An Unmanned Aerial Vehicle (UAV) Water Surface Flow Velocity Measurement Method and Device Based on Dual Doppler Radar

Abstract

The present application provides a method and device for measuring the water surface velocity of an unmanned aerial vehicle (UAV) based on dual Doppler radar, which solves the technical problems of low accuracy of velocity measurement and poor anti-interference ability caused by the distortion of radar signals due to complex water surface flow fields during the existing use of UAVs for flow measurement. It includes obtaining the reflection signal of the radar, preprocessing, denoising, reconstructing, and spatio-temporally aligning the reflection signal using multi-scale topological wavelet decomposition technology to obtain the water surface velocity and water level height, synthesizing the velocity vectors of the water surface velocity to obtain the water surface velocity; performing prediction and time averaging processing to obtain the time-averaged velocity; correcting the water surface velocity and converting it into the vertical average velocity, performing spatial grid division and local velocity calculation to obtain the local velocity of the spatial grid; weighting the time-averaged velocity and the local velocity of the spatial grid to obtain the final velocity, and performing flow integration to obtain the cross-sectional flow rate. The present application can be widely applied to the field of hydrological monitoring technology.

Description

Technical Field

[0001] This application belongs to the technical field of hydrological monitoring. More specifically, it relates to a method and device for measuring the water surface velocity of an unmanned aerial vehicle (UAV) based on dual Doppler radar. Background Art

[0002] Traditional water surface velocity measurement techniques mainly include contact mechanical instruments, acoustic Doppler current profilers, and non-contact radar speed measurement. Contact current meters rely on the direct interaction between mechanical rotors and water flow, and there are problems such as easy wear and being easily stuck by floating objects, and they cannot adapt to high flow velocities or complex water flow environments; although Doppler current profilers can obtain vertical velocity profiles, they have defects such as large volume, high cost, and severe acoustic wave attenuation in turbid water bodies.

[0003] Currently, non-contact radar is applied to the speed measurement field. For example, in the Chinese invention patent with the application number CN202411049268.4, a multi-element UAV flow measurement method and system are proposed. Utilizing the advantages of the UAV being lightweight and not requiring mechanical contact, the accuracy and reliability of water body velocity measurement are improved by integrating multiple data sources. However, when measuring speed in the case of turbulent water surfaces, the clutter interference caused by wind and waves on the water surface significantly reduces the signal-to-noise ratio, and the strong rainfall attenuation effect and the existence of gas-liquid two-phase flow further exacerbate the data distortion. Therefore, there is an urgent need to provide a method and device for measuring the water surface velocity of an UAV to solve the above problems. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a method and device for measuring the water surface velocity of an UAV based on dual Doppler radar, so as to solve the technical problems of low accuracy and poor anti-interference ability in velocity measurement caused by radar signal distortion due to complex water surface flow fields when using UAVs to measure flow in the prior art.

[0005] To achieve the above purpose, the first aspect of the embodiments of this application provides a method for measuring the water surface velocity of an UAV based on dual Doppler radar, including

[0006] Obtain the reflected signal of the radar, and use multi-scale topological wavelet decomposition technology to preprocess the reflected signal to obtain the corrected detail coefficients and multi-scale topological features;

[0007] Denoise and reconstruct the multi-scale topological features and the corrected detail coefficients to obtain a reconstructed signal, perform spatio-temporal alignment to obtain the flow velocity and water level height on the water surface, and perform flow velocity vector synthesis on the flow velocity on the water surface to obtain the water surface velocity;

[0008] Perform prediction and time-averaging processing on the water surface velocity and water level height to obtain the time-averaged flow velocity;

[0009] The water surface velocity is corrected and then converted into the vertical average velocity. Spatial grid division and local velocity calculation are carried out to obtain the local velocity of the spatial grid.

[0010] The time-averaged velocity and the local velocity of the spatial grid are weighted to obtain the final velocity, and flow integration is carried out to obtain the cross-sectional flow rate.

[0011] The preprocessing process includes: decomposing according to the number of layers of the reflection signal to obtain detail coefficients, extracting topological features to obtain multi-scale topological features, and performing denoising and then fusion to obtain the corrected detail coefficients.

[0012] Preferably, the Kalman state equation introducing the gravitational acceleration and the water surface slope is used to predict the water surface velocity and the water level height. The formula is as follows:

[0013] ;

[0014] In the formula, is the water surface velocity at the next moment (k + 1), is the water level height at the next moment (k + 1), is the time step, g is the gravitational acceleration, S is the water surface slope parameter, is the water surface velocity at the current moment k, is the water level height at the current moment k, is the process noise.

[0015] Preferably, before spatio-temporal alignment, the mutual information between the reconstructed signal and the multi-scale topological features is calculated, and the topological consistency of the reconstructed signal is verified using the mutual information to obtain the verified reconstructed signal, including the verified water level signal and the verified water surface reflection signal;

[0016] The verified water level signal is subjected to attitude compensation to obtain the attitude-compensated water level signal;

[0017] The verified water surface reflection signal and the attitude-compensated water level signal are spatio-temporally aligned to obtain the velocity and water level height of the water body surface.

[0018] Preferably, the process of topological feature extraction includes: constructing a complex according to the detail coefficients, calculating the time series of betti numbers for each scale of the complex to obtain multi-scale topological features.

[0019] Preferably, the process of denoising and then fusion includes: incorporating the multi-scale topological features into the noise variance to calculate the corrected noise standard deviation;

[0020] Using the corrected noise standard deviation, the multi-scale topological features and the detail coefficients are fused to obtain the corrected detail coefficients.

[0021] Preferably, the signal-to-noise ratio factor is used as the turbulence correction factor to correct the water surface velocity, and the corrected water surface velocity is converted by the vertical velocity distribution coefficient to obtain the vertical average velocity.

[0022] Preferably, the formula for spatio-temporal alignment is as follows:

[0023] ;

[0024] In the formula, are the reconstructed signals of the dual Doppler radar and the water level radar, which are the fixed delays of the reconstructed forward water surface reflection signal, the backward water surface reflection signal and the water level signal respectively, is the water surface velocity corresponding to the forward Doppler radar on the water body surface, is the water surface velocity corresponding to the backward Doppler radar on the water body surface, is the water level height in the vertical direction of the water level radar, is the reconstructed signal of the forward Doppler radar after removing the delay, is the reconstructed signal of the backward Doppler radar after removing the delay, is the reconstructed signal of the water level radar after removing the delay and attitude compensation.

[0025] Preferably, the process of spatial grid division and local flow velocity calculation includes: dividing the vertical average velocity according to equal width and equal depth and then assigning values to obtain the local flow velocity of the spatial grid.

[0026] Preferably, the process of denoising the multi-scale topological features and the corrected detail coefficients includes: dynamically denoising the multi-scale topological features to generate a hybrid threshold function, and performing joint threshold processing on the corrected detail coefficients according to the hybrid threshold function to obtain the detail coefficients after joint threshold processing.

[0027] In the second aspect of the embodiments of the present application, a drone water surface velocity measurement device based on a dual Doppler radar is provided, which uses the above-mentioned drone water surface velocity measurement method based on a dual Doppler radar, and includes: a drone, with a control terminal inside, a positioning device on the top, an adjusting device and a transmitting and receiving device on the bottom, and both the adjusting device and the transmitting and receiving device are electrically connected to the control terminal;

[0028] The control terminal controls the adjusting device and the transmitting and receiving device to obtain the radar reflection signal;

[0029] The control terminal preprocesses the reflection signal by using the multi-scale topological wavelet decomposition technology to obtain the corrected detail coefficients and multi-scale topological features;

[0030] The multi-scale topological features and the corrected detail coefficients are denoised and reconstructed to obtain a reconstructed signal. Then, spatio-temporal alignment is performed to obtain the flow velocity and water level height on the water surface. The flow velocities on the water surface are synthesized into a flow velocity vector to obtain the water surface flow velocity.

[0031] The water surface flow velocity and water level height are predicted and time-averaged to obtain the time-averaged flow velocity.

[0032] The water surface flow velocity is corrected and then converted into the vertical average flow velocity. Spatial grid division and local flow velocity calculation are performed to obtain the local flow velocity of the spatial grid.

[0033] The time-averaged flow velocity and the local flow velocity of the spatial grid are weighted to obtain the final flow velocity, and then flow integral is performed to obtain the sectional flow rate.

[0034] The beneficial effects of this application are as follows: This application proposes a method and device for measuring the water surface flow velocity of an unmanned aerial vehicle based on a dual Doppler radar. The dual Doppler radar is used to simultaneously obtain the reflection signals from different azimuths on the water surface. At the same time, a multi-scale topological wavelet decomposition technology that combines the Daubechies6 wavelet basis four-layer decomposition method and multi-scale topological technology is used to preprocess the obtained reflection signals to obtain the corrected detail coefficients and multi-scale topological features. Through topological feature extraction, multi-scale topological features are obtained. Then, the multi-scale topological features are introduced into the joint noise estimation of the noise variance, and denoising and reconstruction are performed using dynamic topological threshold calculation and joint threshold processing to achieve topological-wavelet joint denoising and signal reconstruction. The data accuracy is ensured through topological consistency verification, and finally, the spatio-temporal alignment output of each sensor data is performed. It effectively cancels the dynamic disturbance of the unmanned aerial vehicle and environmental interference, significantly improves the measurement stability and anti-noise ability under complex water surface flow fields, and improves the measurement efficiency and measurement accuracy.

[0035] Moreover, this application also sets a vector synthesis algorithm for the dual Doppler radar to fuse the measurement results of multiple Doppler radars to achieve high-precision estimation and output of the water surface flow velocity and sectional flow rate. In addition, the adjustment device adjusts the radar installation angle and position in real time, effectively cancels the measurement errors caused by the unstable hovering of the unmanned aerial vehicle, environmental disturbance, and water surface turbulence, significantly improves the stability and accuracy of the flow velocity measurement data, overcomes the defects of low signal-to-noise ratio compensation, large blind areas, and untimely dynamic response of traditional single radars, realizes robust signal extraction and accurate flow rate determination in complex water surface flow field environments, greatly shortens the device debugging and parameter calibration time, reduces the hardware cost, and improves the applicability, reliability, and anti-interference ability of the unmanned aerial vehicle water surface flow velocity measurement device in various actual application scenarios. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0037] Figure 1 A flow chart of a method for measuring water surface velocity using a UAV based on a dual-Doppler radar according to an embodiment of the present application;

[0038] Figure 2 A schematic diagram of a drone measuring water surface velocity and water depth and vector synthesis angle according to an embodiment of the present application;

[0039] Figure 3 A schematic diagram of attitude compensation for water level signals measured by a drone using water surface flow velocity according to an embodiment of the present application;

[0040] Figure 4 A structural diagram of a dual-Doppler radar-based UAV water surface flow velocity measurement device provided in one embodiment of the present application;

[0041] Figure 5 An exploded diagram of a dual-Doppler radar-based UAV water surface flow velocity measurement device provided in one embodiment of the present application;

[0042] Figure 6 An enlarged view of a flow meter in a dual-Doppler radar-based UAV water surface flow velocity measurement device provided in one embodiment of the present application;

[0043] Figure 7 A perspective view of the upper portion of a flight platform in a dual-Doppler radar-based UAV water surface flow velocity measurement device provided in one embodiment of the present application;

[0044] Figure 8 A perspective view of the lower portion of a flight platform in a dual-Doppler radar-based UAV water surface flow velocity measurement device provided in one embodiment of the present application.

[0045] In the figure: 1. Positioning device; 10. Central processing unit; 11. Data receiver; 12. FPGA; 13. Soft shell protective cover; 14. Bearing; 15. Turntable; 16. Upper connecting pin; 17. Telescopic rod; 18. Rotating shaft; 19. Lower connecting pin; 2. Protective cover; 20. Upper clamp; 21. Flight rod; 22. Lower clamp; 23. Rotor; 24. Motor; 25. Support plate; 26. Upper clamp; 27. Lower clamp; 3. Flow meter; 4. Flight platform; 5. Dual Doppler radar; 6. Water level radar; 9. IMU sensor. DETAILED DESCRIPTION

[0046] In order to make the technical problems, technical solutions and beneficial effects to be solved by this application clearer and more understandable, the following further details this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0047] Please refer to Figure 1 , which is a flowchart of a method for measuring the water surface flow velocity of an unmanned aerial vehicle based on dual Doppler radar provided in the first aspect of an embodiment of this application, including:

[0048] S1: Obtain the reflection signals of the dual Doppler radar and the water level radar located on the monitoring section, including the water surface reflection signal and the water level signal. Among them, the water surface reflection signal includes the forward water surface reflection signal and the backward water surface reflection signal.

[0049] Specifically, control the unmanned aerial vehicle to hover above the monitoring section, and obtain the plane coordinates of the unmanned aerial vehicle through the positioning device. At the same time, the data receiver collects the real-time pitch angle and roll angle data of the flowmeter provided by the IMU sensor, and sends the pitch angle and roll angle data to the FPGA. After the FPGA corresponds the pitch angle and roll angle data with the plane coordinates of the unmanned aerial vehicle, it controls the turntable and telescopic rod of the adjustment device according to the pitch angle and roll angle data, and adjusts the radar installation angle in real time, so that the dual Doppler radar forms a fixed angle with the water flow direction, and the water level radar is perpendicular to the water surface.

[0050] Among them, the self-stabilizing gimbal adjusts the angles and positions of the water level radar and the dual Doppler radar in real time through the PID algorithm (proportional-integral-differential algorithm), ensuring that the water level radar is always perpendicular to the water surface. At the same time, the dual Doppler radar is positioned along the water flow direction and maintains a fixed angle between its emitted wave and the water surface. In this application, the PID algorithm of the self-stabilizing gimbal adopts incremental PID control, and its control quantity output formula is as follows:

[0051] ;

[0052] In the formula, is the angle deviation at the current moment (the difference between the target angle and the angle actually measured by the IMU sensor), is the proportional gain, and its value range is (0.5, 0.8), is the integral gain, and its value range is (0.05, 0.1), is the differential gain, and its value range is (0.1, 0.2), p represents proportional, i represents integral, d represents differential, and k is the discrete time step, representing the current moment. According to the control quantity adjust the position of the flowmeter in real time, so that the water level radar is always perpendicular to the water surface, and the dual Doppler radar maintains a preset angle with the water flow direction.

[0053] The FPGA synchronously triggers the dual Doppler radar to emit electromagnetic waves of 24 GHz and the water level radar to emit pulsed waves of 120 GHz to obtain radar reflection signals, including forward water surface reflection signals, backward water surface reflection signals, and water level signals. Here, the frequency of the radar emission band is not limited and can be set according to actual situations. Within the synchronous time window, the reflection signals obtained by the radar include the following three types:

[0054]

[0055] In the formula, are the water surface reflection signals obtained by the forward Doppler radar in the dual Doppler radar, the water surface reflection signals obtained by the backward Doppler radar in the dual Doppler radar, and the water level signals of the vertical distance obtained by the water level radar respectively. f represents the forward Doppler radar, r represents the backward Doppler radar, h is the vertical ranging of the water level radar, is the synchronous time window, t0 is the initial time, is the sampling time interval. Among them, the length of the time window is synchronized with the radar sampling frequency. is the radial velocity component of the forward Doppler radar in the dual Doppler radar, is the radial velocity component of the backward Doppler radar in the dual Doppler radar, is the true water level, is the additive noise.

[0056] S2: Use the multi-scale topological wavelet decomposition technology to preprocess the obtained reflection signals to obtain the corrected detail coefficients and multi-scale topological features.

[0057] The process of preprocessing using the multi-scale topological wavelet decomposition technology includes: determining the scaling function, wavelet function, and decomposition level of the reflection signal, decomposing according to the level to obtain approximation coefficients and detail coefficients, constructing a complex according to the detail coefficients and calculating the time series of the Betti numbers of each scale complex to obtain multi-scale topological features, incorporating the noise variance to calculate the corrected noise standard deviation, and using the noise standard deviation to fuse each scale topological feature and detail coefficient to obtain the corrected detail coefficients.

[0058] Specifically, use the multi-scale topological wavelet decomposition technology based on the four-layer decomposition method of the Daubechies6 wavelet basis to simultaneously decompose the collected reflection signals to obtain the approximation coefficients and detail coefficients of the layer after decomposition. First, the scaling function of the reflection signal before decomposition and the wavelet function need to meet the following conditions:

[0059]

[0060] In the formula, and are the coefficients of the filter, defined by the Daubechies6 wavelet standard, , is the support length, that is, the non-zero interval of the scaling function in the time domain, and t is the synchronization time window.

[0061] Then, determine the number of layers of wavelet decomposition:

[0062]

[0063] In the formula, J is the number of layers of wavelet decomposition (decomposition scale number), N is the number of sampling points of the reflected signal (length of the radar reflected signal), and it needs to satisfy (feasibility condition of wavelet decomposition).

[0064] Next, decompose the j-th layer of any reflected signal to obtain the approximation coefficient and detail coefficient of the j-th layer. The decomposition formula is as follows:

[0065]

[0066] Among them, is the approximation coefficient of the j-th layer, is the detail coefficient of the j-th layer, n is the discrete time index of the output coefficient, and the actual range is , keep the coefficients of , is the noise estimation based on wavelet coefficients, is the approximation coefficient of the (j - 1)-th layer, w is the low-pass filter used to extract the low-frequency component of the signal, g is the high-pass filter used to extract the high-frequency component of the signal, and k is the discrete time index of the input signal.

[0067] Finally, the multi-scale topological features are extracted from the detail coefficients through FPGA, and the detail coefficients are fused after denoising to obtain the corrected detail coefficients. The specific steps are as follows:

[0068] First, construct an alpha complex based on the filtered detail coefficients at wavelet decomposition scales j = 1,..., J to identify the signal mutation features caused by surface turbulence. The formula for constructing the alpha complex is as follows:

[0069]

[0070] In the formula, is the average amplitude of the radius adaptation and the detail coefficient, which controls the generation of the simplex, is the detail coefficient of the j-th layer, is the radius of the closed ball, is the number of coefficients.

[0071] Next, for the complex at each scale , calculate the time series of its 0-dimensional Betti number to obtain multi-scale topological features, and distinguish the noise in the reflected signal from the true flow velocity signal. The formula for obtaining the multi-scale topological features is as follows:

[0072]

[0073] In the formula, is the multi-scale topological feature, K is the number of topological features detected in the j-th layer, calculated by the Alpha complex, I is the indicator function, which is 1 when the condition is satisfied and 0 otherwise. is the survival time of the k-th topological feature, t is the time series, and is the adaptive persistence threshold for analyzing the persistence of topological features. Making and have the same formula means that the connectivity of the Alpha complex at radius is consistent with the result when the threshold in persistent homology is , ensuring the logical self-consistency of the topological analysis and simplifying the parameter process.

[0074] Furthermore, incorporate the multi-scale topological features into the noise variance calculation to obtain the corrected noise standard deviation. Here, use the topological structure information contained in the multi-scale topological features to optimize the noise modeling and improve the robustness and estimation accuracy of the system. The formula is as follows:

[0075]

[0076] Among them, is the corrected noise standard deviation, is the standard deviation of the input noise, is the local time window length, N is the number of sampling points of the reflected signal, taking 10% of the sampling points of the reflected signal, is the multi-scale topological feature corresponding to the n-th discrete time point.

[0077] Finally, use the corrected noise standard deviation to fuse each scale topological feature with the detail coefficient of the j-th layer of wavelet decomposition to obtain the corrected detail coefficient. The formula is as follows:

[0078]

[0079] Among them, is the corrected detail coefficient, is the hyperbolic tangent function to normalize the topological feature to [-1, 1], is the detail coefficient of the j-th layer, is the multi-scale topological feature corresponding to the nth discrete time point.

[0080] S3: Denoise the multi-scale topological feature according to the corrected detail coefficients and reconstruct to obtain a reconstructed signal. After verification, perform spatio-temporal alignment to obtain the flow velocity and the vertical water level height of the radar corresponding to the water surface of the dual Doppler radar, and perform flow velocity vector synthesis to obtain the water surface flow velocity.

[0081] Specifically, perform dynamic denoising on the multi-scale topological feature according to the corrected detail coefficients to obtain a hybrid threshold function, and perform joint threshold processing to obtain the detail coefficients after joint threshold processing; perform wavelet inverse transform reconstruction on the detail coefficients after joint threshold processing according to the approximation coefficients to obtain a reconstructed signal. After verification, perform spatio-temporal alignment to obtain the flow velocity and the vertical water level height of the radar corresponding to the water surface of the dual Doppler radar, and perform flow velocity vector synthesis to obtain the water surface flow velocity. Among them, the reconstructed signal includes the reconstructed water level signal and the water surface reflection signal.

[0082] Input the multi-scale topological feature into the FPGA according to the corrected detail coefficients for dynamic denoising, and generate a hybrid threshold function related to the radar signal-to-noise ratio to suppress the vibration of the unmanned aerial vehicle and environmental noise interference. Among them, the formula for generating the hybrid threshold function is as follows:

[0083]

[0084] In the formula, is the basic threshold function, is the hybrid threshold function corresponding to the nth discrete time point, is the number of sampling points of the jth layer of the reflection signal, is the multi-scale topological feature corresponding to the nth discrete time point.

[0085] Next, perform joint threshold processing on the hybrid threshold function generated by dynamic denoising according to the corrected detail coefficients to generate a scene-adaptive detection threshold to avoid the limitations of the fixed threshold. Among them, the formula for the joint threshold is as follows:

[0086] ;

[0087] In the formula, is the corrected detail coefficient, is the detail coefficient after joint threshold processing.

[0088] Then, perform wavelet inverse transform reconstruction on the approximation coefficients and the detail coefficients after joint threshold processing to obtain a reconstructed signal, including the reconstructed water level signal and the water surface reflection signal. The formula for obtaining the reconstructed signal is as follows:

[0089]

[0090] In the formula, is the reconstructed signal, is the approximation coefficient, is the scaling function, J is the number of layers of wavelet decomposition, is the detail coefficient after joint threshold processing after reconstruction, is the wavelet function, t is the time window, and n is the number of sampling points of the transmitted signal.

[0091] Again, the topological consistency is verified by calculating the mutual information (MI) between the reconstructed signal and the multi-scale topological features, and the verified reconstructed signal is obtained, including the verified water level signal and the water surface reflection signal. The verification formula is as follows:

[0092]

[0093] In the formula, is the mutual information calculation, is the reconstructed signal, is the topological feature, and t is the time window. When the signal re-acquisition mechanism is triggered, , where J is the number of layers of wavelet decomposition, N is the number of sampling points of the reflection signal, v is the topological consistency verification index, is the reconstructed signal, is the multi-scale topological feature, and I is , which refers to the mutual information calculation, p(x, y) is the joint probability distribution of X and Y, p(x) is the marginal probability distribution of X, and p(y) is the marginal probability distribution of Y.

[0094] Furthermore, please refer to Figure 2 for attitude compensation of the verified water level signal , and the tilted beam is restored to the true height perpendicular to the water surface through geometric correction to obtain the attitude-compensated water level signal , where the formula for attitude compensation is as follows:

[0095]

[0096] In the formula, are respectively the pitch angle and roll angle of the UAV measured by the IMU sensor in sequence, is the reconstructed water level signal, is the attitude-compensated water level signal.

[0097] Perform spatio-temporal alignment on the verified water surface reflection signal and the water level signal after attitude compensation, including spatio-temporal stamp alignment and delay compensation alignment, to obtain the flow velocity of the corresponding water body surface of the dual Doppler radar and the vertical water level height of the radar. The formula for spatio-temporal alignment is as follows:

[0098]

[0099] In the formula, are the reconstructed signals of the dual Doppler radar and the water level radar, and are the fixed delays of the reconstructed forward water surface reflection signal, forward water surface reflection signal and water level signal respectively, is the flow velocity of the corresponding water body surface of the forward Doppler radar, is the flow velocity of the corresponding water body surface of the backward Doppler radar, is the vertical water level height of the water level radar, is the reconstructed signal of the forward Doppler radar after removing the delay, is the reconstructed signal of the backward Doppler radar after removing the delay, is the reconstructed signal of the water level radar after removing the delay and attitude compensation.

[0100] S4: Perform flow velocity vector synthesis based on the flow velocity of the water body surface of the dual Doppler radar to obtain the water surface flow velocity; construct a Kalman state equation to predict the water surface flow velocity and water level height at the current moment, obtain the water surface flow velocity and water level height at the next moment, and perform time averaging on the water surface flow velocity at the next moment to obtain the time-averaged flow velocity.

[0101] Please refer to Figure 2 , is the angle between the beam of the forward Doppler radar and the water flow direction, is the angle between the beam of the backward Doppler radar and the water flow direction, is the radial velocity component of the forward Doppler radar calculated based on the flow velocity of the water body surface of the dual Doppler radar, is the radial velocity component of the backward Doppler radar calculated based on the flow velocity of the water body surface of the dual Doppler radar; according to and , perform flow velocity vector synthesis on the flow velocity of the corresponding water body surface of the dual Doppler radar to obtain the water surface flow velocity. The specific calculation formula is as follows:

[0102]

[0103] In the formula, is the water surface flow velocity, is the radial velocity component of the forward Doppler radar, is the radial velocity component of the backward Doppler radar, is the angle between the beam of the forward Doppler radar and the water flow direction, is the angle between the backward Doppler radar beam and the water flow direction. are the signal-to-noise ratios of the forward Doppler radar and the backward Doppler radar, respectively.

[0104] Construct a Kalman state equation that dynamically adjusts based on process noise and observation noise (introducing the parameters of gravitational acceleration and water surface slope), predict the water surface velocity and water level height at the current moment, and obtain the water surface velocity and water level height at the next moment. The specific formula is as follows:

[0105]

[0106] In the formula, is the water surface velocity at the next moment (k + 1). is the water level height at the next moment (k + 1). is the time step, g is the gravitational acceleration, S is the water surface slope parameter. is the water surface velocity at the current moment (k). is the water level height at the current moment (k). is the process noise.

[0107] Among them, the prediction state of the system (water surface velocity and water level) is associated with the actual sensor measurement data (radar signal) using the observation equation, and the state estimation is optimized through data assimilation. The observation equation is as follows:

[0108]

[0109] In the formula, is the process noise. is the observation noise, g is the gravitational acceleration, S is the water surface slope parameter. is the angle between the beam direction of the forward radar and the water flow direction. is the angle between the beam direction of the backward radar and the water flow direction. is the pitch angle of the UAV. is the water surface velocity of the forward Doppler radar after spatio-temporal alignment. is the water surface velocity of the backward Doppler radar after spatio-temporal alignment. is the vertical water level height of the water level radar after spatio-temporal alignment.

[0110] Furthermore, perform time averaging on the water surface velocity at the next moment obtained from the Kalman state equation to obtain the time-averaged velocity.

[0111] Time averaging refers to performing time-weighted averaging on the water surface velocity at the next moment. The formula is as follows:

[0112]

[0113] In the formula, is the time-averaged velocity, K is the average time window, is the index quantity, is the water surface velocity at the next moment, is the pitch angle of the UAV.

[0114] S5: Use the turbulence correction factor and the vertical velocity distribution coefficient to correct the water surface velocity and convert it into the vertical average velocity, perform spatial grid division and local velocity calculation to obtain the local velocity of the spatial grid; weight the time-averaged velocity and the local velocity of the spatial grid to obtain the final velocity, and perform flow integration to obtain the cross-sectional flow rate.

[0115] First, introduce the signal-to-noise ratio factor as the turbulence correction factor into the water surface velocity, correct the water surface velocity to obtain the corrected water surface velocity, and according to the vertical velocity distribution coefficient convert the corrected water surface velocity to obtain the vertical average velocity. The specific formula is as follows:

[0116]

[0117] In the formula, is the vertical average velocity, is the water surface velocity, is the vertical velocity distribution coefficient, which is dynamically calculated according to the bed roughness and the hydraulic radius , is the signal-to-noise ratio factor, which is used as the turbulence correction factor, is the signal-to-noise ratio of the forward Doppler radar, is the signal-to-noise ratio of the backward Doppler radar.

[0118] Then, divide the vertical average velocity into a grid of N×M. Among them, the horizontal direction is divided equally by width and the vertical direction is divided equally by depth to obtain the divided vertical average velocity .

[0119] Among them, assign the divided average velocity to obtain the local velocity of the spatial grid , and the formula is as follows:

[0120]

[0121] In the formula, is the local velocity of the spatial grid, is the divided average velocity, h is the vertical ranging of the water level radar, is the velocity index (default = 7), where is the water depth at the center point of the grid, i is the horizontal grid cell, and j is the vertical grid cell.

[0122] Finally, the time-averaged flow velocity is used as the baseline value, and is weighted with the local flow velocity of the spatial grid to obtain the final flow velocity of the grid cell , and the flow integral is performed to obtain the cross-sectional flow rate. The specific formula is as follows:

[0123]

[0124] In the formula, is the final flow velocity of the grid cell, is the local flow velocity of the spatial grid, is the time-averaged flow velocity.

[0125] Among them, the calculation formula for the flow integral is as follows:

[0126]

[0127] In the formula, Q is the cross-sectional flow rate, is the number of horizontal grids, is the number of vertical grids, i is the horizontal grid cell, j is the vertical grid cell, is the final flow velocity of the spatial grid cell, is the area of the grid cell, , is the horizontal grid size, is the vertical grid size; is the included angle between the water flow direction and the normal direction of the monitoring section, which is obtained through the PID angle calibration result.

[0128] Please refer to Figure 4 , which is the structural diagram of an unmanned aerial vehicle water surface flow velocity measurement system provided in the second aspect of an embodiment of the present application, including: an unmanned aerial vehicle, with a control terminal inside the unmanned aerial vehicle, a positioning device 1 at the top, an adjusting device and a transmitting and receiving device at the bottom, and the transmitting and receiving device, the positioning device 1 and the adjusting device are all electrically connected to the control terminal.

[0129] The control terminal controls the transmitting and receiving device, the positioning device 1 and the adjusting device to obtain the reflection signals of the dual Doppler radar 5 and the water level radar 6 located on the monitoring section.

[0130] The control terminal preprocesses the reflection signals by using the multi-scale topological wavelet decomposition technology to obtain the corrected detail coefficients and multi-scale topological features;

[0131] The multi-scale topological features and the modified detail coefficients are denoised and then subjected to inverse wavelet transform for reconstruction to obtain a reconstructed signal. Space-time alignment is performed to obtain the flow velocity of the water surface corresponding to the dual Doppler radar 5 and the vertical water level height of the water level radar 6. Flow velocity vectors are synthesized to obtain the water surface flow velocity. A Kalman state equation is constructed to predict the water surface flow velocity and water level height at the next moment, and the time-averaged flow velocity is obtained through time-averaging processing.

[0132] The water surface flow velocity is corrected using a turbulence correction factor and a vertical flow velocity distribution coefficient and then converted into the vertical average flow velocity. Spatial grid division and local flow velocity calculation are performed to obtain the local flow velocity of the spatial grid.

[0133] The time-averaged flow velocity and the local flow velocity of the spatial grid are weighted to obtain the final flow velocity, and flow integration is performed to obtain the sectional flow rate.

[0134] Specifically, please refer to Figure 5 , the drone includes a flight platform 4, and a number of flight rods 21 extend outward from the flight platform 4. A flight mechanism is provided at one end of the flight rod 21 away from the flight platform 4. The flight mechanism is successively provided with a support plate 25, a motor 24 and a rotor 23 from bottom to top. Among them, a control terminal is provided inside the drone, a positioning device 1 is provided at the top, a transmitting and receiving device and an adjusting device are provided at the bottom, and the positioning device 1, the transmitting and receiving device and the adjusting device are all electrically connected to the control terminal.

[0135] Furthermore, a protective cover 2 is installed on the top of the flight platform 4, and the positioning device 1 is arranged on the top of the protective cover 2. In this application, RTK (Real-Time Kinematic) is selected as the positioning device 1 to receive the plane coordinates of the drone in real time. There is no limitation on the positioning device here, and it can be selected according to the actual situation.

[0136] The transmitting and receiving device includes an IMU sensor 9 and a flowmeter 3. The IMU sensor 9 is used to receive the pitch angle and roll angle data of the radar obtained by the flowmeter 3 in real time, and at the same time communicate with the control terminal through the CAN bus.

[0137] Please refer to Figure 6 , the flowmeter 3 includes a dual Doppler radar 5 and a water level radar 6. Among them, the dual Doppler radar 5 includes a forward Doppler radar and a backward Doppler radar, which are symmetrically installed on the front and back sides of the flowmeter 3 respectively, and the included angle between the axis of its transmitting beam and the water flow direction is adjustable, with a range of 30° to 60°. The water level radar 6 is installed at the bottom of the flowmeter 3 and is used to measure the vertical distance of the water surface.

[0138] Please refer to Figure 5, the control terminal is set inside the UAV and is located above the center of the flight platform 4. The upper surface and the lower surface of the flight platform 4 are respectively covered with an upper clamping plate 20 and a lower clamping plate 22, and a number of upper clamping blocks 26 and lower clamping blocks 27 for clamping the upper clamping plate 20 and the lower clamping plate 22 are correspondingly arranged. The centers of the upper clamping plate 20 and the lower clamping plate 22 are symmetrically provided with through holes for positioning the control terminal at the center of the UAV.

[0139] Please refer to Figure 7 , the control terminal includes a central processing unit 10, a data receiver 11 and an FPGA 12. The data receiver 11 is connected to the FPGA 12 through an SPI interface and is used to receive the planar coordinates of the UAV transmitted by the positioning device 1 and the original signals of the dual Doppler radar 5 and the water level radar 6, and communicates with the FPGA 12 through a CAN bus to feedback the received data of the flowmeter 3 in real time.

[0140] Please refer to Figure 8 , the adjusting device is set at the bottom of the UAV and is located between the lower clamping plate 22 and the transmitting and receiving device. The adjusting device includes a bearing 14, a turntable 15 and a telescopic rod 17. The turntable 15 is connected to the telescopic rod 17 through an upper connecting pin 16, and the telescopic rod 17 is rigidly connected to the rotating shaft 18 and the transmitting device through a lower connecting pin 19. The outside of the adjusting device is sleeved with a soft shell protective cover 13 to protect the adjusting device, prevent external interference and make the adjustment more precise.

[0141] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0142] The above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of this application, and should all be included in the protection scope of this application.

Claims

1. A method for measuring water surface velocity using a UAV based on dual Doppler radar, characterized in that: include: Acquire a radar reflection signal, and preprocess the reflection signal using a multi-scale topological wavelet decomposition technique to obtain a corrected detail coefficient and a multi-scale topological feature; Denoising and reconstructing the multi-scale topological features and the corrected detail coefficients to obtain a reconstructed signal, performing spatiotemporal alignment to obtain the flow velocity and water level height of the water body surface, and performing velocity vector synthesis on the flow velocity of the water body surface to obtain the water surface flow velocity; Predicting and time-averaging the water surface velocity and the water level to obtain a time-averaged velocity; The water surface velocity is corrected and converted into a vertical average velocity, and spatial grid division and local velocity calculation are performed to obtain the local velocity of the spatial grid; The time-averaged flow velocity and the local flow velocity of the spatial grid are weighted to obtain a final flow velocity, and flow integration is performed to obtain a cross-sectional flow rate; The preprocessing process includes: decomposing the reflected signal according to the number of layers to obtain detail coefficients, extracting topological features to obtain multi-scale topological features, and performing denoising and fusion to obtain corrected detail coefficients; The formula for the spatiotemporal alignment is as follows: ； Where, are the reconstructed signals of the dual-Doppler radar and the water level radar, and correspond to the fixed delays of the reconstructed forward water surface reflection signal, backward water surface reflection signal and water level signal, respectively. is the velocity of the water surface corresponding to the forward Doppler radar, is the velocity of the water surface corresponding to the backward Doppler radar, is the vertical water level height of the water level radar, De-delayed reconstructed signal for forward Doppler radar, De-delayed reconstructed signal for backward Doppler radar, Reconstructed signal after removing delay and attitude compensation for water level radar; The water surface velocity and the water level are predicted using the Kalman equation of state that introduces gravity acceleration and water surface gradient. The formula is as follows: ； Where, is the water surface velocity at the next moment (k+1), is the water level at the next moment (k+1), is the time step, g is the acceleration of gravity, S is the water surface gradient parameter, is the water surface velocity at the current moment k, is the water level at the current moment k, is the process noise.

2. The method for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar according to claim 1, wherein: Before performing the spatiotemporal alignment, calculating the mutual information between the reconstructed signal and the multi-scale topological feature, and verifying the topological consistency of the reconstructed signal using the mutual information to obtain a verified reconstructed signal, including a verified water level signal and a verified water surface reflection signal; Performing attitude compensation on the verified water level signal to obtain an attitude-compensated water level signal; The verified water surface reflection signal and the water level signal after attitude compensation are aligned in time and space to obtain the flow velocity and water level height of the water body surface.

3. The method for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar according to claim 1, wherein: The process of extracting the topological features includes: constructing a complex according to the detail coefficients, calculating a time series of Betti numbers for the complex at each scale, and obtaining the multi-scale topological features.

4. The method for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar according to claim 1, wherein: The post-denoising fusion process includes: incorporating the multi-scale topological features into the noise variance and calculating a corrected noise standard deviation; The multi-scale topological feature is fused with the detail coefficient using the corrected noise standard deviation to obtain the corrected detail coefficient.

5. The method for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar according to claim 1, wherein: The water surface velocity is corrected using a signal-to-noise ratio factor as a turbulence correction factor to obtain a corrected water surface velocity, and the vertical velocity distribution coefficient is used to convert the corrected water surface velocity to obtain the vertical average velocity.

6. The method for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar according to claim 1, wherein: The process of spatial grid division and local flow velocity calculation includes: dividing the vertical average flow velocity according to equal width and equal depth and then assigning values to obtain the local flow velocity of the spatial grid.

7. The method for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar according to claim 1, wherein: The process of denoising the multi-scale topological features and the corrected detail coefficients includes: dynamically denoising the multi-scale topological features to generate a mixed threshold function, and performing joint threshold processing on the corrected detail coefficients according to the mixed threshold function to obtain the detail coefficients after joint threshold processing.

8. A device for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar, using the method for measuring water surface velocity of an unmanned aerial vehicle based on a dual Doppler radar according to claim 1, characterized in that: include: A drone, wherein the drone has a control terminal inside, a positioning device on the top, and an adjustment device and a transmitting and receiving device on the bottom, wherein the adjustment device and the transmitting and receiving device are both electrically connected to the control terminal; The control terminal controls the regulating device and the transmitting and receiving device to obtain the reflected signal of the radar; The control terminal pre-processes the reflected signal using a multi-scale topological wavelet decomposition technique to obtain a corrected detail coefficient and a multi-scale topological feature; Denoising and reconstructing the multi-scale topological features and the corrected detail coefficients to obtain a reconstructed signal, performing spatiotemporal alignment to obtain the flow velocity and water level height of the water body surface, and performing velocity vector synthesis on the flow velocity of the water body surface to obtain the water surface flow velocity; Predicting the water surface velocity and the water level height and performing time averaging processing to obtain a time average velocity; The water surface velocity is corrected and converted into a vertical average velocity, and spatial grid division and local velocity calculation are performed to obtain the local velocity of the spatial grid; The time-averaged flow velocity and the local flow velocity of the spatial grid are weighted to obtain a final flow velocity, and flow integration is performed to obtain a cross-sectional flow rate; The formula for the spatiotemporal alignment is as follows: ； Where, are the reconstructed signals of the dual-Doppler radar and the water level radar, and correspond to the fixed delays of the reconstructed forward water surface reflection signal, backward water surface reflection signal and water level signal, respectively. is the velocity of the water surface corresponding to the forward Doppler radar, is the velocity of the water surface corresponding to the backward Doppler radar, is the vertical water level height of the water level radar, De-delayed reconstructed signal for forward Doppler radar, De-delayed reconstructed signal for backward Doppler radar, Reconstructed signal after removing delay and attitude compensation for water level radar; The water surface velocity and the water level are predicted using the Kalman equation of state that introduces gravity acceleration and water surface gradient. The formula is as follows: ； Where, is the water surface velocity at the next moment (k+1), is the water level at the next moment (k+1), is the time step, g is the acceleration of gravity, S is the water surface gradient parameter, is the water surface velocity at the current moment k, is the water level at the current moment k, is the process noise.

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