Laser radar hardware parameter design method based on laser radar equation simulation model

Abstract

The invention discloses a laser radar hardware parameter design method based on a laser radar equation simulation model, and belongs to the technical field of laser radar remote sensing detection. Based on a laser radar equation simulation model, laser radar parameters such as different laser wavelengths, energy, pulse widths, divergence angles, field angles, repetition frequencies, telescope calibers and laser inclination angles of visible light bands and chlorophyll concentrations, absorption coefficients, scattering coefficients, scattering phase functions and wind speeds of water bodies can be analyzed; the influence of water body biological optical parameters such as zenith angle and environmental parameters on a multi-platform laser radar receiving signal; and finally, by changing different parameters, the laser radar receiving signal strength is analyzed, and the system parameter design is optimized. The method is high in operation speed and high in calculation efficiency, and input parameters can be conveniently adjusted according to a laser radar system and the environment; the method can be used for simulating signals of shipborne, airborne, satellite-borne and other multi-platform ocean laser radars.

Description

Technical Field

[0001] The invention belongs to the technical field of laser radar remote sensing detection in ocean optics, and specifically relates to a method for designing ocean laser radar hardware parameters based on a laser radar equation simulation model. Background Art

[0002] As a new type of active optical sensing system with vertical profile detection capability, ocean lidar has been widely used after a large number of shipborne and airborne experimental measurements. Ocean lidar uses high-power pulsed laser as a light source, can penetrate the subsurface ocean, and has time resolution capability. At the same time, its profile detection capability of seawater optical parameters makes it possible to observe vertical ocean stratification. At present, the development and detection of ocean lidar are still full of challenges. Due to the strong absorption and scattering of seawater, the laser energy emitted by it decays rapidly in water. Other factors, including the optical properties of seawater and the wavelength of the laser, will also have a significant impact on the intensity and signal-to-noise ratio of the lidar signal. The maximum detectable depth and the ability to invert the profile of water body biological optical parameters will also be affected. Therefore, it is of great significance to study the detection mechanism of ocean lidar and establish an accurate simulation model in the early stage of research and development to evaluate the global ocean detection capability of ocean lidar.

[0003] In the pre-research stage of ocean lidar, it is necessary to simulate and analyze the global ocean detection capability, hardware indicator selection and other issues to guide the design of lidar. Patent 1 (patent application number: CN202111009706.0) proposes an optimal band evaluation method for spaceborne ocean lidar based on the spectral dependence of the diffuse attenuation coefficient of water bodies. By calculating the global detection capabilities of different bands, the optimal band is statistically calculated. Patent 2 (patent application number: CN202010687023.X) proposes a wavelength selection method for dual-wavelength ocean lidar detection. According to the error transmission, the minimum error is calculated to perform dual-wavelength selection. Patent 3 (patent application number: CN202010686197.4) proposes a method for evaluating the detection capability of spaceborne ocean lidar based on the chlorophyll concentration of water bodies. By calculating the signal-to-noise ratio limit, the maximum detection depth of different laser wavelengths under different chlorophyll concentrations is calculated.

[0004] The above methods are all aimed at simulating a certain parameter such as the optimal wavelength or maximum detection depth of the ocean lidar. They do not have multi-parameter analysis capabilities and cannot further provide theoretical support for the parameter design of the ocean lidar hardware system and signal interpretation. Summary of the invention

[0005] In view of the fact that the existing simulation methods do not have the function of designing the parameters of the marine lidar hardware system, the purpose of the present invention is to provide a lidar hardware parameter design method based on the lidar equation simulation model.

[0006] The present invention can analyze the influence of different laser wavelengths, energies, pulse widths, divergence angles, field angles, repetition frequencies, telescope apertures, laser inclination angles and other laser radar parameters in the visible light band, as well as water body bio-optical parameters and environmental parameters such as chlorophyll concentration, absorption coefficient, scattering coefficient, scattering phase function, wind speed, zenith angle and other water body on multi-platform (ship-borne, airborne, satellite-borne) laser radar receiving signals based on the laser radar equation simulation model; finally, by changing different parameters, the laser radar receiving signal strength is analyzed and the system parameter design is optimized.

[0007] To achieve the above object, the present invention is implemented by the following technical solutions:

[0008] A method for designing ocean lidar hardware parameters based on a lidar equation model comprises the following steps:

[0009] Step 1: Obtain the global water intrinsic optical parameter IOPs profile; use the global Chl-a concentration profile dataset of CMEMS (Copernicus Marine Environment Monitoring Service) as input, and combine it with the bio-optical model to simulate the intrinsic optical parameter IOPs profile, such as seawater absorption coefficient and scattering coefficient;

[0010] Step 2: Simulate the global ocean received photon number profile. According to the IOPs profile parameters, calculate the laser radar extinction coefficient and backscattering coefficient, combine the laser radar parameters (including the transmitter and the receiver), substitute them into the laser radar equation, and calculate the global ocean received photon number profile;

[0011] Step 3: Simulate the collected photon number profile. According to the Kurucz solar irradiance spectrum data set, calculate the solar background light received by the lidar, combine the sea surface model and the underwater bubble model, add random noise, and obtain the collected photon number profile;

[0012] Step 4: Calculate the signal-to-noise ratio profile and calculate the maximum detectable depth of the lidar and the global detection capability distribution according to the signal-to-noise ratio threshold;

[0013] Step 5: Analyze the detection capability of the LiDAR by inputting different LiDAR system parameters and environmental parameters, and then design the hardware parameters.

[0014] Furthermore, the bio-optical model used in step 1 is:

[0015] K d =K w +χ <chl> e

[0016] c=a+b

[0017] a=a w +a p +a g

[0018] b=b w +b p

[0019] a p (λ)=A <chl> 1-B

[0020] a g (λ)=0.2a p (440)exp[-0.014(λ-440)]

[0021] b p (λ) = c p (λ)-a p (λ)

[0022]

[0023] Among them, K d is the diffuse attenuation coefficient of seawater, K w , χ, e, A, B are constants related to wavelength, c is the seawater attenuation coefficient, a is the seawater absorption coefficient, b is the seawater scattering coefficient, a w is the absorption coefficient of pure seawater, a ph is the phytoplankton absorption coefficient, a det is the absorption coefficient of nonalgal particles (NAP), a g is the absorption coefficient of colored dissolved organic matter (CDOM, also known as yellow gelbstoff), b w is the scattering coefficient of pure seawater, b p is the particle scattering coefficient, λ is the laser wavelength, and chl is the chlorophyll concentration.

[0024] Furthermore, the calculation method for calculating the seawater photon number profile in step 2 is:

[0025] α=K d +(cK d )exp(-0.85cD)

[0026]

[0027]

[0028] Among them, K is the laser radar system constant, z is the water depth, α(z) is the laser radar extinction coefficient, β(π,z) is the volume scattering coefficient at a scattering angle of 180°, that is, the backscattering coefficient. N(z) is the number of echo photons received by the detector, E 0 is the laser energy, h is the Planck constant, v is the laser frequency, is the number of emitted photons, A is the receiving area of ​​the telescope, H is the detection height of the laser radar, and n is the refractive index of seawater. is the receiving solid angle, T atm is the atmospheric transmittance, T sur is the transmittance of the air-sea interface, η is the light transmission efficiency of the optical system, Δz is the vertical resolution, β p is the scattering function of the particle object, β w is the scattering function of pure sea water, is the particle scattering phase function, is the pure seawater scattering phase function.

[0029] Furthermore, the calculation method of the collected photon number profile in step 3 is:

[0030] N c (z) = N (z) + N sun

[0031]

[0032] Among them, N c is the number of collected photons, N sun is the number of solar background photons, I b is the solar spectral radiance reflected by the atmosphere and the sea surface, Δλ is the filter bandwidth of the detector, Δt is the sampling time, and φ is the field of view.

[0033] In addition, the influence of the sea surface and the underwater bubble layer on the received signal needs to be considered separately. The sea surface model is:

[0034]

[0035] γ sur =γ s +γ f +γ u

[0036]

[0037] 2 >=0.003+0.00512v

[0038]

[0039]

[0040] Among them, N sur is the laser radar echo signal received by the sea surface, N 0 is the number of photons emitted by a single pulse, θ is the excitation tilt angle, γ sur is the laser radar scattering coefficient of the sea surface, which can be expressed as the specular reflection γ s , White Hat Contribution γ f and subsurface contribution γ u ρ is the Fresnel reflection coefficient, 2 ​​>The variance of the wave slope, k, is assumed to be Gaussian s is the foam coverage of the sea surface, v 10 is the wind speed at 10m above sea level, R f is the effective reflectivity of the white cap.

[0041] The underwater bubble model is:

[0042]

[0043] r ref =54.4μm+1.984×10 -6 z

[0044]

[0045] Among them, β bub is the 180° backward scattering coefficient of the bubble, b bub is the bubble scattering coefficient, is the phase function of the bubble particle group, is the average scattering efficiency factor, N(z) is the bubble number density, r is the bubble radius, and r min With r max are the minimum and maximum radii of the particle group, n(r,z) is the bubble size distribution, r ref is the reference radius, is the average geometric cross-sectional area.

[0046] Furthermore, in step 4, the signal-to-noise ratio (SNR) is calculated as follows:

[0047]

[0048] Furthermore, the step 5 specifically includes:

[0049] (1) Maximum water profile detection depth: The water body detection depth is mainly affected by the inherent characteristics of multiple scattering of seawater, a strong scattering medium. Increasing the laser energy and telescope aperture can only improve the detection capability to a limited extent. By inputting different hardware parameters in the simulation model, the lidar profile of the specified water body or the global ocean can be obtained, and then the maximum detectable depth distribution can be obtained according to the set photon number threshold or signal-to-noise ratio threshold. By inputting different system parameters, the global distribution under the specified parameter system can be obtained, and the influence of different parameters on the detection depth can be statistically analyzed to optimize the system design.

[0050] (2) Optimal wavelength: Different wavelengths have different penetration capabilities. Considering the engineering feasibility, in addition to the mature 532nm wavelength, by calculating the improvement of other wavelengths on the global jump layer detection capability, the necessity of selecting other wavelengths is analyzed. Various hardware parameters and environmental parameters are input into the simulation model. By inputting laser wavelengths in different visible light bands, the global maximum detectable depth distribution of lidar corresponding to different wavelengths can be calculated. Through statistical comparison, the average maximum detection depth of different wavelengths in a specific sea area or the world is analyzed to obtain the optimal wavelength, thereby improving the global detection capability under engineering feasibility conditions.

[0051] (3) Dynamic range: Ensure that the sea surface signal is not saturated to facilitate post-pulse correction. When the detector is sampling, a large dynamic range affects the sampling accuracy. According to the lidar parameters input in the simulation model, the echo signals of the sea surface and each underwater depth layer can be obtained, and then the lidar dynamic range can be obtained according to the ratio of the maximum signal to the minimum signal. The laser inclination angle, sea surface roughness, pulse width, etc. have a great influence on the dynamic range. When the input of the model remains consistent, the laser inclination angle, sea surface wind speed (affecting the sea surface roughness), and pulse width are modified separately to obtain the lidar echo signals corresponding to different parameters and analyze the dynamic range. According to the obtained dynamic range, a suitable receiving system is designed to ensure that the detection signal is in the linear range of the detector, or the lidar parameters are adjusted to meet the dynamic range requirements of the selected detector.

[0052] (4) Other hardware indicators: laser energy, telescope aperture, laser divergence angle, receiving field of view angle, platform height, etc. will affect the performance of the lidar. By changing the simulation model input separately, such as different laser energies, the corresponding lidar echo signal can be calculated using the simulation model. The simulation model has built-in CMEMS's global Chl-a concentration profile data set, and then calculates the water parameters of different water media around the world. The simulation analysis of the detection capabilities of each parameter in different water media can be used to optimize the indicator design.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The simulation model based on the laser radar equation of the present invention has fast operation speed and high calculation efficiency. The input parameters can be easily adjusted according to the laser radar system and environment. It can be used to simulate the signals of multi-platform ocean laser radars such as ship-borne, airborne and satellite-borne. The model is based on the global chlorophyll a profile data after quality control, and the vertical distribution of marine bio-optical parameters is calculated as input, which has higher credibility than the vertical uniformity assumption.

[0055] The present invention has the following characteristics: (1) various lidar specifications, sea surface parameters, profile bio-optical parameters, etc. are taken into account; (2) the global Chl-a profile data set is used as input to simulate stratified water; (3) by modifying the parameters of the simulation interface, the lidar signals of various platforms and environments can be easily simulated, thereby analyzing the detection capabilities of different parameters and guiding the design of marine lidar hardware parameters. The simulator was actually verified based on the measured data of airborne lidar, and the results showed that it has extremely strong reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flow chart of the method for simulating and optimizing ocean lidar signals and indicators based on the lidar equation proposed in the present invention.

[0057] Figure 2 The profile signal of the ocean lidar simulated by this method is compared and verified with the measured signal of the airborne lidar.

[0058] Figure 3 The maximum detectable depth results of the global ocean at different wavelengths (443, 486, 532nm) under the specific parameters simulated by this method.

[0059] Figure 4 The profile signal and dynamic range analysis results of the ocean lidar simulated by this method.

[0060] Figure 5 The result diagram of the extreme detection depth distribution corresponding to different field of view angles, zenith angles, and signal-to-noise ratio thresholds of the ocean lidar simulated by this method.

[0061] Figure 6 is the chlorophyll concentration of the three water bodies in Example 2.

[0062] Figure 7 are the IOPs of the three water bodies in Example 2.

[0063] Figure 8 This is a comparison of the maximum detection depths of different wavelengths in Example 2. DETAILED DESCRIPTION

[0064] The technical solution of the present invention is further described below in conjunction with embodiments.

[0065] Example 1

[0066] A method for designing ocean lidar hardware parameters based on lidar equation model, such as Figure 1 As shown, the following steps are included:

[0067] Step 1: Establish a simulation model based on the lidar equation, using the global Chl-a concentration profile dataset of CMEMS (Copernicus Marine Environment Monitoring Service) as input to simulate the intrinsic optical parameters IOPs profile parameters, such as seawater absorption coefficient and scattering coefficient; the bio-optical model is:

[0068] K d =K w +χ <chl> e

[0069] c=a+b

[0070] a=a w +a p +a g

[0071] b=b w +b p

[0072] a p (λ)=A <chl> 1-B

[0073] a g (λ)=0.2a p (440)exp[-0.014(λ-440)]

[0074] b p (λ) = c p (λ)-a p (λ)

[0075]

[0076] Among them, K d is the diffuse attenuation coefficient of seawater, K w , χ, e, A, B are constants related to wavelength, c is the seawater attenuation coefficient, a is the seawater absorption coefficient, b is the seawater scattering coefficient, a w is the absorption coefficient of pure seawater, a ph is the phytoplankton absorption coefficient, a det is the absorption coefficient of nonalgal particles (NAP), a g is the absorption coefficient of colored dissolved organic matter (CDOM, also known as yellow gelbstoff), b w is the scattering coefficient of pure seawater, b p is the particle scattering coefficient, λ is the laser wavelength, and chl is the chlorophyll concentration.

[0077] Step 2: According to the IOPs profile parameters, the laser radar extinction coefficient and the backscattering coefficient are calculated, and the laser radar parameters (including the transmitting end and the receiving end) are substituted into the laser radar equation to calculate the seawater photon number profile; the calculation method for calculating the seawater photon number profile is:

[0078] α=K d +(cK d )exp(-0.85cD)

[0079]

[0080]

[0081] Among them, K is the laser radar system constant, z is the water depth, α(z) is the laser radar extinction coefficient, β(π,z) is the volume scattering coefficient at a scattering angle of 180°, that is, the backscattering coefficient. N(z) is the number of echo photons received by the detector, E 0 is the laser energy, h is the Planck constant, v is the laser frequency, is the number of emitted photons, A is the receiving area of ​​the telescope, H is the detection height of the laser radar, and n is the refractive index of seawater. is the receiving solid angle, T atm is the atmospheric transmittance, T sur is the transmittance of the air-sea interface, η is the light transmission efficiency of the optical system, Δz is the vertical resolution, β p is the scattering function of the particle object, β w is the scattering function of pure sea water, is the particle scattering phase function, is the pure seawater scattering phase function.

[0082] Step 3: Based on the Kurucz solar irradiance spectrum dataset, calculate the solar background light received by the lidar and combine it with random noise to obtain the collected photon number profile; the calculation method of the collected photon number profile is:

[0083] N c (z) = N (z) + N sUn

[0084]

[0085] Among them, N c is the number of collected photons, N sun is the number of solar background photons, I b is the solar spectral radiance reflected by the atmosphere and the sea surface, Δλ is the filter bandwidth of the detector, Δt is the sampling time, and φ is the field of view.

[0086] In addition, the influence of the sea surface and the underwater bubble layer on the received signal needs to be considered separately. The sea surface model is:

[0087]

[0088] γ sur =γ s +γ f +γ u

[0089]

[0090] 2 >=0.003+0.00512v

[0091]

[0092]

[0093] Among them, N sur is the laser radar echo signal received by the sea surface, N 0 is the number of photons emitted by a single pulse, θ is the excitation tilt angle, γ sur ​is the laser radar scattering coefficient of the sea surface, which can be expressed as the specular reflection γ s , White Hat Contribution γ f and subsurface contribution γ u ρ is the Fresnel reflection coefficient, 2 >The variance of the wave slope, k, is assumed to be Gaussian s is the foam coverage of the sea surface, v 10 is the wind speed at 10m above sea level, R f is the effective reflectivity of the white cap.

[0094] The underwater bubble model is:

[0095]

[0096]

[0097] r ref =54.4μm+1.984×10 -6 z

[0098]

[0099] Among them, β bub is the 180° backward scattering coefficient of the bubble, b bub is the bubble scattering coefficient, is the phase function of the bubble particle group, is the average scattering efficiency factor, N(z) is the bubble number density, r is the bubble radius, and r min With r max are the minimum and maximum radii of the particle group, n(r,z) is the bubble size distribution, r ref is the reference radius, is the average geometric cross-sectional area.

[0100] Step 4: Calculate the signal-to-noise ratio profile and calculate the maximum detectable depth and global distribution of the lidar based on the signal-to-noise ratio threshold. The signal-to-noise ratio (SNR) calculation method is:

[0101]

[0102] Step 5: Model validation based on measured data

[0103] The reliability of the simulator is verified by using the measured data obtained from the airborne ocean lidar developed in the early stage. The airborne system parameters and the water parameters measured on site are brought into the simulator to obtain the simulated profile, which is compared with the measured data. Figure 2 As shown. Calculate the relative error, the result shows that the correlation coefficient R 2 It is higher than 0.98, and the average relative error is less than 8%.

[0104] ​Step 6: Analyze the detection capability of the LiDAR by inputting different LiDAR system parameters and environmental parameters, and then design the hardware parameters.

[0105] Maximum water profile detection depth: The water body detection depth is mainly affected by the inherent characteristics of multiple scattering of seawater, a strong scattering medium. Increasing the laser energy and telescope aperture can only improve the detection capability to a limited extent. By inputting different hardware parameters into the simulation model, the lidar profile of the specified water body or the global ocean can be obtained, and then the maximum detectable depth distribution can be obtained according to the set photon number threshold or signal-to-noise ratio threshold, such as Figure 3 By inputting different system parameters, the global distribution under the specified parameter system can be obtained, and the influence of different parameters on the detection depth can be statistically analyzed to optimize the system design.

[0106] Optimal wavelength: Different wavelengths have different penetration capabilities. Considering the engineering feasibility, in addition to the mature 532nm wavelength, by calculating the improvement of other wavelengths on the global jump layer detection capability, the necessity of selecting other wavelengths is analyzed. Various hardware parameters and environmental parameters are input into the simulation model. By inputting laser wavelengths in different visible light bands, the global maximum detectable depth distribution of lidar corresponding to different wavelengths can be calculated. Through statistical comparison, the average maximum detection depth of different wavelengths in specific sea areas or globally is analyzed to obtain the optimal wavelength, and improve global detection capabilities under engineering feasibility conditions.

[0107] Dynamic range: Ensure that the sea surface signal is not saturated to facilitate post-pulse correction. When the detector is sampling, a large dynamic range affects the sampling accuracy. According to the lidar parameters entered in the simulation model, the echo signals of the sea surface and each underwater depth layer can be obtained, and then the lidar dynamic range can be obtained according to the ratio of the maximum signal to the minimum signal. The laser inclination angle, sea surface roughness, pulse width, etc. have a great influence on the dynamic range. When the input of the model remains consistent, the laser inclination angle, sea surface wind speed (affecting the sea surface roughness), and pulse width are modified separately to obtain the lidar echo signals corresponding to different parameters and analyze the dynamic range. Design a suitable receiving system based on the obtained dynamic range to ensure that the detection signal is in the linear range of the detector, or adjust the lidar parameters to meet the selected detector dynamic range requirements. The lidar receiving profile corresponding to different laser inclination angles is as follows: Figure 4 As shown in the figure, as the inclination angle increases, the dynamic range can be effectively reduced, but the depth aliasing range will also increase.

[0108] Other hardware indicators: such as laser energy, telescope aperture, laser divergence angle, receiving field of view angle, platform height, etc. will affect the performance of the lidar. By changing the simulation model input separately, such as different laser energies, the corresponding lidar echo signal can be calculated using the simulation model. The simulation model has a built-in CMEMS global Chl-a concentration profile data set, which can then calculate the water parameters of different water media around the world. Simulation analysis of the detection capabilities of each parameter in different water media can be used to optimize indicator design. The distribution of the limit detection depth of ocean lidar corresponding to different field of view angles, zenith angles, and signal-to-noise ratio thresholds is shown below. Figure 5 As shown, in the extreme detection case of SNR=1, setting the field of view angle to 0.15-0.2 mrad is beneficial to increase the detection depth.

[0109] Example 2

[0110] Taking the selection of the best wavelength as an example, the proportion of the global thermocline depth that can be detected under the same energy and receiving aperture is calculated. The maximum detectable depth of a single photon of visible light is calculated based on the simulation model. The parameters of the satellite-borne lidar used in the simulation are shown in the following table. The cleanest ocean water, the relatively clear South China Sea, and the turbid coastal waters are analyzed. The chlorophyll concentration is as follows: Figure 6 shown.

[0111] Table 1 Simulation parameters

[0112] parameter Value Laser wavelength 400:1:700nm Laser energy 100mJ Receiving telescope aperture 1m Receiving field of view 0.3mrad Track height 360km Optical efficiency 0.6 Quantum efficiency 0.4 Water resolution 1m Atmospheric transmittance 0.7 Air-sea interface transmittance 0.98

[0113] The absorption coefficient, scattering coefficient, and beam attenuation coefficient distribution of 486nm and 532nm water calculated using the simulation model Figure 7 shown.

[0114] For 400nm~700nm, the step interval is 1nm, the lidar echo signals corresponding to the three water bodies are calculated, and the detection depth corresponding to the signal attenuation to a single photon is obtained, such as Figure 8 shown.

[0115] From the calculation results of three typical water bodies, we can see that the clearer the water body, the shorter the optimal penetration wavelength. The optimal wavelength for clean ocean water is about 420-440nm, the optimal wavelength for South China Sea water is about 460-490nm, and the optimal wavelength for turbid nearshore water is about 480-560nm.

[0116] The above results show that the parameter design method provided by the present invention can effectively evaluate the maximum water profile detection depth, optimal detection wavelength, dynamic range design and other laser radar hardware indicator designs by inputting different laser radar hardware parameters and water body and atmospheric environment parameters.

[0117] Finally, although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.< / chl> < / chl> < / chl> < / chl>

Claims

1. A method for designing ocean lidar hardware parameters based on a lidar equation model, characterized in that: The steps include: Step 1: Obtain the global water intrinsic optical parameter IOPs profile; use the global Chl-a concentration profile dataset of CMEMS as input and combine it with the bio-optical model to simulate the intrinsic optical parameter IOPs profile parameters; Step 2: Simulate the global ocean received photon number profile: According to the IOPs profile parameters, calculate the laser radar extinction coefficient and backscattering coefficient, combine the laser radar parameters, substitute them into the laser radar equation, and calculate the global ocean received photon number profile; Step 3: Simulate the collected photon number profile: According to the Kurucz solar irradiance spectrum data set, calculate the solar background light received by the lidar, combine the sea surface model and the underwater bubble model, add random noise, and obtain the collected photon number profile; Step 4: Calculate the signal-to-noise ratio profile and calculate the maximum detectable depth of the lidar and the global detection capability distribution according to the signal-to-noise ratio threshold; Step 5: Analyze the detection capability of the LiDAR by inputting different LiDAR system parameters and environmental parameters, and then design the hardware parameters.

2. The method for designing marine laser radar hardware parameters according to claim 1, characterized in that: The bio-optical model used in step 1 is: K d =K w +x <chl> e < / chl> c=a+b a=a w +a p +a g b=b w +b p a p (λ)=A <chl> 1-B < / chl> a g (λ)=0.2a p (440)exp[-0.014(λ-440)] b p (λ)=c p (l)-a p (l) Among them, K d is the diffuse attenuation coefficient of seawater, K w , χ, e, A, B are constants related to wavelength, c is the seawater attenuation coefficient, a is the seawater absorption coefficient, b is the seawater scattering coefficient, a w is the absorption coefficient of pure seawater, a ph is the phytoplankton absorption coefficient, a det is the absorption coefficient of non-phytoplankton particles, a g is the absorption coefficient of colored soluble organic matter, b w is the scattering coefficient of pure seawater, b p is the particle scattering coefficient, λ is the laser wavelength, and chl is the chlorophyll concentration.

3. The method for designing hardware parameters of ocean laser radar according to claim 1, characterized in that: The calculation method for calculating the seawater photon number profile in step 2 is: α=K d +(cK d )exp(-0.85cD) Where K is the laser radar system constant, z is the water depth, α(z) is the laser radar extinction coefficient, β(π,z) is the volume scattering coefficient at a scattering angle of 180°, that is, the backscattering coefficient; N(z) is the number of echo photons received by the detector, E0 is the laser energy, h is the Planck constant, and v is the laser frequency. is the number of emitted photons, A is the receiving area of ​​the telescope, H is the detection height of the laser radar, and n is the refractive index of seawater. is the receiving solid angle, T atm is the atmospheric transmittance, T sur is the transmittance of the air-sea interface, η is the light transmission efficiency of the optical system, Δz is the vertical resolution, β p is the scattering function of the particle object, β w is the scattering function of pure sea water, is the particle scattering phase function, is the pure seawater scattering phase function.

4. The method for designing marine laser radar hardware parameters according to claim 1, characterized in that: The calculation method of the collected photon number profile in step 3 is: N c (z)=N(z)+N sUn Among them, N c is the number of collected photons, N sun is the number of solar background photons, I b is the solar spectral radiance reflected by the atmosphere and the sea surface, Δλ is the filter bandwidth of the detector, Δt is the sampling time, and φ is the field of view angle; In addition, the influence of the sea surface and the underwater bubble layer on the received signal needs to be considered separately. The sea surface model is: c sur =c s +g f +g u <S 2 >=0.003+0.00512v Among them, N sur is the laser radar echo signal received by the sea surface, N0 is the number of photons emitted by a single pulse, θ is the excitation inclination angle, γ sur is the laser radar scattering coefficient of the sea surface, which can be expressed as the specular reflection γ s , White Hat Contribution γ f and subsurface contribution γ u ; ρ is the Fresnel reflection coefficient, 2 >The variance of the wave slope, k, is assumed to be Gaussian s is the foam coverage of the sea surface, v 10 is the wind speed at 10m above sea level, R f is the effective reflectivity of the white cap;​ The underwater bubble model is: r ref =54.4μm+1.984×10 -6 With Among them, β bub is the 180° backward scattering coefficient of the bubble, b bub is the bubble scattering coefficient, is the phase function of the bubble particle group, is the average scattering efficiency factor, N(z) is the bubble number density, r is the bubble radius, and r min With r max are the minimum and maximum radii of the particle group, n(r,z) is the bubble size distribution, r ref is the reference radius, is the average geometric cross-sectional area.

5. The method for designing hardware parameters of ocean laser radar according to claim 1, characterized in that: In step 4, the signal-to-noise ratio SNR calculation method is:

6. The method for designing hardware parameters of ocean laser radar according to claim 1, characterized in that: The step 5 specifically includes: (1) Maximum water profile detection depth: By inputting different hardware parameters, the lidar profile of a specified water body or global ocean is obtained, and then the maximum detectable depth distribution is obtained according to the set photon number threshold or signal-to-noise ratio threshold. By inputting different system parameters, the global distribution under the specified parameter system is obtained, and the impact of different parameters on the detection depth is statistically analyzed to optimize the system design. (2) Optimal wavelength: By inputting various hardware parameters and environmental parameters, and inputting laser wavelengths in different visible light bands, the global maximum detectable depth distribution of lidar corresponding to different wavelengths can be calculated; by statistical comparison, the average maximum detection depth of different wavelengths in a specific sea area or globally can be analyzed to obtain the optimal wavelength, thereby improving global detection capabilities under engineering feasible conditions; (3) Dynamic range: By inputting the lidar parameters, the echo signals of the sea surface and each underwater depth layer are obtained, and then the lidar dynamic range is obtained according to the ratio of the maximum signal to the minimum signal; the laser inclination angle, sea surface roughness, pulse width, etc. have a great influence on the dynamic range. When the input of the model remains consistent, the laser inclination angle, sea surface wind speed, and pulse width are modified separately to obtain the lidar echo signals corresponding to different parameters and analyze the dynamic range; according to the obtained dynamic range, a suitable receiving system is designed to ensure that the detection signal is in the linear range of the detector, or the lidar parameters are adjusted to meet the dynamic range requirements of the selected detector; (4) Other hardware indicators: such as laser energy, telescope aperture, laser divergence angle, receiving field of view angle, and platform height will affect the performance of the lidar. By changing the input separately, the corresponding lidar echo signal can be calculated. Based on the global Chl-a concentration profile data set, the water parameters of different water media around the world can be calculated. The detection capabilities of each parameter in different water media can be simulated and analyzed to optimize the indicator design.

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