Micro-electro-mechanical gyroscope digital signal processing method and system based on model optimization

Abstract

The invention belongs to the technical field of micro-electro-mechanical sensor digital signal processing, and particularly relates to a micro-electro-mechanical gyroscope digital signal processing method and system based on model optimization, and the method comprises the steps: obtaining the angular velocity values of a static micro-electro-mechanical gyroscope and a test state micro-electro-mechanical gyroscope; carrying out deterministic error compensation on the angular velocity value of the micro-electro-mechanical gyroscope to obtain compensated static data and compensated test state data; analyzing the compensated static data by using an Allan variance method to obtain an angle random walk error as a measurement error, and calculating a measurement error variance; analyzing the compensated test state data, performing corresponding model optimization on the gyroscope data according to different results, and outputting an optimal value of the micro-electro-mechanical gyroscope at a specific moment; and repeatedly analyzing and outputting the optimal value of the MEMS gyroscope at the next moment, analyzing an angular velocity signal output by the MEMS gyroscope in real time, and carrying out automatic model optimization, so that automatic optimization of an error model of the digital filtering method of the MEMS gyroscope is realized, and the digital signal filtering processing precision is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of micro-electromechanical sensor digital signal processing, and in particular relates to a micro-electromechanical gyroscope digital signal processing method and system based on model optimization. Background Art

[0002] In response to the development needs of low-cost, intelligent and miniaturized unmanned swarm systems such as micro-aircraft, micro-electromechanical systems (MEMS) gyroscopes have gradually become one of the important components of inertial measurement in future unmanned swarm systems due to their small size and high integration. However, low-cost MEMS gyroscopes cannot meet the accuracy requirements of micro-unmanned aerial vehicles, which hinders their application in navigation and positioning tasks in unmanned swarm systems. The Kalman filter method can reduce and improve the error of MEMS gyroscopes based on the error model and improve the accuracy. However, the current filtering method faces the problem that a single error model cannot accurately describe the MEMS signal error. In order to effectively improve the output accuracy of gyroscope signals, it is urgent to construct multiple prediction models to accurately describe the output signal characteristics of the gyroscope at each moment, and study the digital signal processing method of MEMS gyroscopes based on model optimization. Summary of the invention

[0003] The purpose of the present invention is to solve the problems in the prior art and provide a method for processing digital signals of a micro-electromechanical gyroscope based on model optimization, so as to overcome the difficulty that a single error model cannot accurately describe the MEMS signal error and realize real-time improvement of the MEMS gyroscope signal accuracy.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A method for processing digital signals of a micro-electromechanical gyroscope based on model optimization, comprising: S1: obtaining a static angular velocity value of a micro-electro-mechanical system gyroscope; and performing deterministic error compensation on the angular velocity value of the micro-electro-mechanical system gyroscope to obtain compensated static data; S2: obtaining the angular velocity value of the MEMS gyroscope in the test state, and performing deterministic error compensation on the angular velocity value of the MEMS gyroscope to obtain compensated test state data; S3: Use the Allan variance method to analyze the compensated static data, obtain the angle random walk error as the measurement error, and calculate the measurement error variance; S4: analyzing the compensated test state data, optimizing the corresponding model for the gyroscope data according to different results, and outputting the optimal value of the MEMS gyroscope at a specific moment; S5: Repeat S4 and output the optimal value of the MEMS gyroscope at the next moment.

[0005] Preferably, in S1, the output serial port data of the static micro-electromechanical gyroscope is obtained, and the output serial port data is converted into a gyroscope angular velocity value. The gyroscope angular velocity value calculation formula is as follows:

[0006] in, is the angular velocity value of the gyroscope; To output serial port data.

[0007] Preferably, the S3 specifically includes: S301: Calculate the Allan variance corresponding to different time intervals; S302: Calculate the Allan standard deviation based on the Allan variance and draw a double logarithmic graph; S303: In the double logarithmic curve graph, the slope is -1 / 2, and the angle random walk error coefficient is read at 1 on the horizontal axis. The angle random walk error is regarded as measurement noise.

[0008] Preferably, in S4, the gyroscope data is subjected to corresponding model optimization according to different results, specifically including: If the compensated data meets the stationarity condition and the residual noise is uncorrelated, the AR first-order steady-state Kalman filter model is selected; If the compensated data meets the stationarity condition, but the residual noise is autocorrelated, the AR generalized difference Kalman filter model is selected; If the compensated data does not meet the stationarity condition, select the AR differential Kalman filter model.

[0009] Preferably, when the AR first-order steady-state Kalman filter model is selected, it includes: S401: For the first-order AR model, calculate k The state space parameters, system noise variance and measurement matrix parameters of the gyroscope at each moment; S402: Combination k The state space parameters of the gyroscope, the system noise variance, the sampling time, the measurement noise variance, and the Kalman filter steady-state variance are calculated; S403: Combined k The state space parameters of the gyroscope, system noise variance, sampling time, measurement noise variance, and Kalman filter steady-state variance are used to calculate the steady-state Kalman gain. S404: Combine the optimal estimate value at the previous moment, the steady-state Kalman gain, k Time measurement values, measurement matrix parameters, estimation k The optimal value of the gyroscope at that moment.

[0010] Preferably, when the AR generalized differential Kalman filter model is selected, it includes: S405: Establishing a residual noise autocorrelation model based on the first-order AR model of the gyroscope and the autocorrelation coefficient; S406: establishing a first-order AR generalized difference model based on the first-order AR model of the gyroscope and the residual noise autocorrelation model; S407: Calculate the predicted value at time k by combining the first-order AR generalized difference model constructed in S406; S408: The first-order AR generalized difference model of S407 is used as the state space model of the gyroscope at time k, enters the Kalman filter, performs Kalman filtering, and estimates the optimal value of the gyroscope at time k.

[0011] Preferably, when the AR differential Kalman filter model is selected, it includes: S409: Calculating the angular velocity difference between two adjacent moments according to the first-order AR model of the gyroscope; S4010: Combine the angular velocity difference between two adjacent moments, build an AR differential Kalman filter model, and calculate the predicted value at moment k; S4011: The AR differential Kalman filter model obtained in S4010 is used as the state space model of the gyroscope at time k and enters the Kalman filter.

[0012] A micro-electromechanical system gyroscope digital signal processing system based on model optimization, comprising: A first acquisition unit is used to acquire a static angular velocity value of a micro-electromechanical system gyroscope; and to perform deterministic error compensation to obtain compensated static data; A second acquisition unit is used to acquire the angular velocity value of the MEMS gyroscope in a test state, and perform deterministic error compensation to obtain compensated test state data; An analysis unit, used for analyzing the compensated static data by using the Allan variance method, obtaining the angle random walk error as the measurement error, and calculating the measurement error variance; A model optimization unit is used to analyze the compensated test state data, optimize the corresponding model for the gyroscope data according to different results, and output the optimal value of the micro-electromechanical gyroscope at a specific moment; The repeated output unit is used to combine with the model optimization unit to output the optimal value of the micro-electromechanical gyroscope at the next moment.

[0013] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any one of the above-mentioned steps of model-based optimization for micro-electromechanical gyroscope digital signal processing is implemented.

[0014] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for processing digital signals of a micro-electromechanical gyroscope based on model optimization are implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a method for processing digital signals of a micro-electromechanical gyroscope based on model optimization, which overcomes the problem that a single error model cannot accurately describe the error of a MEMS signal, and realizes real-time improvement of the signal accuracy of a MEMS gyroscope. The output angular velocity signal of the MEMS gyroscope is analyzed in real time, the error model is identified online, and the first-order AR model, the first-order generalized difference AR model, the first-order difference AR model, etc. used to characterize the error are automatically optimized. The optimal error model is automatically selected and identified in real time online during the signal processing process, and the error model of the digital filtering method of the micro-electromechanical gyroscope is automatically optimized, thereby improving the filtering processing accuracy of the digital signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 is a flow chart of the method of the present invention; Figure 2 is a flow chart of a method according to an embodiment of the present invention; Figure 3 This is the AR first-order steady-state Kalman filter model diagram of the present invention; Figure 4 It is the AR first-order generalized difference Kalman filter model in the present invention.

[0018] Figure 5 It is the AR first-order difference Kalman filter model in the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0022] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0023] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0024] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] The present invention is further described in detail below in conjunction with the accompanying drawings: See also Figure 1 The present application discloses a method for processing digital signals of a micro-electromechanical gyroscope based on model optimization, which is characterized by comprising: S1: obtaining a static angular velocity value of a micro-electro-mechanical system gyroscope; and performing deterministic error compensation on the angular velocity value of the micro-electro-mechanical system gyroscope to obtain compensated static data; S2: Acquire the angular velocity value of the MEMS gyroscope in the test state, and perform deterministic error compensation on the MEMS gyroscope angular velocity value to obtain compensated test state data; S3: Use the Allan variance method to analyze the compensated static data, obtain the angle random walk error as the measurement error, and calculate the measurement error variance; S4: analyzing the compensated test state data, optimizing the corresponding model for the gyroscope data according to different results, and outputting the optimal value of the MEMS gyroscope at a specific moment; S5: Repeat S4 and output the optimal value of the MEMS gyroscope at the next moment.

[0026] This application overcomes the problem that a single error model cannot accurately describe the MEMS signal error, and achieves real-time improvement of the MEMS gyroscope signal accuracy. The MEMS gyroscope output angular velocity signal is analyzed in real time, the error model is identified online, and the first-order AR model, first-order generalized differential AR model, first-order differential AR model, etc. used to characterize the error are automatically optimized. The optimal error model is automatically selected and identified in real time online during the signal processing process, and the error model of the micro-electromechanical gyroscope digital filtering method is automatically optimized, thereby improving the accuracy of digital signal filtering processing.

[0027] In some embodiments, a method for processing digital signals of a micro-electromechanical system gyroscope based on model optimization comprises the following steps: Step 1: Place the MEMS gyroscope on a stationary base, use the serial port receiving assistant to directly collect the serial port data output by the gyroscope, process the raw data output by the serial port according to the serial port definition, and convert it into the gyroscope angular velocity value.

[0028]

[0029] in, is the angular velocity value of the gyroscope; To output serial port data.

[0030] Step 2: Establish a deterministic error model and perform deterministic error compensation on static data.

[0031] Step 3: Fix the MEMS gyroscope in the test environment, collect the angular velocity value of the MEMS gyroscope in the test environment, and perform deterministic error compensation.

[0032] Step 4: Use the Allan variance method to analyze the compensated static data, obtain the angle random walk error as the measurement error, and calculate the measurement error variance.

[0033] Step 5: According to the deterministic error compensation result of step 3, the compensated data is analyzed, the model is optimized for the gyroscope data, and the optimal value of the MEMS gyroscope at a specific moment is output.

[0034] Step 6: Repeat step 5 and output the optimal value of the MEMS gyroscope at the next moment.

[0035] In some embodiments, a method for processing digital signals of a micro-electromechanical system gyroscope based on model optimization comprises the following steps: Step 1: Place the MEMS gyroscope on a stationary base, use the serial port receiving assistant to directly collect the serial port data output by the gyroscope, process the raw data output by the serial port according to the serial port definition, and convert it into the gyroscope angular velocity value.

[0036]

[0037] Step 2: Establish a deterministic error model and perform deterministic error compensation on static data.

[0038] Step 3: Fix the MEMS gyroscope in the test environment, collect the angular velocity value of the MEMS gyroscope in the test environment, and perform deterministic error compensation.

[0039] Step 4: Use the Allan variance method to analyze the compensated static data, obtain the angle random walk error as the measurement error, and calculate the measurement error variance.

[0040] Step 5: According to the deterministic error compensation result of step 3, the compensated data is analyzed, the model is optimized for the gyroscope data, and the optimal value of the MEMS gyroscope at a specific moment is output.

[0041] Specifically, if the compensated data meets the stationarity condition and the residual noise is uncorrelated, the AR first-order steady-state Kalman filter model is selected; Specifically, if the compensated data meets the stationarity condition but the residual noise is autocorrelated, the AR generalized difference Kalman filter model is selected; Specifically, if the compensated data does not meet the stationarity condition, the AR differential Kalman filter model is selected; Step 6: Repeat step 5 and output the optimal value of the MEMS gyroscope at the next moment.

[0042] In some embodiments, a method for processing digital signals of a micro-electromechanical system gyroscope based on model optimization comprises the following steps: Step 1: Place the MEMS gyroscope on a stationary base, use the serial port receiving assistant to directly collect the serial port data output by the gyroscope, process the raw data output by the serial port according to the serial port definition, and convert it into the gyroscope angular velocity value.

[0043]

[0044] Step 2: Establish a deterministic error model and perform deterministic error compensation on static data.

[0045] Step 3: Fix the MEMS gyroscope in the test environment, collect the angular velocity value of the MEMS gyroscope in the test environment, and perform deterministic error compensation.

[0046] Step 4: Use the Allan variance method to analyze the compensated static data, obtain the angle random walk error as the measurement error, and calculate the measurement error variance.

[0047] Step 5: According to the deterministic error compensation result of step 3, the compensated data is analyzed, the model is optimized for the gyroscope data, and the optimal value of the MEMS gyroscope at a specific moment is output.

[0048] Specifically, if the compensated data meets the stationarity condition and the residual noise is uncorrelated, the AR first-order steady-state Kalman filter model is selected, and then step 6 is entered; Specifically, if the compensated data meets the stationarity condition, but the residual noise is autocorrelated, the AR generalized difference Kalman filter model is selected, and then step 7 is entered; Specifically, if the compensated data does not meet the stationarity condition, the AR differential Kalman filter model is selected, and step 8 is entered; Step 6: See Figure 3 , construct an AR first-order steady-state Kalman filter model.

[0049] Step 601: Calculate the state space parameters, system noise variance and measurement matrix parameters of the gyroscope at time k for the first-order AR model; Step 602: Calculate the Kalman filter steady-state variance by combining the state space parameters of the gyroscope at time k, the system noise variance, the sampling time, and the measurement noise variance; Step 603: Calculate the steady-state Kalman gain by combining the state space parameters of the gyroscope at time k, the system noise variance, the sampling time, the measurement noise variance, and the Kalman filter steady-state variance; Step 604: Combine the optimal estimated value at the previous moment, the steady-state Kalman gain, the measured value at moment k, and the measurement matrix parameters to estimate the optimal value of the gyroscope at moment k, and proceed to step 9.

[0050] Step 7: See Figure 4 , construct the AR generalized difference Kalman filter model.

[0051] Step 701: Establish a residual noise autocorrelation model based on the gyroscope first-order AR model and the autocorrelation coefficient; Step 702: Establish a first-order AR generalized difference model based on the first-order AR model of the gyroscope and the residual noise autocorrelation model; Step 703: Calculate the predicted value at time k by combining the first-order AR generalized difference model constructed in step 702; Step 704: The first-order AR generalized difference model of step 703 is used as the state space model of the gyroscope at time k, enters the Kalman filter, performs Kalman filtering, estimates the optimal value of the gyroscope at time k, and enters step 9.

[0052] Step 8: See Figure 5 , construct the AR differential Kalman filter model.

[0053] Step 801: Calculate the angular velocity difference between two adjacent moments according to the first-order AR model of the gyroscope; Step 802: combining the angular velocity difference between two adjacent moments, constructing an AR differential Kalman filter model, and calculating the predicted value at moment k; Step 803: The AR differential Kalman filter model obtained in step 802 is used as the state space model of the gyroscope at time k, enters the Kalman filter, and enters step 9.

[0054] Step 9: Repeat steps 58 to process the gyroscope output data at the next moment.

[0055] [Example] This example uses MPU6050 as the MEMS gyroscope test, with a sampling frequency of 100 Hz. Steps 1 and 2 are the offline preprocessing process of the MEMS gyroscope static data, and steps 3 to 9 are the real-time online filtering process of the gyroscope data to be processed. The process is as follows: Figure 2 shown.

[0056] Step 1: Place the MPU6050 on a static base, with a sampling frequency of 100Hz and a +5V power supply. Use the serial port receiving assistant to collect the serial port data output by the gyroscope for 1h. According to the serial port definition, process the raw data output by the serial port and convert it into the gyroscope angular velocity value.

[0057]

[0058] in, is the angular velocity value of the gyroscope; To output serial port data.

[0059] Step 2: Establish a deterministic error model and compensate for the scale factor, temperature drift, zero bias and other deterministic errors of MPU6050.

[0060] Step 3: Fix the MPU6050 in the test environment, collect the angular velocity value of the MEMS gyroscope in the test environment, and compensate for the deterministic errors such as the scale coefficient, temperature drift, and zero bias of the test data according to the deterministic error model established in step 2.

[0061] Step 4: Use the Allan variance method to analyze the compensated static data. The Allan variance is defined as follows:

[0062] Calculate the Allan variance corresponding to different time intervals, complete the Allan variance estimation, further calculate the Allan standard deviation, and draw a double logarithmic graph. In the Allan standard deviation double logarithmic curve, read the angle random walk error coefficient at the slope of -1 / 2 and the horizontal axis of 1.

[0063] The angle random walk error is regarded as measurement noise, and the variance can be expressed as:

[0064] in, R ARW is the angle random walk error coefficient read from the Allan standard deviation curve, and T is the gyroscope sampling time.

[0065] Step 5: According to the deterministic error compensation result of step 3, analyze the compensated data and perform model optimization on the gyroscope data.

[0066] Step 501: If the compensated data meets the stationarity condition and the residual noise is uncorrelated, select the AR first-order steady-state Kalman filter model and proceed to step 6; Step 502: If the compensated data meets the stationarity condition, but the residual noise is autocorrelated, select the AR generalized difference Kalman filter model and go to step 7; Step 503: If the compensated data does not meet the stationarity condition, select the AR differential Kalman filter model and proceed to step 8; Step 6: Construct an AR first-order steady-state Kalman filter model.

[0067] Step 601: For the first-order AR model, calculate k The state space parameters, system noise variance and measurement matrix parameters of the gyroscope at each moment;

[0068] ω k for k The predicted value at the moment, a k|k-1 represents the state space matrix at the current moment, is a constant, ω k-1 for k The optimal estimate at time -1, μ k is the residual white noise, that is, the system noise, and its variance is .

[0069] Step 602: Combination kThe state space parameters of the gyroscope, the system noise variance, the sampling time, the measurement noise variance, and the Kalman filter steady-state variance are calculated;

[0070] Step 603: Combination k The state space parameters of the gyroscope, system noise variance, sampling time, measurement noise variance, and Kalman filter steady-state variance are used to calculate the steady-state Kalman gain.

[0071] Step 604: Combine the optimal estimate at the last moment, the steady-state Kalman gain, k Time measurement values, measurement matrix parameters, estimation k The optimal value of the gyroscope at that moment, go to step 9.

[0072]

[0073] Step 7: Construct AR generalized difference Kalman filter model; Step 701: According to the gyroscope first-order AR model, μ k As autocorrelated residual noise, combined with the autocorrelation coefficient, the residual noise autocorrelation model can be expressed as:

[0074] In the formula, ρ is the autocorrelation coefficient, ε k is Gaussian white noise.

[0075] Step 702: Based on the first-order AR model of the gyroscope and the residual noise autocorrelation model of step 701, a first-order AR generalized difference model is established:

[0076] Step 703: Combine the first-order AR generalized difference model constructed in step 702 to obtain k Moment prediction value:

[0077] Step 704: The first-order AR generalized difference model obtained in step 703 is used as k The state space model of the gyroscope at this moment enters the Kalman filter and goes to step 9. The Kalman filter algorithm expression is:

[0078]

[0079]

[0080]

[0081]

[0082] in, is the current state matrix, is the measurement matrix, for k The predicted value at the moment, for k The optimal estimate at time -1, is the Kalman one-step prediction error variance matrix, is the Kalman filter gain, is the Kalman filter error variance matrix, and the initial value of the variance is set to , for k The best estimate of time.

[0083] Step 8: Construct the AR differential Kalman filter model.

[0084] Step 801: Calculate the angular velocity difference between two adjacent moments according to the first-order AR model of the gyroscope;

[0085] Step 802: Combine the angular velocity differences between two adjacent moments to construct an AR differential Kalman filter model and calculate k Moment prediction value;

[0086] Step 803: The AR differential Kalman filter model obtained in step 802 is used as k The state space model of the gyroscope at this moment enters the Kalman filter and goes to step 9. The Kalman filter algorithm expression is:

[0087]

[0088]

[0089]

[0090]

[0091] in, is the current state matrix, is the measurement matrix, for k The predicted value at the moment, fork The optimal estimate at time -1, is the Kalman one-step prediction error variance matrix, is the Kalman filter gain, is the Kalman filter error variance matrix, and the initial value of the variance is set to , for k The best estimate of time.

[0092] Step 9: Repeat steps 58 to process the gyroscope output data at the next moment.

[0093] The present application also discloses a micro-electromechanical gyroscope digital signal processing system based on model optimization, comprising: A first acquisition unit is used to acquire a static angular velocity value of a micro-electromechanical system gyroscope; and to perform deterministic error compensation to obtain compensated static data; A second acquisition unit is used to acquire the angular velocity value of the MEMS gyroscope in a test state, and perform deterministic error compensation to obtain compensated test state data; An analysis unit, used for analyzing the compensated static data by using the Allan variance method, obtaining the angle random walk error as the measurement error, and calculating the measurement error variance; A model optimization unit is used to analyze the compensated test state data, optimize the corresponding model for the gyroscope data according to different results, and output the optimal value of the micro-electromechanical gyroscope at a specific moment; The repeated output unit is used to combine with the model optimization unit to output the optimal value of the micro-electromechanical gyroscope at the next moment.

[0094] The present application also discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the above-mentioned steps of model-based optimization for micro-electromechanical gyroscope digital signal processing is implemented.

[0095] The present application also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for processing digital signals of a micro-electromechanical gyroscope based on model optimization are implemented.

[0096] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for processing digital signals of a micro-electromechanical gyroscope based on model optimization, characterized in that: include: S1: Get the static angular velocity value of the MEMS gyroscope; and performing deterministic error compensation on the angular velocity value of the micro-electromechanical system gyroscope to obtain compensated static data; S2: obtaining the angular velocity value of the MEMS gyroscope in the test state, and performing deterministic error compensation on the angular velocity value of the MEMS gyroscope to obtain compensated test state data; S3: Use the Allan variance method to analyze the compensated static data, obtain the angle random walk error as the measurement error, and calculate the measurement error variance; S4: analyzing the compensated test state data, optimizing the corresponding model for the gyroscope data according to different results, and outputting the optimal value of the MEMS gyroscope at a specific moment; S5: Repeat S4 and output the optimal value of the MEMS gyroscope at the next moment.

2. A method for processing digital signals of a micro-electromechanical gyroscope based on model optimization according to claim 1, characterized in that: In S1, the output serial port data of the static micro-electromechanical gyroscope is obtained, and the output serial port data is converted into the gyroscope angular velocity value. The gyroscope angular velocity value calculation formula is as follows: in, is the angular velocity value of the gyroscope; To output serial port data.

3. The method for processing digital signals of a micro-electromechanical gyroscope based on model optimization according to claim 1, characterized in that: The S3 specifically includes: S301: Calculate the Allan variance corresponding to different time intervals; S302: Calculate the Allan standard deviation based on the Allan variance and draw a double logarithmic graph; S303: In the double logarithmic curve graph, the slope is -1 / 2, and the angle random walk error coefficient is read at 1 on the horizontal axis. The angle random walk error is regarded as measurement noise.

4. The method for processing digital signals of a MEMS gyroscope based on model optimization according to claim 1, characterized in that: In S4, the gyroscope data is optimized according to different results, specifically including: If the compensated data meets the stationarity condition and the residual noise is uncorrelated, the AR first-order steady-state Kalman filter model is selected; If the compensated data meets the stationarity condition, but the residual noise is autocorrelated, the AR generalized difference Kalman filter model is selected; If the compensated data does not meet the stationarity condition, select the AR differential Kalman filter model.

5. The method for processing digital signals of a micro-electromechanical gyroscope based on model optimization according to claim 4, characterized in that: When the AR first-order steady-state Kalman filter model is selected, it includes: S401: For the first-order AR model, calculate k The state space parameters, system noise variance and measurement matrix parameters of the gyroscope at each moment; S402: Combination k The state space parameters of the gyroscope, the system noise variance, the sampling time, the measurement noise variance, and the Kalman filter steady-state variance are calculated; S403: Combined k The state space parameters of the gyroscope, system noise variance, sampling time, measurement noise variance, and Kalman filter steady-state variance are used to calculate the steady-state Kalman gain. S404: Combine the optimal estimate value at the previous moment, the steady-state Kalman gain, k Time measurement values, measurement matrix parameters, estimation k The optimal value of the gyroscope at that moment.

6. The method for processing digital signals of a micro-electromechanical gyroscope based on model optimization according to claim 4, characterized in that: When the AR generalized difference Kalman filter model is selected, it includes: S405: Establishing a residual noise autocorrelation model based on the first-order AR model of the gyroscope and the autocorrelation coefficient; S406: establishing a first-order AR generalized difference model based on the first-order AR model of the gyroscope and the residual noise autocorrelation model; S407: Calculate the predicted value at time k by combining the first-order AR generalized difference model constructed in S406; S408: The first-order AR generalized difference model of S407 is used as the state space model of the gyroscope at time k, enters the Kalman filter, performs Kalman filtering, and estimates the optimal value of the gyroscope at time k.

7. The method for processing digital signals of a micro-electromechanical gyroscope based on model optimization according to claim 4, characterized in that: When the AR differential Kalman filter model is selected, it includes: S409: Calculating the angular velocity difference between two adjacent moments according to the first-order AR model of the gyroscope; S4010: Combine the angular velocity difference between two adjacent moments, build an AR differential Kalman filter model, and calculate the predicted value at moment k; S4011: The AR differential Kalman filter model obtained in S4010 is used as the state space model of the gyroscope at time k and enters the Kalman filter.

8. A micro-electromechanical gyroscope digital signal processing system based on model optimization, characterized in that: include: A first acquisition unit, used for acquiring a static micro-electromechanical gyroscope angular velocity value; And perform deterministic error compensation to obtain static data after compensation; A second acquisition unit is used to acquire the angular velocity value of the MEMS gyroscope in a test state, and perform deterministic error compensation to obtain compensated test state data; An analysis unit, used for analyzing the compensated static data by using the Allan variance method, obtaining the angle random walk error as the measurement error, and calculating the measurement error variance; A model optimization unit is used to analyze the compensated test state data, optimize the corresponding model for the gyroscope data according to different results, and output the optimal value of the micro-electromechanical gyroscope at a specific moment; The repeated output unit is used to combine with the model optimization unit to output the optimal value of the micro-electromechanical gyroscope at the next moment.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of model-based optimization of micro-electromechanical gyroscope digital signal processing as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the micro-electromechanical gyroscope digital signal processing method based on model optimization according to any one of claims 1 to 7.

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