Water surface fluctuation compensation method for laser sounding

Abstract

The invention relates to the field of laser water depth detection, in particular to a water surface fluctuation compensation method for laser sounding, and solves the problems of insufficient precision and water surface fluctuation interference in a traditional sounding technology in an extremely shallow water area. The measured water depth is corrected through the turbidity sensor, the IMU zero offset compensation sensor and the laser displacement sensor, and the purpose of improving the measurement accuracy is achieved. According to the scheme, the stability and precision of depth sounding of the extremely shallow water area are remarkably improved, and the method has high environmental adaptability and wide application prospects.

Description

Technical Field

[0001] The present invention relates to the field of laser depth sounding, and particularly to a method for compensating water surface fluctuations for laser depth sounding. Background Art

[0002] In extremely shallow waters, due to frequent and large-amplitude water surface fluctuations, the water surface fluctuations have a significant impact on the depth sounding accuracy. Especially when using traditional single-wavelength laser depth sounding technology, the propagation path of the laser signal between the water surface and the water bottom is interfered by the water surface fluctuations, resulting in a significant decrease in the accuracy of the measurement results. At the same time, the turbidity of the water body and the reflection characteristics of the water bottom also affect the clarity of the laser echo, further reducing the measurement accuracy.

[0003] To overcome these challenges, in recent years, dual-wavelength laser depth sounding technology has gradually become a research hotspot. Dual-wavelength lasers can simultaneously utilize the different penetration capabilities and reflection characteristics of blue and green lasers. By fusing the measurement data of the two wavelengths, the stability and accuracy of the water depth measurement are improved. However, the dual-wavelength laser technology still cannot effectively cope with the errors caused by water surface fluctuations, especially in extremely shallow waters, where the influence of water surface fluctuations is significant.

[0004] To improve the measurement accuracy and reduce the influence of water surface fluctuations on the measurement results, in recent years, researchers have proposed some methods for compensating water surface fluctuations, including using a laser displacement sensor to monitor the water surface height, using an inertial navigation unit (IMU) to monitor the hull attitude change, etc. However, these technologies still have some limitations, such as being unable to perform precise compensation in real time, or being difficult to maintain a high measurement accuracy in complex waters.

[0005] Therefore, it is particularly important to develop a water surface fluctuation compensation device and method suitable for blue-green laser depth sounding of unmanned vessels. Such a device not only needs to overcome the errors caused by water surface fluctuations, but also needs to provide stable measurement results under different water body conditions (such as water turbidity, wave size, etc.). The present invention proposes a method for compensating water surface fluctuations based on dual-wavelength lasers, which combines technologies such as high-frequency laser displacement sensors, IMUs, and Kalman filters, and can monitor and compensate the influence of water surface fluctuations on the measurement results in real time, significantly improving the measurement accuracy and the adaptability of the system. Summary of the Invention

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

[0007] To achieve the above purpose, the technical solution adopted by the present invention is:

[0008] A method for compensating water surface fluctuations for laser depth sounding, comprising:

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

[0010] S2. Lower the turbidity sensor into the water body to be measured, and transmit the real-time water turbidity value T collected by the turbidity sensor to the laser emission controller in real time. The laser emission controller will emit lasers with different parameters according to the real-time change of the water turbidity value T. The photoelectric receiving module will receive the laser echo and calculate the depth H of the water body to be measured;

[0011] S3. Record the height change Δh(t) caused by the surface fluctuation of the water body to be measured in real time through the laser displacement sensor. The IMU zero-bias compensation sensor detects the three-axis movement of the unmanned boat, including the attitude angle θ(t); and use the Beidou positioning module to provide accurate position information to ensure the coordinate and time synchronization during the measurement of the IMU zero-bias compensation sensor, and correct the depth H of the water body to be measured to obtain the new depth of the water body to be measured ;

[0012] S4. Expand the Kalman filter to fuse the attitude angle θ(t) and the height change Δh(t), and calibrate the spatial weight coefficient α of the water surface fluctuation correction parameter and the time dynamic compensation coefficient γ through the least squares method to further trim 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 according to the collected water turbidity value T;

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

[0015] , ;

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

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

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

[0019] ;

[0020] In the formula, c represents the speed of light (c = 3 * m / s), n represents the refractive index of the water body (n 1.33), Expressed as a blue light echo signal, Expressed as a green light echo signal.

[0021] Furthermore, The calculation of

[0022] ;

[0023] In the formula, is the water surface change rate, is the fluctuation height of the water body to be measured, is The corresponding moment.

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

[0025] ;

[0026] In the formula, Represents the water depth state at time k, Represents the water depth state at time k-1, Represents the velocity at time k-1, Represents the actual observation value (sensor measurement value) at time k, ~ N(0, Q) following a normal distribution represents the process noise, ~ N(0, R) following a normal distribution represents the observation noise, where Q and R are calibrated through measured data.

[0027] Furthermore, by calculating the prediction covariance the Kalman gain and the updated covariance The accuracy is further improved.

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

[0029] ;

[0030] In the formula, Represents the inheritance of 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, a lidar and an attitude sensor are configured on the unmanned ship, and the lidar, the attitude sensor, and the photoelectric receiving module are all wirelessly connected to the shore base.

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

[0037] The present invention can obtain the turbidity of the current water body through the turbidity sensor, and emit blue light and green light with different parameters such as frequency and wavelength according to the turbidity of the water body to detect the depth of the current water body. And the obtained water body depth is corrected by fusing the IMU attitude data and the signal of the high-frequency laser displacement sensor through the IMU, and finally further corrected by the Kalman filter to obtain a water body depth with a smaller error. The present invention significantly improves the stability and accuracy of bathymetry in extremely shallow waters, and has strong environmental adaptability and broad application prospects. Description of the Drawings

[0038] Figure 1 It is a schematic diagram of a method for compensating water surface fluctuations for laser bathymetry. Detailed Embodiments

[0039] As Figure 1 shown, a method for compensating water surface fluctuations for laser bathymetry includes: first, hovering the unmanned ship in the water body to be measured, calibrating the height of the IMU zero-bias compensation sensor and the laser displacement sensor on the unmanned ship, recording the initial angle and height of the unmanned ship, calibrating the zero point of the turbidity sensor, and recording the turbidity threshold; then lowering the turbidity sensor into the water body to be measured through shore-based control, and transmitting the water body turbidity value T collected by the turbidity sensor to the laser emission controller in real time. The laser emission controller will emit blue light and green light with different parameters in real time according to the change of the water body turbidity value T, and continuously adjust the weights of the blue light and the green light, so as to obtain more accurate water depth information. The photoelectric receiving module will receive the laser echo, calculate the depth H of the water body to be measured through the received laser echo, and the fusion algorithm of the laser signal automatically corrects the error caused by the change of the water body by comparing the reflection coefficients of the blue and green wavelengths, ensuring a stable measurement result in turbid waters.

[0040] After obtaining the initial depth H of the water body to be measured, the height change Δh(t) caused by the surface fluctuation of the water body to be measured is recorded in real time by a 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 reference, thereby improving the accuracy of data fusion and analysis; and the Beidou positioning module is used to provide accurate position information to ensure the coordinate and time synchronization during the measurement of the IMU zero-bias compensation sensor, and the depth H of the water body to be measured is corrected to obtain the new depth of the water body to be measured ;

[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 of the water surface fluctuation correction parameter and the time dynamic compensation coefficient γ are calibrated by the least squares method to further trim the water surface error

[0042] The laser emission controller alternately emits blue light and green light, and adjusts the weights of the alternately emitted blue light and green light in real time according to the collected water turbidity value T

[0043] Blue light weight and green light weight are calculated as follows

[0044] , ;

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

[0046] After the photoelectric receiving module receives the alternately emitted blue light and green light echo signals, the depth H of the water body to be measured is calculated

[0047] The calculation of the depth H of the water body to be measured is as follows

[0048] ;

[0049] In the formula, c represents the speed of light (c = 3 * m / s), n represents the refractive index of the water body (n 1.33), represents the blue light echo signal represents the green light echo signal

[0050] The measured depth H of the water body is corrected, and the corrected depth is calculated as follows

[0051] ;

[0052] In the formula, is the water surface change rate, is the fluctuation height of the water body to be measured, is the corresponding moment.

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

[0054] ;

[0055] In the formula, represents the water depth state at time k, represents the water depth state at time k-1, represents the velocity at time k-1, represents the actual observed value (sensor measurement value) at time k, ~ N(0, Q) following a normal distribution represents the process noise, ~ N(0, R) following a normal distribution represents the observation noise, where Q and R are calibrated through measured data.

[0056] By calculating the prediction covariance the Kalman gain and the updated covariance the accuracy is further improved.

[0057] The prediction covariance , integrating the uncertainty at the previous moment , superimposing the process noise , reflecting the confidence decay in the prediction stage; the prediction covariance is specifically calculated as follows:

[0058] ;

[0059] In the formula, represents inheriting the uncertainty at the previous moment.

[0060] The Kalman gain , the numerator is the prediction uncertainty, the denominator is the total uncertainty of prediction and observation. When R (sensor noise) is small, →1, more trusting the observed value. The Kalman gain is specifically calculated as follows:

[0061] .

[0062] The updated covariance , The calculation of the updated covariance representing the reduction in uncertainty after information update is specifically as follows:

[0063] 。

[0064] Based on real-time water turbidity data, adjust the power ratio of the blue-green laser to optimize the quality of the echo signal. Use Kalman filtering to correct the laser measurement data and eliminate errors caused by environmental factors, ship motion, etc., ensuring the accuracy of the water depth data. Combine the laser displacement sensor with IMU data to correct the errors caused by water surface fluctuations and minimize water surface interference to the greatest extent. Combine the data of different sensors to automatically adjust the measurement parameters of the blue-green laser to ensure measurement stability and accuracy in complex waters.

[0065] According to real-time water depth data, obstacle detection information, and water area environment, adjust the speed of the unmanned ship. Use lidar data to avoid collisions. Dynamically adjust the sailing speed of the unmanned ship to ensure efficient and accurate measurement in different water depth environments.

[0066] Fuse the blue-green laser bathymetry data, lidar point cloud, Beidou positioning data, and IMU attitude data. Generate a high-precision seabed topographic map and transmit it to the shore-based control center in real time.

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

[0068] The unmanned ship is equipped with a lidar and an attitude sensor. The lidar, attitude sensor, and optoelectronic receiving module are all wirelessly connected to the shore-based station. The laser emission controller and the optoelectronic receiving module are used to measure the water depth and obtain the distance between the unmanned ship and the water bottom in real time to ensure the prevention of grounding accidents; the lidar provides a high-precision surrounding obstacle detection function to help identify obstacles on the water surface and underwater; the attitude sensor monitors the attitude changes of the unmanned ship, including pitch angle, roll angle, and yaw angle, to ensure sailing stability. Through the 5G communication module, the data is uploaded to the shore-based control system in real time, and the operator can grasp the current state of the unmanned ship in real time, understand the bathymetry data of the unmanned ship and the surrounding environmental information.

[0069] According to the detection data of the blue-green laser, the system analyzes the water surface and water bottom conditions in real time. When the water depth H measured by the blue-green laser is less than 5 cm, avoid obstacles in the shallow water area to prevent grounding; when the measured water depth H is 5 cm ≤ H < 20 cm, limit the sailing speed ≤ 0.3 m / s; when H ≥ 20 cm, allow a maximum sailing speed of 1.5 m / s. When the lidar measures that there are obstacles within 5 meters around, send an alarm to the shore-based control system and stop the operation.

[0070] Based on the feedback of these measurement data, the unmanned ship avoids obstacles in shallow water areas, prevents grounding, and ensures measurement accuracy. In shallow water areas, the system automatically reduces the navigation speed to ensure safety; in deep water areas, the speed is appropriately increased to improve operation efficiency.

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

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

[0073] Substitute the values:

[0074] ;

[0075] ;

[0076] Calculate the time difference between the laser emission and the time when the photoelectric receiving module receives the laser. Assume the blue light signal and the green light signal intensities at different time points, and it is necessary to find the maximum composite signal through Find such a time point t that maximizes the composite signal.

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

[0078] .2 m;

[0079] Original sounding data , water surface height change , inclination Spatial weight , time compensation coefficient , water surface change rate .

[0080] Formula:

[0081] Substitute the values:

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

[0083] Then optimize the sounding data through the Kalman filtering algorithm:

[0084] State equation:

[0085] Observation equation:

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

[0087] Prediction:

[0088] Prediction covariance m

[0089] Update: Kalman gain

[0090] Estimate after correction:

[0091]

[0092] Updated 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 those skilled in the art within the scope of the essence of the present invention shall also fall within the protection scope of the present invention.

Claims

1. A method for compensating water surface fluctuations for laser bathymetry, characterized in that Including: S1. Hover the unmanned ship in the water body to be measured, and calibrate the height of the IMU zero-offset compensation sensor and the laser displacement sensor on the unmanned ship, and calibrate the zero point of the turbidity sensor. S2. Lower the turbidity sensor into the water body to be 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 will emit lasers with different parameters according to the real-time change of the water turbidity value T. The photoelectric receiving module will receive the laser echo and calculate the depth H of the water body to be measured. S3. Record the height change Δh(t) caused by the surface fluctuation of the water body to be measured in real time through a laser displacement sensor. The IMU zero-bias compensation sensor detects the three-axis motion of the unmanned ship, including the attitude angle θ(t); and uses the Beidou positioning module to provide accurate position information to ensure the coordinate and time synchronization during the measurement of the IMU zero-bias compensation sensor, and correct the depth H of the water body to be measured to obtain the new depth of the water body to be measured ; S4. The extended Kalman filter fuses the attitude angle θ(t) and the height change Δh(t), and calibrates the spatial weight coefficient α of the water surface fluctuation correction parameter and the time dynamic compensation coefficient γ through the least square method to further correct the water surface error.

2. A method for compensating water surface fluctuations for laser bathymetry according to claim 1, characterized in that, 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 according to the water turbidity value T collected. Blue light weight and green light weight are calculated as follows: , ； In the formula, = 10 NTU is the turbidity threshold, k = 0.2 is the adjustment factor, and e represents the base of the natural logarithm.

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

4. A method for compensating water surface fluctuations for laser bathymetry according to claim 1, characterized in that, The calculation is specifically expressed as follows: ； In the formula, is the water level change rate, is the fluctuation height of the water body to be measured, is the corresponding moment.

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

6. A method for compensating for water surface fluctuations in laser bathymetry according to claim 5, characterized in that, Predict the covariance by calculation , the Kalman gain and update the covariance to further improve the accuracy.

7. A method for compensating for water surface fluctuations in laser bathymetry according to claim 6, characterized in that, Predicted covariance The calculation is specifically as follows: ； wherein, represents the uncertainty inherited from the previous moment.

8. A 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. A method for compensating for water surface fluctuations in laser bathymetry according to claim 6, characterized in that, The calculation of the updated covariance is specifically expressed as follows: 。 10. A method for compensating for water surface fluctuations in laser bathymetry according to claim 1, characterized in that, The unmanned ship is equipped with a lidar and an attitude sensor. The lidar, the attitude sensor and the photoelectric receiving module are all wirelessly connected to the shore base.

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