be effectively solved, so that the efficiency of the on-site calibration error parameters of the accelerometer can be further improved, and the automation degree and the reliability of the on-site calibration error parameters of the accelerometer can be effectively improved.
In order to further effectively improve the efficiency and accuracy of the zero-speed interval detection of the accelerometer, in the embodiment of the field calibration method of the accelerometer provided by the application, referring to fig. 2, step 100 of the field calibration method of the accelerometer specifically includes the following contents:
step 110: and acquiring a current zero-speed interval detection result of the unmanned aerial vehicle according to the current acceleration differential module value, the angular velocity differential module value and the acceleration variance of the unmanned aerial vehicle.
Step 120: and screening burr data in the zero-speed interval detection result based on a finite state machine to obtain a corresponding target detection result.
It can be understood that in the actual situation, the accelerometer and the gyroscope have more intense data jump in the switching process of the motion state and the static state, so that the problem of misjudgment exists in the static interval, and the phenomenon of partial burrs exists in the zero-speed interval judgment of multiple criteria. Therefore, the zero-speed interval detection state machine improves the accuracy of zero-speed interval detection by removing the data (namely the burr data) with the time length of the zero-speed interval being less than 1.5 seconds.
Step 130: and if the target detection result shows that the unmanned aerial vehicle is in a static state after rotation, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle.
As can be seen from the above description, according to the accelerometer field calibration method provided by the embodiment of the present application, the current zero speed interval detection result of the unmanned aerial vehicle is obtained according to the current acceleration differential module value, the angular velocity differential module value and the acceleration variance of the unmanned aerial vehicle, so that accurate and rapid discrimination of the zero speed interval detection of the accelerometer can be further realized, the efficiency and accuracy of the zero speed interval detection of the accelerometer can be further effectively improved, and further the efficiency and accuracy of the accelerometer field calibration error parameters can be effectively improved; and data screening is carried out on the zero-speed interval detection result based on a finite state machine so as to obtain a corresponding target detection result, so that burr data (for example, data with the time length of less than 1.5 seconds) in the zero-speed interval can be effectively removed, and the accuracy of zero-speed interval detection is further improved.
In order to further reduce the computational complexity of the zero-speed interval detection process and improve the detection efficiency, in the embodiment of the accelerometer field calibration method provided by the application, step 110 of the accelerometer field calibration method specifically includes the following contents:
step 111: and acquiring the current acceleration differential module value and angular velocity differential module value of the unmanned aerial vehicle.
Step 112: and judging whether the acceleration differential mode value and the angular velocity differential mode value are smaller than the respective corresponding threshold values, if yes, executing step 113.
Step 113: and acquiring the current acceleration variance of the unmanned aerial vehicle.
Step 114: and judging whether the acceleration variance is smaller than a corresponding threshold value, if so, executing step 115.
Step 115: and acquiring a current zero-speed interval detection result of the unmanned aerial vehicle.
Specifically, aiming at the problems of poor zero-speed interval detection precision, complex calculation and the like in the traditional field calibration method, the method for detecting the zero-speed interval of the finite state machine based on multiple criteria is provided, and whether the finite state machine is in a zero-speed state or not is judged based on 3 judgment criteria of differential modulus values and acceleration variances of acceleration and angular velocity.
In order to reduce unnecessary computation, when the differential modulus value is judged to be in a non-stationary state, no further computation of variance is required; and when the differential modulus value is judged to be in a static state, further determining whether the differential modulus value is in the static state according to the variance dimension.
As can be seen from the above description, in the accelerometer field calibration method provided by the embodiment of the present application, by first determining whether the differential acceleration module value and the differential angular velocity module value are both smaller than the respective corresponding threshold values, if so, the current acceleration variance of the unmanned aerial vehicle is obtained, so that unnecessary computation can be reduced, and if the differential acceleration module value is determined to be in a non-stationary state, no further computation of variance is required; when the differential modulus value is judged to be in a static state, whether the differential modulus value is in the static state is further determined according to the variance dimension, so that the calculation complexity of the zero-speed interval detection process can be further reduced, and the detection efficiency is improved.
In order to further improve the comprehensiveness and effectiveness of calibration errors, in the embodiment of the on-site calibration method for the accelerometer provided by the application, the error parameter set in the on-site calibration method for the accelerometer comprises: and the unmanned aerial vehicle comprises a non-orthogonal axis error rotation angle, zero point offset and scale parameters of an inertial sensor.
From the above description, the method for calibrating the accelerometer on site according to the embodiment of the present application includes: the non-orthogonal axis error rotation angle, zero point offset and scale parameters of the inertial sensor of the unmanned aerial vehicle can further improve the comprehensiveness and effectiveness of calibration errors, and further can effectively improve the MEMS positioning accuracy.
In order to improve the efficiency and reliability of calibrating each error of the inertial sensor corresponding to the error parameter set, in an embodiment of the accelerometer field calibration method provided by the present application, referring to fig. 2, before step 110 of the accelerometer field calibration method, the method specifically further includes the following contents:
step 010: and determining the local gravity acceleration of the unmanned aerial vehicle according to the local latitude value and the local altitude value of the unmanned aerial vehicle.
Step 020: and establishing an error model of the accelerometer of the unmanned aerial vehicle.
Correspondingly, referring to fig. 2, the step 200 of the accelerometer field calibration method specifically includes the following:
step 210: and setting the initial value of the non-orthogonal axis error rotation angle of the inertial sensor of the unmanned aerial vehicle to 0.
Step 220: and simplifying the error model of the accelerometer, and generating an initial value of zero point offset of the inertial sensor based on the result of simplifying the error model.
Step 230: and generating an initial value of the scale parameter of the inertial sensor according to the local gravity acceleration.
Step 240: and generating a parameter initial value of an error parameter set based on the initial value of the non-orthogonal axis error rotation angle, the initial value of the zero point offset and the initial value of the scale parameter.
Step 250: and calibrating the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter respectively by adopting the parameter initial value of the error parameter set.
As can be seen from the above description, in the accelerometer field calibration method provided by the embodiment of the present application, by acquiring a gravitational acceleration and an error model in advance, simplifying the error model of the accelerometer, and generating an initial value of zero offset of the inertial sensor based on some simplified processing results; and generating an initial value of the scale parameter of the inertial sensor according to the local gravity acceleration, so that the efficiency and the reliability of calibrating each error of the inertial sensor corresponding to the error parameter set can be effectively improved, and the efficiency and the reliability of on-site calibration of the accelerometer are further improved.
In order to solve the problem that the Hessian matrix (Hessian) is not positive and irreversible in the calculation step length process of the gauss newton in the calculation process, in the embodiment of the accelerometer field calibration method provided by the application, step 250 of the accelerometer field calibration method specifically comprises the following contents:
step 251: and obtaining a nonlinear least square regression fit optimization function of the error parameter set.
Step 252: optimizing the nonlinear least square regression fit optimization function based on a reliability domain Dogleg algorithm, and iterating the optimized nonlinear least square regression fit optimization function based on the initial parameter value of the error parameter set to obtain the calibration results corresponding to the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter.
It will be appreciated that the trusted domain Dogleg algorithm may specifically refer to a Dogleg algorithm in the trusted domain algorithm, and the trusted domain algorithm (Trust-region methods) is also called TR method, which is an optimization method, and can ensure that the optimization method converges as a whole.
From the above description, it can be known that in the accelerometer field calibration method provided by the embodiment of the application, the nonlinear least squares regression fit optimization function is optimized based on the reliability domain dog algorithm, and the optimized nonlinear least squares regression fit optimization function is iterated based on the parameter initial value of the error parameter set, so that the problem that the Hessian matrix (Hessian) is not positively and irreversibly determined in the Gaussian Newton calculation step length process in the calculation process can be effectively solved, and the stability of the angular velocity meter calibration parameter iterative search process is ensured.
In order to improve the efficiency and reliability of calibrating each error of the inertial sensor corresponding to the error parameter set, in an embodiment of the accelerometer field calibration method provided by the present application, referring to fig. 2, after 250 in step 200 of the accelerometer field calibration method, the method specifically further includes the following contents:
step 260: based on a preset calibration precision factor, whether the calibration result corresponding to each error of the inertial sensor is in a finished state or not is automatically judged.
As can be seen from the above description, according to the on-site calibration method for the accelerometer provided by the embodiment of the application, by automatically determining whether the calibration result corresponding to each error of the inertial sensor is in a completed state based on the preset calibration precision factor, compared with the calibration precision evaluation mode directly adopting the residual square sum, the provided calibration state discrimination calibration based on the calibration precision factor is irrelevant to the zero speed interval size M, and can meet the calibration precision evaluation of the zero speed intervals with different sizes, thereby having more practical value.
For the embodiment of the accelerometer field calibration method, the application also provides an accelerometer field calibration device for realizing the accelerometer field calibration method, referring to fig. 3, the accelerometer field calibration device specifically comprises the following contents:
The zero-speed interval detection module 10 is configured to detect whether the unmanned aerial vehicle is currently in a static state after rotation according to a preset multi-criterion zero-speed interval detection mode, and if so, acquire a parameter initial value of an error parameter set to be calibrated currently by an accelerometer of the unmanned aerial vehicle;
and the error calibration and result judgment module 20 is used for respectively calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial value of the parameter of the error parameter set, and automatically judging whether the corresponding calibration result is in a finished state.
As can be seen from the above description, the accelerometer field calibration device provided by the embodiment of the present application detects whether the unmanned aerial vehicle is currently in a static state after rotation according to the preset multi-criterion zero-speed interval detection mode, so that accurate and rapid discrimination of the zero-speed interval detection of the accelerometer can be realized, the efficiency and accuracy of the zero-speed interval detection of the accelerometer can be effectively improved, and further the efficiency and accuracy of the accelerometer field calibration error parameters can be effectively improved; and because the modes of machine learning and the like are not needed, complex operation is not needed, and a large amount of data training is not needed, the efficiency of the on-site calibration error parameters of the accelerometer can be further improved; by automatically judging whether the error calibration result of the inertial sensor is in a completed state or not, the problems of low calibration efficiency and long calibration time caused by manual judgment of the calibration completion state can be effectively solved, so that the efficiency of the on-site calibration error parameters of the accelerometer can be further improved, and the automation degree and the reliability of the on-site calibration error parameters of the accelerometer can be effectively improved.
In addition, in order to further explain the accelerometer field calibration method of the present application, the present application further provides a specific application example of the accelerometer field calibration method for further explanation, specifically an accelerometer field calibration method based on a finite state machine and a precision factor, which is specifically described as follows:
aiming at the problems of low precision of calibration parameters, complex detection process or low detection efficiency caused by long time consumption and the like caused by the poor detection precision of the existing accelerometer field calibration method, the application provides a zero-speed interval detection method based on multiple criteria, which realizes accurate and rapid judgment of the zero-speed interval of an accelerometer; aiming at the problems of low calibration efficiency and long calibration time caused by manual judgment of the calibration completion state in the existing field calibration method, a calibration precision factor is provided, and whether the calibration state is completed or not is automatically judged.
Referring to fig. 4, the overall calibration flow of the accelerometer field calibration method based on the finite state machine and the precision factor is as follows:
local normal gravity acceleration g l And accelerometer error model
Local gravity acceleration g l The global level surface is typically used to determine an approximation of the normal gravitational field equation. The local normal gravitational field formula is:
g l ＝g e [1-0.0053sin 2 φ+3.0159×10 -6 sin 2 (2φ)]-3.0828×10 -6 h (1)
Wherein g e The gravity acceleration value is the equator, phi is the local latitude value, and h is the local altitude value.
The error model of a low cost accelerometer can be expressed as:
a I ＝T a SM a (a O +b a +v a ) (2)
wherein a is I Acceleration in an orthogonal coordinate system IC, a O For acceleration in the actual accelerometer coordinate system AC,for accelerometer measurement noise, it is generally assumed that 0-mean gaussian distribution is obeyed, SM a Is a scale matrix, b a Is a zero offset vector. Drift b a Typically slowly varying over time, typically modeled as a random walk, during on-site calibration, b can be considered due to the short calibration time a Is a constant value.
(II) finite state machine zero-speed interval detection method based on multiple criteria
Aiming at the problems of poor zero-speed interval detection precision, complex calculation and the like in the traditional field calibration method, the method for detecting the zero-speed interval of the finite state machine based on multiple criteria is provided, and whether the finite state machine is in a zero-speed state or not is judged based on 3 judgment criteria of differential modulus values and acceleration variances of acceleration and angular velocity.
In order to reduce unnecessary computation, when the differential modulus value is judged to be in a non-stationary state, no further computation of variance is required; when the differential modulus value is judged to be in a static state, whether the differential modulus value is in the static state is further determined according to the variance dimension, and in summary, the algorithm flow chart of the zero-speed interval detection based on the multiple criteria is shown in fig. 5.
Taking an accelerometer as an example, analyzing the effectiveness of the differential-based unmanned aerial vehicle motion state judgment, and firstly, carrying out difference on two adjacent sampling moments of a formula (2) to obtain:
equation left side in stationary state of unmanned aerial vehicleShould be a 0 vector because of the matrix SM a T a A non-zero matrix and therefore can be obtained:
in the stationary state of the unmanned aerial vehicle, the above equation satisfies:
wherein th a And judging a threshold value for the static state of the unmanned aerial vehicle, and selecting random walk and Gaussian white noise and the like associated with the accelerometer.
The 3 decision criterion detection methods are as follows:
(1) Differential mode value of accelerometer
Under the static state of the unmanned aerial vehicle, the differential mode value of the output value of the accelerometer is smaller than a set threshold value th a The differential output value module value of the accelerometer at the time t can be obtainedThe method comprises the following steps:
the criteria for determining whether the drone is stationary may be expressed as:
(2) Accelerometer variance
Under the static condition of the unmanned aerial vehicle, the accelerometer outputs a valueThe variance of (2) should be less than the set thresholdBy approximating the accelerometer variance by the accelerometer's sample variance, one can obtain:
where w is the window size and is the window size,as an intra-window average, defined as:
the criteria for determining whether the drone is stationary may be expressed as:
(3) Differential mode value of gyroscope
Under the static condition of the unmanned aerial vehicle, the differential mode value of the output value of the gyroscope is smaller than the set threshold value th η The differential output value module value of the accelerometer at the time t can be obtainedThe method comprises the following steps:
the criteria for determining whether the drone is stationary may be expressed as:
wherein, the judgment criterion of the differential modulus in FIG. 5 is thatAnd->Logic and of (c). In practical situations, because the accelerometer and the gyroscope have intense data jump in the switching process of the motion state and the static state, the problem of misjudgment exists in the static region, and referring to fig. 6, the phenomenon of partial burrs exists in the zero-speed region judgment of multiple criteria. Therefore, a zero-speed interval detection state machine is provided, the state transition is shown in fig. 7, and the accuracy of zero-speed interval detection is improved by removing the data with the time length of the zero-speed interval being less than 1.5 seconds.
(III) zero-point offset and initial value of scale parameter
For the dependence area Dogleg algorithm parameter estimation precision and the error parameter initial value setting correlation, the non-orthogonal axis error of the accelerometer is usually smaller, and the non-orthogonal rotation angle is near 0, so the initial value of the non-orthogonal axis error rotation angle can be selected to be 0, namely alpha yz ＝α zy ＝α zx =0. Therefore, in the initial value determination process, the accelerometer error model of equation (2) can be simplified to be:
When the accelerometer is vertically upwards consistent with the gravity acceleration direction, the accelerometer is vertically upwards consistent with the gravity acceleration direction due to the scale parameterAnd->If the zero point offset is not 0, the zero point offset initial value is:
the scale parameter is then determined from the local gravitational accelerationAnd->
Acceleration field calibration method based on trust domain Dogleg
Accelerometer calibration requires the estimation of zero offset, scale and non-orthogonal axis bias, 9 parameters:
a nonlinear least squares regression fits an optimization function as shown in equation (17):
in the formula of I, I 2 Representing the euclidean norm, M being the number of samples in the zero speed interval,represents the accelerometer sampling value at time k, g l Is the local gravitational acceleration.
Aiming at the problem that the Hessian matrix (Hessian) is not positively fixed and irreversible in the Gaussian Newton step length calculation process in the calculation process, an improved reliability domain Dogleg algorithm is provided based on a singular value decomposition method (Singular Value Decomposition, SVD), and the stability of the angular velocity meter calibration parameter iterative search process is ensured.
According to the trusted domain Dogleg algorithm, a function F (e a ) The method comprises the following steps:
F(e a )＝[f 1 (e a ) f 2 (e a ) …f M (e a )] (18)
wherein the function f k (e a ) The definition is as follows:
the optimization problem corresponding to equation (17) is equivalent to:
the iterative process of the trusted domain Dogleg algorithm is shown in table 1.
TABLE 1
/>
Wherein the iteration termination condition may select an infinitesimal threshold th 1 ，th 2 ，th 3 For example 10 -11 The threshold is chosen independent of the final iteration accuracy.
(V) calibration state discrimination based on calibration precision factors
The conventional method usually adopts a determination method based on residual square sum, but the size of the residual square sum is related to the sampling number M of the zero-speed interval, and the calibration error cannot be correctly represented. In order to evaluate the parameter calibration capability of the accelerometer, the automatic judgment of the calibration state is realized. Firstly, the statistical characteristics of the residual errors in the formula (17) are analyzed, the expectation of the residual errors is deduced, the relation between the expectation and M is proved, a calibration precision factor is provided, and the automatic judgment of the calibration state is realized.
Based on the error model of the accelerometer, assume thatGaussian distribution satisfying independent zero mean homodyne, corrected +.>Should satisfy a Gaussian distribution, i.e. +.>Wherein->The definition is as follows: />
Typically, the dimensionsVery close to 1, for this->Can be approximately equivalent to->Since the means of the corrected XYZ axes are not uniform and the true projection of the gravitational acceleration on the XYZ axes cannot be determined during the calibration process, therefore +.>There is no probability density function in analytical form. The mean value of the residual square sum is analyzed, and based on the relation between the mean value and M, a calibration precision factor of the size of an irrelevant zero-speed interval is provided for calibration state judgment.
Defining a calibration precision factor as r:
the calibration precision factor is irrelevant to the size M of the zero-speed interval, can meet the calibration precision evaluation of the zero-speed intervals with different sizes, and has more practical value compared with the method of directly adopting the residual square sum. The smaller the calibration precision factor, the higher the calibration precision. When the decrease of the calibration precision factor is gentle, the calibration can be considered to be completed.
In summary, the application example of the application can accurately judge the zero-speed space, reduce the burr effect, and particularly can more accurately judge the zero-speed space by the finite state machine zero-speed interval detection method based on multiple criteria; compared with a calibration precision evaluation mode directly adopting residual square sum, the calibration state judgment calibration based on the calibration precision factor is irrelevant to the size M of the zero-speed interval, can meet the calibration precision evaluation of the zero-speed interval with different sizes, has higher practical value, and is a more effective and practical calibration state judgment method of the calibration precision factor.
The embodiment of the application also provides a computer device (i.e. electronic device), which may include a processor, a memory, a receiver and a transmitter, where the processor is configured to perform the accelerometer field calibration method mentioned in the foregoing embodiment, and the processor and the memory may be connected by a bus or other manners, for example, by a bus connection. The receiver may be connected to the processor, memory, by wire or wirelessly. The computer equipment is in communication connection with the accelerometer field calibration device so as to receive real-time motion data from a sensor in the wireless multimedia sensor network and receive an original video sequence from the video acquisition device.
The processor may be a central processing unit (Central Processing Unit, CPU). The processor may also be any other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof.
The memory is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions/modules corresponding to the accelerometer field calibration method in the embodiment of the application. The processor executes the non-transient software programs, instructions and modules stored in the memory to perform various functional applications and data processing of the processor, i.e., to implement the accelerometer field calibration method in the above method embodiments.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory that, when executed by the processor, perform the accelerometer field calibration method of the embodiments.
In some embodiments of the present application, a user equipment may include a processor, a memory, and a transceiver unit, which may include a receiver and a transmitter, the processor, the memory, the receiver, and the transmitter may be connected by a bus system, the memory being configured to store computer instructions, the processor being configured to execute the computer instructions stored in the memory to control the transceiver unit to transmit and receive signals.
As an implementation manner, the functions of the receiver and the transmitter in the present application may be considered to be implemented by a transceiver circuit or a dedicated chip for transceiver, and the processor may be considered to be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.
As another implementation manner, a manner of using a general-purpose computer may be considered to implement the server provided by the embodiment of the present application. I.e. program code for implementing the functions of the processor, the receiver and the transmitter are stored in the memory, and the general purpose processor implements the functions of the processor, the receiver and the transmitter by executing the code in the memory.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, is used for realizing the steps of the accelerometer field calibration method. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disk, a removable memory disk, a CD-ROM, or any other form of storage medium known in the art.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or a combination of both. The particular implementation is hardware or software dependent on the specific application of the solution and the design constraints. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
In this disclosure, features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, and various modifications and variations can be made to the embodiments of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (5)
1. An accelerometer field calibration method, comprising:
determining the local gravity acceleration of the unmanned aerial vehicle according to the local latitude value and the local altitude value of the unmanned aerial vehicle;
establishing an error model of an accelerometer of the unmanned aerial vehicle; the error model of the accelerometer is shown in the following formula:
a I ＝T a SM a (a O +b a +v a )
wherein a is I Acceleration in an orthogonal coordinate system IC, a O For acceleration in the actual accelerometer coordinate system AC,for accelerometer measurement of noise, assume compliance with a 0-mean gaussian distribution, SM a For the scale matrix>Andis a scale parameter; b a Is a zero offset vector; />And->Is a zero offset parameter; alpha yz ,α zy ,α zx Error rotation angle for non-orthogonal axis;
detecting whether the unmanned aerial vehicle is in a static state after rotation currently according to a preset multi-criterion zero-speed interval detection mode, and if so, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle; the error parameter set includes: the non-orthogonal axis error rotation angle, zero point offset and scale parameter of the inertial sensor of the unmanned aerial vehicle;
based on the initial values of the error parameter sets, calibrating each error of the inertial sensor corresponding to the error parameter sets, and automatically judging whether the corresponding calibration result is in a finished state or not;
The method for detecting the error parameter set of the unmanned aerial vehicle comprises the steps of detecting whether the unmanned aerial vehicle is in a static state after rotation at present according to a preset multi-criterion zero-speed interval detection mode, if so, acquiring a parameter initial value of the error parameter set to be calibrated at present of an accelerometer of the unmanned aerial vehicle, and comprising the following steps: acquiring a current acceleration differential module value and an angular velocity differential module value of the unmanned aerial vehicle;
judging whether the acceleration differential module value and the angular velocity differential module value are smaller than respective corresponding threshold values, if yes, acquiring the current acceleration variance of the unmanned aerial vehicle;
judging whether the acceleration variance is smaller than a corresponding threshold value, if so, acquiring a current zero-speed interval detection result of the unmanned aerial vehicle;
screening burr data in the zero-speed interval detection result based on a finite state machine to obtain a corresponding target detection result;
if the target detection result shows that the unmanned aerial vehicle is in a static state after rotation, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle;
correspondingly, the calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial parameter value of the error parameter set includes:
Parameter estimation precision and error parameter initialization aiming at reliability domain Dogleg algorithmThe initial value of the non-orthogonal axis error rotation angle of the inertial sensor of the unmanned aerial vehicle is set to 0, namely alpha yz ＝α zy ＝α zx ＝0；
Simplifying the error model of the accelerometer, and generating an initial value of zero offset of the inertial sensor based on a corresponding simplified processing result; the error model of the simplified accelerometer is shown in the following formula:
acceleration in the orthogonal coordinate system IC at the previous sampling time of the two adjacent sampling times; />Acceleration in an orthogonal coordinate system IC at the previous sampling time under the stationary state of the unmanned aerial vehicle; generating an initial value of a scale parameter of the inertial sensor according to the local gravitational acceleration;
generating a parameter initial value of an error parameter set based on the initial value of the non-orthogonal axis error rotation angle, the initial value of the zero point offset and the initial value of the scale parameter;
calibrating the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter by adopting a parameter initial value of the error parameter set;
the calibrating the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter by adopting the parameter initial value of the error parameter set respectively comprises the following steps:
Acquiring a nonlinear least square regression fit optimization function of the error parameter set;
optimizing the nonlinear least square regression fit optimization function based on a reliability domain Dogleg algorithm, and iterating the optimized nonlinear least square regression fit optimization function based on the initial parameter value of the error parameter set to obtain the calibration results corresponding to the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter.
2. The method for calibrating an accelerometer on site according to claim 1, wherein automatically determining whether the corresponding calibration result is in a completed state comprises:
based on a preset calibration precision factor, whether the calibration result corresponding to each error of the inertial sensor is in a finished state or not is automatically judged.
3. An accelerometer field calibration device, wherein the accelerometer field calibration device is configured to perform the steps of:
determining the local gravity acceleration of the unmanned aerial vehicle according to the local latitude value and the local altitude value of the unmanned aerial vehicle;
establishing an error model of an accelerometer of the unmanned aerial vehicle; the error model of the accelerometer is shown in the following formula:
a I ＝T a SM a (a O +b a +v a )
Wherein a is I Acceleration in an orthogonal coordinate system IC, a O For acceleration in the actual accelerometer coordinate system AC,for accelerometer measurement of noise, assume compliance with a 0-mean gaussian distribution, SM a For the scale matrix>Andis a scale parameter; b a Is a zero offset vector; />And->Is a zero offset parameter; alpha yz ,α zy ,α zx Error rotation angle for non-orthogonal axis;
the accelerometer field calibration device further comprises:
the zero-speed interval detection module is used for detecting whether the unmanned aerial vehicle is in a static state after rotation at present according to a preset multi-criterion zero-speed interval detection mode, and if so, acquiring a parameter initial value of an error parameter set to be calibrated at present of an accelerometer of the unmanned aerial vehicle; the error parameter set includes: the non-orthogonal axis error rotation angle, zero point offset and scale parameter of the inertial sensor of the unmanned aerial vehicle;
the error calibration and result judgment module is used for respectively calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial parameter value of the error parameter set and automatically judging whether the corresponding calibration result is in a finished state or not;
the method for detecting the error parameter set of the unmanned aerial vehicle comprises the steps of detecting whether the unmanned aerial vehicle is in a static state after rotation at present according to a preset multi-criterion zero-speed interval detection mode, if so, acquiring a parameter initial value of the error parameter set to be calibrated at present of an accelerometer of the unmanned aerial vehicle, and comprising the following steps: acquiring a current acceleration differential module value and an angular velocity differential module value of the unmanned aerial vehicle;
Judging whether the acceleration differential module value and the angular velocity differential module value are smaller than respective corresponding threshold values, if yes, acquiring the current acceleration variance of the unmanned aerial vehicle;
judging whether the acceleration variance is smaller than a corresponding threshold value, if so, acquiring a current zero-speed interval detection result of the unmanned aerial vehicle;
screening burr data in the zero-speed interval detection result based on a finite state machine to obtain a corresponding target detection result;
if the target detection result shows that the unmanned aerial vehicle is in a static state after rotation, acquiring a parameter initial value of an error parameter set to be calibrated currently of an accelerometer of the unmanned aerial vehicle;
correspondingly, the calibrating each error of the inertial sensor corresponding to the error parameter set based on the initial parameter value of the error parameter set includes:
setting an initial value of a non-orthogonal axis error rotation angle of an inertial sensor of the unmanned aerial vehicle to 0;
aiming at the dependence area Dogleg algorithm parameter estimation precision and error parameter initial value setting correlation, selecting the initial value of the non-orthogonal axis error rotation angle as 0, namely alpha yz ＝α zy ＝α zx =0, performing a simplification process on the error model of the accelerometer, and generating an initial value of zero point offset of the inertial sensor based on a corresponding simplification process result; the error model of the simplified accelerometer is shown in the following formula:
Acceleration in the orthogonal coordinate system IC at the previous sampling time of the two adjacent sampling times; />Acceleration in an orthogonal coordinate system IC at the previous sampling time under the stationary state of the unmanned aerial vehicle; generating an initial value of a scale parameter of the inertial sensor according to the local gravitational acceleration;
generating a parameter initial value of an error parameter set based on the initial value of the non-orthogonal axis error rotation angle, the initial value of the zero point offset and the initial value of the scale parameter;
calibrating the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter by adopting a parameter initial value of the error parameter set;
the calibrating the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter by adopting the parameter initial value of the error parameter set respectively comprises the following steps:
acquiring a nonlinear least square regression fit optimization function of the error parameter set;
optimizing the nonlinear least square regression fit optimization function based on a reliability domain Dogleg algorithm, and iterating the optimized nonlinear least square regression fit optimization function based on the initial parameter value of the error parameter set to obtain the calibration results corresponding to the non-orthogonal axis error rotation angle, the zero point offset and the scale parameter.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the accelerometer field calibration method of claim 1 or 2 when the computer program is executed.
5. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the accelerometer field calibration method of claim 1 or 2.
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