A new IMU module design method

Abstract

A novel IMU module design method belongs to the field of navigation and positioning technology, and includes the following steps: S1, turntable calibration: use a 6-position turntable calibration algorithm to calibrate the normal temperature zero bias, scale factor, and cross-axis coupling error of the IMU module; S2, temperature compensation: perform multiple temperature cycle experiments on the IMU module to verify its temperature rise and fall consistency, and obtain the zero bias value corresponding to each temperature point, and then use the temperature compensation algorithm to perform full temperature zero bias compensation; S3, low-pass filtering: filter out part of the high-frequency noise and interference, and improve the accuracy and stability of the angular velocity and acceleration measured by the IMU; S4, downsampling: reduce the frequency of the IMU to the use frequency range by the mean value; S5, the IMU module is shipped. Based on an accurate zero-speed judgment algorithm, the present invention can provide a vehicle zero-speed mark and more accurate IMU six-axis data and real-time attitude information, and can improve the positioning accuracy of the combined navigation system without a GNSS signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of navigation and positioning, and specifically relates to a novel IMU module design method. Background Art

[0002] Inertial Navigation System (INS) is an independent navigation and positioning system that uses an inertial measurement unit (IMU) composed of gyroscopes and accelerometers to sense the movement of the carrier in the inertial system. It has outstanding advantages such as continuous output of carrier position information, high short-term navigation accuracy, and complete independence. It is widely used in the fields of vehicle, ship, aircraft navigation, tactical and strategic navigation, etc. IMU (Inertial Measurement Unit) is a device that measures the three-axis angular velocity and acceleration of an object. Gyroscopes and accelerometers are installed on three orthogonal axes in an IMU, with a total of 6 degrees of freedom, to measure the angular velocity and acceleration of an object in three-dimensional space, namely: "6-axis IMU". The error of IMU is composed of zero bias, scale factor, cross-axis coupling error, etc.

[0003] The compensation of IMU error is of great significance for maintaining the accuracy of the GNSS / INS integrated navigation system. By calculating the ALLAN variance, the bias instability and random walk of the accelerometer and gyroscope can be obtained. Bias instability refers to the change of the IMU bias value during operation, which is a random drift within a certain range. The IMU error can be calibrated more effectively through turntable calibration, temperature compensation and other methods, but the bias instability of the IMU cannot be changed, that is, the effect of calibration will weaken over time.

[0004] In a vehicle environment, we often face some parking scenarios. Combined with some attitude information, the zero bias of the accelerometer and gyroscope at the current moment can be re-estimated to a certain extent. Although the existing IMU module can provide IMU 6-axis data and attitude information, the current zero-speed state can be obtained through IMU data, but there is a certain misjudgment rate; in addition, most IMU modules are calibrated through factory calibration to have zero bias error, scale factor error, cross-axis coupling and other errors. However, as the use time accumulates, the calibration parameters will also change, resulting in poor IMU accuracy, and the attitude information it provides will also drift. Summary of the invention

[0005] The present invention provides a novel IMU module design method, which can provide a vehicle zero-speed mark and more accurate IMU six-axis data and real-time attitude information based on an accurate zero-speed judgment algorithm, and can improve the positioning accuracy of the integrated navigation system in the absence of a GNSS signal.

[0006] The technical solution adopted by the present invention is:

[0007] A novel IMU module design method includes the following steps:

[0008] S1. Turntable calibration: Use the 6-position turntable calibration algorithm to calibrate the normal temperature zero bias, scale factor, and cross-axis coupling error of the IMU module;

[0009] S2. Temperature compensation: Perform multiple temperature cycle experiments on the IMU module to verify its temperature rise and fall consistency, and obtain the zero bias value corresponding to each temperature point. Then use the temperature compensation algorithm (table lookup method, polynomial fitting temperature compensation, segmented polynomial fitting temperature compensation) to perform full temperature zero bias compensation.

[0010] S3, low-pass filtering: filter out some high-frequency noise and interference, improve the accuracy and stability of angular velocity and acceleration measured by IMU; automatically adjust the required noise according to different IMUs.

[0011] S4, downsampling: reduce the frequency of IMU to the usage frequency range by averaging;

[0012] S5, IMU module shipped from the factory.

[0013] Furthermore, the internal algorithm flow of the IMU module includes the following steps:

[0014] S11, IMU 6-axis data input;

[0015] S12, data preprocessing, update the latest IMU 6-axis data through calibration parameters, expressed as:

[0016]

[0017] Among them, W x , W y , W z Respectively represent the calibrated gyroscope x, y, z axis data; w x 、w y 、w z Respectively represent the gyroscope x, y, and z axis data before calibration; K gx , K gy , K gz Respectively represent the gyroscope scale factor; b gx , b gy , b gz Respectively represent the gyroscope zero bias; S gyx , S gzx , S gzy , S gxy , S gxz , S gyzRespectively represent the gyroscope installation error factor; a x 、a y 、a z Respectively represent the acceleration x, y, and z axis data before calibration; A x , A y , A z Respectively represent the calibrated gyroscope x, y, and z axis data; K ax , K ay , K az Respectively represent the gyroscope scale factor; b ax , b ay , b az Respectively represent the gyroscope zero bias; S ayx , S azx , S azy , S axy , S axz , S ayz They represent the acceleration installation error factors respectively;

[0018] S13, judging the current zero speed state by a zero speed judgment algorithm;

[0019] S14, if it is in the zero-speed state, the gyroscope output value in the zero-speed interval is calculated and recorded as zero bias, and then the process goes to step S15, and the zero bias and temperature at this time are updated to FLASH. This method can ensure the freshness of the temperature zero bias of the gyroscope in FLASH; if it is not in the zero-speed state, the process goes directly to step S16;

[0020] S15, updating the gyroscope data using the latest zero bias;

[0021] S16, updating the attitude through the accelerometer and gyroscope data;

[0022] S17, output IMU six-axis data, zero-speed status, and attitude information.

[0023] Furthermore, in step S13, the zero speed determination algorithm comprises the following steps:

[0024] S131: Set a sliding window, the size of which is N, and set the gyroscope single-axis zero-speed threshold threthod;

[0025] By testing multiple IMU modules from the same batch, the absolute values ​​of the up and down fluctuations of multiple IMU modules in a static state are obtained.

[0026] S132: Score the updated i-th IMU, and the score is zuptscore i If the score is less than threthod, get 0 points, if the score is greater than threthod, get X i Points, specifically expressed as:

[0027]

[0028] Among them, gyrz represents the input gyroscope Z-axis data, scator_max represents the amplification factor; X i indicates a score greater than the threshold;

[0029] scorezuptscore i They are all integers. Because the part greater than the threshold is amplified by the scator_max factor, this judgment method is very sensitive to the IMU vibration at the start of acceleration, and is also relatively sensitive to some slight vibrations, which can fully meet the in-vehicle scenarios.

[0030] S133: Calculate the zero speed score zuptwindow of the entire interval of the sliding window i :

[0031]

[0032] Where N represents the size of the sliding window;

[0033] S134: If the score in the sliding window is zuptwindow j If it is greater than threshold_window, it is considered that the IMU movement amplitude is large, and the movement state is recorded as zuptstate i If it is 0, it is in motion state, otherwise it is in zero speed state, recorded as 1;

[0034]

[0035] Where threshold_window represents the zero-speed score threshold of the sliding window;

[0036] S135: If it is in the zero speed state, the current gyroscope data and temperature value are used as the latest temperature zero bias.

[0037] Beneficial effects of the present invention:

[0038] (1) The present invention proposes a novel IMU module design method, which can output six-axis data of accelerometer and gyroscope, three-axis attitude, and zero-speed mark, and update the static zero bias of IMU in real time through zero-speed judgment.

[0039] (2) The zero-speed judgment method proposed in the present invention can enable the IMU to give a more accurate zero-speed mark, providing customers with richer IMU information.

[0040] (3) Based on the zero-speed judgment, the temperature-bias data of the IMU is updated in real time, which can effectively solve the problem of IMU bias changing over time. At the same time, with the optimization of the bias, the IMU can provide more stable attitude information.

[0041] The traditional zero-speed judgment method requires the variance of the sliding window to be calculated and a threshold to be set according to the variance. The calculation process is complicated and the misjudgment rate is high. Compared with the traditional method, the method proposed in the present invention is simpler, has a lower algorithm complexity, and is more sensitive to small movements. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is the internal algorithm flow chart of the IMU module;

[0043] Figure 2 It is the gyroscope Z-axis data;

[0044] Figure 3 It is the zero speed detection and speed diagram. DETAILED DESCRIPTION

[0045] A novel IMU module design method includes the following steps:

[0046] S1. Turntable calibration: Use the 6-position turntable calibration algorithm to calibrate the normal temperature zero bias, scale factor, and cross-axis coupling error of the IMU module;

[0047] S2. Temperature compensation: Perform multiple temperature cycle experiments on the IMU module to verify its temperature rise and fall consistency, and obtain the zero bias value corresponding to each temperature point. Then use the temperature compensation algorithm (table lookup method with 20°C intervals) to perform full-temperature zero bias compensation.

[0048] S3, low-pass filtering: filter out some high-frequency noise and interference to improve the accuracy and stability of angular velocity and acceleration measured by IMU;

[0049] S4, downsampling: reduce the frequency of IMU to the usage frequency range by averaging;

[0050] S5, IMU module shipped from the factory.

[0051] The internal algorithm flow of the IMU module is as follows: Figure 1 As shown, the following steps are included:

[0052] S11, IMU 6-axis data input;

[0053] S12, data preprocessing, update the latest IMU 6-axis data through calibration parameters, expressed as:

[0054]

[0055] Among them, W x , W y , W z Respectively represent the calibrated gyroscope x, y, z axis data; w x 、w y 、w z Respectively represent the gyroscope x, y, and z axis data before calibration; K gx , K gy , K gz Respectively represent the gyroscope scale factor; b gx , b gy , b gz Respectively represent the gyroscope zero bias; S gyx , S gzx , S gzy , S gxy , S gxz , S gyz Respectively represent the gyroscope installation error factor; a x 、a y 、a z Respectively represent the acceleration x, y, and z axis data before calibration; A x , A y , A z Respectively represent the calibrated gyroscope x, y, and z axis data; K ax , K ay , K az Respectively represent the gyroscope scale factor; b ax , b ay , b az Respectively represent the gyroscope zero bias; S ayx , S azx , S azy , S axy , S axz , S ayz They represent the acceleration installation error factors respectively;

[0056] S13, judging the current zero speed state by a zero speed judgment algorithm, including the following steps:

[0057] S131: Set a sliding window, the size of which is N, and set the gyroscope single-axis zero-speed threshold threthod;

[0058] By testing multiple IMU modules from the same batch, the absolute values ​​of the up and down fluctuations of multiple IMU modules in a static state are obtained.

[0059] S132: Score the updated i-th IMU, and the score is zuptscore i If the score is less than threthod, get 0 points, if the score is greater than threthod, get X i Points, specifically expressed as:

[0060]

[0061]

[0062] Among them, gyrz represents the input gyroscope Z-axis data, scator_max represents the amplification factor; X i indicates a score greater than the threshold;

[0063] scorezuptscore i They are all integers. Because the part greater than the threshold is amplified by the scator_max factor, this judgment method is very sensitive to the IMU vibration at the start of acceleration, and is also relatively sensitive to some slight vibrations, which can fully meet the in-vehicle scenarios.

[0064] Figure 2 It is the Z-axis change of the gyroscope when the IMU is stationary and moving.

[0065] S133: Calculate the zero speed score zuptwindow of the entire interval of the sliding window i :

[0066]

[0067] Where N represents the size of the sliding window;

[0068] S134: If the score in the sliding window is zuptwindow j If it is greater than threshold_window, it is considered that the IMU movement amplitude is large, and the movement state is recorded as zuptstate i If it is 0, it is in motion state, otherwise it is in zero speed state, recorded as 1;

[0069]

[0070] Where threshold_window represents the zero-speed score threshold of the sliding window;

[0071] Figure 3 It is the result of comparing the zero speed detected by the IMU with the vehicle speed. As can be seen from the figure, compared with the actual speed, the accuracy reaches 100%.

[0072] S135: If it is in the zero speed state, the current gyroscope data and temperature value are used as the latest temperature zero bias.

[0073] S14, if it is in the zero-speed state, the gyroscope output value in the zero-speed interval is calculated and recorded as zero bias, and then the process goes to step S15, and the zero bias and temperature at this time are updated to FLASH. This method can ensure the freshness of the temperature zero bias of the gyroscope in FLASH; if it is not in the zero-speed state, the process goes directly to step S16;

[0074] S15, updating the gyroscope data using the latest zero bias;

[0075] S16, updating the attitude through the accelerometer and gyroscope data;

[0076] S17, output IMU six-axis data, zero-speed status, and attitude information.

[0077] In the present invention, (1) the IMU module hardware includes an IMU chip, FLASH, and MCU. FLASH is used to store parameters for IMU turntable calibration and temperature compensation. MCU is used for zero-speed update, parameter compensation, heading and other algorithms. The turntable calibration and stability compensation of the IMU always have a certain timeliness, so it is far from enough to rely solely on the calibration parameters to ensure the accuracy and stability of the IMU data. (2) The installation error, proportional factor and zero bias of the IMU are calibrated by the turntable. The order is to calibrate the accelerometer first and then the gyroscope. (3) The full-temperature zero bias of the IMU is calibrated by the temperature box. Here, the data of the IMU module at -40℃, -20℃, 0℃, 20℃, 40℃, 60℃, and 80℃ are collected at intervals of 20℃, and calibrated as its temperature-zero bias data. The time for full-temperature calibration is reduced by increasing the temperature interval. (4) The IMU’s zero-speed judgment algorithm is used to update the IMU’s temperature-zero bias in real time. This method can complement the full-temperature calibration and improve the IMU’s calibration efficiency as a whole. At the same time, it can overcome the drawback that the calibration parameters become invalid over time.

Claims

1. A novel IMU module design method, characterized in that: The following steps are involved: S1. Turntable calibration: Use the 6-position turntable calibration algorithm to calibrate the normal temperature zero bias, scale factor, and cross-axis coupling error of the IMU module; S2. Temperature compensation: Perform multiple temperature cycle experiments on the IMU module to verify its temperature rise and fall consistency, obtain the zero bias value corresponding to each temperature point, and then use the temperature compensation algorithm to perform full temperature zero bias compensation; S3, low-pass filtering: filter out some high-frequency noise and interference to improve the accuracy and stability of angular velocity and acceleration measured by IMU; S4, downsampling: reduce the frequency of IMU to the usage frequency range by averaging; S5, IMU module shipped from the factory.

2. A novel IMU module design method as claimed in claim 1, characterized in that: The internal algorithm flow of the IMU module includes the following steps: S11, IMU 6-axis data input; S12, data preprocessing, update the latest IMU 6-axis data through calibration parameters, expressed as: Among them, W x , W y , W z Respectively represent the calibrated gyroscope x, y, z axis data; w x 、w y 、w z Respectively represent the gyroscope x, y, and z axis data before calibration; K gx , K gy , K gz Respectively represent the gyroscope scale factor; b gx , b gy , b gz Respectively represent the gyroscope zero bias; S gyx , S gzx ,S gzy ,S gxy ,S gxz ,S gyz Respectively represent the gyroscope installation error factor; a x 、a y 、a z Respectively represent the acceleration x, y, and z axis data before calibration; A x , A y , A z Respectively represent the calibrated gyroscope x, y, and z axis data; K ax , K ay , K az Respectively represent the gyroscope scale factor; b ax , b ay , b az Respectively represent the gyro zero bias; S ayx , S azx , S azy , S axy , S axz , S ayz They represent the acceleration installation error factors respectively; S13, judging the current zero speed state by a zero speed judgment algorithm; S14, if it is in the zero-speed state, calculate the gyroscope output value in the zero-speed interval, record it as zero bias, and enter step S15 to update the zero bias and temperature at this time to FLASH; if it is not in the zero-speed state, directly enter the state of step S16; S15, updating the gyroscope data using the latest zero bias; S16, updating the attitude through the accelerometer and gyroscope data; S17, output IMU six-axis data, zero-speed status, and attitude information.

3. A novel IMU module design method as claimed in claim 2, characterized in that: In step S13, the zero speed judgment algorithm includes the following steps: S131: Set a sliding window, the size of which is N, and set the gyroscope single-axis zero-speed threshold threthod; S132: Score the updated i-th IMU, and the score is zuptscore i If the score is less than threthod, get 0 points, if the score is greater than threthod, get X i Points, specifically expressed as: Among them, gyrz represents the input gyroscope Z-axis data, scator_max represents the amplification factor; X i indicates a score greater than the threshold; S133: Calculate the zero speed score zuptwindow of the entire interval of the sliding window i : Where N represents the size of the sliding window; S134: If the score in the sliding window is zuptwindow j If it is greater than threshold_window, it is considered that the IMU movement amplitude is large, and the movement state is recorded as zuptstate i If it is 0, it is in motion state, otherwise it is in zero speed state, recorded as 1; Where threshold_window represents the zero-speed score threshold of the sliding window; S135: If it is in the zero speed state, the current gyroscope data and temperature value are used as the latest temperature zero bias.

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