A water surface fluctuation compensation method for laser bathymetry

Abstract

This invention relates to the field of laser water depth detection, specifically to a method for compensating for water surface fluctuations in laser depth measurement. This method addresses the inaccuracy and surface fluctuation interference issues inherent in traditional depth measurement techniques in extremely shallow waters. By using a turbidity sensor, an IMU zero-bias compensation sensor, and a laser displacement sensor, the measured water depth is corrected to improve measurement accuracy. This solution significantly enhances the stability and accuracy of depth measurement in extremely shallow waters, possesses strong environmental adaptability, and offers broad application prospects.

Description

Technical Field

[0001] The present invention relates to the field of laser water depth detection, and in particular to a water surface fluctuation compensation method for laser depth detection. Background Art

[0002] In extremely shallow waters, frequent and large surface fluctuations significantly impact depth measurement accuracy. This is especially true when using traditional single-wavelength laser depth measurement technology. The propagation path of the laser signal between the water surface and the bottom is disrupted by surface fluctuations, significantly reducing measurement accuracy. Furthermore, water turbidity and bottom reflectivity also affect the clarity of the laser echo, further reducing measurement accuracy.

[0003] To overcome these challenges, dual-wavelength laser bathymetry has become a research hotspot in recent years. Dual-wavelength lasers can simultaneously utilize the different penetration and reflection characteristics of blue and green lasers, fusing the measurement data from the two wavelengths to improve the stability and accuracy of water depth measurements. However, dual-wavelength laser technology still cannot effectively address the errors caused by water surface fluctuations, especially in extremely shallow waters, where the impact of surface fluctuations is significant.

[0004] To improve measurement accuracy and reduce the impact of water surface fluctuations on measurement results, researchers have proposed several methods to compensate for water surface fluctuations in recent years, including using laser displacement sensors to monitor water height and inertial navigation units (IMUs) to monitor changes in ship posture. However, these technologies still have limitations, such as the inability to accurately compensate in real time and difficulty maintaining high measurement accuracy in complex waters.

[0005] Therefore, developing a water surface fluctuation compensation device and method suitable for unmanned vessel blue-green laser bathymetry is particularly important. Such a device must not only overcome the errors caused by water surface fluctuations but also provide stable measurement results under varying water conditions (such as turbidity and wave size). This paper proposes a water surface fluctuation compensation method based on dual-wavelength lasers. Combining high-frequency laser displacement sensors, IMUs, and Kalman filtering technologies, this method can monitor and compensate for the impact of water surface fluctuations on measurement results in real time, significantly improving measurement accuracy and system adaptability. Summary of the Invention

[0006] The purpose of the present invention is to provide a water surface fluctuation compensation method for laser depth sounding, which solves the problem of low laser measurement accuracy in complex waters.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0008] A water surface fluctuation compensation method for laser bathymetry, comprising:

[0009] S1. Hover the unmanned vessel in the water body to be measured, and calibrate the IMU zero bias compensation sensor and laser displacement sensor on the unmanned vessel for height, and calibrate the turbidity sensor for zero point;

[0010] S2. Lower the turbidity sensor into the water being measured and transmit the water turbidity value T collected by the turbidity sensor to the laser emission controller in real time. The laser emission controller emits lasers with different parameters based on the real-time changes in the water turbidity value T. The photoelectric receiving module receives the laser echo and calculates the depth H of the measured water body;

[0011] S3. A laser displacement sensor records the height change Δh(t) caused by surface fluctuations in the measured water body in real time. The IMU zero-bias compensation sensor detects the three-axis motion of the unmanned vessel, including the attitude angle θ(t). The Beidou positioning module provides accurate position information to ensure that the coordinates and time of the IMU zero-bias compensation sensor are synchronized during measurement. The measured water depth H is corrected to obtain the new depth of the measured water body. ;

[0012] S4. The extended Kalman filter fuses the attitude angle θ(t) and the height change Δh(t), and calibrates the spatial weight coefficient α and the temporal dynamic compensation coefficient γ of the water surface fluctuation correction parameter through the least squares method to further correct the water surface error.

[0013] Furthermore, the laser emission controller will alternately emit blue light and green light, and adjust the weights of the alternately emitted blue light and green light in real time based on the collected water turbidity value T;

[0014] Blue light weight With green light weight The calculation is specifically expressed as follows:

[0015] , ;

[0016] Where, =10NTU is the turbidity threshold, k=0.2 is the adjustment factor, and e is the base of the natural logarithm.

[0017] Furthermore, after receiving the alternately emitted blue and green light echo signals, the photoelectric receiving module calculates the depth H of the measured water body;

[0018] The calculation of the measured water depth H is specifically expressed as follows:

[0019] ;

[0020] Where c is the speed of light (c=3* m / s), n represents the refractive index of water (n 1.33), Represented as blue light echo signal, Represented as green light echo signal.

[0021] Furthermore, The calculation is specifically expressed as follows:

[0022] ;

[0023] Where, is the water surface change rate, is the measured water body fluctuation height, for Corresponding moment.

[0024] Furthermore, the calculation of the Kalman filter fusion attitude angle θ(t) and height change Δh(t) is specifically expressed as follows:

[0025] ;

[0026] Where, It is represented as the water depth state at time k, It represents the water depth at time k-1, Expressed as the speed at time k-1, Represented as the actual observation value (sensor measurement value) at time k, ~ N(0,Q) follows a normal distribution and is represented as process noise, ~ N(0, R) obeys the normal distribution and is represented as observation noise, where Q and R are calibrated by measured data.

[0027] Furthermore, by calculating the predicted covariance , Kalman gain and update the covariance Further improve accuracy.

[0028] Furthermore, the prediction covariance The calculation is specifically expressed as follows:

[0029] ;

[0030] Where, It is expressed as inheriting the uncertainty of the previous moment.

[0031] Furthermore, the Kalman gain The calculation is specifically expressed as follows:

[0032] .

[0033] Furthermore, the updated covariance calculation is specifically expressed as follows:

[0034] .

[0035] Furthermore, the unmanned vessel is equipped with a laser radar and a posture sensor, and the laser radar, posture sensor and photoelectric receiving module are all wirelessly connected to the shore.

[0036] By adopting the above technical solution, the beneficial technical effects of the present invention are:

[0037] This system uses a turbidity sensor to measure the current water turbidity. It then emits blue and green light of varying frequencies and wavelengths to detect the water's depth. The system then uses an IMU to fuse IMU attitude data with signals from a high-frequency laser displacement sensor to correct the depth. Finally, it uses a Kalman filter to further correct the depth, achieving a low error. This system significantly improves the stability and accuracy of bathymetry in extremely shallow waters, possessing strong environmental adaptability and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of a water surface fluctuation compensation method for laser bathymetry. DETAILED DESCRIPTION

[0039] like Figure 1 A water surface fluctuation compensation method for laser depth measurement is shown. The method includes: first, hovering an unmanned vessel in the measured water body, calibrating the vessel's IMU zero-bias compensation sensor and laser displacement sensor, and recording the vessel's initial angle and altitude. The turbidity sensor is also calibrated to zero, recording the turbidity threshold. The turbidity sensor is then lowered into the measured water body via shore-based control. The water turbidity value T collected by the turbidity sensor is transmitted to a laser emission controller in real time. The laser emission controller emits blue and green light with different parameters based on the real-time changes in the water turbidity value T, and continuously adjusts the weighting of the blue and green lights to obtain more accurate water depth information. An optoelectronic receiving module receives the laser echo and calculates the measured water depth H based on the received laser echo. A laser signal fusion algorithm automatically corrects errors caused by water fluctuations by comparing the reflection coefficients of the blue and green wavelengths, ensuring stable measurement results in turbid waters.

[0040] After obtaining the initial depth H of the measured water body, the height change Δh(t) caused by the surface fluctuation of the measured water body is recorded in real time by the laser displacement sensor. The IMU zero bias compensation sensor detects the three-axis motion of the unmanned ship, including the attitude angle θ(t). Through the PTP protocol, it can ensure that the data of the high-frequency laser displacement sensor, IMU and laser depth sounder have a unified time base, thereby improving the accuracy of data fusion and analysis; and use the Beidou positioning module to provide accurate position information to ensure the coordinate and time synchronization of the IMU zero bias compensation sensor during measurement, and correct the measured water body depth H to obtain the new measured water body depth. ;

[0041] Finally, the attitude angle θ(t) and the height change Δh(t) are fused through the extended Kalman filter, and the spatial weight coefficient α and the time dynamic compensation coefficient γ of the water surface fluctuation correction parameter are calibrated by the least squares method to achieve the purpose of further correcting the water surface error.

[0042] The laser emission controller will emit blue light and green light alternately, and adjust the weight of the alternately emitted blue light and green light in real time according to the collected water turbidity value T;

[0043] Blue light weight With green light weight The calculation is specifically expressed as follows:

[0044] , ;

[0045] Where, =10NTU is the turbidity threshold, k=0.2 is the adjustment factor, and e is the base of the natural logarithm.

[0046] After receiving the alternately emitted blue and green light echo signals, the photoelectric receiving module calculates the depth H of the measured water body;

[0047] The calculation of the measured water depth H is specifically expressed as follows:

[0048] ;

[0049] Where c is the speed of light (c=3* m / s), n represents the refractive index of water (n 1.33), Represented as blue light echo signal, Represented as green light echo signal.

[0050] Correct the measured water depth H. The calculation is specifically expressed as follows:

[0051] ;

[0052] Where, is the water surface change rate, is the measured water body fluctuation height, for Corresponding moment.

[0053] The calculation of the Kalman filter fusion attitude angle θ(t) and height change Δh(t) is specifically expressed as follows:

[0054] ;

[0055] Where, It is represented as the water depth state at time k, It represents the water depth at time k-1, Expressed as the speed at time k-1, Represented as the actual observation value (sensor measurement value) at time k, ~ N(0,Q) follows a normal distribution and is represented as process noise, ~ N(0, R) obeys the normal distribution and is represented as observation noise, where Q and R are calibrated by measured data.

[0056] By calculating the forecast covariance , Kalman gain and update the covariance Further improve accuracy.

[0057] Prediction covariance , integrating the uncertainty of the previous moment , superimposed process noise , reflecting the confidence decay in the prediction stage; prediction covariance The calculation is specifically expressed as follows:

[0058] ;

[0059] Where, It is expressed as inheriting the uncertainty of the previous moment.

[0060] Kalman gain ,molecular Forecast uncertainty, denominator The total uncertainty of prediction and observation, when R (sensor noise) is small, →1, more trust in the observed value Kalman gain The calculation is specifically expressed as follows:

[0061] .

[0062] Update covariance , The uncertainty reduction ratio after the information update is expressed as follows:

[0063] .

[0064] The power ratio of the blue and green lasers is adjusted based on real-time water turbidity data to optimize echo signal quality. A Kalman filter is used to correct laser measurement data, eliminating errors caused by environmental factors and vessel motion, ensuring accurate depth data. The system combines laser displacement sensor and IMU data to correct errors caused by water surface fluctuations and minimize surface disturbances. The system automatically adjusts blue and green laser measurement parameters based on data from various sensors, ensuring measurement stability and accuracy in complex waters.

[0065] The unmanned vessel's speed is adjusted based on real-time water depth data, obstacle detection information, and the water environment. LiDAR data is used to avoid collisions. Dynamic adjustments to the unmanned vessel's speed ensure efficient and accurate measurements in varying water depths.

[0066] The system integrates blue-green laser bathymetric data, lidar point cloud, Beidou positioning data, and IMU attitude data to generate high-precision seabed topographic maps, which are then transmitted to the shore-based control center in real time.

[0067] Through the above implementation plan, the ultra-shallow water unmanned boat depth sounding device based on blue-green laser can effectively overcome the limitations of traditional depth sounding methods and provide high-precision and high-efficiency measurement results.

[0068] The unmanned vessel is equipped with a laser radar and a posture sensor, all of which are wirelessly connected to the shore. The laser emission controller and the photoelectric receiving module are used to measure the water depth and obtain the distance between the unmanned vessel and the bottom in real time, ensuring that bottoming accidents are prevented. The laser radar provides high-precision surrounding obstacle detection, helping to identify obstacles on the water surface and underwater. The posture sensor monitors the unmanned vessel's posture changes, including pitch, roll, and yaw angles, to ensure navigation stability. Through the 5G communication module, data is uploaded to the shore-based control system in real time, allowing the operator to understand the current status of the unmanned vessel, its depth measurement data, and the surrounding environment in real time.

[0069] Based on blue-green laser detection data, the system analyzes the water surface and bottom conditions in real time. When the blue-green laser measures a water depth H less than 5cm, it avoids obstacles in shallow water to prevent hitting the bottom. When the measured water depth H is 5cm≤H<20cm, the navigation speed is limited to ≤0.3m / s; when H is ≥20cm, the maximum speed is allowed to be 1.5m / s. If the laser radar detects an obstacle within 5 meters, an alarm is issued to the shore-based control system and operations are stopped.

[0070] Based on this measurement data, the unmanned vessel avoids obstacles in shallow waters and prevents hitting the bottom, ensuring measurement accuracy. In shallow waters, the system automatically reduces speed to ensure safety; in deep waters, it increases speed appropriately to improve efficiency.

[0071] The present invention is described as follows in conjunction with experimental verification:

[0072] The turbidity of the current water area is read by the turbidity sensor as T=15NTU, the threshold T0=10 NTU, and the adjustment factor k=0.2.

[0073] Substituting the values:

[0074] ;

[0075] ;

[0076] Calculate the time difference between the laser emission and the time when the photoelectric receiving module receives the laser. Assuming that the blue light signal and green light signal The intensity at different time points needs to be To find the maximum composite signal, we need to find the time point t at which the composite signal is the largest.

[0077] Speed ​​of light c = 3* m / s water refractive index n 1.33 The time corresponding to the maximum composite signal is measured as t = 1.064× s ;

[0078] .2m;

[0079] Raw bathymetric data , water surface height changes ,inclination Spatial weight , time compensation coefficient , water surface change rate .

[0080] formula:

[0081] Substituting the values:

[0082] The corrected water depth is 1.2518 m, and the error compensation is 0.0518 m.

[0083] Then use the Kalman filter algorithm to optimize the depth data:

[0084] Equation of state:

[0085] Observation equation:

[0086] Initial state ,speed , time interval , observed values m.

[0087] predict:

[0088] Prediction covariance m

[0089] Update: Kalman Gain

[0090] Revised estimate:

[0091]

[0092] Update covariance .

[0093] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the scope of protection of the present invention.

Claims

1. A method for compensating water surface fluctuations for laser bathymetry, characterized in that: include: S1. Hover the unmanned vessel in the water body to be measured, and calibrate the IMU zero bias compensation sensor and laser displacement sensor on the unmanned vessel for height, and calibrate the turbidity sensor for zero point; S2. Lower the turbidity sensor into the water being measured and transmit the water turbidity value T collected by the turbidity sensor to the laser emission controller in real time. The laser emission controller emits lasers with different parameters based on the real-time changes in the water turbidity value T. The photoelectric receiving module receives the laser echo and calculates the depth H of the measured water body; S3. A laser displacement sensor records the height change Δh(t) caused by surface fluctuations in the measured water body in real time. The IMU zero-bias compensation sensor detects the three-axis motion of the unmanned vessel, including the attitude angle θ(t). The Beidou positioning module provides accurate position information to ensure that the coordinates and time of the IMU zero-bias compensation sensor are synchronized during measurement. The measured water depth H is corrected to obtain the new depth of the measured water body. ; S4. The extended Kalman filter fuses the attitude angle θ(t) and the height change Δh(t), and calibrates the spatial weight coefficient α and the temporal dynamic compensation coefficient γ of the water surface fluctuation correction parameter through the least squares method to further correct the water surface error.

2. The method for compensating water surface fluctuations for laser bathymetry according to claim 1, characterized in that: The laser emission controller will emit blue light and green light alternately, and adjust the weight of the alternately emitted blue light and green light in real time according to the collected water turbidity value T; Blue light weight With green light weight The calculation is specifically expressed as follows: , ； Where, =10NTU is the turbidity threshold, k=0.2 is the adjustment factor, and e is the base of the natural logarithm.

3. The method for compensating water surface fluctuations for laser bathymetry according to claim 2, characterized in that: After receiving the alternately emitted blue and green light echo signals, the photoelectric receiving module calculates the depth H of the measured water body; The calculation of the measured water depth H is specifically expressed as follows: ； In the formula, c represents the speed of light, c=3* m / s, n represents the refractive index of water, n 1.33, Represented as blue light echo signal, Represented as green light echo signal.

4. The method for compensating water surface fluctuations for laser bathymetry according to claim 1, wherein: The calculation is specifically expressed as follows: ； Where, is the water surface change rate, is the measured water body fluctuation height, for Corresponding moment.

5. The method for compensating water surface fluctuations for laser bathymetry according to claim 3, characterized in that: The calculation of the Kalman filter fusion attitude angle θ(t) and height change Δh(t) is specifically expressed as follows: ； Where, It is represented as the water depth state at time k, It represents the water depth at time k-1, Expressed as the speed at time k-1, Represented as the actual observation value at time k, that is, the sensor measurement value, ~ N(0,Q) follows a normal distribution and is represented as process noise, ~ N(0, R) obeys the normal distribution and is represented as observation noise, where Q and R are calibrated by measured data.

6. The method for compensating water surface fluctuations for laser bathymetry according to claim 5, characterized in that: By calculating the forecast covariance , Kalman gain and update the covariance Further improve accuracy.

7. The method for compensating water surface fluctuations for laser bathymetry according to claim 6, characterized in that: Prediction covariance The calculation is specifically expressed as follows: ； Where, It is expressed as inheriting the uncertainty of the previous moment.

8. The method for compensating water surface fluctuations for laser bathymetry according to claim 6, characterized in that: Kalman gain The calculation is specifically expressed as follows: 。 9. The method for compensating water surface fluctuations for laser bathymetry according to claim 6, characterized in that: The updated covariance calculation is specifically expressed as follows: 。 10. The method for compensating water surface fluctuations for laser bathymetry according to claim 1, characterized in that: The unmanned vessel is equipped with a laser radar and a posture sensor, and the laser radar, posture sensor and photoelectric receiving module are all wirelessly connected to the shore.

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