High-precision wind field inversion algorithm based on buoy lidar

Abstract

The application belongs to the technical field of laser radar, and discloses a high-precision wind field inversion algorithm based on a buoy laser radar, which comprises the following steps: S1, a rectangular coordinate system is established with the position of the radar as the origin; the radar performs multi-beam conical scanning measurement of the wind field data of the target airspace with the pitch angle being beta; the radar is controlled to scan according to the set azimuth angle and pitch angle; and the radial data of each beam at each target distance library is detected. Through the detection of the target airspace, the view wind data of each scanning beam is obtained; after the view wind vector is compensated and corrected according to the attitude and motion speed of the buoy platform, the three-dimensional wind field information formed by the wind speed, wind direction and vertical airflow above the buoy platform is obtained through the inversion of the view wind data of the same height layer, so that the precision of the wind field inversion is improved.

Description

Technical Field

[0001] This invention belongs to the field of lidar technology, and in particular relates to a high-precision wind field inversion algorithm based on buoy lidar. Background Technology

[0002] The 21st century is the century of the ocean, and coastal nations have elevated the development of marine economy and marine science and technology to a national strategic level, leading to an increasing demand for marine environmental monitoring. Sea surface wind speed is a crucial sea state parameter that significantly impacts many maritime activities; however, remote sensing of wind speed remains a challenge. Traditional methods of marine wind measurement typically involve erecting a wind measurement tower in a designated sea area and installing wind speed and direction sensors at different heights on the tower. However, establishing such a traditional wind measurement tower requires the construction of pile foundations at sea, resulting in high construction costs, long construction periods, and significant inconvenience to conducting normal sea trials. Furthermore, the wind measurement tower cannot be reused after testing, making it economically inefficient. Buoy-based laser wind radar, on the other hand, offers automated observation in the open ocean, high measurement accuracy, high spatiotemporal resolution, and long-term reliability. It is relatively easy to deploy and install at sea, and can be recovered and reused after dismantling, resulting in lower construction costs and recovery difficulties. It is currently being widely adopted.

[0003] Laser buoy radar is a novel wind measurement system that combines a marine buoy, lidar, and a positioning system. By placing lidar on a marine buoy, it can measure wind force in the open ocean. However, the buoy's movement with waves causes changes in the platform's attitude, leading to measurement errors in the lidar results. To ensure the accuracy of lidar measurement data, a series of corrections are needed for the measured wind field data. Currently, most solutions to this problem focus on correcting the wind field by adjusting the buoy platform's attitude and speed, without considering compensation corrections for line-of-sight wind speeds at different heights for each scanning beam. Summary of the Invention

[0004] The purpose of this invention is to provide a high-precision wind field inversion algorithm based on buoy lidar, in order to solve the problem that existing wind field correction methods almost all start from the attitude and speed of the buoy platform, without considering the technical problem of compensation and correction of the line-of-sight wind speed of each scanning beam at different heights.

[0005] To achieve the above objectives, the specific technical solution of the high-precision wind field inversion algorithm based on buoy lidar of the present invention is as follows:

[0006] A high-precision wind field inversion algorithm based on buoy lidar includes the following steps:

[0007] S1. Establish a rectangular coordinate system with the radar's location as the origin. The radar performs a multi-beam conical scan with an elevation angle of β to measure the wind field data in the target airspace. Control the radar to scan according to the set azimuth and elevation angles, and detect the radial data V of each beam at each target range. r,i (i = 1, 2, ..., 8), the azimuth and elevation of each beam are V r,i (α i If ,β)(i=1,2,…,8), then the radial wind vector in the radar coordinate system can be expressed as: V r,i =(sinα) i cosβ,sinα i cosβ, sinβ).

[0008] S2. The attitude information of the buoy platform in the local sea area is obtained by the motion sensors installed on the buoy, including the heading angle φ, roll angle ψ, and pitch angle ξ. Then the transformation matrix from the turntable coordinate system to the ground coordinate system is:

[0009]

[0010] Radial velocity V r,i (i = 1, 2, ..., 8) becomes V after coordinate transformation. rm,im =T·V r,i T .

[0011] S3. The motion of the buoy, Vp(ν), is obtained by a motion sensor installed on the buoy. E ,ν N ,ν S (Northeast Celestial Coordinate System) is used to obtain the wind speed correction coefficient v. brad =(v E ,v N ,v S ) T ·V rm , im Therefore, the radial velocity v produced solely by the Doppler shift caused by wind field motion is... R,i It can be represented as: v R,i =v brad -v r,i (i = 1, 2, ..., 8).

[0012] S4. Fit each corrected radial velocity.

[0013] v R,i =p0 + p1*R + p2*R 2 +…+pn*R n (i = 1, 2, ..., 8)

[0014] R is the radial distance. Interpolating the radial velocity to the same height h, then:

[0015] v h,i =p0 + p1*h + p2*h 2 +…+pn*h n

[0016] S5. The radial velocity interpolated to the same height layer is denoted as v. h,i (i = 1, 2, ..., 8), its azimuth and elevation in the geographic coordinate system are denoted as (θ). i ,μ i ),but

[0017]

[0018] The optimal solution to this system of equations is obtained through optimization methods, thus yielding the three-dimensional wind field (u,v,w).

[0019] The high-precision wind field inversion algorithm based on buoy lidar of the present invention has the following advantages: by detecting the target airspace, the line-of-sight wind data of each scanning beam is obtained. After compensating and correcting the line-of-sight wind vector according to the attitude and movement speed of the buoy platform, the three-dimensional wind field information formed by wind speed, wind direction and vertical airflow above the buoy platform is obtained by inverting the line-of-sight wind data at the same height layer, thereby improving the accuracy of wind field inversion. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the lidar scanning detection of the buoy platform involved in this invention;

[0021] Figure 2 This is a schematic diagram of the coordinate transformation involved in this invention;

[0022] Figure 3 This is a schematic diagram of radial velocity correction using the method described in this invention. Detailed Implementation

[0023] To better understand the purpose, structure, and function of this invention, the following detailed description of a high-precision wind field inversion algorithm based on buoy lidar, in conjunction with the accompanying drawings, is provided.

[0024] like Figures 1-3 As shown, the high-precision wind field inversion algorithm based on buoy lidar of the present invention includes the following steps:

[0025] Step 1: Establish a rectangular coordinate system with the radar location as the origin. The radar performs a multi-beam conical scan with an elevation angle of β to measure the wind field data in the target airspace. Control the radar to scan according to the set azimuth and elevation angles, and detect the radial data V of each beam at each target range. r,i(i = 1, 2, ..., 8), the azimuth and elevation of each beam are V r,i (α i If ,β)(i=1,2,…,8), then the radial wind vector in the radar coordinate system can be expressed as: V r,i =(sinα) i cosβ,sinα i cosβ, sinβ).

[0026] Step 2: The motion sensors mounted on the buoy acquire the buoy platform's attitude information in the local sea area, including its heading angle. Given the roll angle ψ and the pitch angle ξ, the transformation matrix from the turntable coordinate system to the ground coordinate system is:

[0027]

[0028] Radial velocity V r,i (i = 1, 2, ..., 8) becomes V after coordinate transformation. rm,im =T·V r,i T .

[0029] Step 3: Obtain the buoy's motion Vp (ν) using a motion sensor mounted on the buoy. E ,ν N ,ν S (Northeast Celestial Coordinate System) is used to obtain the wind speed correction coefficient v. brad =(v E ,v N ,v S ) T ·V rm , im Therefore, the radial velocity v produced solely by the Doppler shift caused by wind field motion is... R,i It can be represented as: v R,i =v brad -v r,i (i = 1, 2, ..., 8).

[0030] Step 4: Fit each corrected radial velocity.

[0031] v R,i =p0 + p1*R + p2*R 2 +…+pn*R n (i = 1, 2, ..., 8)

[0032] R is the radial distance. Interpolating the radial velocity to the same height h, then:

[0033] v h,i =p0 + p1*h + p2*h 2 +…+pn*hn

[0034] Step 5: Denote the radial velocity interpolated to the same height layer as v. h,i (i = 1, 2, ..., 8), its azimuth and elevation in the geographic coordinate system are denoted as (θ). i ,μ i ),but

[0035]

[0036] The optimal solution to this system of equations is obtained through optimization methods, thus yielding the three-dimensional wind field (u,v,w).

[0037] This invention obtains line-of-sight wind data for each scanning beam by detecting the target airspace. After compensating and correcting the line-of-sight wind vector according to the buoy platform's attitude and speed, it inverts the line-of-sight wind data at the same altitude layer to obtain three-dimensional wind field information above the buoy platform formed by wind speed, wind direction, and vertical airflow, thus improving the accuracy of wind field inversion.

[0038] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art will be able to make various modifications and improvements without departing from the principles of the present invention, and these modifications and improvements should also be considered to fall within the scope of protection of the present invention.

Claims

1. A high-precision wind field inversion algorithm based on buoy lidar, characterized in that, Includes the following steps: S1. Establish a rectangular coordinate system with the radar's location as the origin. The radar performs a multi-beam conical scan with an elevation angle of β to measure the wind field data in the target airspace. Control the radar to scan according to the set azimuth and elevation angles, and detect the radial data of each beam at each target range. The azimuth and elevation corresponding to each beam are as follows: Then the radial wind vector in the radar coordinate system is expressed as: ; S2. The attitude information of the buoy platform in the local sea area is obtained by motion sensors installed on the buoy, including the heading angle. Roll angle Pitch angle The transformation matrix from the turntable coordinate system to the ground coordinate system is: radial velocity After coordinate transformation, it becomes ; S3, obtain the motion Vp(ν) of the buoy in the northeast celestial coordinate system. E ,ν N ,ν S ), to obtain the wind speed correction coefficient Therefore, the radial velocity generated solely by the Doppler frequency shift caused by wind field motion Represented as: (i=1, 2, ..., 8); S4. Fit each corrected radial velocity. （i=1,2,…,8） Given the radial distance, interpolating the radial velocity to the same height h, then: S5. The radial velocity interpolated to the same height layer is denoted as... (i=1,2,…,8), its azimuth and elevation in the geographic coordinate system are denoted as . ,but The optimal solution to this system of equations is obtained through optimization methods, thus yielding the three-dimensional wind field. .

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