Vehicle-mounted nine-axis gyroscope

Abstract

The invention provides a vehicle-mounted nine-axis gyroscope, which comprises a nine-axis sensor unit, a control chip, a communication module, a power supply management unit and an input / output unit, and the nine-axis sensor unit comprises a triaxial accelerometer used for measuring linear accelerations of an object in X, Y and Z axial directions, the instant speed and the relative displacement of the object are calculated by detecting the force borne by the sensor in the three axial directions; the three-axis gyroscope is used for measuring the angular velocity of the object around three axes, directly reflecting the rotating state and the angular acceleration of the object and acquiring the rotating information of the object in unit time; and the three-axis geomagnetic instrument is used for detecting magnetic field components of an earth magnetic field in X, Y and Z directions and calculating a plurality of geomagnetic angles of the magnetic field relative to the sensor through vector operation in a three-dimensional coordinate system. Through a multi-sensor fusion technology, the accurate course, speed, acceleration and attitude of the vehicle are tracked and calculated in real time, so that reliable navigation in various complex environments is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile inertial navigation, and in particular to a vehicle-mounted nine-axis gyroscope. Background Art

[0002] With the rapid development of the automotive industry, especially the continuous innovation of autonomous driving and autonomous navigation technology, unprecedented high requirements have been placed on vehicle safety, stability and navigation accuracy. Traditional navigation systems mainly rely on the Global Positioning System (GPS) to achieve vehicle positioning and navigation. However, GPS signals are easily lost or become unstable in specific environments such as tunnels, high-rise buildings, and underground parking lots, which greatly limits the reliability and application scope of navigation systems.

[0003] In order to meet the growing demand for high-precision navigation and safety in the automotive industry, vehicle-mounted sensor technology has been significantly developed. Among them, the nine-axis gyroscope, as a high-precision sensor that integrates a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, has gradually become an indispensable component of modern automobiles due to its excellent performance and wide application potential.

[0004] The nine-axis gyroscope can measure the vehicle's angular velocity, attitude, acceleration and other multi-dimensional information in real time and accurately. Through advanced multi-sensor fusion algorithms such as Kalman filtering and complementary filtering, it can effectively integrate these data information and monitor key parameters such as vehicle acceleration, deceleration, speed, and heading in real time. This high-precision data fusion not only provides a reliable foundation for the vehicle's inertial navigation system, but also provides continuous and stable navigation information for the vehicle in environments where the GPS signal is missing or weakened, greatly improving the robustness and applicability of the navigation system.

[0005] More importantly, the nine-axis gyroscope's miniaturized design and high integration make it easy to embed into various electronic devices in the vehicle without taking up too much space. At the same time, its high-precision measurement capability enables the vehicle to calculate and update its key dynamic state information such as three-axis acceleration, three-axis angular velocity, three-axis Euler angle, three-axis geomagnetic angle and quaternion in real time, providing strong support for the vehicle's stable control and safe driving. However, the nine-axis gyroscope in the prior art still has some problems and shortcomings, such as unstable navigation system signals, insufficient road condition monitoring accuracy, untimely safety accident warnings, and insufficient vehicle posture monitoring accuracy. Summary of the invention

[0006] The purpose of the present invention is to provide a vehicle-mounted nine-axis gyroscope. By integrating a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, the nine-axis gyroscope can provide more accurate vehicle dynamic information, especially when the traditional navigation system signal is unstable or lost. Its core advantage is that through multi-sensor fusion technology, it can track and calculate the vehicle's accurate heading, speed, acceleration and attitude in real time, thereby ensuring reliable navigation in various complex environments.

[0007] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0008] A vehicle-mounted nine-axis gyroscope, comprising:

[0009] Nine-axis sensor unit: used to monitor the angular velocity, acceleration and direction information of the vehicle. The nine-axis sensor unit includes a three-axis gyroscope, a three-axis accelerometer and a three-axis magnetometer. The nine-axis sensor unit is electrically connected to the control chip through an A / D converter to convert the monitored signal into an electrical signal; among them, the three-axis accelerometer is used to measure the linear acceleration of the object in the three axes of X, Y and Z. By detecting the force exerted on the sensor in the three axes and combining time integration, the instantaneous speed and relative displacement of the object are calculated; the three-axis gyroscope is used to measure the angular velocity of the object around the three axes, directly reflecting the rotation state and angular acceleration of the object, and obtaining the rotation information of the object in unit time; the three-axis magnetometer is used to detect the magnetic field components of the earth's magnetic field in the three directions of X, Y and Z, and calculates multiple geomagnetic angles of the magnetic field relative to the sensor through vector operations in the three-dimensional coordinate system;

[0010] Control chip: used to receive and process the electrical signals from the nine-axis sensor unit, execute the inertial navigation algorithm, and analyze the relative position and motion state of the vehicle;

[0011] Communication module: used to connect with the control chip to realize two-way communication between the vehicle and the outside world, and used to receive vehicle speed and acceleration signals, and locate the vehicle in combination with the nine-axis gyroscope sensor;

[0012] Power management unit: used to manage the power supply of the on-board nine-axis gyroscope, including batteries and power management chips. The power management chip is connected to the control chip to manage the activation and sleep of the terminal. At the same time, it feeds back the current status to the control chip when charging, and the control chip then calibrates the sensor and communication module;

[0013] Input / output unit: includes a touch screen or a key panel with indicator lights, which is used to receive control commands input by the user and output feedback from the control chip to the user.

[0014] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, when using the vehicle-mounted nine-axis gyroscope for data processing, it includes: collecting real-time three-axis acceleration, three-axis angular velocity and three-axis geomagnetic data, and pre-processing the real-time data; applying a low-pass filter to filter out high-frequency noise from the pre-processed data, and using weighted averaging technology to process the filtered data; and applying Kalman filtering technology to perform real-time state estimation.

[0015] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, a Kalman filter module is applied to receive three-axis acceleration (Ax, Ay, Az), three-axis angular velocity (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) after data processing as input, and the state estimation of the system is continuously updated and optimized by combining predictions based on known motion models and observations based on sensor measurements; the acceleration, angular velocity and geomagnetic data processed by the Kalman filter are fused through a nine-axis fusion algorithm, and the quaternion representation method is used to calculate the posture information of the object according to the fused posture data; after obtaining the posture information represented by the quaternion, the three-axis Euler angle is further solved to provide posture data for the vehicle's posture monitoring and intelligent driving system.

[0016] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, when the Kalman filter processing module performs state estimation, the specific steps include:

[0017] Initialization phase: setting the system's initial state estimate and initial error covariance;

[0018] Prediction stage: Based on the dynamic model of the system, the state estimate at the previous moment and the known motion model are used to predict the state and error covariance at the current moment through the state transfer matrix and the control input matrix;

[0019] Measurement update phase: Calculate the measurement residual based on the sensor measurement value at the current moment, and fuse the measurement value into the predicted state through the Kalman gain matrix to obtain the optimal state estimate at the current moment;

[0020] Covariance update phase: Based on the updated state estimate and Kalman gain matrix, the error covariance is updated to reflect the uncertainty of the updated state estimate;

[0021] Iterative processing: Repeat the prediction phase to the covariance update phase until the predetermined number of iterations is reached or the convergence condition is met, thereby obtaining the final state estimate.

[0022] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, the acceleration, angular velocity and geomagnetic data processed by Kalman filtering are fused through a nine-axis fusion algorithm, including:

[0023] The received three-axis acceleration data (Ax, Ay, Az), three-axis angular velocity data (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) are processed by Kalman filtering to remove noise and errors, and the filtered acceleration data (Ax_f, Ay_f, Az_f), angular velocity data (Wx_f, Wy_f, Wz_f) and geomagnetic data (Mx_f, My_f, Mz_f) are obtained;

[0024] Define the state vector, which contains the quaternion (q0, q1, q2, q3) representing the object's posture, use the angular velocity data (Wx_f, Wy_f, Wz_f) filtered by the gyroscope as the control input, and predict the state vector at the next moment through the state transfer equation;

[0025] Build an observation model that relates the filtered accelerometer data (Ax_f, Ay_f, Az_f) and the filtered magnetometer data (Mx_f, My_f, Mz_f) to the state vector;

[0026] The predicted state vector is fused with the observations obtained through the observation model using the extended Kalman filter algorithm. During the fusion process, the attitude estimate obtained by integrating the gyroscope is corrected using the data from the accelerometer and magnetometer.

[0027] Extract the quaternion (q0, q1, q2, q3) from the fused state vector, and calculate the three-axis Euler angle of the object, including heading angle, pitch angle, and roll angle, through the conversion formula from quaternion to Euler angle;

[0028] Output the fused attitude information, including the attitude represented by three-axis Euler angles and / or quaternion.

[0029] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, the nine-axis sensor unit also includes a sensor for monitoring the reverse acceleration generated by the collision between the terminal and a local position of the entire vehicle. When the reverse acceleration generated by the collision is monitored, the terminal is activated, and the inertial navigation algorithm is continuously performed to analyze the relative position of the terminal; wherein the terminal is a device or system that receives and processes the data of the vehicle-mounted nine-axis gyroscope.

[0030] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, the control chip also includes a positioning trigger source. When the sensor is monitored to have shifted, the Bluetooth transceiver is activated to receive the Bluetooth signal that generates the displacement, the received signal strength indication RSSI value of the Bluetooth beacon at the position where the displacement occurs in the vehicle is measured, and the position of the gyroscope inertial navigation positioning terminal in the vehicle is located using the sensor.

[0031] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, the three-axis acceleration data of the terminal is monitored in real time. When the reverse acceleration caused by the collision is monitored, at the moment of collision detection, the current acceleration, angular velocity and geomagnetic data are recorded as the initial state of the inertial navigation algorithm, and the inertial navigation system built into the terminal, that is, the vehicle-mounted nine-axis gyroscope, is activated. Starting from the initial state at the time of collision detection, the acceleration and angular velocity data are continuously collected, and the inertial navigation algorithm is applied to perform attitude estimation and position update; based on the continuous attitude and speed information output by the inertial navigation algorithm, the three-dimensional relative position of the terminal relative to the collision occurrence point is calculated by integration, and the relative position information of the terminal is output. If the relative position or motion state of the terminal exceeds a preset safety range or threshold, an alarm mechanism is triggered.

[0032] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, after data preprocessing, a low-pass filter is selected as a signal processing tool, and the type of the low-pass filter is determined according to the IMU data characteristics;

[0033] Set the filter cutoff frequency, which should be lower than the highest frequency of the valid signal and higher than the lowest frequency of the expected noise;

[0034] According to the selected filter type and cutoff frequency, the specific parameters of the filter are calculated and determined, and the pre-processed data is input into the configured low-pass filter for filtering, filtering out high-frequency noise components higher than the cutoff frequency, and outputting a smooth signal containing low-frequency effective dynamic information;

[0035] Verify the filtered data and evaluate whether the filtering effect meets the expected requirements by comparing the spectral characteristics, signal-to-noise ratio and other indicators of the data before and after filtering.

[0036] According to a vehicle-mounted nine-axis gyroscope provided by the present invention, the filtered data is processed using a weighted average technology, including:

[0037] After low-pass filtering, a series of filtered angular velocity and acceleration data are obtained;

[0038] Set a weighted average time window that contains a certain number of the latest data points;

[0039] Assign a weight value to each data point in the time window. The weight value is determined by the time when the data point was collected. The most recently collected data point is given the largest weight, while the earlier collected data points are gradually given smaller weights.

[0040] Calculate the weighted average, that is, multiply each data point in the time window by its corresponding weight value, then add all the products and divide them by the sum of the weight values ​​to get the weighted average data;

[0041] The weighted averaged data is used as the angular velocity and acceleration values ​​at the current moment for subsequent attitude estimation;

[0042] As new data is continuously collected, the data points and corresponding weight values ​​within the time window are updated, and the steps of calculating the weighted average are repeated to achieve real-time weighted average processing.

[0043] It can be seen that compared with the prior art, the present invention has the following significant beneficial effects:

[0044] 1. The in-vehicle nine-axis gyroscope of the present invention integrates a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and can accurately measure the vehicle's angular velocity, attitude, acceleration and other multi-dimensional information in real time. This high-precision measurement capability provides a solid foundation for the vehicle's navigation, control and safety performance.

[0045] 2. The in-vehicle nine-axis gyroscope of the present invention integrates multiple sensors into a compact unit, realizing the miniaturization and lightness of the sensor. This highly integrated design not only reduces the complexity and cost of the vehicle electronic system, but also improves the reliability and maintainability of the system.

[0046] 3. The in-vehicle nine-axis gyroscope of the present invention adopts advanced multi-sensor fusion algorithms, such as Kalman filtering and complementary filtering, which can effectively integrate data from different sensors and improve the overall performance and accuracy of the system. This fusion technology enables the vehicle to maintain stable navigation and attitude control under various driving conditions.

[0047] 4. In an environment where the GPS signal is lost or weakened, the vehicle-mounted nine-axis gyroscope of the present invention can provide continuous and stable navigation information. By combining the data of the accelerometer and the gyroscope, the system can update the vehicle's position and posture in real time to ensure the accuracy of navigation.

[0048] 5. The in-vehicle nine-axis gyroscope of the present invention can monitor the posture changes of the vehicle in real time and promptly warn the driver of the possible risk of rollover. In addition, in the automatic driving system, the precise monitoring capability of the gyroscope helps to improve the response speed and decision-making accuracy of the driving system, ensuring the safety during driving.

[0049] 6. The present invention can provide the driver with more accurate and stable driving feedback by accurately monitoring the vehicle's posture and acceleration changes. This improvement helps the driver better understand the vehicle's dynamics and improve driving comfort and stability.

[0050] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1The present invention is a schematic diagram of a vehicle-mounted nine-axis gyroscope embodiment.

[0052] Figure 2 It is a flow diagram of a method implemented in an embodiment of a vehicle-mounted nine-axis gyroscope of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. 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.

[0054] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] See also Figure 1 and Figure 2 The present invention provides a vehicle-mounted nine-axis gyroscope, comprising:

[0056] Nine-axis sensor unit: used to monitor the vehicle's angular velocity, acceleration and direction information. The nine-axis sensor unit includes a three-axis gyroscope, a three-axis accelerometer and a three-axis magnetometer. The nine-axis sensor unit is electrically connected to the control chip through an A / D converter to convert the monitored signals into electrical signals; among them, the three-axis accelerometer is used to measure the linear acceleration of the object in the three axes of X, Y and Z. By detecting the forces exerted on the sensor in the three axes and combining time integration, the instantaneous speed and relative displacement of the object are calculated; the three-axis gyroscope is used to measure the angular velocity of the object around the three axes, directly reflecting the rotation state and angular acceleration of the object, and obtaining the rotation information of the object in unit time; the three-axis magnetometer is used to detect the magnetic field components of the earth's magnetic field in the three directions of X, Y and Z, and calculates multiple geomagnetic angles of the magnetic field relative to the sensor through vector operations in the three-dimensional coordinate system.

[0057] Control chip: used to receive and process electrical signals from the nine-axis sensor unit, execute inertial navigation algorithms, and analyze the relative position and motion status of the vehicle.

[0058] Communication module: used to connect with the control chip to realize two-way communication between the vehicle and the outside world, and used to receive vehicle speed and acceleration signals, and locate the vehicle in combination with the nine-axis gyroscope sensor.

[0059] Power management unit: used to manage the power supply of the vehicle-mounted nine-axis gyroscope, including batteries and power management chips. The power management chip is connected to the control chip and is used to manage the activation and sleep of the terminal. At the same time, it feeds back the current status to the control chip when charging. The control chip then calibrates the sensor and communication module.

[0060] Input / output unit: includes a touch screen or a key panel with indicator lights, which is used to receive control commands input by the user and output feedback from the control chip to the user.

[0061] In this embodiment, firstly, the normal operation of each component of the system can be ensured by initializing the hardware self-check, interface configuration and data processing module. Among them, it is necessary to ensure that the IMU is correctly connected to the vehicle's control system and that the power supply and communication interface of the IMU are working properly to avoid failures during the data acquisition process. Next, the sampling rate and range of the IMU are initialized, and the filtering algorithm is calibrated to obtain the zero bias data of the IMU.

[0062] Nine-axis IMU is an important sensor device, widely used in aerospace, automobile, robot, virtual reality and smart phone. It can provide real-time information about the motion state of an object, including acceleration, angular velocity and attitude. Nine-axis IMU is usually composed of three main sensors: three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. The three-axis accelerometer is responsible for measuring the linear acceleration of the object in the three axes of X, Y and Z. According to Newton's second law (F=ma), the speed and displacement of the object are calculated by detecting the force applied to the sensor; the three-axis gyroscope measures the angular velocity of the object around the three axes, reflecting the rotation state of the object, and can obtain the rotation information of the object through the angular displacement per unit time; the three-axis magnetometer detects the magnetic field components of the earth's magnetic field in the three directions of X, Y and Z, and calculates the geomagnetic angles such as the pitch angle and yaw angle of the magnetic field through vector operations in the three-dimensional coordinate system (such as the inverse tangent function), thereby providing the direction information of the object relative to the geomagnetic field, assisting attitude estimation and navigation positioning.

[0063] After completing the above preparations, you can start collecting data output by the IMU in real time, including acceleration, angular velocity, and geomagnetic angle information, which will serve as the basis for subsequent analysis. Using this data, you can calculate three Euler angles (roll angle, pitch angle, and yaw angle) to describe the direction and attitude of the object. The roll angle represents the rotation angle of the object around the front-to-back axis, the pitch angle represents the rotation angle of the object around the left-right axis, and the yaw angle represents the rotation angle of the object around the vertical axis.

[0064] After the nine-axis gyroscope obtains three-axis acceleration, three-axis angular velocity, and three-axis geomagnetic data, the accuracy of the data usually needs to be optimized through calibration due to possible sensor errors or external environmental interference. The calibration process first collects the original acceleration and angular velocity data in a stationary state and performs zero bias calibration to eliminate static errors. Next, the output of the geomagnetic sensor is calibrated through an ellipsoid fitting algorithm to eliminate hardware bias and external interference to ensure the accuracy of the magnetic field data. In order to improve the accuracy of angular velocity measurement, the raw angular velocity data is also processed to remove noise and errors. Finally, the system adjusts the appropriate range according to the specific application requirements and performs zero bias calibration again to ensure accurate measurement of the sensor within the entire range. Through this series of calibration operations, the nine-axis gyroscope can provide more accurate and reliable attitude estimation and navigation information.

[0065] Regarding the installation position of the vehicle-mounted nine-axis gyroscope in this embodiment, it is usually necessary to consider the vicinity of the center of gravity of the vehicle in order to more accurately perceive the overall motion state of the vehicle. At the same time, the installation position should avoid strong vibration and electromagnetic interference to ensure the normal operation of the gyroscope and the accuracy of the data. According to the specific structure and design of the vehicle, the vehicle-mounted nine-axis gyroscope may be installed at the center of mass of the vehicle, on the floor of the rear compartment, on the floor of the cab, etc. The specific installation position needs to be determined according to the actual situation of the vehicle and the specification requirements of the gyroscope.

[0066] The terminal of this embodiment can be an independent electronic device, such as a vehicle-mounted computer, a navigator or a data recorder, or a part integrated in a vehicle control system. The main functions of the terminal include receiving data sent by the gyroscope, performing data processing and analysis (such as attitude solution, position estimation, etc.), and possible data display and alarm functions.

[0067] When using the on-board nine-axis gyroscope for data processing, it includes: collecting real-time three-axis acceleration, three-axis angular velocity and three-axis geomagnetic data, and pre-processing the real-time data; applying a low-pass filter to filter out high-frequency noise from the pre-processed data, and using weighted averaging technology to process the filtered data; and applying Kalman filtering technology for real-time state estimation.

[0068] Among them, after data preprocessing, a low-pass filter is selected as a signal processing tool, and the type of low-pass filter is determined according to the characteristics of IMU data; the cutoff frequency of the filter is set, and the cutoff frequency should be lower than the highest frequency of the effective signal and higher than the lowest frequency of the expected noise; according to the selected filter type and cutoff frequency, the specific parameters of the filter are calculated and determined, and the preprocessed data is input into the configured low-pass filter for filtering, filtering out high-frequency noise components higher than the cutoff frequency, and outputting a smooth signal containing low-frequency effective dynamic information; the filtered data is verified, and by comparing the spectral characteristics, signal-to-noise ratio and other indicators of the data before and after filtering, it is evaluated whether the filtering effect meets the expected requirements.

[0069] The filtered data is processed using a weighted average technique, including:

[0070] After low-pass filtering, a series of filtered angular velocity and acceleration data are obtained;

[0071] Set a weighted average time window that contains a certain number of the latest data points;

[0072] Assign a weight value to each data point in the time window. The weight value is determined by the time when the data point was collected. The most recently collected data point is given the largest weight, while the earlier collected data points are gradually given smaller weights.

[0073] Calculate the weighted average, that is, multiply each data point in the time window by its corresponding weight value, then add all the products and divide them by the sum of the weight values ​​to get the weighted average data;

[0074] The weighted averaged data is used as the angular velocity and acceleration values ​​at the current moment for subsequent attitude estimation;

[0075] As new data is continuously collected, the data points and corresponding weight values ​​within the time window are updated, and the steps of calculating the weighted average are repeated to achieve real-time weighted average processing.

[0076] Specifically, when processing the data collected by the on-board nine-axis gyroscope, the angular velocity and acceleration measured by sensors such as gyroscopes and accelerometers are often affected by various errors, such as measurement error, noise, and drift. In order to improve the data quality and accurately reflect the real motion state of the vehicle, a series of data processing technologies are usually required, such as low-pass filtering, weighted averaging, and Kalman filtering.

[0077] First, the data acquisition stage obtains the original angular velocity and acceleration data from the sensor. These data may be interfered by high-frequency noise, affecting their accuracy and stability. Therefore, data preprocessing is crucial. At this stage, the raw data needs to be cleaned, obvious noise removed, and zero-point correction performed to ensure that the sensor readings in a stationary state are close to the theoretical value, thereby improving data quality.

[0078] After completing the data preprocessing, the data is first processed using a low-pass filter. By selecting appropriate filter parameters and setting an appropriate cutoff frequency, the low-pass filter can remove high-frequency noise above the cutoff frequency and retain effective low-frequency signals. This process makes the data smoother and reduces the interference of noise and environmental factors on the measurement results. Low-pass filtering effectively removes unnecessary high-frequency components while retaining low-frequency dynamic information, allowing the data to more accurately reflect the actual motion state of the vehicle.

[0079] Next, the low-pass filtered data is further processed using weighted averaging technology. In the weighted averaging process, different weight values ​​are set to emphasize the importance of the latest data, so that the currently collected data has a larger proportion in the final calculation, which can reduce the impact of historical data on the results and more accurately reflect real-time dynamic changes. Therefore, weighted averaging helps to further smooth the data and reduce the impact of noise on the final results, thereby improving the reliability of posture estimation.

[0080] Finally, Kalman filtering technology is applied to real-time state estimation of dynamic systems. Kalman filtering gradually improves the estimation accuracy by continuously updating the estimate of the previous state and combining it with the current measurement value. It can effectively process noisy data and track the state changes of the system in real time, thereby improving the prediction accuracy of the motion state. In the case of dynamic changes and noise interference, Kalman filtering plays a vital role and can provide more stable and accurate output.

[0081] By combining low-pass filtering, weighted averaging and Kalman filtering, the data quality can be significantly improved to obtain a more stable and reliable data sequence. These finely processed data provide a solid foundation for subsequent posture analysis and motion state inference, helping to achieve more accurate dynamic tracking and analysis.

[0082] In this embodiment, a Kalman filter module is applied to receive the three-axis acceleration (Ax, Ay, Az), three-axis angular velocity (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) after data processing as input, and continuously update and optimize the state estimation of the system by combining the prediction based on the known motion model and the observation based on the sensor measurement value; the acceleration, angular velocity and geomagnetic data after Kalman filter processing are fused through the nine-axis fusion algorithm, and the quaternion representation is used to calculate the posture information of the object according to the fused posture data; after obtaining the posture information represented by the quaternion, the three-axis Euler angle is further solved to provide posture data for the vehicle's posture monitoring and intelligent driving system.

[0083] Among them, when the Kalman filter processing module performs state estimation, the specific steps include:

[0084] Initialization phase: setting the system's initial state estimate and initial error covariance;

[0085] Prediction stage: Based on the dynamic model of the system, the state estimate at the previous moment and the known motion model are used to predict the state and error covariance at the current moment through the state transfer matrix and the control input matrix;

[0086] Measurement update phase: Calculate the measurement residual based on the sensor measurement value at the current moment, and fuse the measurement value into the predicted state through the Kalman gain matrix to obtain the optimal state estimate at the current moment;

[0087] Covariance update phase: Based on the updated state estimate and Kalman gain matrix, the error covariance is updated to reflect the uncertainty of the updated state estimate;

[0088] Iterative processing: Repeat the prediction phase to the covariance update phase until the predetermined number of iterations is reached or the convergence condition is met, thereby obtaining the final state estimate.

[0089] Specifically, the acceleration, angular velocity and geomagnetic data processed by Kalman filtering are fused through a nine-axis fusion algorithm, including:

[0090] The received three-axis acceleration data (Ax, Ay, Az), three-axis angular velocity data (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) are processed by Kalman filtering to remove noise and errors, and the filtered acceleration data (Ax_f, Ay_f, Az_f), angular velocity data (Wx_f, Wy_f, Wz_f) and geomagnetic data (Mx_f, My_f, Mz_f) are obtained;

[0091] Define the state vector, which contains the quaternion (q0, q1, q2, q3) representing the object's posture, use the angular velocity data (Wx_f, Wy_f, Wz_f) filtered by the gyroscope as the control input, and predict the state vector at the next moment through the state transfer equation;

[0092] Build an observation model that relates the filtered accelerometer data (Ax_f, Ay_f, Az_f) and the filtered magnetometer data (Mx_f, My_f, Mz_f) to the state vector;

[0093] The predicted state vector is fused with the observations obtained through the observation model using the extended Kalman filter algorithm. During the fusion process, the attitude estimate obtained by integrating the gyroscope is corrected using the data from the accelerometer and magnetometer.

[0094] Extract the quaternion (q0, q1, q2, q3) from the fused state vector, and calculate the three-axis Euler angle of the object, including heading angle, pitch angle, and roll angle, through the conversion formula from quaternion to Euler angle;

[0095] Output the fused attitude information, including the attitude represented by three-axis Euler angles and / or quaternion.

[0096] Specifically, the Kalman filter combines the three-axis acceleration (Ax, Ay, Az), three-axis angular velocity (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) provided by the IMU, and continuously updates the system's state estimate by prediction and observation. First, the current state is predicted using the known motion model and the previous state, and then the sensor's measurement value is observed and updated, the prediction result is corrected, and the error covariance is calculated to evaluate the uncertainty of the system. In this way, the Kalman filter can effectively improve the system's response speed and accuracy to dynamic environments. At the same time, the fused data can be used to calculate the attitude of the object, usually represented by quaternions to avoid the universal lock problem in the traditional Euler angle representation, thereby improving the stability of the attitude estimation.

[0097] Quaternion is an effective way to represent three-dimensional rotation, which can overcome the problem of loss of freedom that may occur in the Euler angle during rotation. In practical applications, the angle between the vehicle and the x-axis and y-axis can be calculated through quaternion data. Usually, the x-axis represents the direction of the vehicle's forward movement. The angle with the x-axis can be used to determine whether the vehicle is driving uphill or horizontally. Specifically, when the angle is greater than 20°, it means that the vehicle is going uphill; when the angle is less than -20°, it means that the vehicle is going downhill; when the angle is between -20° and 20°, the vehicle is in a horizontal driving state. At the same time, the y-axis represents the direction perpendicular to the ground. The angle with the y-axis can be used to determine whether the vehicle has a roll risk. When the angle is greater than 10°, it means that the left side of the vehicle is tilted; when the angle is less than -10°, it means that the right side of the vehicle is tilted. These angle analyses provide an important basis for vehicle posture monitoring, which helps to optimize driving decisions and improve driving safety.

[0098] During the processing, Kalman filtering is used to filter out high-frequency noise and retain low-frequency signals, thereby obtaining a more stable attitude estimation. At the same time, the nine-axis fusion algorithm and quaternion method effectively fuse the data of the gyroscope, accelerometer, and magnetometer to solve the three-axis Euler angle and avoid the gimbal lock problem. Finally, in the post-processing and verification stage, the fusion results will be evaluated, and the algorithm will be continuously optimized based on actual application feedback to ensure the accuracy and reliability of the output results. This series of processes ensures the accuracy and stability of vehicle attitude estimation and provides solid support for intelligent driving systems.

[0099] Finally, the processed IMU data (including three-axis acceleration, three-axis angular velocity, three-axis Euler angle, three-axis geomagnetic angle and quaternion) is transmitted to external devices through the UART interface to ensure the real-time, reliability and integrity of the data.

[0100] In this embodiment, the nine-axis sensor unit also includes a sensor for monitoring the reverse acceleration generated by the collision between the terminal and a local position of the vehicle. When the reverse acceleration generated by the collision is detected, the terminal is activated, and the inertial navigation algorithm is continuously performed to analyze the relative position of the terminal; wherein the terminal is a device or system that receives and processes the data of the vehicle-mounted nine-axis gyroscope.

[0101] Among them, the three-axis acceleration data of the terminal is monitored in real time. When the reverse acceleration caused by the collision is monitored, the current acceleration, angular velocity and geomagnetic data are recorded as the initial state of the inertial navigation algorithm at the moment of collision detection, and the built-in inertial navigation system of the terminal, that is, the on-board nine-axis gyroscope, is activated. Starting from the initial state at the time of collision detection, the acceleration and angular velocity data are continuously collected, and the inertial navigation algorithm is applied for attitude estimation and position update; based on the continuous attitude and velocity information output by the inertial navigation algorithm, the three-dimensional relative position of the terminal relative to the collision point is calculated by integration, and the relative position information of the terminal is output. If the relative position or motion state of the terminal exceeds the preset safety range or threshold, the alarm mechanism is triggered.

[0102] In this embodiment, the control chip also includes a positioning trigger source. When the sensor is detected to be displaced, the Bluetooth transceiver is activated to receive the Bluetooth signal that generates the displacement, and the received signal strength indication RSSI value of the Bluetooth beacon at the displaced position in the vehicle is measured, and the gyroscope inertial navigation positioning terminal of the sensor is used to locate the position in the vehicle.

[0103] In summary, the vehicle-mounted nine-axis gyroscope of this embodiment integrates a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and can accurately measure the vehicle's angular velocity, attitude, acceleration and other multi-dimensional information in real time. This high-precision measurement capability provides a solid foundation for the vehicle's navigation, control and safety performance.

[0104] Furthermore, the vehicle-mounted nine-axis gyroscope of this embodiment integrates multiple sensors into a compact unit, realizing miniaturization and lightness of the sensor. This highly integrated design not only reduces the complexity and cost of the vehicle electronic system, but also improves the reliability and maintainability of the system.

[0105] Furthermore, the vehicle-mounted nine-axis gyroscope of this embodiment adopts advanced multi-sensor fusion algorithms, such as Kalman filtering and complementary filtering, which can effectively integrate data from different sensors and improve the overall performance and accuracy of the system. This fusion technology enables the vehicle to maintain stable navigation and attitude control under various driving conditions.

[0106] Furthermore, in this embodiment, when the GPS signal is lost or weakened, the vehicle-mounted nine-axis gyroscope can provide continuous and stable navigation information. By combining the data of the accelerometer and gyroscope, the system can update the vehicle's position and posture in real time to ensure the accuracy of navigation.

[0107] Furthermore, the on-board nine-axis gyroscope of this embodiment can monitor the posture changes of the vehicle in real time and promptly warn the driver of the possible risk of rollover. In addition, in the automatic driving system, the precise monitoring capability of the gyroscope helps to improve the response speed and decision-making accuracy of the driving system and ensure the safety during driving.

[0108] Furthermore, by accurately monitoring the vehicle's attitude and acceleration changes, the on-board nine-axis gyroscope can provide the driver with more accurate and stable driving feedback. This improvement helps the driver better understand the vehicle's dynamics and improves driving comfort and stability.

[0109] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A vehicle-mounted nine-axis gyroscope, characterized in that: include: Nine-axis sensor unit: used to monitor the angular velocity, acceleration and direction information of the vehicle. The nine-axis sensor unit includes a three-axis gyroscope, a three-axis accelerometer and a three-axis magnetometer. The nine-axis sensor unit is electrically connected to the control chip through an A / D converter to convert the monitored signal into an electrical signal; among them, the three-axis accelerometer is used to measure the linear acceleration of the object in the three axes of X, Y and Z. By detecting the force exerted on the sensor in the three axes and combining time integration, the instantaneous speed and relative displacement of the object are calculated; the three-axis gyroscope is used to measure the angular velocity of the object around the three axes, directly reflecting the rotation state and angular acceleration of the object, and obtaining the rotation information of the object in unit time; the three-axis magnetometer is used to detect the magnetic field components of the earth's magnetic field in the three directions of X, Y and Z, and calculates multiple geomagnetic angles of the magnetic field relative to the sensor through vector operations in the three-dimensional coordinate system; Control chip: used to receive and process the electrical signals from the nine-axis sensor unit, execute the inertial navigation algorithm, and analyze the relative position and motion state of the vehicle; Communication module: used to connect with the control chip to realize two-way communication between the vehicle and the outside world, and used to receive vehicle speed and acceleration signals, and locate the vehicle in combination with the nine-axis gyroscope sensor; Power management unit: used to manage the power supply of the on-board nine-axis gyroscope, including batteries and power management chips. The power management chip is connected to the control chip to manage the activation and sleep of the terminal. At the same time, it feeds back the current status to the control chip when charging, and the control chip then calibrates the sensor and communication module; Input / output unit: includes a touch screen or a key panel with indicator lights, which is used to receive control commands input by the user and output feedback from the control chip to the user.

2. The vehicle-mounted nine-axis gyroscope according to claim 1, characterized in that: When using the on-board nine-axis gyroscope for data processing, it includes: collecting real-time three-axis acceleration, three-axis angular velocity and three-axis geomagnetic data, and pre-processing the real-time data; applying a low-pass filter to filter out high-frequency noise from the pre-processed data, and using weighted averaging technology to process the filtered data; and applying Kalman filtering technology for real-time state estimation.

3. The vehicle-mounted nine-axis gyroscope according to claim 2, characterized in that: The Kalman filter module is applied to receive the processed three-axis acceleration (Ax, Ay, Az), three-axis angular velocity (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) as input, and continuously updates and optimizes the system state estimation by combining the prediction based on the known motion model and the observation based on the sensor measurement value; the acceleration, angular velocity and geomagnetic data processed by the Kalman filter are fused through the nine-axis fusion algorithm, and the quaternion representation is used to calculate the posture information of the object according to the fused posture data; after obtaining the posture information represented by the quaternion, the three-axis Euler angle is further solved to provide posture data for the vehicle's posture monitoring and intelligent driving system.

4. The vehicle-mounted nine-axis gyroscope according to claim 2, characterized in that: When the Kalman filter processing module performs state estimation, the specific steps include: Initialization phase: setting the system's initial state estimate and initial error covariance; Prediction stage: Based on the dynamic model of the system, the state estimate at the previous moment and the known motion model are used to predict the state and error covariance at the current moment through the state transfer matrix and the control input matrix; Measurement update phase: Calculate the measurement residual based on the sensor measurement value at the current moment, and fuse the measurement value into the predicted state through the Kalman gain matrix to obtain the optimal state estimate at the current moment; Covariance update phase: Based on the updated state estimate and Kalman gain matrix, the error covariance is updated to reflect the uncertainty of the updated state estimate; Iterative processing: Repeat the prediction phase to the covariance update phase until the predetermined number of iterations is reached or the convergence condition is met, thereby obtaining the final state estimate.

5. The vehicle-mounted nine-axis gyroscope according to claim 3, characterized in that: The acceleration, angular velocity and geomagnetic data processed by Kalman filtering are fused through a nine-axis fusion algorithm, including: The received three-axis acceleration data (Ax, Ay, Az), three-axis angular velocity data (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) are processed by Kalman filtering to remove noise and errors, and the filtered acceleration data (Ax_f, Ay_f, Az_f), angular velocity data (Wx_f, Wy_f, Wz_f) and geomagnetic data (Mx_f, My_f, Mz_f) are obtained; Define the state vector, which contains the quaternion (q0, q1, q2, q3) representing the object's posture, use the angular velocity data (Wx_f, Wy_f, Wz_f) filtered by the gyroscope as the control input, and predict the state vector at the next moment through the state transfer equation; Build an observation model that relates the filtered accelerometer data (Ax_f, Ay_f, Az_f) and the filtered magnetometer data (Mx_f, My_f, Mz_f) to the state vector; The predicted state vector is fused with the observations obtained through the observation model using the extended Kalman filter algorithm. During the fusion process, the attitude estimate obtained by integrating the gyroscope is corrected using the data from the accelerometer and magnetometer. Extract the quaternion (q0, q1, q2, q3) from the fused state vector, and calculate the three-axis Euler angle of the object, including heading angle, pitch angle, and roll angle, through the conversion formula from quaternion to Euler angle; Output the fused attitude information, including the attitude represented by three-axis Euler angles and / or quaternion.

6. The vehicle-mounted nine-axis gyroscope according to claim 1, characterized in that: The nine-axis sensor unit also includes a sensor for monitoring the reverse acceleration caused by a collision between the terminal and a local position of the vehicle. When the reverse acceleration caused by the collision is detected, the terminal is activated and the inertial navigation algorithm is continuously performed to analyze the relative position of the terminal. The terminal is a device or system that receives and processes the data of the vehicle-mounted nine-axis gyroscope.

7. The vehicle-mounted nine-axis gyroscope according to claim 1, characterized in that: The control chip also includes a positioning trigger source. When the sensor is detected to be displaced, the Bluetooth transceiver is activated to receive the Bluetooth signal that generates the displacement, and the received signal strength indication RSSI value of the Bluetooth beacon at the displaced position in the vehicle is measured, and the gyroscope inertial navigation positioning terminal of the sensor is used to locate the position in the vehicle.

8. The vehicle-mounted nine-axis gyroscope according to claim 6, characterized in that: The three-axis acceleration data of the terminal is monitored in real time. When the reverse acceleration caused by the collision is detected, the current acceleration, angular velocity and geomagnetic data are recorded as the initial state of the inertial navigation algorithm at the moment of collision detection, and the built-in inertial navigation system of the terminal, that is, the on-board nine-axis gyroscope, is activated. Starting from the initial state at the time of collision detection, the acceleration and angular velocity data are continuously collected, and the inertial navigation algorithm is applied for attitude estimation and position update; based on the continuous attitude and velocity information output by the inertial navigation algorithm, the three-dimensional relative position of the terminal relative to the collision point is calculated by integration, and the relative position information of the terminal is output. If the relative position or motion state of the terminal exceeds the preset safety range or threshold, the alarm mechanism is triggered.

9. The vehicle-mounted nine-axis gyroscope according to claim 2, characterized in that: After data preprocessing, a low-pass filter is selected as a signal processing tool, and the type of low-pass filter is determined according to the characteristics of the IMU data; Set the filter cutoff frequency, which should be lower than the highest frequency of the valid signal and higher than the lowest frequency of the expected noise; According to the selected filter type and cutoff frequency, the specific parameters of the filter are calculated and determined, and the pre-processed data is input into the configured low-pass filter for filtering, filtering out high-frequency noise components higher than the cutoff frequency, and outputting a smooth signal containing low-frequency effective dynamic information; Verify the filtered data and evaluate whether the filtering effect meets the expected requirements by comparing the spectral characteristics, signal-to-noise ratio and other indicators of the data before and after filtering.

10. The method according to claim 8, characterized in that The method of using a weighted average technique to process the filtered data includes: After low-pass filtering, a series of filtered angular velocity and acceleration data are obtained; Set a weighted average time window that contains a certain number of the latest data points; Assign a weight value to each data point in the time window. The weight value is determined by the time when the data point was collected. The most recently collected data point is given the largest weight, while the earlier collected data points are gradually given smaller weights. Calculate the weighted average, that is, multiply each data point in the time window by its corresponding weight value, then add all the products and divide them by the sum of the weight values ​​to get the weighted average data; The weighted averaged data is used as the angular velocity and acceleration values ​​at the current moment for subsequent attitude estimation; As new data is continuously collected, the data points and corresponding weight values ​​within the time window are updated, and the steps of calculating the weighted average are repeated to achieve real-time weighted average processing.

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