A laser radar attenuation estimation method and system based on empirical model

Abstract

The present invention discloses a laser radar attenuation estimation method and system based on an empirical model, the method comprising: constructing a first empirical model according to the influence of atmospheric molecules on the attenuation characteristics of the laser radar; constructing a second empirical model according to the influence of atmospheric particles on the transmission of the laser radar in the atmosphere; integrating the first empirical model and the second empirical model to obtain a final empirical model; obtaining atmospheric parameters and importing them into the final empirical model to obtain an attenuation estimation result. The system comprises: a first empirical model construction module, a second empirical model construction module, an integration module and an attenuation estimation module. By using the present invention, the attenuation result of the laser radar propagating in the atmosphere can be generated quickly and accurately. As a laser radar attenuation estimation method and system based on an empirical model, the present invention can be widely used in the field of laser radar.

Description

Technical Field

[0001] The present invention relates to the field of laser radar, and in particular to a laser radar attenuation estimation method and system based on an empirical model. Background Art

[0002] Lidar uses laser as radiation source and carrier frequency, and has the advantages of short wavelength, high beam quality, and strong directivity, and is widely used in atmospheric detection. In atmospheric detection, the main factor affecting the detection capability of Lidar is the transmission characteristics of Lidar energy signal in the atmosphere. When Lidar is transmitted in the atmosphere, it will interact with gas molecules and aerosols in the atmosphere, causing attenuation of laser transmission.

[0003] The influence of the atmosphere on the attenuation of lidar signals mainly includes two aspects: on the one hand, the radiation energy loss caused by the absorption and scattering effects of atmospheric molecules, and on the other hand, the signal attenuation caused by the absorption and scattering effects of aerosol particles suspended in the atmosphere (such as haze, clouds, fog, rain, snow). Existing theoretical models of attenuation often require large-scale experiments for parameter determination, with large amounts of calculation and poor real-time performance, so the results may have little practical value. The existing empirical and semi-empirical models cannot be constructed for long-distance lidar experiments, resulting in relatively large error precision. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a lidar attenuation estimation method and system based on an empirical model, which can universally, quickly and accurately evaluate the attenuation characteristics of lidar transmission in the atmosphere.

[0005] The first technical solution adopted by the present invention is: a laser radar attenuation estimation method based on an empirical model, comprising the following steps:

[0006] The first empirical model is constructed based on the influence of atmospheric molecules on the attenuation characteristics of the lidar;

[0007] A second empirical model is constructed based on the impact of atmospheric aerosols on the transmission of lidar in the atmosphere;

[0008] Integrate the first empirical model and the second empirical model to obtain a final empirical model;

[0009] The atmospheric parameters are obtained and imported into the final empirical model to obtain the attenuation estimation results.

[0010] Furthermore, it also includes:

[0011] The final empirical model is analyzed to obtain the factors affecting the attenuation characteristics of the LiDAR;

[0012] According to the influencing factors of the lidar attenuation characteristics, attenuation suppression measures are proposed.

[0013] Furthermore, the step of constructing a first empirical model according to the influence of atmospheric molecules on the attenuation characteristics of the laser radar specifically includes:

[0014] According to the influence of atmospheric molecules on the attenuation characteristics of lidar, the first theoretical model was established based on Rayleigh scattering theory analysis;

[0015] Ignore the absorption effect of atmospheric molecules (k m (r)), based on the molecular scattering effect, the first theoretical model is mathematically analyzed and simplified to obtain the first empirical model.

[0016] Further, the formula of the first empirical model is expressed as follows:

[0017]

[0018] In the above formula, β m represents the molecular scattering coefficient, N represents the molecular number density, P represents the pressure, T represents the temperature, Ar represents the influencing parameter, and λ represents the laser radar wavelength.

[0019] Furthermore, the step of constructing a second empirical model based on the influence of atmospheric aerosols on the transmission of lidar in the atmosphere specifically includes:

[0020] According to the influence of atmospheric particles on the transmission of LiDAR in the atmosphere, the second theoretical model is established based on Mie scattering theory.

[0021] The extinction cross section in the second theoretical model is simplified and function fitting is performed to obtain the second empirical model.

[0022] Further, for foggy weather, the formula of the second empirical model is expressed as follows:

[0023] η n (h) = 1.93e -10 (erf(10.52r2-4.53)-erf(10.52r1-4.53))

[0024] +2.93e -10 (erf(12.36r2-3.84)-erf(12.36r1-3.84))

[0025] +1.20e -10 (erf(7.75r2-1.64)-erf(7.75r1-1.64))

[0026] In the above formula, η n (h) represents the aerosol effect coefficient η n (h) = kn (h)+β n (h), r2 represents the upper limit of particle radius, and r1 represents the lower limit of particle radius.

[0027] Further, the final empirical model formula is:

[0028]

[0029]

[0030] In the above formula, k m (r), k n (r) represent the absorption coefficient caused by atmospheric molecules and aerosol, β m (r), β n (r) are the scattering coefficients caused by atmospheric molecules and aerosols, H represents the propagation distance, k t,λ Represents the attenuation coefficient of the medium.

[0031] The second technical solution adopted by the present invention is: a laser radar attenuation estimation system based on an empirical model, comprising:

[0032] A first empirical model building module, used to build a first empirical model according to the influence of atmospheric molecules on the attenuation characteristics of the laser radar;

[0033] A second empirical model building module is used to build a second empirical model according to the influence of atmospheric aerosol on the transmission of lidar in the atmosphere;

[0034] An integration module, used for integrating the first empirical model and the second empirical model to obtain a final empirical model;

[0035] The attenuation estimation module is used to obtain atmospheric parameters and import them into the final empirical model to obtain the attenuation estimation results.

[0036] The beneficial effects of the method and system of the present invention are as follows: under the theoretical framework of Mie and Rayleigh scattering, the present invention calculates and analyzes the attenuation characteristics of lidar in the most common rainy and foggy atmospheric environments, respectively constructs and integrates empirical models of the influence of atmospheric molecules and aerosols, and the proposed models have better real-time performance and smaller errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of the steps of a laser radar attenuation estimation method based on an empirical model of the present invention;

[0038] Figure 2 Schematic diagram of the change of the At parameter ratio with temperature (T) in a specific embodiment of the present invention;

[0039] Figure 3is a parameter of a specific embodiment of the present invention (C ext *n(r)) and the schematic diagram of the fitting with the particle radius (r);

[0040] Figure 4 It is a schematic diagram of the variation of the transmittance (T) difference between the empirical model of a specific embodiment of the present invention and the theoretical model of the real part of different refractive indices with the propagation distance (H);

[0041] Figure 5 It is a schematic diagram of the variation of the transmittance (T) difference between the empirical model of a specific embodiment of the present invention and the theoretical model of the imaginary part of different refractive indices with the propagation distance (H);

[0042] Figure 6 It is a schematic diagram of the difference in transmittance (T) between the empirical model of a specific embodiment of the present invention and the theoretical model of different wavelengths as a function of the propagation distance (H);

[0043] Figure 7 It is a structural block diagram of a laser radar attenuation estimation system based on an empirical model of the present invention. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only provided for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0045] like Figure 1 As shown, the present invention provides a laser radar attenuation estimation method based on an empirical model, the method comprising the following steps:

[0046] S1. Construct the first empirical model based on the influence of atmospheric molecules on the attenuation characteristics of the lidar;

[0047] S1.1. Establish the first theoretical model based on the influence of atmospheric molecules on the attenuation characteristics of the lidar;

[0048] When the lidar signal propagates in the atmosphere, due to the presence of various molecules and aerosols in the atmosphere, part of the light signal energy is absorbed and scattered, causing it to attenuate as the propagation distance increases. According to the Beer-Lambert-Bouguer law, assuming that the atmospheric medium is in a uniform state, the atmospheric transmittance is expressed as follows:

[0049]

[0050] Among them, F 0,λ 、F λ are the laser radiation flux before and after emission, respectively; H represents the propagation distance; k t,λis the attenuation coefficient of the scattering or absorbing medium; k m (r), k n (r) absorption coefficient caused by atmospheric molecules and aerosols respectively; β m (r), β n (r) are the scattering coefficients caused by atmospheric molecules and aerosols, respectively;

[0051] The influence of atmospheric molecules on the attenuation characteristics of lidar mainly includes absorption (k m (r)) and scattering (β m (r)). The specific analysis of the theoretical models of these two parts is as follows:

[0052] The molecular absorption effect is generally calculated by line-by-line integration, and the specific calculation is shown in Formula 2:

[0053] k m (h) = Sf(v-v0) (2)

[0054] Where S is the intensity of the absorption line; f represents the line type of the molecular absorption line; v0 is the center line frequency; v is the wave number;

[0055] The spectral line intensity (S) can be further expressed by formula 3:

[0056]

[0057] Among them, S(T ref ) is the reference temperature (T ref ) under the spectral line intensity; Q(T) is expressed as the overall partition function of the molecule; E η is the low-state energy of the transition; c2 is the second radiation constant; for the line type (f), there are mainly Doppler line type, Lorentz line type and Voigt line type. The most widely used is the Doppler line type, and its calculation formula is as follows:

[0058]

[0059] n is the molecular number density, ξ is a constant, T is the temperature, M represents the molecular mass, and c is the speed of light. The absorption line parameters of atmospheric molecules can be obtained from the HITRAN database developed by the U.S. Air Force Cambridge Research Laboratory (AFCRL) to calculate and analyze the molecular absorption coefficient.

[0060] The molecular scattering effect is mainly analyzed by Rayleigh scattering theory, and the specific calculation is shown in formula 5:

[0061]

[0062] Among them, for the real part of the air index (m s) and the real part of the atmospheric refractive index (mi), as shown in Formulas 6 and 7 respectively:

[0063]

[0064]

[0065] Where N is the molecular number density under pressure (P) and temperature (T); N s is the molecular number density under standard atmospheric conditions;

[0066] S1.2. Ignoring the absorption effect of atmospheric molecules, the first theoretical model is analyzed and simplified based on the molecular scattering effect to obtain the first empirical model.

[0067] The molecular absorption effect of the signal mainly comes from the absorption spectrum characteristics, and the absorption coefficient depends on the emission wave frequency. From the center frequency values ​​of the molecular absorption spectrum collected by the HITRAN database, it can be seen that most bands show high transmittance, and these bands are also called "atmospheric windows". Therefore, it can be considered that the atmospheric absorption effect has little effect on the transmission of the lidar and can be ignored.

[0068] The following mainly considers the analysis and simplification of the molecular scattering effect. The process is as follows:

[0069] First, the real part of the air index (m s ) analysis and simplification, when the laser radar wavelength value is taken as λ=0.5,2um, the simulation results are shown in Table 1 (parameters A, B, C and D are m s ): The values ​​of parameters A, B and parameters C, D differ by nearly 100 times, so the empirical model can ignore the influence of parameters C and D; comparing the values ​​of parameters A and B, it can be found that their sizes differ by at least 100 times their difference, so they can be approximated by a single parameter B formula.

[0070] Table 1 Effect of wavelength on parameters in the real part of air index

[0071]

[0072] Note:

[0073] In summary, we can get:

[0074]

[0075] Then, the parameter Ar in formula 10 (such as formula 9) is analyzed and analyzed.

[0076]

[0077]

[0078] From formula (9) and the above formula, we can see that there are two factors that affect the parameter (Ar): temperature (T) and pressure (P). Taylor expansion of Ar yields:

[0079] Ar=Ar1+Ar2+Ar3

[0080] =3.6e -58 P5(At1) -1 +2.6e -52 P 4 (At2) -1 +4.7e -47 P 3 (At3) -1 (11)

[0081] in:

[0082] At1=1.8e -10 T 5 +2e -7 T 4 +8.1e -5 T 3 +0.01T 2 +T (12)

[0083] At2=4.9e -8 T 4 +4e -5 T 3 +0.01T 2 +T (13)

[0084] At3=1.4e -5 T 3 +0.01T 2 +T (14)

[0085] Simulate At3 / At1 and At3 / At2, T = 0 ~ 400 ° C, the results are as follows Figure 2 As shown: We found that the values ​​of At3 / At1 and At3 / At2 decrease with the increase of temperature T. Therefore, Ar3 / Ar1 and Ar3 / Ar2 will increase with the increase of temperature T and decrease with the increase of pressure P. When the temperature T = 0.001 ° C and the pressure P = 400 kPa, the ratio is the smallest, Ar3 / Ar1 = 8.1598e 5 ,Ar3 / Ar2=451.9247. Therefore Ar≈Ar3. In summary:

[0086]

[0087] S2, constructing a second empirical model based on the influence of atmospheric particles on the transmission of lidar in the atmosphere;

[0088] S2.1. According to the influence of atmospheric particles on the transmission of lidar in the atmosphere, the second theoretical model is established based on Mie scattering theory;

[0089] The influence of atmospheric aerosol on the transmission of lidar in the atmosphere mainly comes from particle absorption and scattering. Both particle absorption and scattering can be analyzed using Mie scattering theory. The particle absorption coefficient (k n (h)) and scattering coefficient (β n (h)) is calculated as follows:

[0090]

[0091]

[0092] C ext =C abs +C sca (18)

[0093] η n (h) = k n (h)+β n (h) (19)

[0094] From formula (28), we can know that the particle effect coefficient (η n (h)) is expressed as the particle absorption coefficient (k n (h)) and scattering coefficient (β n (h)) and the extinction cross section (C ext ) is the absorption cross section (C abs ) and scattering cross section (C sca ). The absorption and scattering cross sections can be further expressed as follows:

[0095]

[0096]

[0097]

[0098]

[0099] Among them, l = 2π / λ is the wave number; a n and b n is the scattering coefficient; x = lr is the size parameter; j k , is a spherical Bessel function; μ and μ1 are the magnetic permeability of the sphere and the surrounding medium, respectively:

[0100] For the particle distribution n(r), the classic Gamma distribution type is mainly compared, and the expression is as follows.

[0101]

[0102] S2.2. Simplify the extinction cross section in the second theoretical model and perform function fitting to obtain a second empirical model.

[0103] Based on the theoretical model analysis of aerosol absorption and scattering mentioned above, we simplify and analyze it as follows. The specific process is as follows:

[0104] First: the extinction cross section (C ext ) analysis and simplification, C ext It is composed of refractive index (iR = real + imag * i), wavelength (λ), and particle radius (r). The following mainly analyzes the effects of these parameters on the extinction cross section (C ext )’s impact.

[0105] As for the influence of the imaginary part of the refractive index (imag), the corresponding parameter values ​​are shown in Table 2. The calculated results show that for the same particle radius and different imaginary parts of the refractive index, the extinction cross-section values ​​and differences between them are quite different; for example, when the particle radius is 337.1um, the extinction cross-section value is of the order of e -3 , but the magnitude of the difference between them is e -5 Therefore, the imaginary part can be taken as a constant. For the analysis of the influence of the real part of the refractive index and the wavelength, it is found that the calculated extinction cross section value is also quite different from the difference value, and both can be taken as constants for calculation. ext ) is mainly affected by the particle radius (r), and is a function of r. In this paper, real = 1.6, imag = 5e -6 ,λ=1.06um.

[0106] Table 2 Values ​​of parameters such as the imaginary part of the refractive index

[0107] real imag r(um) Δr λ(um) 1.33 <![CDATA[1,2~9e -2 ,…,1,2~9e -9 ]]> <![CDATA[10 -3 ~10 3 ]]> 0.01 0.532

[0108] Then: select different particle size distributions (n(r)), particle effect coefficients (η n ) is used for function fitting. The particle size distribution is mainly described by generalized Gamma distribution. The parameter values ​​of the two size distribution models of rain and fog are shown in Table 3:

[0109] Table 3 Size distribution parameter values ​​of rain and fog types

[0110]

[0111] Among them, Fog, Haze and Rain represent fog, haze and rain respectively, Heavy / Moderate / Chu&Hogg Fog, Haze H / L / M, Rain L / M, etc. represent different types of fog, haze and rain respectively, which are mainly classified according to their particle radius and density. The widely used model is the Gamma distribution model (also known as the Khragian-Mazin model) with c=2, d=1. In each size distribution type, the particle effect coefficient (η n ), and the widely used distribution (Haze H) is initially selected for analysis. The analysis of other distribution types is similar.

[0112] For Haze H type, the particle radius range is r = 10 -3 ~10um, based on Matlab curve fitting tool, we use Gaussian function to fit, the result is as follows Figure 3 As shown, the fitting parameters (C ext *n(r)) and its integral to obtain the particle effect coefficient (η n ) are as follows:

[0113]

[0114]

[0115] Note: erf is the error function, r2 and r1 represent the upper and lower limits of the particle radius, respectively.

[0116] S3, integrating the first empirical model and the second empirical model to obtain a final empirical model;

[0117] Combining the construction of the molecular and particle empirical models in step S1 and step S2, we can finally establish an empirical model for transmittance calculation based on the HazeH size type distribution (defined as T-Haze-H). The specific expression is as follows:

[0118]

[0119]

[0120] S4. Obtain atmospheric parameters and import them into the final empirical model to obtain attenuation estimation results.

[0121] Specifically, lidar has important applications in the observation of terrestrial species and marine fish schools. It can be based on parameters such as atmospheric temperature, pressure and atmospheric molecular density provided by the model parameters of the US or international standard atmosphere (1976) (temperature and pressure parameters can also be measured through our experimental equipment (such as thermometers, mercury barometers, etc.)), the radius of particles obtained from the data released by the Earth Observation Satellite Program (CERS) launched by NASA and the Earth Environment Observation Satellite launched by our country, as well as the design parameters of the lidar itself (laser wavelength, etc.). By inputting these parameters into the established empirical model, the atmospheric attenuation results under different environments can be calculated, and then the attenuation characteristics of lidar transmission in different environments (land and ocean) can be analyzed more accurately and efficiently. At present, we have carried out analysis on the detection distance range of lidar targets on land and the detection depth of marine fish, providing technical support for lidar to effectively detect targets and the hardware system design of lidar (selection of indicators such as lidar wavelength, emission energy, and receiving aperture).

[0122] As a further preferred embodiment of the method, it also includes:

[0123] S5. Analyze the final empirical model to obtain the influencing parameters of the laser radar attenuation characteristics;

[0124] S6. Propose attenuation suppression measures based on the influencing parameters of the laser radar attenuation characteristics.

[0125] Specifically, through the constructed empirical model (Equations 27-28), it can be found that the attenuation characteristics of the lidar in the atmosphere are mainly affected by multiple parameters such as atmospheric temperature (T), pressure (P), wavelength (λ), atmospheric molecular density (N), particle radius (r) and observation distance (H). In order to reduce the attenuation of the lidar in the atmospheric environment, it can be suppressed from two aspects. On the one hand, the detection experiment can be carried out under the conditions of suitable temperature and pressure, relatively clean atmospheric environment (lower atmospheric molecular density and smaller particle radius), such as clear sky weather, which can effectively reduce the attenuation of the lidar in the atmosphere; on the other hand, from the perspective of the system design of the lidar, a lidar hardware system with a higher wavelength that meets the experimental index requirements can be selected, which will also reduce the signal attenuation in the atmospheric environment to a certain extent.

[0126] As a further preferred embodiment of the method, it also includes:

[0127] S7. Compare the errors of the final empirical model with other empirical and semi-empirical models.

[0128] Regarding error analysis, it is mainly divided into two parts (comparison with theoretical models and other empirical models).

[0129] In terms of theoretical model: We modeled the empirical model (T-Haze-H) based on the type of haze size distribution (Haze H), and performed differential analysis with the theoretical model results obtained with different values ​​of three parameters (real part of refractive index, imaginary part (imag), wavelength (λ)). Considering the effects of different real part, imaginary part and wavelength of refractive index, the total values ​​of each parameter are shown in Table 4. The results of the established empirical model and theoretical model are compared as shown in Table 4. Figure 4-6 In addition to the error comparison, the average calculation time of the theoretical and empirical models (average value after 5 repeated runs), the preliminary statistical results are shown in Table 5:

[0130] Table 4 Values ​​of parameters such as the real part of the refractive index

[0131] real Δreal imag r(um) Δr λ(um) H(km) ΔH 1.2～2.8 0.4 <![CDATA[5e -6 ]]> <![CDATA[10 -3 ~10]]> <![CDATA[10 -4 ]]> 1.06 0-3 0.01

[0132] Table 5 Average calculation time of theoretical model and established empirical model

[0133] type Real part of refractive index Imaginary part of refractive index wavelength Theoretical Model 40.63s 35.05s 45.61s Empirical Model 0.74s 0.68s 0.69s

[0134] From the above charts, we know that the maximum error of the T-Haze-H empirical model is less than 1.5%, 1.2%, and 1.5% respectively when compared with the theoretical model results obtained by selecting different values ​​of the real part, imaginary part, and wavelength of the refractive index, and the average calculation time is improved by 39.88s, 34.37s, and 44.92s respectively. Therefore, it is preliminarily believed that the established empirical model has small errors and high time efficiency.

[0135] In terms of empirical models: the error and calculation time are also compared with the existing fog empirical models. Here, the newly established T-Haze-H empirical model is also selected for analysis. Table 3 shows the values ​​of Haze H size distribution parameters. The relationship between size distribution parameters (a, b) and parameters visibility (V) and water content (W) is shown in equations 29-30:

[0136]

[0137]

[0138] Where: According to the advection fog and radiation fog characteristics of fog, the relationship between visibility (V) and water content (W) is obtained by formula 31:

[0139]

[0140] Combining equations (29)-(31), the visibility (V) and water content (W) under the two fog characteristics of advection fog and radiation fog can be calculated from the size distribution parameters of Haze H. The values ​​of other parameters are shown in Table 6.

[0141] Table 6 Parameter values

[0142] real imag r(um) Δr λ(um) 1.60 <![CDATA[5e -6 ]]> <![CDATA[10 -3 ~10]]> <![CDATA[10 -4 ]]> 1.06

[0143] The simulation results show that when different propagation distances are selected, in advection fog, the error range of T-Haze-H is 0-1.2%, and the existing empirical model is 50%-90%. In terms of average calculation time, T-Haze-H ranges from 0.74 to 0.76s, and the existing empirical model is 0.05 to 0.1s; in radiation fog, the error range of T-Haze-H is 0.06%-1.2%, and the existing empirical model is 0.18%-90%. In terms of average calculation time, T-Haze-H ranges from 0.72s to 0.75s, and the existing empirical model is 0.06s to 0.1s. Therefore, the established empirical model will have smaller errors than the existing ones, and the calculation time will be longer, but the overall calculation time will be shorter.

[0144] like Figure 2 As shown, a laser radar attenuation estimation system based on an empirical model includes:

[0145] A first empirical model building module, used to build a first empirical model according to the influence of atmospheric molecules on the attenuation characteristics of the laser radar;

[0146] A second empirical model building module is used to build a second empirical model according to the influence of atmospheric aerosol on the transmission of lidar in the atmosphere;

[0147] An integration module, used for integrating the first empirical model and the second empirical model to obtain a final empirical model;

[0148] The attenuation estimation module is used to obtain atmospheric parameters and import them into the final empirical model to obtain the attenuation estimation results.

[0149] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0150] A laser radar attenuation estimation device based on empirical model:

[0151] at least one processor;

[0152] at least one memory for storing at least one program;

[0153] When the at least one program is executed by the at least one processor, the at least one processor implements the laser radar attenuation estimation method based on the empirical model as described above.

[0154] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0155] A storage medium storing processor-executable instructions, characterized in that the processor-executable instructions are used to implement a laser radar attenuation estimation method based on an empirical model as described above when executed by the processor.

[0156] The contents of the above method embodiments are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0157] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A laser radar attenuation estimation method based on an empirical model, characterized in that: The following steps are involved: The first empirical model is constructed based on the influence of atmospheric molecules on the attenuation characteristics of the lidar; A second empirical model is constructed based on the impact of atmospheric aerosols on the transmission of lidar in the atmosphere; Integrate the first empirical model and the second empirical model to obtain a final empirical model; Obtain atmospheric parameters and import them into the final empirical model to obtain attenuation estimation results; The final empirical model formula: In the above formula, k m (r), k n (r) represent the absorption coefficient caused by atmospheric molecules and aerosol, β m (r), β n (r) are the scattering coefficients caused by atmospheric molecules and aerosols, H represents the propagation distance, k t,λ represents the attenuation coefficient of the medium, N represents the molecular number density, P represents the pressure, T represents the temperature, and λ represents the laser radar wavelength.

2. The method for estimating laser radar attenuation based on an empirical model according to claim 1, characterized in that: Also includes: The final empirical model is analyzed to obtain the influencing parameters of the lidar attenuation characteristics; Attenuation suppression measures are proposed based on the influencing parameters of the lidar attenuation characteristics.

3. According to the method for estimating laser radar attenuation based on an empirical model as claimed in claim 1, the step of constructing a first empirical model according to the influence of atmospheric molecules on the laser radar attenuation characteristics specifically comprises: According to the influence of atmospheric molecules on the attenuation characteristics of lidar, the first theoretical model was established based on the analysis of Rayleigh scattering theory; Ignoring the absorption effect of atmospheric molecules, the first theoretical model is analyzed and simplified based on the molecular scattering effect to obtain the first empirical model.

4. The method for estimating laser radar attenuation based on an empirical model according to claim 3, characterized in that: The formula of the first empirical model is as follows: In the above formula, β m represents the molecular scattering coefficient.

5. The method for estimating laser radar attenuation based on an empirical model according to claim 4, characterized in that: The step of constructing a second empirical model based on the influence of atmospheric aerosol on the transmission of the lidar in the atmosphere specifically includes: According to the influence of atmospheric aerosol on the transmission of lidar in the atmosphere, the second theoretical model is established based on the Mie scattering theory. The extinction cross section in the second theoretical model is simplified and function fitting is performed to obtain the second empirical model.

6. The method for estimating laser radar attenuation based on an empirical model according to claim 3, characterized in that: For foggy weather, the formula of the second empirical model is as follows: η n (h)=1.93e -10 (erf(10.52r2-4.53)-erf(10.52r1-4.53)) +2.93e -10 (erf(12.36r2-3.84)-erf(12.36r1-3.84)) +1.20e -10 (erf(7.75r2-1.64)-erf(7.75r1-1.64)) In the above formula, η n (h) represents the aerosol effect coefficient, r2 represents the upper limit of the particle radius, and r1 represents the lower limit of the particle radius.

7. A laser radar attenuation estimation system based on an empirical model, characterized in that: include: A first empirical model building module, used to build a first empirical model according to the influence of atmospheric molecules on the attenuation characteristics of the laser radar; A second empirical model building module is used to build a second empirical model according to the influence of atmospheric aerosol on the transmission of lidar in the atmosphere; An integration module, used for integrating the first empirical model and the second empirical model to obtain a final empirical model; Attenuation estimation module, used to obtain atmospheric parameters and import them into the final empirical model to obtain attenuation estimation results; The final empirical model formula: In the above formula, k m (r), k n (r) represent the absorption coefficient caused by atmospheric molecules and aerosol, β m (r), β n (r) are the scattering coefficients caused by atmospheric molecules and aerosols, H represents the propagation distance, k t,λ represents the attenuation coefficient of the medium, N represents the molecular number density, P represents the pressure, T represents the temperature, and λ represents the laser radar wavelength.

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