LiDAR hardware parameter design method based on LiDAR equation simulation model

Abstract

The present invention discloses a method for designing laser radar hardware parameters based on a laser radar equation simulation model, and belongs to the field of laser radar remote sensing detection technology. Based on the laser radar equation simulation model, the present invention can analyze the influence of laser radar parameters such as different laser wavelengths, energies, pulse widths, divergence angles, field angles, repetition frequencies, telescope apertures, laser inclination angles 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, etc. on multi-platform laser radar receiving signals; finally, by changing different parameters, the laser radar receiving signal strength is analyzed and the system parameter design is optimized. The present invention has fast operation speed and high calculation efficiency, and the input parameters can be conveniently 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 shipborne, airborne and satellite-borne.

Description

Technical Field

[0001] The present invention belongs to the technical field of laser radar remote sensing detection in ocean optics, and in particular 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 capabilities, ocean lidar has been widely used after numerous shipborne and airborne experimental measurements. Using high-power pulsed lasers as a light source, ocean lidar can penetrate the subsurface ocean and possesses temporal resolution. Furthermore, its ability to profile the optical parameters of seawater makes it possible to observe vertical ocean stratification. Currently, the development and detection of ocean lidar remain fraught with challenges. Due to the strong absorption and scattering of seawater, the emitted laser energy rapidly attenuates in water. Other factors, including the optical properties of seawater and the laser wavelength, also significantly affect the intensity and signal-to-noise ratio of the lidar signal. This also impacts the maximum detectable depth and the ability to invert the optical parameter profiles of aquatic organisms. Therefore, it is crucial to study the detection mechanisms of ocean lidar and establish accurate simulation models in the early stages of development to assess its global ocean exploration capabilities.

[0003] During the preliminary research phase of ocean lidar, it is necessary to conduct simulation analysis on global ocean detection capabilities, hardware indicator selection, and other issues to guide lidar design. Patent 1 (patent application number: CN202111009706.0) proposes a method for evaluating the optimal band of 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 determined. Patent 2 (patent application number: CN202010687023.X) proposes a wavelength selection method for dual-wavelength ocean lidar detection. Based on error propagation, 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 of the ocean lidar, such as the optimal wavelength or maximum detection depth. They do not have the ability to analyze multiple parameters and cannot further provide theoretical support for the parameter design of the ocean lidar hardware system and signal interpretation. Summary of the Invention

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

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

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

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

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

[0010] Step 2: Simulate the global ocean received photon number profile. Based on the IOPs profile parameters, calculate the lidar extinction coefficient and backscatter coefficient. Combined with the lidar parameters (including the transmitter and receiver), substitute them into the lidar equation to calculate the global ocean received photon number profile.

[0011] Step 3: Simulate the collected photon number profile. Based on the Kurucz solar irradiance spectrum dataset, calculate the solar background light received by the lidar. Combined with the sea surface model and the underwater bubble model, random noise is added to 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 based on the signal-to-noise ratio threshold;

[0013] Step 5: By inputting different LiDAR system parameters and environmental parameters, analyze the LiDAR detection capability and conduct hardware parameter design.

[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] Where K is the lidar system constant, z is the water depth, α(z) is the lidar 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 ν 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 lidar, 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.

[0028] Furthermore, the calculation method of the collected photon number profile in step 3 is:

[0029] N c (z)=N(z)+N sun

[0030]

[0031] 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, is the field of view angle.

[0032] 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:

[0033]

[0034] γ sur =γ s +γ f +γ u

[0035]

[0036] 2 >=0.003+0.00512v

[0037]

[0038] 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 lidar 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.

[0039] The underwater bubble model is:

[0040]

[0041] r ref =54.4μm+1.984×10 -6 z

[0042]

[0043] 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, 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.

[0044] Furthermore, in step 4, the signal-to-noise ratio (SNR) is calculated as follows:

[0045]

[0046] Furthermore, the step 5 specifically includes:

[0047] (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 into the simulation model, the lidar profile of a specified water body or the global ocean can be obtained, and then the maximum detectable depth distribution can be obtained based on 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 impact of different parameters on the detection depth can be statistically analyzed to optimize the system design.

[0048] (2) Optimal wavelength: Different wavelengths have different penetration capabilities. Considering the engineering feasibility, in addition to the mature 532nm wavelength, the necessity of selecting other wavelengths is analyzed by calculating the improvement of global jump layer detection capabilities by other wavelengths. By inputting various hardware parameters and environmental parameters into the simulation model and 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, thereby improving global detection capabilities under engineering feasibility conditions.

[0049] (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 laser radar parameters input in the simulation model, the echo signals of the sea surface and each underwater depth layer can be obtained, and then the laser radar dynamic range can be obtained based on 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 sea surface roughness), and pulse width are modified separately to obtain the laser radar 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 laser radar parameters are adjusted to meet the dynamic range requirements of the selected detector.

[0050] (4) Other hardware indicators: Laser energy, telescope aperture, laser divergence angle, receiving field of view angle, platform height, etc. all affect lidar performance. By changing the simulation model input individually, 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 water parameters for 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.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] This simulation model, based on lidar equations, boasts fast computational speed and high efficiency, and its input parameters can be easily adjusted based on the lidar system and environment. It can be used to simulate signals from multiple ocean lidar platforms, including shipborne, airborne, and satellite-based ones. The model uses quality-controlled global chlorophyll-a profile data to calculate the vertical distribution of optical parameters of marine organisms, providing greater confidence than the assumption of vertical uniformity.

[0053] The present invention has the following features: (1) It takes into account various lidar specifications, sea surface parameters, and bio-optical profile parameters; (2) It uses a global Chl-a profile dataset as input to simulate stratified water; and (3) By modifying the parameters of the simulation interface, it can conveniently simulate lidar signals from various platforms and environments, thereby analyzing the detection capabilities of different parameters and guiding the design of marine lidar hardware parameters. The simulator was actually verified based on measured data from airborne lidar, and the results showed that it has extremely strong reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Flowchart of the ocean lidar signal simulation and index optimization method based on the lidar equation proposed in the present invention.

[0055] 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.

[0056] Figure 3 The profile signal and dynamic range analysis results of the ocean lidar simulated by this method.

[0057] Figure 4 The distribution results of the extreme detection depth corresponding to different field of view angles, zenith angles, and signal-to-noise ratio thresholds of the ocean lidar simulated by this method.

[0058] Figure 5 is the chlorophyll concentration of the three water bodies in Example 2.

[0059] Figure 6 are the IOPs of the three water bodies in Example 2.

[0060] Figure 7 This is a comparison of the maximum detection depths of different wavelengths in Example 2. DETAILED DESCRIPTION

[0061] The technical solution of the present invention is further described below with reference to embodiments.

[0062] Example 1

[0063] A method for designing marine lidar hardware parameters based on the lidar equation model, such as Figure 1 As shown, the following steps are included:

[0064] Step 1: Establish a simulation model based on the lidar equation, using the global Chl-a concentration profile dataset from the Copernicus Marine Environment Monitoring Service (CMEMS) as input to simulate intrinsic optical parameters (IOPs) profile parameters, such as seawater absorption and scattering coefficients. The bio-optical model is:

[0065] K d =K w +x <chl> e

[0066] c=a+b

[0067] a=a w +a p +a g

[0068] b=b w +b p

[0069] a p (λ)=A <chl> 1-B

[0070] a g (λ)=0.2a p (440)exp[-0.014(λ-440)]

[0071] b p (λ)=c p (λ)-a p (λ)

[0072]

[0073] 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.

[0074] Step 2: Based on the IOPs profile parameters, calculate the lidar extinction coefficient and backscattering coefficient, combine the lidar parameters (including the transmitter and receiver), substitute them into the lidar equation, and calculate the seawater photon number profile; the calculation method for calculating the seawater photon number profile is:

[0075] α=K d +(cK d )exp(-0.85cD)

[0076]

[0077] Where K is the lidar system constant, z is the water depth, α(z) is the lidar 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 lidar, 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.

[0078] 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 for the collected photon number profile is:

[0079] N c (z)=N(z)+N sun

[0080]

[0081] 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, is the field of view angle.

[0082] 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:

[0083]

[0084] γ sur =γ s +γ f +γ u

[0085]

[0086] 2 >=0.003+0.00512v

[0087]

[0088] 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 lidar 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.

[0089] The underwater bubble model is:

[0090]

[0091] r ref =54.4μm+1.984×10 -6 z

[0092]

[0093] 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, 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.

[0094] 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:

[0095]

[0096] Step 5: Model validation based on measured data

[0097] 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 results show that the correlation coefficient R 2 The average relative error is less than 8%.

[0098] Step 6: By inputting different LiDAR system parameters and environmental parameters, analyze the LiDAR detection capability and conduct hardware parameter design.

[0099] ​Maximum water profile detection depth: Water detection depth is primarily affected by the inherent multiple scattering characteristics of seawater, a strongly scattering medium. Increasing laser energy or telescope aperture, for example, can only improve detection capabilities to a limited extent. By inputting different hardware parameters into the simulation model, lidar profiles can be generated for a specific water body or the global ocean. Furthermore, the maximum detectable depth distribution can be determined based on a set photon count threshold or signal-to-noise ratio threshold. By inputting different system parameters, a global distribution can be obtained for a given parameter system. The impact of different parameters on detection depth can be statistically analyzed to optimize system design.

[0100] Optimal wavelength: Different wavelengths have different penetration capabilities. Considering engineering feasibility, we analyze the necessity of selecting other wavelengths, in addition to the mature 532nm wavelength, by calculating how other wavelengths improve global thermocline detection capabilities. By inputting various hardware and environmental parameters into the simulation model and laser wavelengths within the visible light band, we can calculate the global maximum detectable depth distribution of lidar corresponding to different wavelengths. By statistically comparing and analyzing the average maximum detectable depth of different wavelengths in specific sea areas or globally, we can determine the optimal wavelength and improve global detection capabilities within engineering feasibility.

[0101] 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 greater impact on the dynamic range. When the input of the model remains consistent, the laser inclination angle, sea surface wind speed (affecting 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 dynamic range requirements of the selected detector. The lidar receiving profile corresponding to different laser inclination angles is as follows: Figure 3 As shown in Figure 2, as the tilt angle increases, the dynamic range can be effectively reduced, but the depth aliasing range will also increase.

[0102] 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. The simulation analysis of the detection capabilities of various parameters in different water media can be used to optimize the indicator design. The distribution of the extreme detection depth of the ocean lidar corresponding to different field of view angles, zenith angles, and signal-to-noise ratio thresholds is as follows. Figure 4 As shown in FIG, in the extreme detection case of SNR=1, setting the field of view angle to 0.15-0.2 mrad is beneficial to increasing the detection depth.

[0103] Example 2

[0104] Taking the selection of the optimal wavelength as an example, the proportion of the global thermocline depth that can be detected under the same energy and receiving aperture conditions is calculated. The maximum detectable depth of visible light single photons is calculated based on the simulation model. The parameters of the satellite-borne lidar used in the simulation are shown in the table below. The analysis was conducted on three different water bodies: the cleanest ocean water, the relatively clear South China Sea, and the turbid nearshore water. The chlorophyll concentration is as follows: Figure 5 shown.

[0105] Table 1 Simulation parameters

[0106]

[0107]

[0108] The absorption coefficient, scattering coefficient, and beam attenuation coefficient distribution of 486nm and 532nm water bodies calculated using the simulation model Figure 6 shown.

[0109] For the range of 400nm to 700nm, with a step interval of 1nm, the lidar echo signals corresponding to the three water bodies were calculated, and the detection depth corresponding to the signal attenuation to single photons was obtained, as shown in the figure. Figure 7 shown.

[0110] Calculations for three typical water bodies show that the clearer the water, the shorter the optimal penetration wavelength. The optimal wavelength for clean ocean water is approximately 420-440nm, for South China Sea waters it is approximately 460-490nm, and for turbid nearshore waters it is approximately 480-560nm.

[0111] 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 lidar hardware indicator designs by inputting different lidar hardware parameters and water body and atmospheric environment parameters.

[0112] Finally, although this specification is described in terms of 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 marine 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 CMEMS global Chl-a concentration profile dataset 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: Calculate the lidar extinction coefficient and backscattering coefficient based on the IOPs profile parameters, and substitute the lidar parameters into the lidar equation to calculate the global ocean received photon number profile; Step 3: Simulate the collected photon number profile: Based on the Kurucz solar irradiance spectrum dataset, calculate the solar background light received by the lidar. Combined with the sea surface model and underwater bubble model, random noise is added to 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 based on the signal-to-noise ratio threshold; Step 5: By inputting different LiDAR system parameters and environmental parameters, the LiDAR detection capability is analyzed to perform hardware parameter design. 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 based on 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, inputting laser wavelengths in different visible light bands, the global maximum detectable depth distribution of lidar corresponding to different wavelengths is calculated; through statistical comparison, the average maximum detectable depth of different wavelengths in specific sea areas or globally is analyzed to obtain the optimal wavelength, thereby improving global detection capabilities under engineering implementation 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 based on the ratio of the maximum signal to the minimum signal; the laser inclination angle, sea surface roughness, and pulse width 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: Laser energy, telescope aperture, laser divergence angle, receiving field of view angle, and platform height will all affect the performance of the lidar. By changing the input separately, the corresponding lidar echo signal is calculated. Based on the global Chl-a concentration profile data set, the water parameters of different water media around the world are calculated; the detection capabilities of each parameter in different water media are simulated and analyzed to optimize the indicator design.

2. The method for designing marine laser radar hardware parameters according to claim 1, wherein: 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)=X <chl> 1-Y < / 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, X, Y 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 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, <chl> is the chlorophyll concentration.< / chl> 3. The method for designing marine lidar hardware parameters according to claim 2, wherein: The calculation method for calculating the seawater photon number profile in step 2 is: α(z′)=K d +(cK d )exp(-0.85cD) Where K is the lidar system constant, z is the water depth, α(z′) is the lidar extinction coefficient, β(π) 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 lidar, 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 lidar hardware parameters according to claim 3, wherein: 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, 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 lidar scattering coefficient of the sea surface, expressed as specular reflection γ s , white hat contribution γ f and subsurface contribution γ u The sum of the addition; ρ is the Fresnel reflection coefficient, 2 > is the variance of the wave slope of the Gaussian distribution, k 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 =ra+1.984×10 -6 z 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 bub (z) is the bubble number density, r is the bubble radius, 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; the unit of ra is μm, and the value is 54.

4.

5. The method for designing hardware parameters of a marine laser radar according to claim 4, wherein: In step 4, the signal-to-noise ratio (SNR) is calculated as follows:

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