Pan-tilt-free dynamic compensation water flow velocity measurement system used in vibration environment

Abstract

The invention discloses a pan-tilt-free dynamic compensation water flow velocity measurement system used in a vibration environment. The pan-tilt-free dynamic compensation water flow velocity measurement system comprises a multi-source sensor module, a dynamic motion compensation module, a self-adaptive Kalman filter bank and a data credibility evaluation module, the multi-source sensor module is used for collecting platform attitude information, water flow original speed and platform height. The dynamic motion compensation module comprises an attitude solution sub-module and an error calculation sub-module and is used for realizing flow velocity dynamic compensation under the condition of no holder structure; the adaptive Kalman filter bank comprises an angular velocity filter and a flow velocity filter, the angular velocity filter dynamically adjusts filtering parameters according to the angular motion intensity of the platform, and the flow velocity filter dynamically switches processing strategies according to the effectiveness of a water flow signal measured by the radar; and the data credibility evaluation module is used for carrying out stability judgment based on a sliding window variance-trend joint detection algorithm. According to the invention, attitude calculation and motion compensation are fused, and accurate measurement of the water flow velocity of the holder-free platform in a vibration environment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water flow velocity measurement, and particularly to a non-panning dynamic compensation water flow velocity measurement system for a vibrating environment. Background Art

[0002] In many fields such as fluid environment monitoring, inland water body investigation, urban flood control, and water resource scheduling, the real-time measurement of water flow velocity has become one of the key sensing means. Traditional water flow velocity measurement methods mostly rely on the installation of devices on stationary platforms, such as deploying radar speedometers, ultrasonic flow meters, etc. on fixed bridges, embankments or ground brackets. Although such measurement means can provide relatively accurate and stable data in a static environment, they are subject to many limitations when applied on dynamic platforms.

[0003] With the wide application of mobile platforms such as unmanned aerial vehicles and unmanned boats, hydrological monitoring based on dynamic platforms has become a trend. However, when a speed measurement sensor is installed on such a platform, due to inevitable angular changes such as pitch, roll, and yaw during the movement of the platform, its own dynamic attitude will introduce additional errors to the measured flow velocity data. For example, when a millimeter-wave radar measures the water surface speed, the rotation of the platform may cause the beam direction to deviate from the measurement target direction, and even measure the speed component formed by the movement of the platform itself. In addition, the high-frequency interference generated by platform vibration will also be superimposed on the flow velocity signal, making the stability of the original measurement data poor and the credibility low.

[0004] To reduce the interference of platform movement on the measurement results, attitude compensation methods are mainly used for correction in existing research, such as static compensation using the angles measured by an IMU. However, such methods usually assume that the platform attitude changes slowly or regularly, and it is difficult to adapt to the fast and complex dynamic attitude changes in the real environment. At the same time, existing filtering methods such as simple low-pass filtering or Kalman filters with fixed parameters often have problems such as response lag and signal distortion when dealing with violent movement or high-noise input. In addition, the credibility evaluation of the original water flow data is also relatively weak, lacking a joint analysis of data stability and trend, resulting in subsequent hydrological modeling and control strategies relying on unreliable data and posing potential risks.

[0005] Especially in the current large number of unmanned platform applications, in order to save weight and energy consumption and simplify the structural design, the traditional mechanical panning system is often cancelled, making sensors such as radars unable to work in a constant attitude. The application environment of such a non-panning structure further exacerbates the dynamic coupling problem between the sensor and the platform attitude, and further increases the challenges to attitude solution, motion compensation, and data filtering technologies.

[0006] Therefore, how to provide a non-panning dynamic compensation water flow velocity measurement system for a vibrating environment is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] An object of the present invention is to provide a non-gimbal dynamic compensation water flow velocity measurement system for a vibrating environment. The present invention integrates attitude solution and error compensation, combines adaptive filtering with a sliding window credibility evaluation mechanism, and realizes high-precision dynamic measurement of water flow velocity on a non-gimbal platform in a vibrating environment. The system has the advantages of strong real-time performance, strong anti-disturbance ability, and applicability to complex platforms, solves the problems of insufficient dynamic compensation accuracy, lagging filtering response, and difficult evaluation of data reliability in the prior art, and is applicable to mobile measurement scenarios such as unmanned aerial vehicles and unmanned boats.

[0008] The non-gimbal dynamic compensation water flow velocity measurement system for a vibrating environment according to an embodiment of the present invention includes a multi-source sensor module, a dynamic motion compensation module, an adaptive Kalman filter bank, and a data credibility evaluation module; The multi-source sensor module includes a three-axis MEMS inertial measurement unit, a millimeter-wave radar flow velocity sensor, and an ultrasonic altimeter, which are used to collect platform attitude information, raw water flow velocity, and platform height respectively; The dynamic motion compensation module includes an attitude solution sub-module and an error calculation sub-module. The attitude solution sub-module is used to perform attitude solution based on the platform attitude information and estimate the attitude change of the platform in a vibrating state in real time; the error calculation sub-module removes the error velocity component introduced by the platform's own motion from the raw water flow velocity according to the platform attitude information and the angular velocity change situation, and realizes the dynamic compensation of the flow velocity under the condition of a non-gimbal structure; The adaptive Kalman filter bank includes an angular velocity filter and a flow velocity filter. The angular velocity filter dynamically adjusts the filtering parameters according to the platform angular motion intensity, and the flow velocity filter dynamically switches the processing strategy according to the validity of the water flow signal measured by the radar, and performs different filtering processes for zero values, small signals, and valid signals respectively; The data credibility evaluation module is used to judge the stability of the water flow measurement data after dynamic compensation and filtering processing based on the sliding window variance-trend joint detection algorithm.

[0009] Optionally, the working frequency of the millimeter-wave radar flow velocity sensor is 24 GHz, the speed measurement range is 0.1 m / s to 20 m / s, and the measurement accuracy is 0.01 m / s.

[0010] Optionally, the working frequency of the ultrasonic altimeter is 80 GHz, the range is 0.2 m to 40 m, and the measurement accuracy is ±2 mm.

[0011] Optionally, the attitude resolver module uses the Mahony complementary filtering algorithm and the fourth-order Runge-Kutta integration method to perform attitude resolution on the platform attitude information. The Mahony complementary filtering algorithm is based on the measurement information of the three-axis gyroscope and accelerometer, and realizes angular velocity correction through non-linear error feedback, including: Let be the attitude quaternion of the platform at time , representing the rotation state of the platform from the inertial coordinate system to the body coordinate system, and the initial value is the unit quaternion ; Obtain the angular velocity vector measured by the three-axis gyroscope, where are the angular velocities around the X, Y, and Z axes respectively, and obtain the gravity direction vector measured by the accelerometer; Based on the attitude quaternion calculate the currently estimated gravity direction vector : ; where, represents the conjugate quaternion of the attitude quaternion , represents the quaternion multiplication operation; Perform a vector cross product operation on the gravity direction vector measured by the accelerometer and the currently estimated gravity direction vector to obtain the gravity direction error vector ; Construct a correction angular velocity vector with proportional-integral feedback: ; where, represents the proportional gain coefficient, represents the integral gain coefficient, represents the time integral of the gravity direction error vector , and correspondingly construct the correction angular velocity quaternion ; Use the fourth-order Runge-Kutta integration method to perform numerical integration on the quaternion differential equation, and the integration time step is , and the update process is: ; ; ; ; The finally updated attitude quaternion is: ; Among them, , , and represent the intermediate increments of the Runge - Kutta integration, reflecting the differential change trend, represents the updated attitude quaternion.

[0012] Optionally, the error calculation sub - module includes an on - line calibration unit for radar installation parameters, a beam direction compensation unit, and a motion error velocity compensation unit; The on - line calibration unit for radar installation parameters is used to combine the updated attitude quaternion output by the attitude solution operator module to real - time correct the installation position parameters of the millimeter - wave radar in the body coordinate system. The on - line calibration function of the installation position parameters satisfies: ; Among them, represents the installation position vector of the millimeter - wave radar in the body coordinate system, and represent the nominal installation position of the millimeter - wave radar in the horizontal plane of the body, and represent the on - line correction value of the horizontal installation deviation, represents the column - vector form; The beam direction compensation unit calculates the beam direction compensation factor according to the angle between the radar beam direction and the horizontal plane: ; Among them, represents the projection of the radar beam direction on the platform horizontal plane, obtained by rotating the static beam direction vector based on the attitude quaternion, represents the norm; The motion error velocity compensation unit calculates the error velocity component of the platform's own rotational motion in three - dimensional space on the original water flow velocity: ; Among them, represents the error velocity component, represents the rotation matrix from the body coordinate system to the inertial coordinate system, represents the angular velocity vector; Removing the error velocity component from the original water flow velocity realizes the dynamic compensation for the errors introduced by the platform motion under the condition of no pan - tilt structure.

[0013] Optionally, the angular velocity filter adopts a two - stage series structure, including: The first stage is a moving average filter, which is used to preliminarily smooth the original angular velocity data to suppress high-frequency noise fluctuations. The width of the moving average filter window is 5 to 15 data points; The second stage is - - a filter, which is used to further predict the angular velocity trend based on the results of the moving average filter and enhance the response ability to dynamic changes. The - - filtering parameters of the filter are dynamically adjusted according to the current motion state of the platform. The adjustment rules are as follows: When the platform is in the static mode, set ; When the platform is in the dynamic mode, set ; Among them, represents the position update coefficient, represents the velocity update coefficient, represents the acceleration update coefficient; The platform state is judged by the angular velocity amplitude. If the angular velocity is lower than the set amplitude threshold, it enters the static mode; otherwise, it enters the dynamic mode; The combination of the two-stage filtering structure realizes the enhancement of the stability and the adjustment of the response performance of the angular velocity signal under different vibration intensities, and provides a more reliable angular velocity input for the attitude solution operator module and the error calculation sub-module.

[0014] Optionally, the flow velocity filter includes a zero value processing unit, a small signal holding unit, and an effective signal tracking unit; The zero value processing unit is used to identify and eliminate invalid water flow signals that are continuously zero. When the continuous zero value exceeds the set duration threshold, the processing is triggered. The set range of the duration threshold is 30 to 100 sampling points, which is used to eliminate the influence of false data caused by long-term signal absence or echo failure; The small signal holding unit is used to hold small signals. The activation threshold setting range is 0.05 m / s to 0.2 m / s. When it detects that the signal amplitude is lower than the activation threshold but persists, it automatically maintains the validity of the small signal; The effective signal tracking unit is used to perform dynamic smoothing processing on the confirmed effective signals. The response speed is controlled by a time constant. The set range of the time constant is 0.1 s to 1.0 s, which is used to balance the response delay and the data fluctuation suppression ability.

[0015] Optionally, the sliding window variance-trend joint detection algorithm specifically includes: Divide the continuous water flow measurement data into a sequence of sliding windows. The minimum stable point number for each sliding window is set in the range of 10 to 50, and the sliding window step size is set in the range of 1 to 5 data points; For the data within each sliding window, calculate the coefficient of variation as the stability index: ; Among them, represents the coefficient of variation, represents the standard deviation of the data samples within the sliding window, represents the average value of the data samples within the sliding window. When , determine that the current sliding window is a stable interval; Use the first-order difference method to calculate the trend index for judging whether there is a continuous change trend in the data: ; Among them, represents the trend index, represents the total number of data samples in the sliding window, represents the th data sample within the sliding window, represents the th data sample within the sliding window, represents the sign function. When , determine that the current sliding window is an interval without significant trend change.

[0016] The beneficial effects of the present invention are as follows: First of all, by introducing an attitude solution strategy that combines the Mahony complementary filtering algorithm and the fourth-order Runge-Kutta integration method, the present invention effectively improves the real-time performance and stability of attitude estimation in a dynamic environment. Compared with traditional methods that only rely on gyroscope integration or simple Kalman filtering, this solution structure can use the accelerometer to correct the long-term integration error and reduce attitude fluctuations through high-order numerical integration, so as to accurately obtain the platform attitude information even under severe vibration or rapid flight conditions.

[0017] Secondly, in terms of error compensation, the present invention constructs an error velocity model based on attitude quaternions, angular velocity vectors, and radar installation parameters, dynamically eliminates the non-fluid velocity components introduced by the platform's rotational motion, and realizes precise compensation for the original water flow velocity data under the condition of no pan-tilt structure. At the same time, the system integrates an online calibration mechanism for the radar installation position and a beam direction projection compensation mechanism, so that the measurement direction of the radar is always geometrically consistent with the true water surface flow direction, reducing the measurement deviation caused by the structural installation error from the source.

[0018] In addition, in terms of data processing, the present invention designs a set of adaptive Kalman filter banks, and configures a two-stage filtering structure for the different characteristics of the angular velocity and flow velocity signals respectively. Among them, the angular velocity filter adopts a series structure of moving average and - - filters, and automatically adjusts the filtering parameters by detecting the motion state of the platform, improving the anti-noise and response capabilities under different dynamic working conditions. The flow velocity filter is provided with three types of processing units: zero-value processing, small-signal holding, and effective signal tracking, which can respectively cope with various complex signal states such as still water surface, disturbed micro-flow, and normal flow, significantly improving the continuity and reliability of the water flow velocity data.

[0019] Finally, to further ensure the credibility of the system output data, the present invention constructs a sliding window variance-trend joint detection algorithm. By analyzing the coefficient of variation and the first-order trend index of the measurement data, it can accurately identify the stable section and the non-trend change region of the data, endowing the system with the ability to quantitatively judge the credibility of the measurement results. This mechanism not only improves the system's automatic recognition ability for low-quality data, but also provides an important reliability basis for subsequent data fusion and hydrological modeling. Description of the Drawings

[0020] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings: Figure 1 is a schematic structural diagram of the non-panning dynamic compensation water flow velocity measurement system for vibration environment proposed by the present invention; Figure 2 is a flowchart of Mahony complementary filtering and fourth-order Runge-Kutta integration of the attitude solution operator module of the non-panning dynamic compensation water flow velocity measurement system for vibration environment proposed by the present invention; Figure 3 is a schematic diagram of the motion compensation principle in the error calculation sub-module of the non-panning dynamic compensation water flow velocity measurement system for vibration environment proposed by the present invention. Detailed Embodiment

[0021] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic way, so they only show the components related to the present invention.

[0022] Referring to Figures 1-3 , the non-panning dynamic compensation water flow velocity measurement system for vibration environment includes a multi-source sensor module, a dynamic motion compensation module, an adaptive Kalman filter bank, and a data credibility evaluation module; The multi-source sensor module includes a three-axis MEMS inertial measurement unit, a millimeter wave radar flow velocity sensor and an ultrasonic altimeter, which are used to respectively collect platform attitude information, water flow original velocity and platform height; The dynamic motion compensation module includes an attitude solver module and an error calculation submodule. The attitude solver module is used to perform attitude calculation based on the platform attitude information and estimate the attitude change of the platform in a vibrating state in real time. The error calculation submodule removes the error velocity component introduced by the platform's own motion from the original velocity of the water flow according to the platform attitude information and angular velocity changes, thereby realizing dynamic compensation of the flow velocity without a pan-tilt structure. The adaptive Kalman filter group includes an angular velocity filter and a flow rate filter. The angular velocity filter dynamically adjusts the filter parameters according to the angular motion intensity of the platform. The flow rate filter dynamically switches the processing strategy according to the effectiveness of the water flow signal measured by the radar, and performs different filtering processes on zero values, small signals and effective signals respectively. The data credibility assessment module is used to make stability judgment on the water flow measurement data after dynamic compensation and filtering based on the sliding window variance-trend joint detection algorithm.

[0023] This system achieves high-precision flow velocity perception in a vibrating environment and without a gimbal by building an integrated multi-module water flow velocity measurement architecture. The overall system integrates a multi-source sensor module, a dynamic motion compensation module, an adaptive Kalman filter group, and a data credibility assessment module to form a closed data flow loop. Compared with traditional static platforms or structures that rely on mechanical gimbals, this system does not rely on external stabilization equipment, and completely uses algorithms to perceive and compensate for platform posture and motion errors in real time, adapting to the complex dynamic characteristics of the platform in non-stationary motion in the air or on the water. It is particularly suitable for lightweight platforms such as drones and unmanned ships, and realizes a flow velocity monitoring solution that takes into account miniaturization, intelligence, and high robustness.

[0024] In this embodiment, the operating frequency of the millimeter wave radar flow velocity sensor is 24 GHz, the velocity measurement range is 0.1 m / s to 20 m / s, and the measurement accuracy is 0.01 m / s.

[0025] The operating frequency of the millimeter wave radar sensor is set to 24GHz. It has strong anti-interference ability and penetration, and can maintain stable speed measurement in complex water surface environments. Its speed measurement range covers the flow velocity range of common natural water bodies, with an accuracy of 0.01m / s, ensuring the data sampling fineness and speed change response capability. The use of radar units of this specification enables this system to capture weak flows and high dynamic changes while ensuring the measurement distance, meeting the needs of various application scenarios from steady water flow to rapid fluctuations, improving the versatility and measurement accuracy of the system, and is particularly suitable for actual deployment in complex external environments to perform long-term, continuous flow velocity observation tasks.

[0026] In this embodiment, the operating frequency of the ultrasonic altimeter is 80 GHz, the measuring range is 0.2 m to 40 m, and the measuring accuracy is ±2 mm.

[0027] The ultrasonic altimeter with an operating frequency of 80GHz enables the platform to accurately obtain the relative height information with respect to the water surface when flying or floating. It has a wide range and high measurement accuracy, and can effectively adapt to the platform's height change monitoring under different flight altitudes or wave conditions, ensuring that the radar velocity data accurately corresponds to the actual water surface distance. As an important auxiliary quantity for error compensation and attitude dynamic solution, the height data can effectively improve the measurement accuracy and the integrity of the compensation model. By introducing a high-frequency and high-precision altimeter, the system's ability to perceive the platform's attitude changes is effectively enhanced, and the spatial resolution and geometric registration capability of the velocity measurement are improved.

[0028] In this implementation, the attitude solver module uses the Mahony complementary filtering algorithm and the fourth-order Runge-Kutta integration method to solve the attitude information of the platform. The Mahony complementary filtering algorithm is based on the measurement information of the three-axis gyroscope and the accelerometer, and realizes angular velocity correction through nonlinear error feedback, including: set up For the platform at all times The attitude quaternion represents the rotation state of the platform from the inertial coordinate system to the body coordinate system. The initial value is the unit quaternion ; Get the angular velocity vector measured by the three-axis gyroscope ,in They are the angular velocities around the X, Y, and Z axes respectively, and the gravity direction vector measured by the accelerometer is obtained ; Based on attitude quaternion Calculate the current estimated gravity direction vector : ; in, Quaternion representing attitude The conjugate quaternion of Represents quaternion multiplication operation; The gravity direction vector measured by the accelerometer The current estimated gravity direction vector Perform vector cross multiplication to obtain the gravity direction error vector ; Constructing a Corrected Angular Velocity Vector with Proportional-Integral Feedback : ; in, represents the proportional gain coefficient, represents the integral gain coefficient, represents the gravity direction error vector of the time integral, and correspondingly constructs the correction angular velocity quaternion ; The fourth-order Runge-Kutta integration method is used to numerically integrate the quaternion differential equation, and the integration time step is , and the update process is as follows: ; ; ; ; The finally updated attitude quaternion is: ; where , , and represent the intermediate increments of the Runge-Kutta integration, reflecting the differential change trend, represents the updated attitude quaternion.

[0029] By introducing the Mahony complementary filtering algorithm and the fourth-order Runge-Kutta numerical integration method, the system can stably output high-precision attitude estimation results in a violently vibrating or high-dynamic environment. The Mahony filter combines accelerometer and gyroscope data, corrects the angular velocity drift through non-linear feedback, and effectively resists the long-term error accumulation of the inertial measurement unit; the Runge-Kutta integration method further improves the numerical stability and accuracy of the solution process. This combined scheme does not require the support of an external magnetometer, has high computational efficiency and strong real-time performance, and is especially suitable for resource-constrained platforms. The attitude information, as the core basis for error compensation and radar beam angle correction, directly determines the measurement accuracy, and the high reliability of this module greatly enhances the overall performance of the system.

[0030] In this embodiment, the error calculation sub-module includes an on-line calibration unit for radar installation parameters, a beam direction compensation unit, and a motion error velocity compensation unit; The on-line calibration unit for radar installation parameters is used to combine the updated attitude quaternion output by the attitude solution sub-module to real-time correct the installation position parameters of the millimeter-wave radar in the body coordinate system. The on-line calibration function of the installation position parameters satisfies: ; where represents the installation position vector of the millimeter-wave radar in the body coordinate system, and Indicates the nominal installation position of the millimeter-wave radar in the horizontal plane of the airframe, and represents the online correction value of the horizontal installation deviation, represents the column vector form; The beam direction compensation unit calculates the beam direction compensation factor according to the angle between the radar beam direction and the horizontal plane : ; ; wherein, represents the projection of the radar beam direction on the platform horizontal plane, obtained by rotating the static beam direction vector based on the attitude quaternion, represents the norm; The motion error velocity compensation unit calculates the error velocity component of the platform's own rotational motion in three-dimensional space on the original water flow velocity: ; wherein, represents the error velocity component, represents the rotation matrix from the airframe coordinate system to the inertial coordinate system, represents the angular velocity vector; The error velocity component is removed from the original water flow velocity to achieve dynamic compensation for the errors introduced by the platform motion under the condition of no pan-tilt structure.

[0031] The error compensation sub-module accurately eliminates the radar speed error caused by the platform motion through multi-level modeling. First, the system supports the online calibration mechanism of the radar installation position, which can dynamically correct the actual position deviation and eliminate the influence of the structural assembly error on the speed measurement direction; second, the calculation method of the beam direction compensation factor combines the attitude solution result to effectively correct the deviation of the radar oblique beam in the non-horizontal state; third, the rotation motion model between the angular velocity and the installation vector is introduced to compensate for the additional velocity component generated by the platform's own rotation in the radar perception.

[0032] In addition, it should be noted that the platform rotation error velocity component and the projection deviation caused by the radar beam direction belong to two different sources of measurement errors, which are processed by different compensation mechanisms. The platform rotation error velocity component is calculated through the cross product relationship between the angular velocity and the radar installation position vector, combined with the rotation matrix generated by the attitude solution, and is used to compensate for the additional velocity of the radar measurement value caused by the platform's rotation around the three-dimensional axis. Its calculation result is a vector quantity, reflecting the perturbation of the instantaneous motion state on the speed measurement result. And the beam direction compensation factor is a geometric projection factor calculated based on the angle between the radar beam and the horizontal plane in the platform coordinate system, mainly used to correct the measurement deviation caused by the inconsistency between the radar speed measurement direction and the actual horizontal direction. Since is a scalar compensation factor and cannot be directly involved in the calculation of three-dimensional velocity components. However, it can be used in the velocity output stage after compensation to further restore the true horizontal component of the velocity measured by the radar, thereby improving the consistency between the measurement data and the true water flow state. The two are used in combination to form a full-link error control mechanism from dynamic disturbance compensation to direction correction, ensuring that the system can still stably output high-precision water flow velocity data under the condition of no pan-tilt.

[0033] In this embodiment, the angular velocity filter adopts a two-stage series structure, including: The first stage is a moving average filter, which is used to perform preliminary smoothing on the original angular velocity data to suppress high-frequency noise fluctuations. The width of the moving average filter window is 5 to 15 data points; The second stage is - - a filter, which is used to further predict the angular velocity trend based on the results of the moving average filter and enhance the response ability to dynamic changes. The filtering parameters of the - - filter are dynamically adjusted according to the current motion state of the platform. The adjustment rule is: When the platform is in the static mode, set ; When the platform is in the dynamic mode, set ; Among them, represents the position update coefficient, represents the velocity update coefficient, represents the acceleration update coefficient; The platform state is judged by the angular velocity amplitude. If the angular velocity is lower than the set amplitude threshold, it enters the static mode; otherwise, it enters the dynamic mode; The combination of the two-stage filtering structure realizes the enhancement of the stability and the adjustment of the response performance of the angular velocity signal under different vibration intensities, providing a more reliable angular velocity input for the attitude solution operator module and the error calculation sub-module.

[0034] The angular velocity signal is vulnerable to high-frequency noise and transient disturbances in a vibrating environment, which affects the stability of attitude solution. This system adopts a two-stage filtering structure. The front stage is a moving average filter, which can effectively suppress instantaneous spikes and high-frequency jitter signals; the latter stage introduces - - The filter has good dynamic tracking ability and state prediction performance. More importantly, the parameters of the filter can be adaptively adjusted according to the platform motion state, enabling the system to automatically switch the filtering intensity between static and dynamic states, thereby balancing the response speed and filtering stability. This structure significantly improves the quality of the attitude input angular velocity signal and provides a solid foundation for subsequent dynamic compensation.

[0035] In this embodiment, the flow velocity filter includes a zero-value processing unit, a small-signal holding unit, and an effective-signal tracking unit; The zero-value processing unit is used to identify and eliminate invalid water flow signals that are continuously zero. When the continuous zero value exceeds the set duration threshold, processing is triggered. The set range of the duration threshold is 30 to 100 sampling points, which is used to eliminate the influence of false data caused by long-term signal absence or echo failure; The small-signal holding unit is used to perform holding processing on small signals. The set range of the activation threshold is 0.05m / s to 0.2m / s. When it detects that the signal amplitude is lower than the activation threshold but persists, it automatically maintains the validity of the small signal; The effective-signal tracking unit is used to perform dynamic smoothing processing on the confirmed effective signals. The response speed is controlled by the time constant, and the set range of the time constant is 0.1s to 1.0s, which is used to balance the response delay and the ability to suppress data fluctuations.

[0036] The water flow signal has strong time-varying and polymorphic characteristics. Especially in a complex external environment, various states such as sensor echo failure, small disturbances, or effective flow may occur. The flow velocity filter of this system is divided into three strategies: zero-value processing, small-signal holding, and effective-signal tracking, which can perform hierarchical processing for different flow velocity characteristics. Zero-value processing eliminates invalid data, small-signal holding prevents small fluctuations from being misjudged as noise, and effective-signal tracking smooths the real water flow signal to avoid over-response. Through this type of adaptive mechanism, the system effectively improves the continuity, authenticity, and anti-interference ability of the flow velocity data, showing stronger data stability and credibility in actual measurements.

[0037] In this embodiment, the sliding window variance-trend joint detection algorithm specifically includes: Dividing the continuous water flow measurement data into a sliding window sequence, the set range of the minimum stable number of points for each sliding window is 10 to 50, and the set range of the sliding window step size is 1 to 5 data points; For the data within each sliding window, calculate the coefficient of variation as the stability index: ; where, represents the coefficient of variation, represents the standard deviation of the data samples within the sliding window, represents the average value of data samples within the sliding window. When occurs, it is determined that the current sliding window is a stable interval; The first-order difference method is used to calculate the trend index for determining whether there is a continuous change trend in the data: ; Among them, represents the trend index, represents the total number of data samples in the sliding window, represents the th data sample within the sliding window, represents the th data sample within the sliding window, represents the sign function. When occurs, it is determined that the current sliding window is an interval without significant trend changes.

[0038] The data credibility evaluation module dynamically identifies the stability and trend of measurement data from the perspective of time series by introducing a sliding window analysis mechanism. It judges the degree of fluctuation by calculating the coefficient of variation of the data within the window, and combines first-order difference trend detection to identify whether the current data is in a stable and reliable state. The system uses the evaluation result as an output confidence index, which can be used for external output marking, data selection, or automatic rejection to improve the security and intelligence of backend data processing. Compared with the traditional unfiltered measurement method, this mechanism endows the system with the ability to identify reliable sections, making the measurement result no longer a passive acquisition, but having a credibility level and quality judgment function, significantly enhancing the application value.

[0039] Embodiment 1: To verify the feasibility of the present invention in implementation, the present invention is applied to an actual measurement task of dynamic water velocity monitoring based on an unmanned platform. A multi-rotor unmanned aerial vehicle is selected as the test platform, and the measurement system described in the present invention is carried on it. The system integrates a multi-source sensor module (including a three-axis MEMS inertial measurement unit, a millimeter-wave radar velocity sensor, and an ultrasonic altimeter), a dynamic motion compensation module, an adaptive Kalman filter bank, and a data credibility evaluation module.

[0040] The aircraft flies at a height of about 3 meters to 4 meters above the water area along a preset route. During the execution process, due to wind disturbance and platform attitude changes, the system continuously faces the challenges of high-frequency vibration and attitude disturbance. Under the condition that the platform has no gimbal structure, the attitude information is accurately solved by the attitude solution operator module using Mahony complementary filtering and the fourth-order Runge-Kutta integration method, and the attitude quaternion is output for subsequent compensation. During the test, the maximum pitch angle fluctuation of the platform reaches ±14°, and the maximum yaw angle change rate reaches 22° per second, verifying the practicability of the system under severe dynamic conditions.

[0041] Meanwhile, the system compensates in real time for the measurement errors caused by the rotational motion of the platform through the error calculation sub-module, calculates the spatial interaction between the platform angular velocity and the radar installation position, obtains the error velocity component and eliminates it from the original radar flow velocity data, thereby obtaining the true water flow velocity. The maximum instantaneous value of the error velocity reaches 0.65 m / s, and the error is reduced to 0.07 m / s after compensation.

[0042] To further improve the stability and accuracy of the signal, the angular velocity filter adopts a two-stage structure: the front-stage moving average filter suppresses high-frequency jitter, and the - - filter switches the filtering parameters according to the real-time angular velocity amplitude to achieve the adaptive switching of the filter between the static and dynamic modes. Compared with the original angular velocity data, the peak fluctuation of the filtered signal decreases by 45.3%, the standard deviation decreases by 52.3%, and the jitter amplitude of the attitude solution decreases by 41.7%.

[0043] The radar flow velocity data is processed by the flow velocity filter and classified and processed for different states. In the section with long-term echo failure, the system successfully eliminates about 120 groups of consecutive zero-value points, accounting for 6.7% of the total sampling points; in the micro-flow section (<0.2 m / s), the small signal holding module effectively identifies and stably outputs the flow velocity data, with an average deviation of less than 0.03 m / s; in the medium-high speed flow region (1.2 - 2.0 m / s), the tracking module can keep the response time within 0.3 seconds to effectively cope with the actual flow velocity fluctuations.

[0044] A total of 24,000 groups of data are collected in the test task. After being processed by the data credibility evaluation module, effective sections with high stability and no obvious trend drift are identified. The coefficient of variation and the first-order difference trend joint index analysis method are used to score each group of data. Compared with the water surface flow velocity comparison device, the average error of this system remains below 0.08 m / s.

[0045] Table 1 Statistical table of flow velocity compensation effects under different attitude change conditions of the platform Table 2 Comparison table of angular velocity signal characteristics before and after filter processing Table 3 Evaluation results table of data stability in different flow velocity sections Based on the experimental data in Table 1 to Table 3, the performance of the non-panning dynamic compensation water flow velocity measurement system in the vibration environment of this embodiment is fully verified, demonstrating significant technical advantages and practical application value.

[0046] First, as can be seen from Table 1, in the flight missions of routes with drastic changes in different postures, the original flow velocity measurement errors are generally high, reaching up to 0.65 m / s. After being compensated and processed by the system of the present invention, the errors are significantly reduced to between 0.05 and 0.09 m / s, and the improvement rates of the flow velocity measurement deviations all exceed 78%, up to 89.2% at most. This shows that the system has a significant effect in dynamic compensation, can effectively eliminate the error velocity components brought by the platform rotation movement, and ensures the authenticity of the measurement data.

[0047] Secondly, as can be seen from the comparison of the angular velocity filtering effects in Table 2, through the cascaded processing of the moving average filter and - - the filter, the peak fluctuation of the angular velocity signal is reduced by 45.3%, and the standard deviation is reduced by 52.3%. The high-frequency vibration interference is significantly suppressed, and the average response delay is maintained at the millisecond level, indicating that the system has excellent anti-interference ability and real-time performance at the front end of attitude solution, providing a more reliable input data basis for subsequent attitude and error calculations.

[0048] Finally, the data stability analysis in Table 3 further verifies the wide adaptability of the system to different flow velocity sections. In the low flow velocity region (0.1 - 0.5 m / s) and the high flow velocity region (1.5 - 2.5 m / s), the system can stably output high-confidence data, and the proportion of valid data generally exceeds 78%, and the average confidence level remains above 88.6%, proving that the system can achieve continuous and reliable data acquisition in complex water surface environments.

[0049] The embodiments fully show that the system of the present invention can achieve high-precision dynamic compensation for the water flow velocity in the measurement scenarios without a pan-tilt structure and with significant attitude disturbances, and maintain the stability and credibility of the measurement data, having wide practical deployment feasibility and engineering promotion prospects. The system not only maintains high-reliability output in the case of drastic attitude changes and significant signal quality fluctuations, but also demonstrates good real-time performance and algorithm flexibility, is applicable to various mobile platforms such as unmanned aerial vehicles and unmanned boats, and has important application values in the fields of intelligent water conservancy, emergency patrol measurement, and field scientific monitoring.

[0050] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent replacements or changes, and should be covered by the protection scope of the present invention.

Claims

1. A water flow velocity measurement system for dynamic compensation without a pan-tilt in a vibrating environment, characterized in that, It includes a multi-source sensor module, a dynamic motion compensation module, an adaptive Kalman filter bank, and a data credibility evaluation module; The multi-source sensor module includes a three-axis MEMS inertial measurement unit, a millimeter-wave radar flow velocity sensor, and an ultrasonic altimeter, which are used to collect platform attitude information, raw water flow velocity, and platform height respectively; The dynamic motion compensation module includes an attitude solution operator module and an error calculation sub-module. The attitude solution operator module is used to perform attitude solution based on the platform attitude information and estimate the attitude change of the platform under the vibration state in real time. The error calculation sub-module removes the error velocity component introduced by the platform's own motion from the raw water flow velocity according to the platform attitude information and the angular velocity change situation, realizing the dynamic compensation of the flow velocity under the condition of no pan-tilt structure; The adaptive Kalman filter bank includes an angular velocity filter and a flow velocity filter. The angular velocity filter dynamically adjusts the filtering parameters according to the platform angular motion intensity, and the flow velocity filter dynamically switches the processing strategy according to the validity of the water flow signal measured by the radar, and performs different filtering processes for zero value, small signal, and valid signal respectively; The data credibility evaluation module is used to judge the stability of the water flow measurement data after dynamic compensation and filtering processing based on the sliding window variance-trend joint detection algorithm.

2. The no-pan-tilt dynamic compensation water flow velocity measurement system for a vibrating environment according to claim 1, wherein The working frequency of the millimeter-wave radar flow velocity sensor is 24 GHz, the velocity measurement range is 0.1 m / s to 20 m / s, and the measurement accuracy is 0.01 m / s.

3. The water flow velocity measurement system for dynamic compensation without a pan-tilt under a vibration environment according to claim 1, wherein The working frequency of the ultrasonic altimeter is 80 GHz, the measurement range is 0.2 m to 40 m, and the measurement accuracy is ±2 mm.

4. The non-panning dynamic compensation water flow velocity measurement system for a vibration environment according to claim 1, wherein The attitude solution operator module uses the Mahony complementary filtering algorithm and the fourth-order Runge-Kutta integration method to perform attitude solution on the platform attitude information. The Mahony complementary filtering algorithm is based on the measurement information of the three-axis gyroscope and accelerometer, and realizes the angular velocity correction through nonlinear error feedback, including: Let be the attitude quaternion of the platform at time , representing the rotation state of the platform from the inertial coordinate system to the body coordinate system, and the initial value is the unit quaternion ; Obtain the angular velocity vector measured by the three-axis gyroscope , where are the angular velocities about the X, Y, and Z axes respectively, and obtain the gravity direction vector measured by the accelerometer ; Based on the attitude quaternion Calculate the currently estimated gravity direction vector : ； Among them, represents the conjugate quaternion of the attitude quaternion , and represents the quaternion multiplication operation; The gravity direction vector measured by the accelerometer is subjected to a vector cross product operation with the currently estimated gravity direction vector to obtain the gravity direction error vector ; Construct a corrected angular velocity vector with proportional-integral feedback : ； Among them, represents the proportional gain coefficient, represents the integral gain coefficient, represents the time integral of the gravity direction error vector and correspondingly constructs the correction angular velocity quaternion ; The fourth-order Runge-Kutta integration method is used to numerically integrate the quaternion differential equation, and the integration time step is , and the update process is as follows: ； ； ； ； The finally updated attitude quaternion is: ； Among them, , , and represent the intermediate increments of the Runge-Kutta integration, reflecting the differential change trend, represents the updated attitude quaternion.

5. The no-panhead dynamic compensation water flow velocity measurement system for vibrating environments according to claim 1, characterized in that, The error calculation sub-module includes an online calibration unit for radar installation parameters, a beam direction compensation unit, and a motion error velocity compensation unit; The online calibration unit for radar installation parameters is used to combine the updated attitude quaternion output by the attitude solution operator module and correct the installation position parameters of the millimeter-wave radar in the body coordinate system in real time. The online calibration function of the installation position parameters satisfies: ； Among them, represents the installation position vector of the millimeter-wave radar in the body coordinate system, and represent the nominal installation positions of the millimeter-wave radar in the body horizontal plane, and represent the online correction values of the horizontal installation deviation, represents the column vector form; The beam direction compensation unit calculates a beam direction compensation factor according to the angle between the radar beam direction and the horizontal plane , as follows: ； Among them, represents the projection of the radar beam direction on the platform horizontal plane, which is obtained by rotating the static beam direction vector based on the attitude quaternion. represents the norm. The motion error velocity compensation unit calculates the error velocity component of the platform's own rotational motion in the three-dimensional space on the raw water flow velocity: ； wherein, represents the error velocity component, represents the rotation matrix from the body coordinate system to the inertial coordinate system, represents the angular velocity vector; Remove the error velocity component from the raw water flow velocity to realize the dynamic compensation of the error introduced by the platform motion under the condition of no pan-tilt structure.

6. The no-panhead dynamic compensation water flow velocity measurement system for a vibration environment according to claim 1, characterized in that The angular velocity filter adopts a two-stage series structure, including: The first stage is a moving average filter, which is used to perform preliminary smoothing processing on the raw angular velocity data to suppress high-frequency noise fluctuations. The width of the moving average filter window is 5 to 15 data points; The second level is - - a filter for further predicting the angular velocity trend based on the moving average filter result and enhancing the response ability to dynamic changes.

7. The no-panhead dynamic compensation water flow velocity measurement system for a vibration environment according to claim 6, wherein The - - filtering parameters of the filter are dynamically adjusted according to the current motion state of the platform, and the adjustment rule is: When the platform is in the static mode, set ; When the platform is in dynamic mode, set ; Among them, represents the position update coefficient, represents the speed update coefficient, represents the acceleration update coefficient; The platform state is judged by the angular velocity amplitude. If the angular velocity is lower than the set amplitude threshold, it enters the static mode; otherwise, it enters the dynamic mode.

8. The non-panning dynamic compensation water flow velocity measurement system for a vibrating environment according to claim 1, characterized in that The flow velocity filter includes a zero-value processing unit, a small-signal holding unit, and a valid signal tracking unit.

9. The no-panhead dynamic compensation water flow velocity measurement system for a vibration environment according to claim 8, wherein The zero-value processing unit is used to identify and eliminate invalid water flow signals that are continuously zero. It is triggered for processing when the continuous zero value exceeds a set duration threshold. The set range of the duration threshold is 30 to 100 sampling points, which is used to eliminate the influence of false data caused by long-term signal absence or echo failure. The small-signal holding unit is used to perform holding processing on small signals. The set range of the activation threshold is 0.05 m / s to 0.2 m / s. When it detects that the signal amplitude is lower than the activation threshold but persists, it automatically maintains the validity of the small signal. The valid signal tracking unit is used to perform dynamic smoothing processing on the confirmed valid signals. The response speed is controlled by a time constant. The set range of the time constant is 0.1 s to 1.0 s, which is used to balance the response delay and the data fluctuation suppression ability.

10. The no-panhead dynamic compensation water flow velocity measurement system for vibration environment according to claim 1, wherein The sliding window variance-trend joint detection algorithm specifically includes: Dividing the continuous water flow measurement data into a sliding window sequence. The set range of the minimum stable number of points for each sliding window is 10 to 50, and the set range of the sliding window step size is 1 to 5 data points. For the data within each sliding window, calculating the coefficient of variation as a stability index: ； Among them, represents the coefficient of variation, represents the standard deviation of the data samples within the sliding window, represents the average value of the data samples within the sliding window. When , it is determined that the current sliding window is a stable interval; Using the first-order difference method to calculate the trend index, which is used to judge whether there is a continuous change trend in the data: ； Among them, represents a trend indicator, represents the total number of data samples in the sliding window, represents the th data sample within the sliding window, represents the th data sample within the sliding window, represents the sign function. When , it is determined that the current sliding window is an interval without significant trend change.

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