38 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 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.

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.

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.

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 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 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.

The application provides a compressed air energy storage and release system control method, device, equipment and medium, relates to the compressed air energy storage power station control technical field, and the control method comprises the following steps: generating a frequency modulation total power instruction according to the power grid frequency deviation; decomposing the frequency modulation total power instruction into a high-frequency fast-changing power component instruction and a low-frequency slow-changing power component instruction through a complementary filtering power hierarchical decomposition module; a first active disturbance rejection controller adopts a first group of control parameters to control and calculate the high-frequency fast-changing power component instruction and then outputs a first adjustment amount; a second active disturbance rejection controller adopts a second group of control parameters to control and calculate the low-frequency slow-changing power component instruction and then outputs a second adjustment amount; the first adjustment amount and the second adjustment amount are superimposed, are limited in amplitude after rate change, and are converted into an opening degree instruction of an adjusting valve to control the opening degree of the adjusting valve through the opening degree instruction. Thus, the application can maintain fast and stable frequency regulation performance in the whole working condition range.

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.

This invention relates to the field of attitude control technology, and more particularly to a complementary filter gain adjustment method based on a KNN classifier. The method includes: during the model training phase, acquiring triaxial accelerometer data; calculating the accelerometer output modulus and variance values ​​at each time step; constructing a dataset containing the modulus, variance, and adaptive threshold; inputting the training data into the KNN algorithm to construct a KNN model; during the real-time attitude calculation phase, calculating the modulus and variance values ​​using the real-time acquired triaxial accelerometer data, and then inputting them into the KNN model to obtain the adaptive threshold; multiplying the adaptive threshold by a scalar gain to obtain a corrected gain; and substituting the corrected gain into the final attitude estimation formula to calculate the attitude. This invention addresses the deficiency of existing complementary filter algorithms in accurately and adaptively adjusting the scalar gain during attitude calculation, leading to insufficient accuracy and reliability in attitude calculation.

The application discloses a fast complementary filter method for attitude estimation by using a low-cost MARG sensor, and comprises the following steps: 1) estimating an attitude quaternion by using data output by an accelerometer; 2) using a gravity filter for compensation of a gyroscope; and 3) fusing the gravity and the magnetometer. The application discloses the fast complementary filter method for attitude estimation by using the low-cost MARG sensor, and converts the attitude estimation problem based on the accelerometer into a linear problem in view of a nonlinear problem in the attitude quaternion calculation process. In order to improve the accuracy of MARG attitude estimation, a new complementary filter structure is designed, and the new complementary filter structure is characterized in that the gravity is estimated by fusing the measurements of the accelerometer and the gyroscope. Meanwhile, the magnetic distortion detection is introduced into the algorithm, and the output of the magnetometer is fused with the estimated gravity based on the Markley algorithm.

The invention discloses a mobile equipment attitude estimation method and device based on IMU nine-axis data, and the method comprises the steps: firstly initializing a quaternion describing the attitude of mobile equipment by taking an east-north-sky coordinate system as a world coordinate system according to the data of an acceleration sensor and a magnetic field sensor in an initial window; the environment magnetic field distribution in a complex environment is approximated to a multi-magnetic dipole magnetic field distribution model, and a weight attenuation geomagnetic field estimation method is designed according to the attenuation characteristics of the magnetic dipole model; then, a multi-layer sliding window method is used, and a gyroscope, an acceleration sensor and a magnetic field sensor are respectively used for estimating quaternion, a roll angle, a pitch angle and a yaw angle of the mobile equipment; finally, the fusion effect of nine-axis sensor data estimation equipment attitude is optimized by using a self-adaptive complementary filter. The precision and the stability of attitude estimation of the mobile equipment can be remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle control, and provides a pipeline unmanned aerial vehicle escape control method and system after air crash, and the method comprises the steps: carrying out the preprocessing and calibration of IMU sensor data; carrying out attitude estimation by adopting EKF (Extended Kalman Filter) and complementary filtering progressive weighted fusion; establishing a stability evaluation model based on the curvature characteristics of the bent pipe, and performing multi-level stability judgment; performing hierarchical adjustment on the fusion weight according to the stability level; calculating a safe rolling area through geometric constraints of the spherical protective cover and the pipeline, and establishing an electrically controlled delay prediction model; and multiple parameters are fused to generate a pre-compensation control instruction, and the pre-compensation control instruction is converted into a PWM electric adjusting signal. According to the method, the safety rolling area and the electric speed controller delay prediction model are calculated through the spherical protection cover and the pipeline geometric constraint, the pre-compensation control instruction is generated according to the collision risk assessment result, and the precision and reliability of unmanned aerial vehicle escape control are improved.

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 invention discloses a robot teleoperation control method, system, device and equipment and a storage medium. The method comprises the following steps: acquiring state parameters of a target part of an operator acquired by a sensor, wherein the state parameters comprise acceleration and angular velocity; performing fusion processing on the acceleration and the angular velocity based on a complementary filtering algorithm to obtain the attitude and the relative displacement of the target part; determining a to-be-converted target coordinate system of the robot according to the type of the posture; converting the attitude and the relative displacement to the target coordinate system to obtain a target attitude and a target position; and a joint instruction is generated based on the target posture and the target position, and the joint instruction is sent to the robot so that the robot can execute the joint instruction. According to the method, the position and posture of the robot are directly controlled according to natural movement of the operator, and the teleoperation efficiency and safety can be improved.

The invention discloses a sensor data discrete fusion modeling method, a fusion method, equipment and a storage medium, and the modeling method comprises the steps: constructing a sensor data continuous fusion model based on a complementary filtering algorithm; converting the sensor data continuous fusion model into a continuous state space equation; converting the system matrix of the continuous state space equation into a transfer function; converting the transfer function to a time domain to obtain a system matrix time domain expression, and further calculating an input matrix time domain expression; and obtaining a discrete state space equation according to the system matrix time domain expression, the input matrix time domain expression and the continuous state space equation, namely obtaining a sensor data discrete fusion model. According to the method, the dynamic performance and the precision of the original continuous system are kept to the maximum theoretically, and additional discretization errors caused by damage of module separation to the integrity of the original continuous system design in traditional complementary filtering discretization are avoided.

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.