19 results about "Complementary filter" patented technology

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 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 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.

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.

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.

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.

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 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 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.

The application relates to a 3D simulation head movement method and device, a storage medium and an electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: acquiring posture data of the actual head posture of a target patient at present through a nine-axis IMU built in a convenient eyepiece; pre-processing and calibrating the posture data respectively to obtain processed data; fusing gyroscope data, accelerometer data and magnetometer data in the processed data through a complementary filtering algorithm to obtain a head posture solution result of the target patient; generating a target quaternion under a world coordinate system according to the head posture solution result, the target quaternion being a quaternion representing the actual head posture; adjusting the posture of a head three-dimensional model of the target patient to the actual head posture according to the target quaternion; and synchronously displaying the head three-dimensional model in the actual head posture and the actual video frame of the target patient at present in a terminal. The application has the effect of improving the accuracy of examination results.

The invention discloses a floating type wind power platform attitude monitoring and early warning control system. The system comprises an attitude sensing module, a data preprocessing module, a central processing module, an early warning module, a regulation and control execution module and a power supply module. The attitude sensing module collects data in multiple dimensions, after dual preprocessing of wavelet threshold denoising and Kalman filtering, the central processing module calculates attitude parameters through a complementary filtering algorithm, the attitude abnormality level is judged by combining a BP neural network dynamic early warning threshold model, and then the early warning module is triggered to conduct graded early warning. Meanwhile, the regulation and control execution module is driven to correct the posture hierarchically; and the power supply module adopts photovoltaic and lithium battery cooperative power supply. According to the invention, high-precision real-time monitoring, dynamic intelligent early warning and active linkage regulation and control of the platform attitude are realized, the early warning accuracy and the platform operation safety are improved, and the method is suitable for a deep and far sea floating type wind power platform scene.

The application discloses a pose solution method based on complementary filtering and unscented Kalman fusion, and particularly relates to the technical field of waterways, and the specific solution steps are as follows: S1: for a nonlinear system, the system state vector is iteratively updated by using unscented Kalman filtering; the application fuses Mohony complementary filtering and unscented Kalman filtering (UKF) algorithm, replaces the pose angle directly calculated from acceleration and magnetic field intensity information with the pose angle obtained by solving based on the complementary filtering algorithm from the perspective of making the measurement information more accurate, improves the overall pose solution accuracy; meanwhile, the framework of the fusion algorithm is based on the unscented Kalman filtering algorithm, the probability distribution of the nonlinear function is approximated by unscented transformation, the high-order truncation error caused by Taylor expansion of the extended Kalman filtering algorithm is avoided, and the pose solution accuracy for the nonlinear system is improved.

The application discloses a method for obtaining real-time attitude data and real-time angular velocity data of a guided shell, and comprises the following steps: obtaining measured angular velocity data, measured acceleration data and measured magnetic force data of the guided shell; normalizing the measured angular velocity data, the measured acceleration data and the measured magnetic force data to obtain standard angular velocity data, standard acceleration data and standard magnetic force data; designing a finite-time complementary filter based on attitude kinematics of flight of the guided shell; inputting the standard angular velocity data, the standard acceleration data and the standard magnetic force data into the finite-time complementary filter to obtain angular velocity measurement deviation estimation, acceleration estimation and magnetic force estimation; and calculating the real-time attitude data and the real-time angular velocity data of the guided shell based on the angular velocity measurement deviation estimation, the acceleration estimation and the magnetic force estimation. The application can quickly and accurately obtain the real-time attitude data and the real-time angular velocity data of the guided shell.

The application discloses a dynamic self-adaptive attitude estimation method based on an inertial sensor, and comprises the following steps: acquiring carrier maneuvering state data based on an inertial sensor and an attitude estimation result; acquiring adaptive compensation coefficients and gyroscope zero offset estimation data from the carrier maneuvering state data; processing the adaptive compensation coefficients and the gyroscope zero offset estimation data to generate a complementary filtering algorithm; and acquiring an adaptive attitude estimation result based on the complementary filtering algorithm. Under static conditions, the method can achieve good attitude estimation effect; under dynamic conditions, the method has higher precision than traditional complementary filtering; and the method has smaller influence from maneuvering than traditional complementary filtering.

This invention provides a positioning method, apparatus, device, medium, and legged robot for a legged robot. The method includes: obtaining a first velocity vector of the legged robot in a world coordinate system based on angle sensor data at each knee joint using a kinematic method; obtaining a second velocity vector of the legged robot in the world coordinate system based on acceleration sensor data of the legged robot's body; fusing the first and second velocity vectors using a complementary filter to obtain a target velocity of the legged robot in the world coordinate system, wherein the cutoff frequency of the complementary filter is predetermined by fusing terrain information; and obtaining the position information of the legged robot in the world coordinate system based on the target velocity using an integration method. This invention can improve the accuracy of local positioning of legged robots.

This invention discloses an automatic vehicle horizontal attitude control method and computer device based on a vehicle MEMS system. The method includes: calculating the initial pitch angle and initial roll angle of the vehicle body to establish an initial horizontal attitude; calculating first observation data for the pitch angle and roll angle; calculating second observation data for the pitch angle and roll angle; fusing the first and second observation data based on a complementary filtering algorithm and optimizing the attitude estimation results using a Kalman filter; determining the target horizontal attitude of the vehicle body according to the driving mode and calculating the target pitch angle; calculating the deviation between the real-time pitch angle and the target pitch angle; if the deviation exceeds a set deviation threshold, generating a suspension control command; sending the suspension control command to the air suspension ECU; controlling the independent raising and lowering of each suspension according to the suspension control command to adjust the vehicle attitude; stopping the adjustment after the vehicle body reaches the target horizontal attitude. This invention enables automated control of the vehicle's horizontal attitude.

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