Vehicle-mounted nine-axis gyroscope

Abstract

The application provides a vehicle-mounted nine-axis gyroscope, which comprises a nine-axis sensor unit, a control chip, a communication module, a power management unit and an input / output unit, wherein the nine-axis sensor unit comprises: a three-axis accelerometer, which is used for measuring linear acceleration of an object in X, Y and Z three axial directions, and calculating instantaneous speed and relative displacement of the object by detecting force suffered by the sensor in the three axial directions; a three-axis gyroscope, which is used for measuring angular velocity of the object around the three axes, directly reflecting rotation state and angular acceleration of the object, and acquiring rotation information of the object in a unit time; and a three-axis magnetometer, which is used for detecting magnetic field components of the earth magnetic field in X, Y and Z three directions, and calculating multiple geomagnetic angles of the magnetic field relative to the sensor through vector operation in a three-dimensional coordinate system.The application realizes real-time tracking and calculation of accurate heading, speed, acceleration and attitude of a vehicle through a multi-sensor fusion technology, so as to guarantee reliable navigation under various complex environments.

Description

TECHNICAL FIELD

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

[0002] With the rapid development of the automotive industry, especially the continuous innovation of automatic driving and autonomous navigation technology, unprecedented high requirements are put forward for the safety, stability and navigation accuracy of vehicles. The traditional navigation system mainly relies on the global positioning system (GPS) to realize the positioning and navigation of vehicles. However, GPS signals are easily lost or become unstable in specific environments such as tunnels, high-rise dense areas, underground parking lots, etc., which greatly limits the reliability and application range of the navigation system.

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

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

[0005] Especially important is that the miniaturized design and high integration of the nine-axis gyroscope make it easy to embed into various electronic devices of the vehicle without occupying 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 magnetic angle and quaternion in real time, providing strong support for the stable control and safe driving of the vehicle. However, the nine-axis gyroscope in the prior art still has some problems and shortcomings, such as unstable navigation system signal, insufficient road condition monitoring accuracy, untimely safety accident warning and insufficient vehicle body attitude monitoring accuracy, etc. SUMMARY

[0006] The purpose of the present application is to provide a vehicle-mounted nine-axis gyroscope, which can provide more accurate vehicle dynamic information by integrating a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer. The core advantage is that the vehicle's accurate heading, speed, acceleration and attitude can be tracked and calculated in real time through multi-sensor fusion technology, thereby ensuring reliable navigation in various complex environments.

[0007] The present application achieves the above-mentioned purpose by the following technical solutions:

[0008] A vehicle-mounted nine-axis gyroscope comprises:

[0009] A nine-axis sensor unit is used to monitor the angular velocity, acceleration and direction information of the vehicle. The nine-axis sensor unit comprises a three-axis gyroscope, a three-axis accelerometer and a three-axis magnetometer. The nine-axis sensor unit is electrically connected to a control chip through an A / D converter, and is used to convert the monitored signals into electrical signals. The three-axis accelerometer is used to measure the linear acceleration of an object in X, Y and Z three axial directions. By detecting the force on the sensor in three axial directions 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 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 X, Y and Z three directions. The magnetic field relative to the sensor is calculated through vector operation in a three-dimensional coordinate system.

[0010] A control chip is used to receive and process the electrical signals from the nine-axis sensor unit, execute an inertial navigation algorithm, and analyze the relative position and motion state of the vehicle.

[0011] A communication module is used to connect with the control chip, realize the bidirectional communication between the vehicle and the outside world, receive the vehicle speed and acceleration signals, and combine the nine-axis gyroscope sensor to position the vehicle.

[0012] A power management unit is used to manage the power supply of the vehicle-mounted nine-axis gyroscope, comprising a battery and a power management chip. The power management chip is connected with the control chip, and is used to manage the activation and dormancy of the terminal, and feedback the current state to the control chip when charging, so that the control chip calibrates the sensor and the communication module.

[0013] An input / output unit comprises a touch screen or a key panel with an indicator light, and is used to receive the control commands input by the user, and output the feedback of the control chip to the user.

[0014] The application provides a vehicle-mounted nine-axis gyroscope, which comprises the following steps of 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 high-frequency noise from the pre-processed data, and processing the filtered data by using a weighted average technique; and applying a Kalman filtering technique to perform real-time state estimation.

[0015] The application provides a vehicle-mounted nine-axis gyroscope, which applies a Kalman filtering module 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, continuously updates and optimizes state estimation of the system by combining prediction based on a known motion model and observation based on sensor measurement values; fuses the acceleration, angular velocity and geomagnetic data after Kalman filtering by using a nine-axis fusion algorithm, and calculates attitude information of an object according to fused attitude data by using a quaternion representation method; and further calculates three-axis Euler angles after obtaining the attitude information represented by the quaternion, so as to provide attitude data for attitude monitoring and an intelligent driving system of the vehicle.

[0016] The application provides a vehicle-mounted nine-axis gyroscope, and the Kalman filtering processing module specifically comprises the following steps during state estimation:

[0017] An initialization stage: setting initial state estimation and initial error covariance of the system;

[0018] A prediction stage: predicting the state and error covariance at the current time by using the state estimation at the previous time and a known motion model through a state transition matrix and a control input matrix according to a dynamic model of the system;

[0019] A measurement update stage: calculating a measurement residual according to the sensor measurement value at the current time, and fusing the measurement value into the predicted state through a Kalman gain matrix, so as to obtain the optimal state estimation at the current time;

[0020] A covariance update stage: updating the error covariance according to the updated state estimation and the Kalman gain matrix, so as to reflect the uncertainty of the updated state estimation;

[0021] An iterative processing stage: repeating the prediction stage to the covariance update stage until a predetermined iteration number is reached or a convergence condition is met, so as to obtain the final state estimation.

[0022] The application provides a vehicle-mounted nine-axis gyroscope, which fuses the acceleration, angular velocity and geomagnetic data after Kalman filtering by using a nine-axis fusion algorithm, and comprises the following steps:

[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 respectively subjected to Kalman filtering processing to remove noise and error, so as to obtain 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);

[0024] A state vector is defined, which contains a quaternion (q0, q1, q2, q3) representing the attitude of the object, the filtered angular velocity data (Wx_f, Wy_f, Wz_f) of the gyroscope is used as a control input, and the state vector at the next time is predicted through a state transition equation;

[0025] An observation model is constructed, which associates the filtered data (Ax_f, Ay_f, Az_f) of the accelerometer and the filtered data (Mx_f, My_f, Mz_f) of the magnetometer with the state vector;

[0026] An extended Kalman filtering algorithm is used to fuse the predicted state vector with the observation value obtained through the observation model; in the fusion process, the data of the accelerometer and the magnetometer are used to correct the attitude estimation obtained by integrating the gyroscope;

[0027] The quaternion (q0, q1, q2, q3) is extracted from the fused state vector, and the three-axis Euler angles of the object, including the heading angle, the pitch angle and the roll angle, are calculated through a conversion formula from the quaternion to the Euler angle;

[0028] The fused attitude information, including the three-axis Euler angle and / or the attitude represented by the quaternion, is output.

[0029] According to the vehicle-mounted nine-axis gyroscope provided by the application, the nine-axis sensor unit further comprises a sensor for monitoring the reverse acceleration generated by the collision between the terminal and the local position of the whole vehicle, and 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 for receiving and processing the data of the vehicle-mounted nine-axis gyroscope.

[0030] According to the vehicle-mounted nine-axis gyroscope provided by the application, the control chip further comprises a positioning trigger source, and when the displacement of the sensor is monitored, the Bluetooth transceiver receives the Bluetooth signal generated by the displacement, measures the received signal strength indication RSSI value of the Bluetooth beacon at the displacement position in the whole vehicle, and uses the gyroscope of the sensor to position the terminal in the whole vehicle.

[0031] According to the vehicle-mounted nine-axis gyroscope provided by the application, three-axis acceleration data of a terminal is monitored in real time, when reverse acceleration caused by a collision is monitored, current acceleration, angular velocity and geomagnetic data are recorded as initial state of an inertial navigation algorithm at the moment of collision detection, a built-in inertial navigation system of the terminal, i.e. the vehicle-mounted nine-axis gyroscope, is activated, and continuous acceleration and angular velocity data are collected starting from the initial state at the moment of collision detection, and the inertial navigation algorithm is applied for attitude estimation and position updating; based on continuous attitude and speed information output by the inertial navigation algorithm, three-dimensional relative position of the terminal relative to a collision point is calculated through integration, and relative position information of the terminal is output, and 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 the vehicle-mounted nine-axis gyroscope provided by the application, 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 characteristics of IMU data.

[0033] The cutoff frequency of the filter is set, which should be lower than the highest frequency of the effective 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, the preprocessed data is input into the configured low-pass filter, and filtering processing is performed, high-frequency noise components higher than the cutoff frequency are filtered out, and a smooth signal containing low-frequency effective dynamic information is output.

[0035] The filtered data is verified, and whether the filtering effect meets the expected requirement is evaluated by comparing the spectral characteristics, signal-to-noise ratio and other indicators of the data before and after filtering.

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

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

[0038] A time window for weighted average is set, and the window contains a certain number of latest data points.

[0039] A weight value is assigned to each data point in the time window, and the weight value is determined according to the collection time of the data point, the latest collected data point is given the largest weight, and the earlier collected data point is gradually given smaller weight.

[0040] The weighted average value is calculated, i.e. each data point in the time window is multiplied by its corresponding weight value, then all products are added, and the sum is divided by the sum of the weight values, to obtain the data after weighted average.

[0041] The data after the weighted average processing is used as the angular velocity and acceleration value at the current time 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 step of calculating the weighted average is repeatedly performed to achieve real-time weighted average processing.

[0043] Therefore, compared with the prior art, the present application has the following remarkable beneficial effects:

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

[0045] 2. The vehicle-mounted nine-axis gyroscope integrates multiple sensors in a compact unit, achieving miniaturization and lightweight of the sensors. This high-integration design not only reduces the complexity and cost of the vehicle's electronic system, but also improves the reliability and maintainability of the system.

[0046] 3. The vehicle-mounted nine-axis gyroscope uses advanced multi-sensor fusion algorithms such as Kalman filtering and complementary filtering to effectively integrate data from different sensors, improving 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 environments where GPS signals are lost or weakened, the vehicle-mounted nine-axis gyroscope can provide continuous and stable navigation information. By combining the data from the accelerometer and gyroscope, the system can update the vehicle's position and attitude in real time, ensuring the accuracy of navigation.

[0048] 5. The vehicle-mounted nine-axis gyroscope can monitor the vehicle's attitude changes in real time and timely warn the driver of possible rollover risks. In addition, in an autonomous driving system, the precise monitoring capability of the gyroscope helps to improve the response speed and decision accuracy of the driving system, ensuring safety during driving.

[0049] 6. By accurately monitoring the vehicle's attitude and acceleration changes, the vehicle-mounted nine-axis gyroscope can provide more accurate and stable driving feedback to the driver. This improvement helps the driver better understand the vehicle's dynamics, improving driving comfort and stability.

[0050] The application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1is a schematic diagram of an embodiment of a vehicle-mounted nine-axis gyroscope of the present application.

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

[0053] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the present application will be described below in detail with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0054] Reference herein to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive or alternative embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined.

[0055] Reference is made to Figure 1 with Figure 2 The present application provides a vehicle-mounted nine-axis gyroscope, comprising:

[0056] The nine-axis sensor unit is used to monitor the angular velocity, acceleration, and directional information of the vehicle. The nine-axis sensor unit comprises 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, and is used to convert the monitored signals into electrical signals. The three-axis accelerometer is used to measure the linear acceleration of an object in X, Y, and Z three axial directions. The force experienced by the sensor in the three axial directions is detected, and the instantaneous speed and relative displacement of the object are calculated by time integration. The three-axis gyroscope is used to measure the angular velocity of the object around the three axes, and directly reflects the rotation state and angular acceleration of the object, and obtains the rotation information of the object in a unit time. The three-axis magnetometer is used to detect the magnetic field components of the earth's magnetic field in X, Y, and Z three directions, and calculates multiple geomagnetic angles of the magnetic field relative to the sensor through vector operation in a three-dimensional coordinate system.

[0057] The control chip is used to receive and process the electrical signals from the nine-axis sensor unit, execute an inertial navigation algorithm, and analyze the relative position and motion state of the vehicle.

[0058] The communication module is used to connect with the control chip, realize the bidirectional communication between the vehicle and the outside world, receive the vehicle speed and acceleration signals, and combine the nine-axis gyroscope sensor to position the vehicle.

[0059] Power management unit: for managing the power supply of the vehicle-mounted nine-axis gyroscope, including battery and power management chip, the power management chip is connected with the control chip, used for managing the activation and sleep of the terminal, and feeding back the current state to the control chip when charging, and the control chip calibrates the sensor and communication module.

[0060] Input / output unit: including touch screen or key panel with indicator light, used for receiving user input control commands and outputting control chip feedback to users.

[0061] In this embodiment, first, through hardware self-checking, interface configuration and initialization of data processing module, the normal work of each component of the system can be ensured. Among them, it is necessary to ensure that the IMU is correctly connected to the control system of the vehicle, and the power supply and communication interface of the IMU are normal, so as to avoid faults in the data acquisition process. Then, the sampling rate and range of the IMU are initialized, and the filter 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 fields. It can provide real-time motion state information of objects, including acceleration, angular velocity and attitude, etc. Nine-axis IMU is usually composed of three main sensors: three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. Three-axis accelerometer is responsible for measuring the linear acceleration of object in X, Y, Z three axes, according to Newton's second law (F=ma), the velocity and displacement of object are calculated by detecting the force on the sensor; three-axis gyroscope measures the angular velocity of object around three axes, reflecting the rotation state of object, and can obtain the rotation information of object through the angular displacement per unit time; three-axis magnetometer detects the magnetic field components of earth's magnetic field in X, Y, Z three directions, and calculates the magnetic angles such as pitch angle and yaw angle through vector operation in three-dimensional coordinate system (such as arctangent function), so as to provide the direction information of object relative to earth's magnetic field, assisting attitude estimation and navigation positioning.

[0063] After the above preparation work is completed, real-time collection of IMU output data can be started, including acceleration, angular velocity and magnetic angle information, which will be the basis for subsequent analysis. Using these data, three Euler angles (roll angle, pitch angle and yaw angle) can be calculated to describe the direction and attitude of the object. Roll angle represents the rotation angle of object around front-back axis, pitch angle represents the rotation angle of object around left-right axis, and yaw angle represents the rotation angle of object around vertical axis.

[0064] After acquiring three-axis acceleration, three-axis angular velocity, and three-axis geomagnetic data, the nine-axis gyroscope may have sensor errors or external environmental disturbances, and the accuracy of the data usually needs to be optimized through calibration. The calibration process first collects the original acceleration and angular velocity data in a static state, and performs zero offset calibration to eliminate static errors. Next, the output of the geomagnetic sensor is calibrated by an ellipsoid fitting algorithm to eliminate hardware bias and external interference and ensure the accuracy of the magnetic field data. In order to improve the accuracy of angular velocity measurement, the original 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 offset 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] The installation position of the vehicle-mounted nine-axis gyroscope in this embodiment usually needs to be considered near 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 behind the vehicle cabin, on the floor of the driver's cabin, etc. The specific installation position needs to be determined according to the actual situation of the vehicle and the specifications of the gyroscope.

[0066] The terminal of this embodiment can be a standalone electronic device such as a vehicle-mounted computer, a navigation device, or a data recorder, or it can be integrated into the vehicle control system. The main functions of the terminal include receiving data sent by the gyroscope, processing and analyzing data (such as attitude calculation, position estimation, etc.), and possible data display and alarm functions.

[0067] 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, preprocessing real-time data; applying a low-pass filter to filter out high-frequency noise from the preprocessed data, using weighted average technology to process the filtered data; 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 the low-pass filter is determined according to the characteristics of the IMU data; the cutoff frequency of the filter is set, which 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, the preprocessed data is input into the configured low-pass filter, and the filtering processing is performed, the high-frequency noise component higher than the cutoff frequency is filtered out, and the signal containing low-frequency effective dynamic information is output. Smooth; verify the filtered data, compare the spectral characteristics, signal-to-noise ratio and other indicators of the data before and after filtering, and evaluate whether the filtering effect meets the expected requirements.

[0069] Among them, 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] A weighted average time window is set, which contains a certain number of latest data points;

[0072] Each data point in the time window is assigned a weight value, which is determined according to the collection time of the data point. The latest collected data point is given the largest weight, and the earlier collected data point is gradually given a smaller weight;

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

[0074] The data after weighted average processing is used as the angular velocity and acceleration value at the current time, which is used for subsequent attitude estimation;

[0075] With the continuous collection of new data, the data points and corresponding weight values in the time window are updated, and the step of calculating the weighted average value is repeated to realize real-time weighted average processing.

[0076] Specifically, when processing the data collected by the vehicle-mounted 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 errors, noise and drift. In order to improve the data quality and accurately reflect the true motion state of the vehicle, a series of data processing techniques such as low-pass filtering, weighted averaging and Kalman filtering are usually required.

[0077] Firstly, the data collection stage obtains raw angular velocity and acceleration data from sensors, which may be disturbed by high-frequency noise, affecting their accuracy and stability. Therefore, data preprocessing is crucial. In this stage, the original data needs to be cleaned to remove obvious noise and zero-point correction to ensure that the sensor readings in the static state are close to the theoretical value, thereby improving data quality.

[0078] After completing data preprocessing, a low-pass filter is first used to process the data. 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 valid low-frequency signals. This process makes the data smoother, reducing noise and environmental factors that interfere with measurement results. Low-pass filtering effectively removes unnecessary high-frequency components while retaining low-frequency dynamic information, making the data more accurately reflect the true motion state of the vehicle.

[0079] Next, the weighted average technique is used to further process the low-pass filtered data. In the weighted average process, different weight values are set to emphasize the importance of the latest data, so that the currently collected data has a greater proportion in the final calculation. This 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, reducing the impact of noise on the final results, thereby improving the reliability of attitude estimation.

[0080] Finally, Kalman filtering technology is applied to real-time state estimation of dynamic systems. Kalman filtering continuously updates the estimate of the previous state and combines the current measurement value to gradually improve the estimation accuracy. 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 crucial role in providing more stable and accurate output.

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

[0082] In the embodiment, the Kalman filtering module receives the processed three-axis acceleration (Ax, Ay, Az), three-axis angular velocity (Wx, Wy, Wz) and three-axis geomagnetic data (Mx, My, Mz) as inputs, and continuously updates and optimizes the state estimation of the system by combining the prediction based on the known motion model and the observation based on the sensor measurements; the acceleration, angular velocity and geomagnetic data processed by the Kalman filtering are fused by the nine-axis fusion algorithm, and the quaternion representation method is used to calculate the attitude information of the object according to the fused attitude data; after obtaining the attitude information represented by the quaternion, the three-axis Euler angles are further calculated, which provides the attitude data for the attitude monitoring and intelligent driving system of the vehicle.

[0083] In the state estimation process, the specific steps of the Kalman filtering processing module include:

[0084] Initialization stage: set the initial state estimation and initial error covariance of the system;

[0085] Prediction stage: according to the dynamic model of the system, use the state estimation at the previous time and the known motion model, and through the state transition matrix and the control input matrix, predict the state and error covariance at the current time;

[0086] Measurement update stage: according to the sensor measurements at the current time, calculate the measurement residual, and through the Kalman gain matrix, fuse the measurements into the predicted state, so as to obtain the optimal state estimation at the current time;

[0087] Covariance update stage: according to the updated state estimation and the Kalman gain matrix, update the error covariance to reflect the uncertainty of the updated state estimation;

[0088] Iterative processing: repeat the prediction stage to the covariance update stage until the predetermined number of iterations is reached or the convergence condition is met, so as to obtain the final state estimation.

[0089] Specifically, the acceleration, angular velocity and geomagnetic data processed by the Kalman filtering are fused by the 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 respectively processed by the 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 a state vector containing a quaternion (q0, q1, q2, q3) representing the object's pose, and use the filtered angular velocity data (Wx_f, Wy_f, Wz_f) from the gyroscope as control input to predict the next state vector through a state transition equation;

[0092] Construct 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] Use the Extended Kalman Filter algorithm to fuse the predicted state vector with the observations obtained through the observation model. During the fusion process, the data from the accelerometer and magnetometer are used to correct the pose estimate obtained by integrating the gyroscope;

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

[0095] Output the fused pose information, including the three-axis Euler angles and / or the quaternion representation of the pose.

[0096] Specific ID, Kalman filter combines the three-axis acceleration (Ax, Ay, Az), three-axis angular velocity (Wx, Wy, Wz) and three-axis magnetic data (Mx, My, Mz) provided by the IMU, and uses prediction and observation to continuously update the system's state estimation. First, use the known motion model and the previous state to predict the current state, then update the measurement value through the sensor to correct the prediction result, and calculate the error covariance to evaluate the uncertainty of the system. In this way, Kalman filter can effectively improve the response speed and accuracy of the system in dynamic environment. At the same time, the fused data can be used to calculate the object's pose, usually represented by quaternion to avoid the gimbal lock problem in traditional Euler angle representation, so as to improve the stability of pose estimation.

[0097] Quaternions are an effective representation of three-dimensional rotation, which can overcome the problem of loss of freedom in rotation process. In practical applications, the quaternion data can be used to calculate the angles between the vehicle and the x-axis and the y-axis. Generally, the x-axis represents the direction of the vehicle's forward movement, and the angle with the x-axis can determine the uphill or downhill driving state of the vehicle. Specifically, when the angle is greater than 20°, it indicates that the vehicle is climbing uphill; when the angle is less than -20°, it indicates that the vehicle is descending 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, and the angle with the y-axis can determine whether the vehicle has a risk of rolling. When the angle is greater than 10°, it indicates that the left side of the vehicle is tilted; when the angle is less than -10°, it indicates that the right side of the vehicle is tilted. These angle analyses provide important basis for vehicle attitude monitoring, which helps to optimize driving decisions and improve driving safety.

[0098] During processing, Kalman filtering is used to filter out high-frequency noise and retain low-frequency signals, resulting in more stable attitude estimation. At the same time, the nine-axis fusion algorithm and quaternion method effectively fuse the data of gyroscopes, accelerometers and magnetometers, and solve the three-axis Euler angles, avoiding the problem of gimbal lock. Finally, in the post-processing and verification stage, the fusion results will be evaluated, and the algorithm will be continuously optimized according to the feedback of practical applications to ensure the accuracy and reliability of the output results. This series of processes ensures the accuracy and stability of vehicle attitude estimation, providing 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 magnetic angle and quaternion) are transmitted to external devices through the UART interface to ensure 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 of the terminal with the local position of the whole 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. The terminal is a device or system that receives and processes vehicle-mounted nine-axis gyroscope data.

[0101] The three-axis acceleration data of the real-time monitoring terminal is monitored, when the reverse acceleration generated 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, the built-in inertial navigation system of the terminal, namely the vehicle-mounted nine-axis gyroscope, is activated, the initial state at the moment of collision detection is taken as the starting point, the acceleration and angular velocity data are continuously collected, and the inertial navigation algorithm is applied for attitude estimation and position updating; 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 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 the embodiment, the control chip further includes a positioning trigger source, when displacement of the sensor is monitored, the Bluetooth transceiver is activated to receive the Bluetooth signal generated by the displacement, the received signal strength indication RSSI value of the Bluetooth beacon at the displacement position in the whole vehicle is measured, and the position of the terminal in the whole vehicle is positioned by using the gyroscope inertial navigation of the sensor.

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

[0104] Further, the vehicle-mounted nine-axis gyroscope of the embodiment integrates multiple sensors in a compact unit, realizing miniaturization and lightweight of the sensors. Such high-integration design not only reduces the complexity and cost of the vehicle electronic system, but also improves the reliability and maintainability of the system.

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

[0106] Further, in the environment where 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 the gyroscope, the system can update the position and attitude of the vehicle in real time, ensuring the accuracy of navigation.

[0107] Further, the vehicle-mounted nine-axis gyroscope of the embodiment can monitor the attitude change of the vehicle in real time and timely warn the driver of the possible risk of rollover. In addition, in the automatic driving system, the accurate monitoring capability of the gyroscope helps to improve the response speed and decision accuracy of the driving system, ensuring the safety during driving.

[0108] Further, by precisely monitoring the vehicle's attitude and acceleration changes, the vehicle-mounted nine-axis gyroscope can provide more accurate and stable driving feedback to the driver. This improvement helps the driver better master the vehicle dynamics and improves the comfort and stability of driving.

[0109] The above embodiments are only preferred embodiments of the present application, and cannot be used to limit the scope of protection of the present application. Any non-essential changes and substitutions made by those skilled in the art on the basis of the present application shall fall within the scope of protection of the present application.

Claims

1. A vehicle-mounted nine-axis gyroscope, characterized in that, include: The nine-axis sensor unit monitors the vehicle's angular velocity, acceleration, and orientation. It 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 via an A / D converter to convert the monitored signals into electrical signals. The three-axis accelerometer measures the linear acceleration of an object along the X, Y, and Z axes. By detecting the forces acting on the sensor along these three axes and integrating the data over time, the instantaneous velocity and relative displacement of the object are calculated. The three-axis gyroscope measures the angular velocity of the object around the three axes, directly reflecting the object's rotational state and angular acceleration, and obtaining rotational information per unit time. The three-axis magnetometer detects the magnetic field components of the Earth's magnetic field in the X, Y, and Z directions, calculating multiple magnetic angles relative to the sensor through vector operations in a three-dimensional coordinate system. Control chip: Used to receive and process electrical signals from the nine-axis sensor unit, execute inertial navigation algorithms, and analyze the vehicle's relative position and motion state; Communication module: used to connect with the control chip to realize two-way communication between the vehicle and the outside world, and to receive vehicle speed and acceleration signals, and to locate the vehicle in conjunction with the nine-axis gyroscope sensor; Power Management Unit: Used to manage the power supply of the vehicle-mounted nine-axis gyroscope, including the battery and power management chip. 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, and the control chip then calibrates the sensors and communication modules. Input / output unit: including a touch screen or a button panel with indicator lights, used to receive control commands input by the user and output feedback from the control chip to the user; When using an onboard nine-axis gyroscope for data processing, the process includes: acquiring real-time three-axis acceleration, three-axis angular velocity, and three-axis geomagnetic data; preprocessing the real-time data; applying a low-pass filter to remove high-frequency noise from the preprocessed data; processing the filtered data using a weighted average technique; and applying Kalman filtering techniques for real-time state estimation. The system uses a Kalman filter module to receive processed triaxial acceleration (Ax, Ay, Az), triaxial angular velocity (Wx, Wy, Wz), and triaxial geomagnetic data (Mx, My, Mz) as input. By combining predictions based on a known motion model and observations based on sensor measurements, the system's state estimation is continuously updated and optimized. The Kalman-filtered acceleration, angular velocity, and geomagnetic data are fused using a nine-axis fusion algorithm, and quaternion representation is used to calculate the object's attitude information based on the fused attitude data. After obtaining the quaternion-represented attitude information, the triaxial Euler angles are further calculated to provide attitude data for vehicle attitude monitoring and intelligent driving systems. The process of fusing the acceleration, angular velocity, and geomagnetic data after Kalman filtering using a nine-axis fusion algorithm includes: The received triaxial acceleration data (Ax, Ay, Az), triaxial angular velocity data (Wx, Wy, Wz), and triaxial geomagnetic data (Mx, My, Mz) are processed by Kalman filtering to remove noise and errors, resulting in 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). Define a state vector containing quaternions (q0, q1, q2, q3) representing the object's attitude. Use the gyroscope-filtered angular velocity data (Wx_f, Wy_f, Wz_f) as the control input, and predict the state vector at the next moment through the state transition equation. An observation model is constructed that associates the accelerometer-filtered data (Ax_f, Ay_f, Az_f) and the magnetometer-filtered data (Mx_f, My_f, Mz_f) with the state vector; The extended Kalman filter algorithm is used to fuse the predicted state vector with the observation values ​​obtained through the observation model; during the fusion process, data from the accelerometer and magnetometer are used to correct the attitude estimate obtained by the gyroscope integration. The quaternions (q0, q1, q2, q3) are extracted from the fused state vector, and the three-axis Euler angles of the object, including yaw angle, pitch angle, and roll angle, are calculated using the quaternion to Euler angle conversion formula. Output the fused attitude information, including attitude represented by three-axis Euler angles and / or quaternions.

2. The vehicle-mounted nine-axis gyroscope according to claim 1, characterized in that: The Kalman filter processing module performs the following steps during state estimation: Initialization phase: Set the initial state estimate and initial error covariance of the system; Prediction phase: Based on the system's dynamic model, using the state estimate from the previous moment and the known motion model, the current state and error covariance are predicted through the state transition matrix and control input matrix. Measurement update phase: Based on the sensor measurements at the current moment, the measurement residual is calculated, and the measurements are fused into the predicted state through the Kalman gain matrix to obtain the optimal state estimate at the current moment; Covariance update stage: Based on the updated state estimate and Kalman gain matrix, update the error covariance to reflect the uncertainty of the updated state estimate; Iterative processing: Repeat the prediction stage to the covariance update stage until the predetermined number of iterations is reached or the convergence condition is met, so as to obtain the final state estimate.

3. 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 generated by a collision between the terminal and a local part 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. The terminal is a device or system that receives and processes data from the on-board nine-axis gyroscope.

4. The vehicle-mounted nine-axis gyroscope according to claim 1, characterized in that: The control chip also includes a positioning trigger source. When displacement of the sensor is detected, the Bluetooth transceiver is activated to receive the Bluetooth signal that caused the displacement, and the signal strength indicator RSSI value of the Bluetooth beacon at the displacement location in the vehicle is measured. The position of the terminal in the vehicle is then determined using the sensor's gyroscope inertial navigation system.

5. The vehicle-mounted nine-axis gyroscope according to claim 3, characterized in that: The system monitors the terminal's three-axis acceleration data in real time. When a collision-induced reverse acceleration is detected, the system records the current acceleration, angular velocity, and geomagnetic data at the instant of collision detection as the initial state for the inertial navigation algorithm. This activates the terminal's built-in inertial navigation system, namely the vehicle-mounted nine-axis gyroscope. Starting from the initial state at the time of collision detection, the system continuously collects acceleration and angular velocity data and applies the inertial navigation algorithm for attitude estimation and position update. Based on the continuous attitude and velocity information output by the inertial navigation algorithm, the system calculates the terminal's three-dimensional relative position to the collision point through integration and outputs the terminal's relative position information. If the terminal's relative position or motion state exceeds a preset safety range or threshold, an alarm mechanism is triggered.

6. The vehicle-mounted nine-axis gyroscope according to claim 1, characterized in that: After data preprocessing, a low-pass filter is selected as the signal processing tool, and the type of low-pass filter is determined based on the characteristics of the IMU data. Set the cutoff frequency of the filter. This cutoff frequency should be lower than the highest frequency of the effective signal and higher than the lowest frequency of the expected noise. Based on the selected filter type and cutoff frequency, calculate and determine the specific parameters of the filter, input the preprocessed data into the configured low-pass filter for filtering, filter out high-frequency noise components above the cutoff frequency, and output a smooth signal containing effective low-frequency dynamic information. The filtered data is verified by comparing the spectral characteristics and signal-to-noise ratio of the data before and after filtering to evaluate whether the filtering effect meets the expected requirements.

7. The vehicle-mounted nine-axis gyroscope according to claim 5, characterized in that, The process 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 time window for the weighted average, which contains a certain number of the latest data points; Each data point within the time window is assigned a weight value, which is determined based on the data point's collection time. The most recently collected data point is given the largest weight, while earlier collected data points are gradually given smaller weights. To calculate the weighted average, multiply each data point within the time window by its corresponding weight value, sum all the products, and then divide by the sum of the weight values ​​to obtain the weighted average data. The weighted average 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 step of calculating the weighted average is repeated to achieve real-time weighted average processing.

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