Kalman filtering parameter setting method and device, equipment, medium and product

Abstract

The application provides a Kalman filtering parameter setting method, device, equipment, medium and product, and relates to the technical field of digital processing. The method comprises the following steps: obtaining angular velocity measurement values measured by a MEMS gyroscope at each time in a previous period; calculating autocorrelation coefficients between the angular velocity measurement values, and determining the autocorrelation coefficients as a state transition matrix corresponding to the MEMS gyroscope in a current period. The application can realize dynamic updating of the state transition matrix under long-term operation conditions of the MEMS gyroscope, effectively offset the influence caused by temperature drift, and finally achieve the purpose of improving filtering precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital processing, and in particular to a Kalman filtering parameter setting method, device, equipment, medium and product. BACKGROUND

[0002] Micro-Electro-Mechanical System (MEMS) gyroscopes have been widely and importantly applied in many fields such as aerospace, automotive navigation, industrial automation, etc. due to their low cost, small size, light weight, and other significant advantages.

[0003] MEMS gyroscopes generally have the inherent characteristic of high random noise content during operation, which directly affects the accuracy of the output angular velocity data. To effectively suppress noise and improve data quality, Kalman filtering algorithm has become a common technical means for processing MEMS gyroscope measurement data. In the prior art, for example, the patent document CN119245619A discloses a "Kalman filtering parameter setting method, device, electronic equipment and storage medium", which mainly aims at the application scenario where the measured object is in a low speed interval, and proposes a scheme for quickly setting Kalman filtering parameters, aiming to track and adapt to the dynamic performance changes of MEMS gyroscopes in real time.

[0004] However, the above parameter setting method has certain limitations: it directly sets the state transition matrix to a uniform distribution corresponding to a fixed numerical value. In the short-term operation condition of MEMS gyroscopes, this parameter setting method can meet the basic requirements; but when MEMS gyroscopes are in long-term operation conditions, as the operation time prolongs, the working temperature will continue to rise, and the measurement signal measured by the MEMS gyroscope will inevitably be affected by the temperature change and produce drift. At this time, if the above fixed state transition matrix is continued to be used, it will be difficult to achieve the ideal filtering effect, resulting in a significant decrease in filtering accuracy, which cannot meet the requirements of MEMS gyroscope data accuracy in actual applications. SUMMARY

[0005] The embodiments of the present application provide a Kalman filtering parameter setting method, device, equipment, medium and product to solve the problem of low filtering accuracy of the existing parameter setting method in long-term operation conditions.

[0006] In a first aspect, the embodiments of the present application provide a Kalman filtering parameter setting method applied to a MEMS gyroscope, wherein the Kalman filtering parameters include a state transition matrix.

[0007] The method comprises:

[0008] obtaining the angular velocity measurement value measured by the MEMS gyroscope at each time in the last period;

[0009] The autocorrelation coefficient between each angular velocity measurement is calculated, and the autocorrelation coefficient is determined as a state transition matrix corresponding to a current period of the MEMS gyroscope.

[0010] In a possible implementation, the calculation of the autocorrelation coefficient between each angular velocity measurement comprises:

[0011] The product of the angular velocity measurements at each two adjacent time points is calculated, and the product is accumulated to obtain a signal change trend item;

[0012] The signal change trend item is normalized based on the angular velocity measurements at each time point to obtain the autocorrelation coefficient.

[0013] In a possible implementation, the normalization of the signal change trend item based on the angular velocity measurements at each time point to obtain the autocorrelation coefficient comprises:

[0014] According to The autocorrelation coefficient is determined;

[0015] wherein, the autocorrelation coefficient is denoted as, the signal change trend item is denoted as, the number of time points contained in each period is denoted as, denotes the angular velocity measurement of the MEMS gyroscope at the time point, denotes the angular velocity measurement of the MEMS gyroscope at the time point.

[0016] In a possible implementation, the Kalman filtering parameters further comprise: a system state transition variance;

[0017] After the angular velocity measurements measured by the MEMS gyroscope at each time point in a previous period are obtained, the method further comprises:

[0018] The angular velocity measurements are low-pass filtered to obtain angular velocity filtered values corresponding to each time point in the previous period of the MEMS gyroscope;

[0019] Initial upper and lower limit values of a speed corresponding to the MEMS gyroscope are obtained, and the initial upper and lower limit values of the speed are updated based on the angular velocity filtered values to obtain updated upper and lower limit values of the speed;

[0020] Based on the updated upper and lower limit values of the speed, a system state transition variance corresponding to a current period of the MEMS gyroscope is determined.

[0021] In a possible implementation, the updating the initial speed upper and lower limit values based on the angular velocity filtered values to obtain updated speed upper and lower limit values comprises:

[0022] respectively calculating a difference value between each angular velocity measurement value and a corresponding angular velocity filtered value to obtain a difference value data sequence;

[0023] calculating a root mean square corresponding to the difference value data sequence, and determining the root mean square as a speed variation;

[0024] determining a difference value between the initial speed lower limit value and the speed variation as an updated speed lower limit value;

[0025] determining a sum value between the initial speed upper limit value and the speed variation as an updated speed upper limit value.

[0026] In a possible implementation, the determining the system state transition variance corresponding to the current period of the MEMS gyroscope based on the updated speed upper and lower limit values comprises:

[0027] calculating a uniform distribution variance of the updated speed lower limit value and the updated speed upper limit value, and determining the uniform distribution variance as the system state transition variance.

[0028] In a second aspect, an embodiment of the present application provides a Kalman filter parameter setting device, applied to a MEMS gyroscope, and Kalman filter parameters comprise: a state transition matrix;

[0029] The device comprises:

[0030] an acquisition module, configured to acquire angular velocity measurement values measured by the MEMS gyroscope at each time in a previous period;

[0031] a setting module, configured to calculate an autocorrelation coefficient between each angular velocity measurement value, and determine the autocorrelation coefficient as a state transition matrix corresponding to a current period of the MEMS gyroscope.

[0032] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the method in the first aspect or any possible implementation manner of the first aspect when executing the computer program.

[0033] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method in the first aspect or any possible implementation manner of the first aspect.

[0034] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising a computer program which, when executed by a processor, implements the method in the first aspect or any possible implementation manner of the first aspect.

[0035] Embodiments of the present application consider that the angular velocity measurement value of the MEMS gyroscope in the low variable speed running scene has significant time correlation, and the autocorrelation coefficient can accurately depict the correlation between the signals. From the physical meaning, the state transition matrix in the Kalman filter is used to characterize the correlation of the system state at adjacent time points, and the two have inherent consistency in function. Therefore, embodiments of the present application determine the autocorrelation coefficient as the state transition matrix, so that the filter parameters are accurately matched with the characteristics of the gyroscope signal.

[0036] When the MEMS gyroscope is in a long-term running condition, the temperature drift will cause the time correlation of the signal to change dynamically. For this, embodiments of the present application periodically calculate the autocorrelation coefficient of the corresponding period in real time, realize dynamic updating of the state transition matrix, so that it can adapt to the evolution law of the system state in real time, effectively offset the influence of temperature drift, and ultimately realize the purpose of improving the filtering precision. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is an implementation flowchart of the Kalman filter parameter setting method provided by an embodiment of the present application;

[0038] Figure 2 is an implementation flowchart of the Kalman filter parameter setting method provided by another embodiment of the present application;

[0039] Figure 3 is a structural schematic diagram of the Kalman filter parameter setting device provided by an embodiment of the present application;

[0040] Figure 4 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0041] In the following, embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0042] The applicant has found that in the application scene where the measured object is in the low variable speed interval, when the MEMS gyroscope is in a long-term continuous running condition, the internal temperature of the device and the ambient temperature will inevitably continue to rise. The change of temperature will significantly affect the core sensing element and the electronic characteristics of the MEMS gyroscope, causing the time-varying drift of the statistical characteristics (such as variance, correlation) of the random noise.

[0043] The use of a fixed state transition matrix in related technologies cannot effectively characterize and track changes in system dynamics caused by factors such as temperature drift. The increasing mismatch between the state transition matrix and the actual system model directly leads to a decrease in the estimation accuracy of the Kalman filter algorithm, significantly deteriorating the filtering effect and making it difficult to meet the reliability requirements of MEMS gyroscope data in high-precision, long-endurance applications.

[0044] In order to improve the filtering accuracy under long-term operating conditions, this embodiment of the invention dynamically updates the state transition matrix of the current cycle based on the autocorrelation coefficient corresponding to the angular velocity measurement value of the MEMS gyroscope in the previous cycle. This enables the state transition matrix to adapt to the evolution of the system state in real time, effectively offsetting the effects of temperature drift, and ultimately achieving the goal of improving filtering accuracy.

[0045] Here, we will first give a brief introduction to the Kalman filter algorithm.

[0046] The Kalman filter algorithm, when performing real-time filtering on angular velocity data acquired by a MEMS gyroscope, mainly includes a prediction stage and an update and correction stage. The prediction stage can be represented by the following formulas (1) and (2):

[0047] (1)

[0048] (2)

[0049] in, express Predicted angular velocity at time [time]. Represents the state transition matrix. express The estimated angular velocity at time t. Represents the input matrix, express Time-based control input, express The covariance matrix of the angular velocity prediction error at time t. express The covariance matrix of the angular velocity estimation error at time t. Indicates the transpose factor. This represents the variance of the system state transition.

[0050] In the prediction phase, the Kalman filter algorithm can predict the angular velocity at the current moment based on the angular velocity estimate of the previous moment, thus obtaining the predicted angular velocity value at the current moment.

[0051] The update and correction phase can be represented by the following formulas (3), (4), and (5):

[0052] (3)

[0053] (4)

[0054] (5)

[0055] wherein, denotes the Kalman gain at the time instant, denotes the observation matrix, denotes the measurement noise variance, denotes the angular velocity estimation value at the time instant, denotes the angular velocity measurement value at the time instant, denotes the angular velocity estimation error state covariance at the time instant, denotes the unit matrix.

[0056] The Kalman filtering algorithm can update and correct the angular velocity prediction value at the current time instant in the update correction stage, obtain the angular velocity estimation value at the current time instant, and output the angular velocity estimation value as the real angular velocity value after filtering.

[0057] When the Kalman filtering algorithm is used to filter the MEMS gyroscope in real time, the state transition matrix, the input matrix, the system state transition variance and the measurement noise variance and other parameters need to be parameter tuned to determine the values of the parameters.

[0058] The embodiment of the present application mainly aims at the application scene that the measured object is in the low speed interval and the MEMS gyroscope is in the long-term running condition. Considering that the existing state transition matrix tuning method has the problem of low filtering precision due to the temperature drift of the MEMS gyroscope in the long-term running condition, a new Kalman filtering parameter tuning method is proposed to solve the above problems.

[0059] Referring to Figure 1 , a flowchart of the implementation of the Kalman filtering parameter tuning method provided by the embodiment of the present application is shown, and the details are as follows:

[0060] In step 101, the angular velocity measurement value measured by the MEMS gyroscope at each time instant in the last period is obtained.

[0061] The embodiment of the present application can divide the running period of the MEMS gyroscope into multiple periods, and obtain the angular velocity measurement value measured by the MEMS gyroscope at each time instant in the last period in real time during the running period of the MEMS gyroscope, so as to update the state transition matrix of the MEMS gyroscope in the current period.

[0062] Here, the period length can be determined according to an actual temperature rise, and the temperature of the MEMS gyroscope remains unchanged or within an allowable error range in each period. Exemplarily, the period length can be 1 hour.

[0063] In step 102, the autocorrelation coefficient between each angular velocity measurement value is calculated, and the autocorrelation coefficient is determined as the state transition matrix of the MEMS gyroscope corresponding to the current period.

[0064] The applicant has found through research that the angular velocity measurement values measured in a low variable speed running scenario have strong time correlation, and satisfy a first-order autoregressive model: , wherein represents white noise at the moment.

[0065] The autocorrelation coefficient can accurately depict the correlation between such signals. The state transition matrix in Kalman filtering is used to represent the correlation rule of the system state at adjacent moments. The autocorrelation coefficient and the state transition matrix have inherent consistency in function.

[0066] Therefore, the embodiment of the application directly determines the autocorrelation coefficient corresponding to the last period as the state transition matrix of the current period, so that the filter parameters are accurately matched with the gyroscope signal characteristics, and the dynamic update of the state transition matrix is realized.

[0067] In some embodiments, for each angular velocity measurement value measured by the MEMS gyroscope in the last period, the product of the angular velocity measurement values at each two adjacent moments can be calculated, and the product is accumulated to obtain a signal change trend item. Then, based on the angular velocity measurement value at each moment, the signal change trend item is normalized to obtain the autocorrelation coefficient.

[0068] The calculation formula of the autocorrelation coefficient can be expressed as: .

[0069] , wherein represents the autocorrelation coefficient, represents the signal change trend item, represents the number of moments included in each period, represents the angular velocity measurement value measured by the MEMS gyroscope at the moment, represents the angular velocity measurement value measured by the MEMS gyroscope at the moment.

[0070] Here, the signal change trend item represents the sum of the products of the signal values at adjacent moments, and reflects the cooperative change trend of the two. ​​​denotes the square sum of the signal value at the previous time, and is used to normalize the signal change trend item, so that the autocorrelation coefficient The value range of the autocorrelation coefficient

[0071] The autocorrelation coefficient Intuitively reflects the time continuity of the gyro angular velocity signal in the low variable speed interval. The autocorrelation coefficient The closer to 1, the stronger the linear correlation of the signals at adjacent times, and the better the stability of the gyro output; the autocorrelation coefficient The greater the deviation from 1, the more significant the influence of noise or drift on the signal, and the weaker the correlation.

[0072] In Kalman filtering, the state transition matrix has a physical meaning of describing "how the system state at the previous time evolves to the current time state". For the MEMS gyro running at low variable speed, the time correlation of the angular velocity measurement value is the core feature of state evolution.

[0073] The autocorrelation coefficient calculated by the above formula in the embodiment of the application, is essentially a statistical fitting of this evolution law. When the MEMS gyro is running for a long time and is affected by temperature drift, the autocorrelation coefficient will dynamically adjust with the change of signal correlation, and at this time, the autocorrelation coefficient is used as the state transition matrix , so that the state transition matrix can match the real state evolution law of the system in real time, and avoid the decline of filtering precision caused by the fixed state transition matrix.

[0074] In the embodiment of the application, at the initial stage of the running of the MEMS gyro (for example, the first cycle), the initial value of the state transition matrix is set to a uniform distribution corresponding to any numerical value in the range of (1±Δ); Δ represents the setting error. Here, the setting error can be determined according to the actual situation, and the embodiment of the application does not make specific limitation on this. Exemplarily, Δ=0, that is, the state transition matrix is set to a uniform distribution corresponding to 1.

[0075] Here, determining the autocorrelation coefficient as the state transition matrix means that the uniform distribution corresponding to the autocorrelation coefficient is determined as the state transition matrix.

[0076] Compared with the prior art, the embodiment of the application optimizes the characteristics of the MEMS gyro in the long-term running condition of the low variable speed running scene: the measured angular velocity measurement value has significant time correlation, and the autocorrelation coefficient can accurately describe the correlation between the signals.

[0077] From the physical meaning, the state transition matrix in Kalman filtering is just used to characterize the correlation law of the system state at adjacent time, and both have inherent consistency in function. Therefore, the embodiment of the application directly determines the autocorrelation coefficient as the state transition matrix, so as to make the filtering parameters accurately match the characteristics of the gyro signal.

[0078] When the gyro is operated for a long time, the temperature drift will cause the time correlation of the signal to dynamically change. In this regard, the embodiment of the application realizes dynamic updating of the state transition matrix by periodically calculating the autocorrelation coefficient of the corresponding period in real time, so that it can adapt to the evolution law of the system state in real time and effectively offset the influence of the temperature drift.

[0079] The embodiment of the application uses the dynamic description ability of the autocorrelation coefficient to the time correlation of the signal to make the Kalman filtering algorithm more suitable for the actual running characteristics of the MEMS gyro, and especially in the long-term running scene, the filtering accuracy can be significantly improved, and the problem that the fixed state transition matrix in the prior art is difficult to adapt to the environmental changes is solved.

[0080] On the basis of setting the state transition matrix, another embodiment of the application further provides a new Kalman filtering parameter setting method for setting the parameters of the system state transition variance.

[0081] Referring to Figure 2 , a flowchart of the implementation of the Kalman filtering parameter setting method provided by another embodiment of the application is shown, and the details are as follows:

[0082] Step 201, obtaining the angular velocity measurement value measured by the MEMS gyro at each time in the last period.

[0083] For the implementation of step 201, refer to the above Figure 1 The implementation of the corresponding embodiment is not repeated here.

[0084] Step 202, performing low-pass filtering on the angular velocity measurement value to obtain the angular velocity filtered value corresponding to the MEMS gyro at each time in the last period.

[0085] Here, the role of low-pass filtering is to retain the relatively stable low-frequency component (i.e., the useful signal of the MEMS gyro measurement, reflecting the real speed trend) in the signal. For example, the bandwidth of the low-pass filtering can be 10 Hz.

[0086] Step 203, obtaining the initial speed upper and lower limit value of the MEMS gyro, and updating the initial speed upper and lower limit value based on the angular velocity filtered value to obtain the updated speed upper and lower limit value.

[0087] When the measured object is in the low variable speed interval, that is, the real angular velocity of the MEMS gyroscope is in the low variable speed interval. The embodiment of the present application can be used to limit the measured object in the low variable speed interval by limiting the real angular velocity of the MEMS gyroscope to be greater than a first angular velocity value and less than a second angular velocity value, and the difference between the second angular velocity value and the first angular velocity value is less than a set threshold. Here, the values of the first angular velocity value, the second angular velocity value and the set threshold can be determined according to the actual situation, and the embodiment of the present application does not make specific limitation. Exemplarily, the set threshold in the embodiment of the present application can be any value in 0~25° / s.

[0088] Here, the initial speed lower limit value is the first angular velocity, and the initial speed upper limit value is the second angular velocity. The initial speed range of the MEMS gyroscope can be represented as , ). Wherein, represents the initial speed lower limit value, represents the initial speed upper limit value.

[0089] In some embodiments, when updating the initial speed upper and lower limit values, the difference between each angular velocity measurement value and the corresponding angular velocity filtered value can be calculated first to obtain a difference data sequence; then, the root mean square corresponding to the difference data sequence is calculated, and the root mean square is determined as the speed change; subsequently, the difference between the initial speed lower limit value and the speed change is determined as the updated speed lower limit value, and the sum of the initial speed upper limit value and the speed change is determined as the updated speed upper limit value.

[0090] Here, the difference between the angular velocity measurement value at each time and the corresponding angular velocity filtered value is calculated to obtain the difference corresponding to multiple times, forming a difference data sequence.

[0091] The difference between the angular velocity measurement value and the corresponding angular velocity filtered value is essentially the high-frequency fluctuation component left after filtering out the low-frequency useful signal. This part of fluctuation is mainly composed of random noise (including interference introduced by temperature drift, mechanical vibration, etc.), that is, white noise in the first-order autoregressive model .

[0092] The embodiment of the present application can effectively quantify the amplitude of the white noise s by calculating the root mean square corresponding to the difference data sequence (here, the root mean square is represented by ).

[0093] During the operation of the MEMS gyroscope, the white noise (amplitude ) can cause the actual fluctuation range of the speed change to expand. The existence of noise can make the speed lower limit value smaller than the initial speed lower limit value (as low as ), and can also make the speed upper limit value larger than the initial speed upper limit value (as high as

[0094] Therefore, the speed range is updated to (v , ) in the embodiment of the application, wherein represents the updated lower limit value of the speed, represents the updated upper limit value of the speed.

[0095] In step 204, the system state transition variance of the MEMS gyroscope corresponding to the current period is determined based on the updated speed upper and lower limit values.

[0096] In the application scenario of the low variable speed interval, the difference between the angular velocities of the measured object at adjacent two time points is small. Correspondingly, the angular velocity difference sensed by the MEMS gyroscope at adjacent two time points is small. On this basis, the embodiment of the application can assume that the angular velocity difference sensed by the MEMS gyroscope at adjacent two time points is subject to a uniform distribution within a certain range.

[0097] When the real angular velocity range of the MEMS gyroscope is (v , ), it can be assumed that the angular velocity difference within adjacent two time points is subject to a uniform distribution within the interval (v , ).

[0098] On this basis, when the system state transition variance in the Kalman filtering algorithm is parameter tuned, the embodiment of the application can calculate the uniform distribution variance of the updated lower limit value of the speed and the updated upper limit value of the speed, and determine the uniform distribution variance as the system state transition variance.

[0099] Here, the system state transition variance is used to represent the error between the state transition matrix and the actual process, and this parameter reflects the uncertainty caused by imperfect model or external disturbance and other factors in the state transition process. When the angular velocity difference sensed by the MEMS gyroscope conforms to the uniform distribution, the uniform distribution variance can be used to represent the uncertainty caused by various factors in the state transition process, so as to realize the tuning of the system state transition variance.

[0100] In some embodiments, the uniform distribution variance of the updated lower limit value of the speed and the updated upper limit value of the speed can be calculated according to .

[0101] wherein, represents the uniform distribution variance.

[0102] The embodiment of the application is to use the amplitude s ​The upper and lower limits of the MEMS gyroscope are updated, the assumption of uniform distribution is closer to the actual physical scene, the system state transition variance can cover the additional fluctuations introduced by noise, the adaptability of the Kalman filtering algorithm to actual disturbances is improved, and the filtering precision is improved.

[0103] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the application.

[0104] The following is the device embodiment of the application, and for details not described in detail, reference can be made to the corresponding method embodiments described above.

[0105] Figure 3 The structure diagram of the Kalman filtering parameter setting device provided by the embodiment of the application is shown, only the part related to the embodiment of the application is shown for convenience of description, and the details are as follows:

[0106] As shown in Figure 3 The Kalman filtering parameter setting device 3 includes an acquisition module 31 and a setting module 32.

[0107] The acquisition module 31 is configured to acquire the angular velocity measurement value measured by the MEMS gyroscope at each time in the last period.

[0108] The setting module 32 is configured to calculate the autocorrelation coefficient between each angular velocity measurement value, and determine the autocorrelation coefficient as the state transition matrix corresponding to the current period of the MEMS gyroscope.

[0109] In a possible implementation, the setting module 32 is specifically configured to:

[0110] Calculate the product of the angular velocity measurement values at each two adjacent time points, and accumulate the product to obtain a signal change trend item;

[0111] Based on the angular velocity measurement value at each time point, the signal change trend item is normalized to obtain the autocorrelation coefficient.

[0112] In a possible implementation, the setting module 32 is specifically configured to:

[0113] According to The autocorrelation coefficient is determined;

[0114] Wherein, The autocorrelation coefficient is represented by r, The signal change trend item is represented by s, The number of time points contained in each period is represented by N, The autocorrelation coefficient is represented by r. a measured value of an angular velocity measured by a MEMS gyroscope at a time point, indicates a measured value of an angular velocity measured by a MEMS gyroscope at a time point.

[0115] In a possible implementation, the Kalman filtering parameter further includes: a system state transition variance;

[0116] The setting module 32 is further configured to:

[0117] perform low-pass filtering on the measured value of the angular velocity to obtain an angular velocity filtered value corresponding to the MEMS gyroscope at each time point in a previous period;

[0118] obtain an initial speed upper and lower limit value corresponding to the MEMS gyroscope, and update the initial speed upper and lower limit value based on the angular velocity filtered value to obtain an updated speed upper and lower limit value;

[0119] determine a system state transition variance corresponding to the MEMS gyroscope in a current period based on the updated speed upper and lower limit value.

[0120] In a possible implementation, the setting module 32 is specifically configured to:

[0121] respectively calculate a difference value between each measured value of the angular velocity and a corresponding angular velocity filtered value to obtain a difference value data sequence;

[0122] calculate a root mean square corresponding to the difference value data sequence, and determine the root mean square as a speed variation;

[0123] determine a difference value between the initial speed lower limit value and the speed variation as an updated speed lower limit value;

[0124] determine a sum value between the initial speed upper limit value and the speed variation as an updated speed upper limit value.

[0125] In a possible implementation, the setting module 32 is specifically configured to:

[0126] calculate a uniform distribution variance of the updated speed lower limit value and the updated speed upper limit value, and determine the uniform distribution variance as the system state transition variance.

[0127] The apparatus embodiment can be used to implement the above method embodiments, and has the same technical principles and implementation effects as the above method embodiments, which will not be described here.

[0128] Figure 4 is a schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 4As shown, the electronic device 4 of the embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. The processor 40 implements the steps of each of the above method embodiments when executing the computer program 42. Alternatively, the processor 40 implements the functions of each of the modules / units in each of the above apparatus embodiments when executing the computer program 42.

[0129] For example, the computer program 42 can be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 42 in the electronic device 4.

[0130] The electronic device 4 can include, but is not limited to, the processor 40 and the memory 41. Those skilled in the art can understand that the electronic device 4 can include more or fewer components than those shown, or combine certain components, or include different components, such as an input / output device, a network access device, a bus, etc. Figure 4 The electronic device 4 is only an example and does not constitute a limitation on the electronic device 4, which can include more or fewer components than those shown, or combine certain components, or include different components, such as an input / output device, a network access device, a bus, etc.

[0131] The processor 40 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0132] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. provided on the electronic device 4. Further, the memory 41 can include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used to store the computer program 42 and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0133] For the convenience and brevity of description, only the above-mentioned division of each functional module / unit is exemplified, and in actual application, the above-mentioned functions can be completed by different functional modules / units according to needs. The above-mentioned modules / units can be realized in the form of hardware, software or a combination of hardware and software.

[0134] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method in each method embodiment described above is realized.

[0135] The embodiment of the present application also provides a computer program product, which comprises a computer program. When the computer program is executed by a processor, the method in each method embodiment described above is realized.

[0136] The computer program comprises computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), electric carrier wave signal, telecommunication signal and software distribution medium, etc.

[0137] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments. If there is no special description and logical conflict, the terms and / or descriptions of different embodiments are consistent and can be mutually referred to, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0138] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A Kalman filter parameter tuning method, characterized in that, The Kalman filter parameters applied to the MEMS gyroscope include a state transition matrix; The method comprises: obtaining angular velocity measurement values measured by the MEMS gyroscope at each time in a previous period; calculating autocorrelation coefficients between the angular velocity measurement values, and determining a uniform distribution corresponding to the autocorrelation coefficients as a state transition matrix corresponding to the MEMS gyroscope in a current period; The Kalman filter parameters further include a system state transition variance; After the angular velocity measurement values measured by the MEMS gyroscope at each time in the previous period are obtained, the method further comprises: performing low-pass filtering on the angular velocity measurement values to obtain angular velocity filtered values corresponding to the MEMS gyroscope at each time in the previous period; obtaining initial upper and lower speed limit values corresponding to the MEMS gyroscope, and updating the initial upper and lower speed limit values based on the angular velocity filtered values to obtain updated upper and lower speed limit values; determining the system state transition variance corresponding to the MEMS gyroscope in the current period based on the updated upper and lower speed limit values; The updating of the initial upper and lower speed limit values based on the angular velocity filtered values to obtain the updated upper and lower speed limit values comprises: calculating differences between each angular velocity measurement value and a corresponding angular velocity filtered value to obtain a difference data sequence; calculating a root mean square corresponding to the difference data sequence, and determining the root mean square as a speed change amount; determining a difference between the initial lower speed limit value and the speed change amount as an updated lower speed limit value; determining a sum of the initial upper speed limit value and the speed change amount as an updated upper speed limit value.

2. The Kalman filter parameter tuning method of claim 1, wherein, The calculation of the autocorrelation coefficients between the angular velocity measurement values comprises: calculating a product of angular velocity measurement values at each two adjacent times, and accumulating the product to obtain a signal change trend item; normalizing the signal change trend item based on the angular velocity measurement values at each time to obtain the autocorrelation coefficients.

3. The Kalman filter parameter tuning method of claim 2, wherein, The normalization of the signal change trend item based on the angular velocity measurement values at each time to obtain the autocorrelation coefficients comprises: According to determining the autocorrelation coefficients; wherein, denotes the autocorrelation coefficient, denotes the signal trend item, denotes the number of time instants contained in each period, denotes the angular velocity measurement value measured by the MEMS gyroscope at the time instant, denotes the angular velocity measurement value measured by the MEMS gyroscope at the time instant.

4. The Kalman filter parameter tuning method of claim 1, wherein, The determination of the system state transition variance corresponding to the MEMS gyroscope in the current period based on the updated upper and lower speed limit values comprises: calculating a uniform distribution variance of the updated lower speed limit value and the updated upper speed limit value, and determining the uniform distribution variance as the system state transition variance.

5. A Kalman filter parameter tuning apparatus, characterized by, The Kalman filter parameters applied to the MEMS gyroscope include a state transition matrix; The device comprises: an obtaining module configured to obtain angular velocity measurement values measured by the MEMS gyroscope at each time in a previous period; a setting module configured to calculate autocorrelation coefficients between the angular velocity measurement values, and determine a uniform distribution corresponding to the autocorrelation coefficients as a state transition matrix corresponding to the MEMS gyroscope in a current period; The Kalman filter parameters further include a system state transition variance; After the angular velocity measurement values measured by the MEMS gyroscope at each time in the previous period are obtained, the method further comprises: performing low-pass filtering on the angular velocity measurement values to obtain angular velocity filtered values corresponding to the MEMS gyroscope at each time in the previous period; An initial speed upper and lower limit value corresponding to the MEMS gyroscope is acquired, and the initial speed upper and lower limit value is updated based on the angular velocity filtered value to obtain an updated speed upper and lower limit value; Based on the updated speed upper and lower limit value, a system state transition variance corresponding to the MEMS gyroscope in a current period is determined; The updating of the initial speed upper and lower limit value based on the angular velocity filtered value to obtain an updated speed upper and lower limit value comprises: A difference value between each angular velocity measurement value and the corresponding angular velocity filtered value is calculated to obtain a difference value data sequence; A root mean square corresponding to the difference value data sequence is calculated, and the root mean square is determined as a speed change amount; A difference value between the initial speed lower limit value and the speed change amount is determined as an updated speed lower limit value; A sum value between the initial speed upper limit value and the speed change amount is determined as an updated speed upper limit value.

6. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 4.

8. A computer program product, characterised in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 4.

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