60 results about "Complementary filter" patented technology

The invention discloses a wind power generation tower pose monitoring method based on a GNSS / MEMS IMU / magnetometer combination, and the method comprises the steps: carrying out the estimation of a state vector containing the position, speed, attitude error, sensor zero offset and scale factor error through error state Kalman filtering based on the GNSS / MEMS IMU combination, and carrying out the correction and fusion of GNSS and IMU data through a lever arm effect; a complementary filtering algorithm is adopted, an error vector is constructed by combining measured values of an accelerometer and a magnetometer, a gyro angular velocity is corrected through a PI controller, an attitude quaternion is recurred, a course angle is calculated, and the course angle is used as an auxiliary observed quantity to update filtering. According to the invention, by adding course information provided by the MEMS IMU and the magnetometer, the position and the attitude are comprehensively estimated, and high-precision six-degree-of-freedom pose monitoring is realized.

The invention discloses a permanent magnet synchronous motor current dynamic coupling correction method, which comprises the following steps of collecting motor three-phase stator current, performing Clark transformation to obtain a current value under an alpha-beta static coordinate system, and meanwhile, collecting through a position sensor to obtain an electrical angle true value; performing low-pass filtering calculation on the current value under the alpha-beta static coordinate system to obtain a filtering current value, and performing operation through a sliding-mode observer and a PLL phase-locked loop to obtain an electrical angle observation value; calculating to obtain a compensation adjustment coefficient according to the true value and the observed value of the electrical angle, and further calculating to obtain a current value after phase offset compensation; and performing complementary filtering calculation on the filtering current value and the current value after phase offset compensation to obtain a coupling correction current value for current loop calculation, inputting the coupling correction current value into a motor control current loop PID algorithm, and generating a PWM driving signal to drive a motor in combination with vector control. According to the method, the problems of high-frequency interference and phase deviation of current sampling can be solved, so that the motor control efficiency and the dynamic performance are improved.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses a pipeline robot positioning method, system and device based on an odometer wheel and an IMU and a medium, and relates to the technical field of robot positioning and pipeline detection. The method comprises the following steps: determining an initial attitude angle of the pipeline robot in a static state according to acceleration data and magnetic force data collected by an IMU (Inertial Measurement Unit); based on a compensated angular velocity obtained by denoising angular velocity data collected by an IMU by using a complementary filtering algorithm, updating an initial attitude angle by using an Euler angle method to obtain an attitude angle matrix, and performing optimal estimation by using a nonlinear state estimation algorithm; performing consistency verification on the pulse data of the plurality of odometer wheels, determining corresponding effective pulse data through weighted correction, and then determining a mileage increment; and based on the optimized attitude angle matrix, the mileage increment is projected to a navigation coordinate system so as to solve the real-time position coordinate of the pipeline robot. The problems that the pipeline robot is poor in trafficability and low in positioning precision when facing a complex pipe network can be solved.

The invention discloses an unmanned aerial vehicle attitude control anomaly detection method and system based on data driving, and belongs to the technical field of unmanned aerial vehicle detection.The method comprises the steps that firstly, a complementary filtering model and an LSTM model are trained based on sensor data and flight attitude data of a four-rotor unmanned aerial vehicle in a historical time period; the trained complementary filtering model and the trained LSTM model output first attitude data and second attitude data; determining an attitude data error based on the attitude data of the previous moment output by the LSTM model and the actual attitude data of the unmanned aerial vehicle at the corresponding moment, and performing weighted fusion on the first attitude data and the second attitude data at the current moment according to the attitude data error to obtain unmanned aerial vehicle attitude prediction data; and finally, determining a confidence interval according to the residual error of the real unmanned aerial vehicle attitude data in the unmanned aerial vehicle attitude prediction data and the historical data, and detecting the attitude of the unmanned aerial vehicle according to the confidence interval. The method effectively improves the accuracy and reliability of unmanned aerial vehicle attitude control anomaly detection, and has important practical significance for improving the safety and reliability of unmanned aerial vehicle flight.

The invention relates to the technical field of broadband signal synthesis and high-speed digital-to-analog conversion, in particular to an oversampling-based real-time frequency interleaving digital-to-analog conversion system aliasing suppression method and system, and aims to solve the problem of spectrum aliasing caused by non-ideal characteristics of a digital sub-band decomposition filter and an analog filter under a real-time architecture of the system. In the invention, the sub-band decomposition is completed by adopting the linear phase complementary filter bank, so that distortion introduced by a transition band of a digital filter is avoided; by restraining system parameters such as passband and stopband frequencies and local oscillation frequency of a sub-band decomposition filter, a protection band is set by an oversampling mechanism, a frequency margin is reserved for a transition band of the filter, and aliasing components are suppressed. According to the method, extraction aliasing of a digital domain and mirror image aliasing of an analog domain are avoided at the same time from the system architecture, the frequency spectrum purity of output signals is improved, the method is suitable for a real-time system for processing continuous data streams, and technical support is provided for achieving a high-performance broadband signal source.

The invention discloses a hydraulic support attitude sensing method, device and equipment and a medium, and relates to the technical field of hydraulic support regulation, and the method comprises the steps: obtaining the relative attitude angles of a top beam and a shield beam relative to a base under a target coordinate system, and obtaining the absolute attitude angles of the top beam, the shield beam and the base under a geographic coordinate system; fusing the relative attitude angle and the absolute attitude angle to obtain attitude angles of the top beam and the shield beam; according to the method, the absolute attitude angles of the top beam, the shield beam and the base are obtained by using a complementary filtering algorithm under the condition of reducing noise interference, and the relative attitude angles and the absolute attitude angles are fused by using extended Kalman filter (EKF), so that the drift error of the IMU technology during attitude sensing of the hydraulic support is offset, and the attitude sensing accuracy of the hydraulic support is improved. And accurate attitude angles of the top beam and the shield beam of the hydraulic support are obtained.

The invention discloses a fan variable pitch bearing intelligent monitoring method and system based on multi-parameter fusion, and the method comprises the steps: respectively calculating angles based on a three-axis acceleration and a three-axis angular velocity, and obtaining a final angle through complementary filtering fusion; carrying out ensemble empirical mode decomposition on the three-axis vibration signals, respectively extracting low-frequency vibration characteristics, high-frequency vibration characteristics and angle variation based on a plurality of signal components, carrying out fusion decision on the low-frequency vibration characteristics, the high-frequency vibration characteristics and the angle variation by adopting a D-S evidence theory to obtain a total confidence coefficient, and judging a fault in combination with a preset fault threshold to obtain a first fault diagnosis result; the low-frequency vibration characteristics, the high-frequency vibration characteristics and the angle variation are input into a deep residual network diagnosis model for processing, and a second fault diagnosis result is obtained; and comparing the first fault diagnosis result with the second fault diagnosis result, outputting a final diagnosis result if the first fault diagnosis result is consistent with the second fault diagnosis result, and performing diagnosis again if the second fault diagnosis result is inconsistent. The limitation of a single sensor frequency band is broken through, a D-S evidence theory is innovatively adopted to carry out multi-source evidence fusion decision, and the fault recognition rate is improved.

The invention belongs to the technical field of positioning and navigation, and provides a geomagnetic sequence positioning method based on BiLSTM and multi-head attention. The method comprises the following steps: firstly, acquiring data of geomagnetism, an accelerometer, a gyroscope and other sensors by using a smart phone, and dividing a geomagnetism sequence into a plurality of subsequences to enhance the time sequence modeling capability; then constructing a multi-scale feature extraction model: extracting deep time sequence features of each sub-sequence by adopting a bidirectional long-short term memory network (BiLSTM), and constructing global representation through splicing; a self-adaptive multi-head attention mechanism is introduced, magnetic signal key features are captured from multiple dimensions, feature weights are dynamically adjusted in combination with a gating mechanism, and important information enhancement is achieved. And the fused multi-scale features output initial position estimation through a full connection layer. In order to further improve the positioning precision, data of a magnetometer and a gyroscope are fused through complementary filtering to correct a course angle, a geomagnetic positioning result serves as an observation value, a pedestrian track plotting result serves as a state prediction value, and final fusion positioning is achieved through particle filtering.

A multi-sensor fusion positioning method for a magnetic adsorption wall-climbing robot is suitable for a high-robustness and high-precision positioning task of a complex surface of a steel structure, and is characterized in that the method constructs a fusion framework based on an extended Kalman filter (EKF), and integrates data of three types of heterogeneous sensors including an inertial measurement unit (IMU), a wheel type odometer (Odom) and an ultra wide band (UWB); according to the method, the high-frequency dynamic response capability provided by an IMU is utilized, displacement increment estimation output by Odom is combined, and a dynamic complementary filter is constructed under the short-time scale to achieve estimation of the attitude and the displacement state; on this basis, a geometric residual weighting mechanism is introduced to optimize UWB original ranging data, and finally, a UWB positioning result based on complementary filtering state prediction and residual optimization is jointly input into an extended Kalman filter to realize position estimation of a robot in a two-dimensional space. Therefore, the positioning precision, the error convergence speed and the sensor fault-tolerant capability of the system in a complex environment are improved.

This application discloses a multi-sensor fusion method and system for measuring the ground speed of a maglev train. The method comprises the following steps: S1: fusing the detection results of two ultrasonic speed radars installed on the ground to obtain radar speed data of the maglev train; S2: fusing image data of the maglev train collected by a binocular camera installed on the ground to obtain camera speed data of the maglev train; and S3: fusing the radar speed data and camera speed data using complementary filtering to obtain the actual speed of the maglev train. This application utilizes a lower-precision, lower-frequency speed measurement device to collect raw data, and achieves higher-precision, higher-frequency speed acquisition through multiple data fusion methods, thus meeting system requirements. This method eliminates the need to install additional devices on the maglev train, make any modifications to the maglev train, or transmit data back to the ground via wireless communication or other means, thereby ensuring the safe operation of the maglev train.

The invention discloses a rotary table control method and device based on gyroscope-coded disc complementary filtering, and belongs to the field of control engineering. The method comprises the following steps: acquiring a gyroscope attitude angle measurement value of a turntable; acquiring a coded disc attitude angle measurement value of the turntable, and judging a coded disc state based on a signal fed back by the coded disc; based on the coded disc state, performing complementary filtering on the gyroscope attitude angle measurement value and the coded disc attitude angle measurement value to obtain a fused attitude angle quaternion estimation value; and calculating a pitch angle, a roll angle and a yaw angle of the rotary table based on the fusion attitude angle quaternion estimation value so as to construct a PID controller to perform steady-state control on the attitude of the rotary table. According to the scheme, the state of the coded disc is judged based on the signal fed back by the coded disc, the measurement results of the gyroscope and the coded disc are fused by using the complementary filtering algorithm, and then PID control is constructed to carry out steady-state control on the turntable, so that the accuracy, the real-time performance and the reliability of turntable attitude angle measurement control under the stormy wave condition can be improved.

The invention relates to a posture sensing method and device based on a flexible wearable robot, and belongs to the technical field of rehabilitation robot application, and the method comprises the steps: synchronously collecting standard motion data and real-time motion data of a user; performing coarse attitude calculation through a complementary filtering algorithm to obtain initial six-degree-of-freedom attitude data; calibrating the initial six-degree-of-freedom attitude data based on the deformation resistance change data, the real-time environment temperature data and a pre-constructed attitude deformation calibration model, completing attitude drift correction and motion artifact elimination, and obtaining calibrated six-degree-of-freedom attitude data; based on the calibrated six-degree-of-freedom attitude data, extracting user exclusive action characteristics and temperature correlation characteristics, and dynamically correcting core parameters of the attitude deformation calibration model according to the user exclusive action characteristics and the temperature correlation characteristics; and inputting the real-time action data into the corrected attitude deformation calibration model, and outputting real-time attitude data. The method has the effect of improving the attitude sensing precision and real-time performance.

The invention discloses an automatic driving platform of a small unmanned helicopter, which is characterized in that angular velocity, acceleration and inclination angle data are acquired in real time through a high-precision micro-mechanical sensor module, quaternions are updated in real time and normalized by combining an attitude calculation module and adopting a rotating quaternion method and a fourth-order Runge-Kutta numerical integration method, and the accuracy of the attitude calculation module is improved. The precision and the real-time performance of attitude calculation are effectively improved, and the problems of insufficient real-time performance and low precision of attitude calculation in the background technology are solved. And the data fusion module fuses data of the gyroscope, the accelerometer and the inclinometer based on a complementary filtering algorithm, corrects attitude calculation errors, compensates the defects that the precision of a sensor is low and the sensor is susceptible to noise interference, and ensures long-term stability and dynamic accuracy of attitude information. The attitude control module adopts a cascade PID controller, performs stage processing on outer ring attitude angle control and inner ring angular velocity control, solves the problem of dynamic coupling existing in attitude control, and remarkably improves the dynamic response performance and the disturbance rejection capability of the system.

This invention discloses a posture monitoring method for smart AI glasses based on multimodal fusion, relating to the field of smart wearable technology. The method includes: collecting head motion data, denoising and standardizing it to generate processed head motion data; using a complementary filter algorithm to fuse the processed head motion data with angular velocity variance, and parsing pitch and roll attitude data to generate posture angle data; analyzing the changing trends of the posture angle data through a multi-stage abnormal behavior recognition algorithm, comparing it with historical behavior patterns to obtain abnormal behavior types, and adjusting the recognition criteria to determine whether the user has an undesirable posture; triggering an alarm signal when an abnormal posture is detected; and analyzing the user's behavior patterns and environmental changes when a corresponding feedback signal is triggered to generate a posture monitoring report. This invention improves the monitoring accuracy of smart glasses, provides an efficient feedback mechanism, and enhances user safety.

The application discloses a fusion filtering method of a target tracking signal, and is based on a tracking control system composed of a master control unit, a driving unit and a motion platform; the master control unit comprises a fusion filter and a tracking loop controller; and the motion platform comprises a platform shaft system, a gyroscope and a visual sensing system; the method of the application performs complementary filtering on the miss distance and other sensor signals in an optoelectronic system; compared with the miss distance, the filtered signals can significantly improve the signal frequency and reduce the system delay, and thus the tracking performance of the optoelectronic system is improved.

The invention discloses a multi-feature fusion wrist strap type tumble detection and tumble direction identification method, which comprises the following steps of: designing and manufacturing a wrist strap type data acquisition system, acquiring triaxial acceleration, angular velocity and magnetic field intensity in real time, calculating quaternion of acquired data through a complementary filtering algorithm, and outputting a hand posture by the quaternion; constructing an acceleration, angular velocity and quaternion initial time series data set, and dividing the time series data set into a test set and a training set; a deep learning pre-training model of CNN-GRU-Attention is constructed by the training set; and taking the test set as the input of a tumble detection and tumble direction pre-training model, and carrying out performance evaluation on the tumble detection and tumble direction identification model in which quaternions are introduced as characteristic values. According to the method, key motion information is extracted from multi-source time sequence features, and time features in sequence data are better extracted, so that the problems of low accuracy, high false alarm rate, poor stability, single detection and the like of wrist strap type detection due to high degree of freedom of hand movement are solved.

A power transmission tower tilt monitoring method, system and device based on complementary filtering and particle filtering, the monitoring method first initializes a particle set based on particle filtering, then updates the tilt angle estimation value of each particle in the set based on the measurement information provided by the gyroscope, fuses the measurement information of the accelerometer and the gyroscope through the complementary filter to update the particle weight, and then obtains the tilt angle prediction value of the power transmission tower through weighted summation, and finally iterates to gradually approach the actual tilt angle. In the application, the particle state of each time step is dynamically updated according to the particle filtering, and the particle weight of each time step is dynamically updated according to the fusion information of the complementary filtering, while considering the influence of environmental noise, thereby improving the measurement accuracy of the tilt angle of the power transmission tower.

This invention relates to an attitude calculation method for an unmanned aerial vehicle (UAV) attitude reference system, belonging to the field of UAV navigation. In this invention, the collected sensor data is first filtered to obtain an initial unit quaternion from the navigation coordinate system to the vehicle coordinate system. The accelerometer output is normalized and an error vector is constructed with the normalized gravity vector. The vehicle motion mode is determined by the accelerometer and gyroscope outputs, and the gain coefficient is adaptively adjusted to calculate the gyroscope error correction, thus completing the attitude update and correcting the horizontal attitude. Simultaneously, the magnetometer output is evaluated; if the magnetometer output is valid, the heading angle is corrected, and the corrected three-axis attitude angles are output in real time. This invention uses only quaternions for calculation, avoiding the calculation of rotation matrices and improving calculation efficiency. Compared to traditional complementary filtering algorithms, it incorporates vehicle motion mode judgment and adaptive complementary filter parameter adjustment, avoiding erroneous attitude corrections by the accelerometer under high dynamic conditions. By applying a second-order complementary filter to process accelerometer and magnetometer data for attitude compensation, horizontal attitude correction can be avoided when magnetometer data is unavailable, achieving decoupling of attitude correction.

The invention discloses a multi-IMU (Inertial Measurement Unit) distributed attitude estimation adaptive fault tolerance method based on closed-loop feedback. According to the method, a closed-loop feedback architecture of'calibration data drive threshold generation, threshold guidance fault detection and detection result optimization fusion weight 'is constructed. The method comprises the following steps: deploying a plurality of IMU nodes and marking microsecond-level timestamps, and realizing online calibration by utilizing multi-surface overturning so as to obtain relative attitude constants among the nodes; dynamically generating a self-adaptive threshold strongly correlated with the node pair based on the calibration data, and continuously updating to compensate for sensor aging and temperature drift; carrying out Byzantine fault-tolerant level fault detection by adopting a space-time redundancy mechanism; and finally, performing weight-adaptive complementary filtering fusion on normal nodes according to a detection result. According to the method, the problems of threshold stiffness, calibration dependence on tools, weak fault-tolerant capability and the like in the prior art are thoroughly solved, high-reliability and high-precision attitude estimation is realized, and the method is easy to operate and maintain.

The invention discloses a rotary table pitching positioning method and device based on gyroscope-coded disc complementary filtering, and belongs to the field of rotary table positioning. The method comprises the following steps: determining a pitch angle estimated value of a turntable by utilizing an angular velocity fed back by a gyroscope; obtaining a pitch angle measurement value of the coded disc to the turntable, and judging the coded disc state based on a signal fed back by the coded disc; and on the basis of the coded disc state, performing complementary filtering on the pitch angle estimated value determined by the gyroscope and the pitch angle measured value fed back by the coded disc to obtain a fused pitch angle estimated value. According to the scheme, the advantages of the gyroscope and the coded disc are combined, so that the defect that the system reliability is insufficient when only a single sensor coded disc exists is overcome; and the coded disc state is judged based on the signal fed back by the coded disc, and the measurement results of the gyroscope and the coded disc are fused based on the coded disc state by combining a complementary filtering algorithm, so that the accuracy, real-time performance and reliability of the rotary table pitch angle measurement under the stormy wave condition can be improved.

This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a UAV servo flap system, a UAV wing, and the UAV itself. The system includes a flap actuator, an inertial measurement module, an airspeed detection module, and an onboard control module. The onboard control module fuses inertial data and airspeed data in real time through a data fusion unit, employing a composite filtering architecture combining a Kalman filter and a complementary filter. A dynamic flap angle calculation unit, based on an aerodynamic model and flight state parameters, uses a gradient descent method to optimize and solve for the optimal flap deflection angle. An error compensation unit corrects inertial measurement errors and the effects of structural elastic deformation online, thereby achieving accurate perception and real-time estimation of flight state, improving flap control accuracy and response speed, and enhancing the reliability of the UAV under complex flight conditions.

The application discloses a gait analysis method and system fusing a depth camera and an inertial sensor, and the method comprises the following steps: collecting human gait data; pre-processing the original human gait data; performing gait-related calculation by using the processed data; performing sensor data fusion by using a fusion algorithm; and performing gait analysis. The application fuses the noisy accelerometer and the gyro with drift in the inertial measurement unit by using a complementary filter, so that more accurate joint angle results are obtained, the IMU is installed on a limb in an arbitrary direction, does not depend on a uniform magnetic field, avoids noise and errors caused by magnetic field interference, is suitable for single-limb rotation, and combines the joint angle results calculated by using the depth camera data, fuses the depth camera and the IMU data, and when a motion with a larger measurement error is detected, the depth camera and the IMU are used to mutually correct the joint angle, so that the calculation precision is improved without reducing the operation efficiency.

The application provides a real-time acupoint tracking method and system and a guasha robot, and relates to the technical field of computer vision recognition. The tracking method comprises a static positioning stage, a two-stage training strategy for fusing traditional Chinese medicine meridian prior knowledge is used to improve the YOLO-Pose model, bone landmark point detection accuracy is improved through meridian perception approximate training and adaptive fine adjustment, and a standardized coordinate system is established based on the bone degree and inch method to realize accurate acupoint mapping; in the dynamic tracking stage, a two-stage optimization complementary filter is used to smooth the acupoint coordinates in the video sequence, and high-frequency jitter is inhibited through a speed smoothing mechanism and historical trajectory weighted fusion. The application solves the problems of traditional guasha relying on experience and large dynamic tracking jitter, improves image detection accuracy, optimizes the balance relationship between smoothness, response speed and accuracy of the dynamic trajectory generated by the image, and realizes high-precision and high-real-time acupoint positioning and tracking.

The invention discloses a star sensor and gyroscope data frequency domain fusion method and system based on complementary filtering, and belongs to the technical field of spaceflight, and the method comprises the following steps: obtaining a satellite attitude measured by a star sensor and a satellite triaxial angular velocity measured by a gyroscope; converting the triaxial angular velocity of the satellite measured by the gyroscope into a quaternion form; compensating the satellite attitude measured by the star sensor based on the quaternion form of the satellite angular velocity measured by the gyroscope to obtain the quaternion form of the satellite angular velocity measured by the star sensor; fusing the quaternion forms of the satellite angular velocity measured by the star sensor and the satellite angular velocity measured by the gyroscope by adopting a complementary filtering frequency domain fusion method to obtain a fused angular velocity; and performing delay compensation and interpolation on the satellite attitude measured by the star sensor by using the fusion angular velocity to obtain fused satellite attitude data. According to the invention, the data of the star sensor and the gyroscope are fused by adopting a complementary filtering frequency domain fusion method, and a fusion speed with good full-band performance is obtained.

The invention discloses a low-complexity multi-resolution structure design method for a space-time frequency sensing system. The method comprises the following steps: designing a filter bank based on frequency response masking; performing multiple interpolation on a prototype filter and a complementary filter in the space-time frequency sensing system, and then multiplying the prototype filter and the complementary filter by a shielding filter to obtain a synthesis filter; performing conjugate frequency modulation optimization: designing a prototype low-pass filter and a prototype shielding filter based on the synthesis filter, and synthesizing the prototype low-pass filter and the prototype shielding filter into a synthesis filter after conjugate frequency modulation optimization; and realizing multi-resolution configuration based on coefficient extraction: performing coefficient extraction on the synthesis filter after conjugate frequency modulation optimization to obtain equivalent prototype filters with different bandwidths, and realizing signal multi-resolution analysis. According to the invention, the resolution can be dynamically adjusted according to different conditions, the consumption of multiplier resources is significantly reduced while high performance is maintained, and the method is suitable for a complex electromagnetic environment.

The application discloses a wind power tower pose monitoring method based on GNSS / MEMS IMU / magnetometer combination, comprising the following steps: based on GNSS / MEMS IMU combination, using error state Kalman filtering to estimate a state vector containing position, velocity, attitude error and sensor zero offset, proportional factor error, and fusing GNSS and IMU data through a lever arm effect correction; using a complementary filtering algorithm, combining accelerometer and magnetometer measurement values to construct an error vector, correcting gyro angular velocity through a PI controller and recursively calculating attitude quaternions, calculating a heading angle, and taking the heading angle as auxiliary observation to update filtering. The application realizes high-precision six-degree-of-freedom pose monitoring by increasing heading information provided by MEMS IMU and a magnetometer and comprehensively estimating position and attitude.

The invention discloses a trailer rolling resistance measuring and calculating method and system based on gradient and acceleration correction, and belongs to the field of tire road surface action contact surface mechanics. Whether the number of times that the real-time longitudinal acceleration is larger than the preset maximum acceleration exceeds a set first threshold value or the number of times that the real-time angular velocity is larger than the preset maximum angular velocity exceeds a second threshold value occurs, and if not, data fusion is conducted on the real-time vehicle speed, the real-time longitudinal acceleration and the real-time angular velocity through a Kalman filter, and the final road gradient is obtained; if yes, correcting the real-time longitudinal acceleration in combination with the real-time angular velocity to obtain a corrected acceleration, and performing data fusion on the real-time vehicle speed, the corrected acceleration and the real-time angular velocity by using a complementary filter to obtain a final road gradient; and calculating the rolling resistance of the unpowered trailer based on the final road gradient. The road gradient is calculated by selecting a proper algorithm according to the road condition, and then the accurate rolling resistance is obtained.

The application discloses a kind of based on data-driven unmanned vehicle attitude control anomaly detection method and system, belong to unmanned vehicle detection technical field, this method is first based on the sensor data and flight attitude data of quadrotor unmanned vehicle of historical time period, respectively train complementary filter model and LSTM model, the first attitude data and second attitude data are outputted in trained complementary filter model and LSTM model;Then based on the attitude data error of last time attitude data and corresponding time unmanned vehicle actual attitude data determined by LSTM model output, according to attitude data error, the first attitude data and second attitude data of current time are weighted fusion, obtain unmanned vehicle attitude prediction data;Finally, according to the residual error of unmanned vehicle attitude prediction data and true unmanned vehicle attitude data in historical data determines confidence interval, according to confidence interval, the attitude of unmanned vehicle is detected;The method effectively improves the accuracy and reliability of unmanned vehicle attitude control anomaly detection, and has important practical significance for improving the safety and reliability of unmanned vehicle flight.

The invention discloses an unmanned aerial vehicle navigation system and method in a high-dynamic throwing environment. The system comprises a mother inertial navigation system, a main inertial navigation system, a backup inertial navigation system, a satellite navigation module, a barometer, an extended Kalman filtering module and a complementary filter. The method comprises the following steps: carrying out transfer alignment on a main inertial navigation by a mother inertial navigation before throwing, judging whether the main inertial navigation exceeds a range after throwing, updating an extended Kalman filter by using a main inertial navigation attitude when the main inertial navigation does not exceed the range, carrying out attitude estimation by switching to a complementary filter based on a backup inertial navigation when the main inertial navigation does not exceed the range, and carrying out weighted fusion on double inertial navigation attitudes after the motion is stable. And finally, after the satellite signal is recovered, resetting the extended Kalman filter and injecting navigation data. According to the invention, continuous and reliable output of navigation information in a high-dynamic throwing process is realized, and adaptability and control stability of the unmanned aerial vehicle in an extreme environment are improved.