A multi-mems imu chip module, performance optimization method and computer system

Abstract

The application discloses a multi-MEMS IMU chip module and a performance optimization method, performance of each IMU is evaluated in different temperature intervals, and respective weights are calculated in each temperature interval, whether the current IMU is in a stationary state is judged by using acceleration and angular velocity information of the IMU, the current acceleration quality is judged according to the acceleration module length of the stationary state, and the weight proportion is adjusted in real time, in the IMU motion state, high-frequency dynamic information of a gyroscope of the IMU chip and low-frequency steady-state information of an accelerometer are fused by using an extended Kalman filter model, weights of abnormal IMUs are screened out and reduced, and data fusion processing is applied in the final output result. The application has the advantages that the precision problem caused by consistency difference in the manufacturing process of a single IMU is solved, and the overall measurement precision and reliability of the IMU module are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent system inertial navigation, and particularly relates to a multi-MEMS IMU chip module, a performance optimization method and a computer system. BACKGROUND

[0002] An important problem faced by micro-electro-mechanical system (MEMS) based inertial measurement units (IMUs) in wide applications is the poor consistency of chip performance. Even for IMU chips produced in the same batch, their performance may still have significant differences. Such consistency differences have a negative impact on the measurement accuracy and reliability of IMUs.

[0003] To overcome this problem, a multi-MEMS IMU chip module is proposed, which uses multiple IMU chips simultaneously to improve the overall measurement performance through redundancy design. However, each IMU chip is affected by various factors such as materials and manufacturing processes during production, leading to performance differences between different chips and ultimately affecting the accuracy of measurement results. Different chips perform differently at different temperature ranges, which is also an important factor affecting measurement accuracy. This is an area that needs to be improved. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a multi-MEMS IMU chip module and a performance optimization method to improve the overall measurement accuracy and reliability of the IMU module.

[0005] To solve the above technical problems, the present application provides a performance optimization method for a multi-MEMS IMU chip module, comprising:

[0006] 1) Perform performance evaluation on each IMU chip in different temperature intervals to obtain their respective performance indicators; based on the performance indicators in each temperature interval, calculate the weight of each IMU chip in different temperature intervals;

[0007] The performance evaluation includes measuring the zero bias fluctuation amplitude, zero bias instability, random walk, and scale factor of the IMU chip in the current temperature range.

[0008] First, standardize each performance indicator to a unified range [0, 1] for comparison; the standardization method is maximum-minimum normalization, and the indicator value obtained after standardization is ;

[0009] Next, assign a weight factor Q to each performance indicator, which represents the importance of the indicator to the chip performance evaluation;

[0010] Finally, according to the standardized indicator value The weights W of the IMU chips are calculated using a weighted average method, along with the weighting factors Q of each indicator. Based on the weights of the IMU chips, the impact of different IMU chips on the final output data of the multi-IMU chip module is determined.

[0011] 2) Differentiate between different temperature ranges and different axes, and calculate a set of weights for each;

[0012] The normal operating temperature range of each IMU chip is divided into n intervals, where T1 represents the temperature range from T... min1 ℃ to T max1 ℃, T2 indicates from T min2 ℃ to T max2 ℃, and so on, T n-1 Indicates from T min（n-1） ℃ to T max（n-1） ℃, T n Indicates from T minn ℃ to T maxn ℃, where T max（n-1） = T minn The performance of the IMU chip was evaluated within each temperature range.

[0013] Furthermore, the performance of the IMU chips is evaluated, and the performance of each IMU chip is further refined to different axes (x, y, z). That is, the weight of the same IMU chip is different on different axes. Based on the performance indicators of different chips on different axes, the weight of each IMU chip on different axes is calculated.

[0014] 3) Determine the current operating status of the IMU chip:

[0015] The acceleration and angular velocity information of the IMU chip are used to determine whether the IMU chip is currently stationary.

[0016] When the IMU chip is stationary, the weight of abnormal IMU chips is reduced by comparing the acceleration magnitude of the stationary state with the current acceleration magnitude, and the weight ratio is dynamically adjusted to make the attitude estimation in the stationary state more stable. Based on the adjusted weight, the outputs of multiple IMU chips in each temperature range are fused to obtain the measurement results of each temperature range. The final measurement result is obtained by combining the measurement results of each temperature range.

[0017] When the IMU chip is in motion, an extended Kalman filter model is used to fuse the high-frequency dynamic information from the gyroscope of the IMU chip with the low-frequency steady-state information from the accelerometer to obtain a more accurate and stable attitude. The weight of abnormal IMU chips is reduced, the weight ratio is dynamically adjusted, and the adjusted weight is fused with the output of the IMU chip. The outputs of multiple IMU chips in each temperature range are fused to obtain the measurement results for each temperature range. The final measurement result is obtained by combining the measurement results of each temperature range.

[0018] The weight calculation employs a data fusion processing method, which combines the outputs of different IMU chips according to their weights. The weights of multiple IMU chips are shown below:

[0019] ;

[0020] in: , The actual number of IMU chips is T, the current temperature is n, and the temperature range is n.

[0021] The present invention also provides a multi-MEMS IMU chip module, including multiple IMU chips and a data processing module, wherein the data processing module is used to implement a performance optimization method for the multi-MEMS IMU chip module.

[0022] The present invention also provides a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for performance optimization of a multi-MEMS IMU chip module.

[0023] The beneficial effects of this invention are as follows:

[0024] 1) Multi-IMU redundancy design improves accuracy: By employing multiple MEMS IMU chips and combining them with a data fusion processing output method, this invention effectively solves the accuracy problem caused by inconsistencies in the manufacturing process of a single IMU chip; the multi-IMU chip module can minimize the impact of errors from individual chips and improve measurement accuracy by performing data fusion processing on the outputs of multiple chips.

[0025] 2) Dynamic Weight Allocation Based on Temperature Range: The performance of MEMS IMU chips fluctuates with temperature changes. Under different temperature conditions, key performance indicators such as the zero-bias fluctuation amplitude, zero-bias instability, random walk, and scaling factor of the IMU chip in the current temperature range will vary. This invention divides the entire temperature range into multiple intervals, evaluates the performance of the IMU chip in each interval, and dynamically adjusts the weight of each IMU chip under different temperature conditions based on actual measurement data. This method ensures that the system maintains high measurement accuracy under different temperature environments, thereby enhancing the system's adaptability and environmental robustness.

[0026] 3) This invention employs different processing methods for IMU chips under different motion states: In a stationary state, the acceleration modulus information is used to evaluate the acceleration output quality of each IMU chip, and the weight ratio is dynamically adjusted to make the attitude estimation more stable in a stationary state; In a moving state, the gyroscope and accelerometer data of the IMU are affected by high-frequency vibration and low-frequency drift. This invention uses the Extended Kalman Filter (EKF) model to effectively fuse the high-frequency dynamic information of the IMU chip's gyroscope with the low-frequency steady-state information of the accelerometer, significantly improving the accuracy of attitude calculation in dynamic environments;

[0027] 4) Real-time elimination of abnormal IMU chip outputs to enhance system robustness: The advantage of multi-IMU chip redundancy design is that when multiple IMU chips are working simultaneously, abnormal chips that significantly deviate from the output results of most IMU chips can be screened out by comparing the attitude estimation results of different IMU chips. In the data fusion processing, this invention dynamically reduces the weight of these abnormal IMU chips to prevent their inaccurate data from affecting the final measurement results, thereby improving the system's anti-interference capability and overall robustness.

[0028] 5) Adaptive weight calculation and precise output control: This invention adopts a data fusion processing method. By evaluating the performance of each IMU chip in different temperature ranges, its weight is obtained. Then, according to the working state of the IMU chip, the weight ratio is further adjusted so that the final output attitude and measurement results can accurately reflect the actual state. Especially in the static state, this invention can adjust the weight in real time through the acceleration modulus and dynamically adjust the output quality.

[0029] 6) Comprehensive Temperature Range Output Ensures Full-Range Accuracy: The final measurement result is obtained by combining the measurement results within each temperature range. This method ensures that the performance differences of the IMU chip under different temperature environments are effectively managed. Especially in application scenarios with drastic temperature changes, the design of this invention can ensure that the measurement results maintain high accuracy throughout the entire temperature range, making it suitable for a wide range of application environments.

[0030] 7) Improve overall system reliability: The multi-IMU chip redundancy design combined with dynamic weight adjustment enables the entire system to maintain high reliability in harsh working environments. Even if some IMU chips perform poorly under certain conditions, their impact can be reduced by adjusting the weights, thereby ensuring the accuracy and reliability of the final measurement results. Attached Figure Description

[0031] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0032] Figure 1 This is a flowchart of a specific embodiment of the present invention;

[0033] Figure 2 This is a functional diagram of a single IMU chip according to a specific embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of a multi-MEMS IMU chip module according to a specific embodiment of the present invention;

[0035] Figure 4 This is a zoning diagram of the normal operating temperature range of the IMU chip in this invention;

[0036] Figure 5 This is a temperature zoning diagram of a specific embodiment of the present invention;

[0037] Figure 6 This is a flowchart of the EKF fusion model according to a specific embodiment of the present invention. Detailed Implementation

[0038] To better understand the present invention, a specific example is provided below. It should be noted that the example given is provided for illustrative purposes only and is not intended to limit the scope of the invention. The present invention can be implemented and applied through other different examples and methods.

[0039] It should be noted that the illustrations in this embodiment are only schematic representations of the basic concept of the present invention. The figures only show components related to the present invention and are not drawn according to the actual number, shape, and size of the components in implementation. In actual implementation, the shape, quantity, and proportion of each component may change according to actual needs, and the component layout may be more complex.

[0040] like Figure 3 As shown, a complete MEMS IMU chip module contains multiple IMU chips, evenly distributed according to a certain arrangement rule, with equal spacing between each IMU chip and parallel alignment. Figure 2As shown, each IMU chip outputs the acceleration and angular velocity of the current motion state through an accelerometer and a gyroscope. Since there are performance differences between each IMU chip, affecting the accuracy of the final measurement results, this invention provides a performance optimization method for multi-MEMS IMU chip modules to address this technical problem. Figure 1 As shown, it includes:

[0041] Step S1: Based on the performance of different chips in different temperature ranges, evaluate the performance of each IMU chip in different temperature ranges and obtain its respective performance indicators; based on the performance indicators in each temperature range, calculate the weight of each IMU chip in different temperature ranges.

[0042] The performance evaluation includes measuring the zero bias fluctuation amplitude, zero bias instability, random walk, and scaling factor of the IMU chip in the current temperature range;

[0043] First, each performance metric is standardized to a uniform range [0, 1] for comparison; the standardization method is min-max normalization.

[0044] For a given index value Xi, such as the zero-bias fluctuation amplitude, zero-bias instability, random walk, or scaling factor in the current temperature range, the standardization formula is as follows:

[0045] ;

[0046] Where: Xmin is the minimum value of the indicator; Xmax is the maximum value of the indicator; These are standardized values, ranging from [0, 1].

[0047] Based on the standardized index values The weights of the IMU chip are calculated using a weighted average method, taking into account the weighting factors Qi of each indicator. The calculation formula is as follows:

[0048] ;

[0049] Where: n is the total number of performance indicators; Qi is the weight factor of the i-th performance indicator, satisfying... ; It is the standardized value of the i-th performance index, and its range is [0, 1].

[0050] like Figure 4 As shown, the normal operating temperature range of each IMU chip is divided into n intervals, where T1 represents the temperature range from T... min1 ℃ to T max1 ℃, T2 indicates from T min2 ℃ to T max2℃, and so on, T n-1 Indicates from T min（n-1） ℃ to T max（n-1） ℃, T n Indicates from T minn ℃ to T maxn ℃, where T max（n-1） = T minn The performance of the IMU chip was evaluated within each temperature range.

[0051] like Figure 5 As shown in the specific embodiment of the present invention, the normal operating temperature range of the IMU chip is divided into five intervals: T1 (-40℃ to -18℃), T2 (-18℃ to 4℃), T3 (4℃ to 26℃), T4 (26℃ to 48℃), and T5 (48℃ to 70℃). Based on the performance indicators within each temperature interval, the weight of each IMU chip in different temperature intervals is calculated.

[0052] The performance of IMU chips is evaluated, and the performance of each IMU chip is further refined to different axes (x, y, z). Since different chips perform differently on different axes, the weights of different axes for the same chip also vary. Specifically, the zero-bias fluctuation amplitude, zero-bias instability, random walk, and scaling factor of the current temperature range are all different for the same chip on the x, y, and z axes. Finally, based on the weight calculation method, the weight values ​​for each axis are also different.

[0053] For the same axis, the weights of different chips are calculated using a weighted average method; if the current temperature is T, the weights of multiple IMU chips in the five temperature ranges are calculated as follows:

[0054] ;

[0055] in: , This refers to the actual number of IMU chips.

[0056] Step S2: Determine whether the IMU is currently stationary based on the acceleration and angular velocity information of the IMU chip.

[0057] The acceleration components output by multiple three-axis IMU chips are respectively The acceleration modulus measured by each IMU chip is calculated as follows:

[0058] ;

[0059] By comparing the acceleration modulus with gravitational acceleration The difference is used to determine whether the change in acceleration is close to zero;

[0060] ;

[0061] in: It is the magnitude of gravitational acceleration. It is a threshold;

[0062] When the change in acceleration and angular velocity are close to zero, it is determined to be in a state of rest.

[0063] Based on the acceleration modulus of the stationary state, the acceleration modulus of all IMU chips is compared. Some IMU chips whose estimated attitude deviates significantly from that of most IMU chips are screened out, and the weight of abnormal IMU chips is reduced. The weight of each IMU chip is adjusted according to the acceleration modulus. Data fusion processing is performed on the outputs of multiple IMU chips in each temperature range to obtain the measurement results for each temperature range. The final measurement result is obtained by combining the measurement results of each temperature range.

[0064] The high-frequency component of the gyroscope accurately reflects dynamic changes in attitude, while the low-frequency component of the accelerometer requires the average acceleration over a period of time to estimate the average attitude. Under the motion state of the IMU chip, an Extended Kalman Filter (EKF) model is used to fuse the high-frequency dynamic information from the gyroscope and the low-frequency steady-state information from the accelerometer, thereby obtaining a more accurate and stable attitude estimate. The EKF fusion process is as follows: Figure 6 As shown:

[0065] 1) The first step is the prediction step, where the state vector is defined as shown in equation (6), which includes the attitude quaternion. and gyroscope deviation ;

[0066] ;

[0067] The state prediction equation is used to predict the state at the next moment, based on the current state and the angular velocity measurement from the gyroscope. , as in equation (7);

[0068] ;

[0069] in: It is the control input, that is, the measurement value of the gyroscope. It is process noise;

[0070] State transition function As shown in equation (8):

[0071] ;

[0072] in: To represent quaternion multiplication, It is the time step;

[0073] To predict the state at the next moment, it is also necessary to evaluate the uncertainty of that state. This uncertainty is the covariance. Therefore, the covariance is updated using the covariance prediction formula, as shown in equation (9):

[0074] ;

[0075] The observation equation is used to associate the accelerometer measurements with the state vector, and is in the form of equation (10);

[0076] ;

[0077] in: These are the measurements from the accelerometer. It measures noise;

[0078] Observation function Represented as equation (11):

[0079] ;

[0080] in: It is composed of quaternions This represents a rotation matrix;

[0081] 2) Enter the update step, dynamically balance the weights of prediction and measurement, and adjust the influence of measurement residuals on state update, that is, calculate the Kalman gain, as shown in equation (12):

[0082] ;

[0083] 3) By fusing predicted and measured values ​​to reduce the impact of measurement noise, the best estimate of the current state is obtained, i.e., the updated state estimate is obtained, as shown in equation (13):

[0084] ;

[0085] 4) Quantify the uncertainty of the updated state estimate to provide a basis for the prediction steps at the next time step, that is, update the covariance estimate, as shown in equation (14):

[0086] ;

[0087] Similarly, some IMU chips that estimate the pose significantly deviate from the pose estimated by most IMU chips are selected, and the weights of these abnormal IMU chips are adjusted in real time.

[0088] The adjusted weights are then weighted and averaged with the output of the IMU chip to calculate the final acceleration and angular velocity, as shown in equation (15):

[0089] ;

[0090] in: The latest weights are obtained through real-time adjustments following the steps described above. and These are the angular velocities and accelerations measured by the IMU chip or obtained by EKF fusion, respectively.

[0091] 6) Based on the adjusted weights, perform data fusion processing on the outputs of multiple IMU chips in each temperature range to obtain the measurement results for each temperature range; combine the measurement results of each temperature range to obtain the final measurement result.

[0092] This invention also provides a multi-MEMS IMU chip module, including multiple IMU chips and a data processing module. The data processing module evaluates the performance of each IMU chip in different temperature ranges and calculates its respective weight in each temperature range. It uses the acceleration and angular velocity information of the IMU chips to determine whether the current IMU is in a stationary state, determines the current acceleration mass based on the acceleration magnitude in the stationary state, and adjusts the weight ratio in real time. When the IMU chips are in motion, an extended Kalman filter model is used to fuse the high-frequency dynamic information of the gyroscope of the IMU chip with the low-frequency steady-state information of the accelerometer, filtering out and reducing the weight of abnormal IMU chips. Based on the adjusted weights, data fusion processing is applied to the final output to obtain the measurement results for each temperature range. The final measurement result is obtained by combining the measurement results of each temperature range.

[0093] The present invention also provides a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for performance optimization of a multi-MEMS IMU chip module.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A performance optimization method for a multi-MEMS IMU chip module, characterized in that: include: 1) Evaluate the performance of each IMU chip in different temperature ranges, obtain its respective performance indicators, and calculate the weight of each IMU chip in different temperature ranges based on the performance indicators in each temperature range. 2) Determine the current operating status of the IMU chip: The acceleration and angular velocity information of the IMU chip are used to determine whether the IMU chip is currently stationary. When the IMU chip is stationary, the weight of the abnormal IMU chip is reduced by comparing the acceleration magnitude of the stationary state with the current acceleration magnitude, and the weight ratio is dynamically adjusted. When the IMU chip is in motion, the extended Kalman filter model is used to fuse the high-frequency dynamic information of the IMU chip's gyroscope with the low-frequency steady-state information of the accelerometer, reduce the weight of abnormal IMU chips, and dynamically adjust the weight ratio. 3) Based on the adjusted weights, the outputs of multiple IMU chips in each temperature range are fused to obtain the measurement results for each temperature range. The final measurement result is obtained by combining the measurement results of each temperature range.

2. The performance optimization method for a multi-MEMS IMU chip module according to claim 1, characterized in that: The performance evaluation includes measuring the zero-bias fluctuation amplitude, zero-bias instability, random walk, and scaling factor of the IMU chip in the current temperature range, which are the performance indicators.

3. The performance optimization method for a multi-MEMS IMU chip module according to claim 1, characterized in that: The weights are calculated using a weighted average method, and the weights of multiple IMU chips coaxial are shown below: ； in: , The actual number of IMU chips is T, the current temperature is n, and the temperature range is n.

4. The performance optimization method for a multi-MEMS IMU chip module according to claim 1, characterized in that: In the static state, the acceleration components output by the three-axis IMU chip are respectively The acceleration modulus measured by each IMU chip is calculated as follows: ； in: , This refers to the actual number of IMU chips. By comparing the acceleration modulus with gravitational acceleration The difference is used to determine whether the change in acceleration is close to zero; ； in, It is the magnitude of gravitational acceleration. It is a threshold.

5. The performance optimization method for a multi-MEMS IMU chip module according to claim 1, characterized in that: The process of fusing the high-frequency dynamic information from the gyroscope of the IMU chip with the low-frequency steady-state information from the accelerometer using the Extended Kalman Filter (EKF) model involves the following steps: 1) The first step is the prediction step, which defines the state vector, including the attitude quaternion. Deviation from gyroscope ; ； in: This represents the state vector of the i-th IMU chip. This represents the attitude quaternion of the i-th IMU chip. This represents the deviation of the gyroscope in the i-th IMU chip; The state prediction equation is used to predict the state at the next moment, based on the current state and the angular velocity measurement from the gyroscope. As shown in the following formula; ； in: It is the control input, that is, the measurement value of the gyroscope. It is process noise; State transition function It can be expressed as follows: ； in: To represent quaternion multiplication, It is the time step; The covariance is updated using the covariance prediction formula, as shown below: ； The observation equation is used to correlate the accelerometer measurements with the state vector, and takes the form shown below: ； in: These are the measurements from the accelerometer. It measures noise; Observation function It is expressed as follows: ； in: It is composed of quaternions The rotation matrix is ​​represented; 2) Enter the update step, dynamically balance the weights of prediction and measurement, and adjust the impact of measurement residuals on state updates, which is to calculate the Kalman gain, as shown in the following formula: ； 3) By fusing the predicted and measured values, the best estimate of the current state is obtained, i.e., the updated state estimate is shown in the following formula: ； 4) Update the covariance estimate as shown in the following formula: 。 6. The performance optimization method for a multi-MEMS IMU chip module according to claim 1, characterized in that: The adjusted weights are then weighted and averaged with the IMU chip output to calculate the final acceleration and angular velocity, as shown in the following formula: ； in: It is the adjusted weight of the i-th IMU chip. and These are the angular velocity and acceleration measured by the i-th IMU chip in the k-th frame of data, or obtained after EKF fusion. This refers to the actual number of IMU chips. and These are the angular velocities and accelerations of all chips in the k-th frame of data, respectively, after weighted averaging.

7. A multi-MEMS IMU chip module, comprising multiple IMU chips and a data processing module, wherein the data processing module is used to implement the performance optimization method according to any one of claims 1 to 6.

8. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of the performance optimization method according to any one of claims 1-6.

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