Methods, devices, electronic equipment, media, and products for simulating lidar signals

Abstract

This invention relates to the field of marine lidar technology, and discloses a method, device, electronic equipment, medium, and product for simulating lidar signals. The method includes: acquiring the hardware parameters of the lidar and the optical parameters of the target marine water body; establishing an elastic scattering analytical model corresponding to the target marine water body; establishing an inelastic scattering analytical model corresponding to the target marine water body; establishing a polarization scattering analytical model corresponding to the target marine water body, wherein the polarization scattering analytical model is used to characterize the changes in polarization state and transmission direction of photon packets during transmission and scattering; and, under the conditions of the hardware and optical parameters, using a semi-analytical Monte Carlo simulation method, analyzing the elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target marine water body. This invention can achieve accurate and efficient simulation of novel marine lidar signals in complex aquatic environments.

Description

Technical Field

[0001] This invention relates to the field of marine lidar technology, specifically to a method, apparatus, electronic device, medium, and product for simulating lidar signals. Background Technology

[0002] With the continuous development of lidar technology, lidar has been widely used in water quality monitoring, marine life detection, and benthic environment surveying. By analyzing the reflection signals (echo signals) of the laser beam emitted by lidar interacting with substances in the water at the surface and underwater, information about the marine environment can be efficiently acquired over a large area. Therefore, accurately simulating lidar echo signals is key to improving the accuracy of marine information detection using lidar remote sensing technology.

[0003] In related technologies, the simulation of lidar signals in the ocean mainly relies on elastic scattering mechanisms. However, the marine environment is complex, with factors such as water inhomogeneity and the coexistence of multiple scattering mechanisms. When dealing with complex marine environments, the above methods are difficult to accurately reflect the actual situation, resulting in significant differences between simulation results and actual observation data. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, medium, and product for simulating lidar signals to solve the problem of significant discrepancies between simulation results and actual observation data.

[0005] In a first aspect, the present invention provides a method for simulating lidar signals, comprising: acquiring the hardware parameters of the lidar and the optical parameters of the target ocean water body; establishing an elastic scattering analytical model corresponding to the target ocean water body, wherein the elastic scattering analytical model is used to characterize the return probability of photon packets emitted by the lidar after elastic scattering; establishing an inelastic scattering analytical model corresponding to the target ocean water body, wherein the inelastic scattering analytical model is used to characterize the return probability of photon packets after inelastic scattering; establishing a polarization scattering analytical model corresponding to the target ocean water body, wherein the polarization scattering analytical model is used to characterize the changes in polarization state and transmission direction of photon packets during transmission and scattering; and, under the conditions of the hardware and optical parameters, analyzing the elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model based on a semi-analytical Monte Carlo simulation method to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water body.

[0006] This embodiment unifies elastic and inelastic scattering processes within the same simulation framework, elevating the semi-analytical simulation technology of polarization lidar signals from a single perspective of elastic scattering to a multi-perspective combining inelastic and elastic scattering, as well as scattering polarization states. This enables the simulation results to fully cover all channels of a multi-channel marine lidar, achieving accurate and efficient simulation of novel marine lidar signals in complex aquatic environments. The simulation results are closer to actual observation data, and complete simulation data support is provided for the development and verification of multi-parameter joint inversion algorithms.

[0007] In one optional implementation, the inelastic scattering analytical model includes a water molecule scattering analytical model, a fluorescence scattering analytical model, and a Raman scattering analytical model. The inelastic scattering analytical model corresponding to the target ocean water body is established, including: establishing a water molecule scattering analytical model based on the scattering coefficient and scattering phase function of Brillouin scattering; establishing a fluorescence scattering analytical model based on the scattering coefficient and scattering phase function of chlorophyll fluorescence scattering; and establishing a Raman scattering analytical model based on the scattering coefficient and scattering phase function of water body Raman scattering.

[0008] This embodiment uses a semi-analytical Monte Carlo simulation method to simulate the laser echo signals from the particle scattering, water molecule scattering, fluorescence scattering, and Raman scattering channels of a marine lidar system. While retaining the advantage of the Monte Carlo simulation method in its flexible numerical processing of multiple scattering processes, it uses analytical models to explicitly calculate the key physical processes in signal transmission, accurately describing the contribution ratio of different physical processes to the laser echo signal.

[0009] In one alternative implementation, the optical parameters include chlorophyll concentration at different seabed depths. Before establishing an analytical model of fluorescence scattering based on the scattering coefficient and scattering phase function of chlorophyll fluorescence scattering, the method includes: determining the scattering coefficient of chlorophyll fluorescence scattering based on the chlorophyll concentration and absorption spectrum of chlorophyll at different seabed depths.

[0010] In one optional implementation, the elastic scattering analytical model includes a particle scattering analytical model. Establishing an elastic scattering analytical model corresponding to the target ocean water body includes: establishing a particle scattering analytical model based on the scattering coefficient and scattering phase function of Mie scattering.

[0011] In one optional implementation, a polarization scattering analytical model corresponding to the target ocean water body is established, including: determining the Mueller matrix based on the amplitude of Mie scattering, wherein the Mueller matrix is ​​used to characterize the change law of photon polarization state during scattering; determining the polarization scattering phase function based on the Mueller matrix and the initial Stokes vector, and using the polarization scattering phase function as the polarization scattering analytical model, wherein the initial Stokes vector is used to represent the polarization state of the photon packet before the scattering process.

[0012] In one optional implementation, the hardware parameters include the field of view of the detector in the lidar, and the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water include the expected contribution values ​​of the echo photons generated by elastic scattering reaching the detector and the expected contribution values ​​of the echo photons generated by inelastic scattering reaching the detector. Under the conditions of hardware and optical parameters, based on the semi-analytical Monte Carlo simulation method, the analytical models of elastic scattering, inelastic scattering, and polarization scattering are analyzed, including: simulating the lidar emitting multiple photon packets in the target ocean water under the conditions of hardware and optical parameters; tracking each... The simulation determines whether the photon packet's motion direction falls within the field of view. If the photon packet falls within the field of view, the elastic scattering analytical model and the polarization scattering analytical model are called to determine the return probability of the photon packet after elastic scattering, and the inelastic scattering analytical model is called to determine the return probability of the photon packet after inelastic scattering. Based on the return probabilities and return times of multiple photon packets after elastic scattering, the expected contribution value of the echo photons generated by elastic scattering reaching the detector is obtained. Based on the return probabilities and return times of multiple photon packets after inelastic scattering, the expected contribution value of the echo photons generated by inelastic scattering reaching the detector is obtained.

[0013] Secondly, the present invention provides a simulation device for lidar signals, comprising: an acquisition module for acquiring the hardware parameters of the lidar and the optical parameters of the target ocean water; an elastic model establishment module for establishing an elastic scattering analytical model corresponding to the target ocean water, wherein the elastic scattering analytical model is used to characterize the return probability of photon packets emitted by the lidar after elastic scattering; an inelastic model establishment module for establishing an inelastic scattering analytical model corresponding to the target ocean water, wherein the inelastic scattering analytical model is used to characterize the return probability of photon packets after inelastic scattering; a polarization model establishment module for establishing a polarization scattering analytical model corresponding to the target ocean water, wherein the polarization scattering analytical model is used to characterize the changes in polarization state and transmission direction of photon packets during transmission and scattering; and a simulation module for, under the conditions of hardware and optical parameters, analyzing the elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model based on a semi-analytical Monte Carlo simulation method to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water.

[0014] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the laser radar signal simulation method of the first aspect or any corresponding embodiment described above.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for simulating lidar signals according to the first aspect or any corresponding embodiment thereof.

[0016] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute a method for simulating lidar signals according to the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for simulating lidar signals according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating another method for simulating lidar signals according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the simulated laser echo signals from the particle scattering, water molecule scattering, fluorescence scattering, and Raman scattering channels of a high-spectral-resolution lidar (HSRL). Figure 5 A schematic diagram of the simulation results of the total polarization signal, parallel component, and perpendicular component of parallel linearly polarized light emitted by a high spectral resolution lidar (HSRL) after scattering. Figure 6 This is a schematic flowchart illustrating a method for simulating lidar signals according to an embodiment of the present invention. Figure 7 This is a structural block diagram of a laser radar signal simulation device according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0022] For example, application 101 is software (such as SuperMC) capable of performing semi-analytical Monte Carlo simulation methods. Figure 1 In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as simulation pages, settings pages, query pages, etc.

[0023] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a terminal device, fixed terminal, or portable terminal, including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, and computing devices in cloud environments.

[0024] It should be noted that, Figure 1This is merely an example of an application scenario and does not limit the scope of protection of this invention. The embodiments described below primarily pertain to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or by application 101 in collaboration with its server (e.g., server 120).

[0025] LiDAR technology has wide applications in marine exploration and remote sensing, including water quality monitoring, marine life detection, and benthic environment surveying. By emitting laser light and receiving its reflected signals, lidar can efficiently acquire information about the marine environment over a large area.

[0026] Traditional lidar primarily relies on elastic scattering mechanisms, mainly Mie scattering, to acquire information about the marine environment. However, the complexity of the marine environment, such as the non-uniformity of water bodies and the coexistence of multiple scattering mechanisms, poses numerous challenges to the practical application of traditional methods.

[0027] There are three main methods for simulating lidar signals: analytical methods, numerical simulation methods, and semi-analytical simulation methods. Analytical methods typically rely on simplified radiative transfer physics models, offering fast computation but limited accuracy, and are difficult to accurately simulate multiple scattering processes in complex marine environments with significant vertical stratification. Numerical simulation methods, such as the Monte Carlo method, track the random scattering process of a large number of photons in the marine water medium based on probabilistic statistical principles and solve the radiative transfer equations under arbitrary detection geometries. They offer high accuracy but are computationally intensive and inefficient.

[0028] Among these methods, analytical methods based on simplified radiative transfer physical models typically assume uniform optical properties of water and neglect polarization state changes, resulting in limited ability to describe multiple scattering effects and complex air-sea interfaces, making it difficult to meet the requirements of high-precision ocean profiling. Monte Carlo-based numerical simulation methods are computationally expensive, facing an inherent contradiction between high computational cost and computational accuracy, and also lack the ability to jointly model the dynamic changes of complex air-sea interfaces and the stratified structure of water bodies.

[0029] The semi-analytical simulation method combines the advantages of analytical and numerical simulation methods. It abandons the traditional numerical simulation approach of directly statistically analyzing photon events, instead analytically superimposing the radiative contributions of photons and then deterministically solving the light field distribution by combining boundary conditions and the radiative transfer equation. Simultaneously, it employs a targeted sampling strategy, sampling only the effective photon paths that can reach the detector, avoiding repeated tracking of a large number of invalid photons. Related technologies propose a full-link simulation method for spaceborne atmospheric and oceanic profiling lidar echo signals. It establishes optical scattering models for multiple media layers, including the atmosphere, air-sea interface, and seawater, and imports these models into a semi-analytical Monte Carlo simulation model to achieve efficient simulation of ocean lidar signals.

[0030] However, most existing semi-analytical simulation methods focus on simulating elastic scattering processes. When dealing with complex marine environments, they often fail to accurately reflect the actual situation, resulting in significant differences between simulation results and actual observation data.

[0031] In view of this, the present invention provides a method, device, electronic device, medium and product for simulating lidar signals. When performing semi-analytical Monte Carlo simulation of the transmission process of photon packets emitted by lidar in a set marine water environment, it not only considers the elastic scattering of photon packets, but also the inelastic scattering and polarization effects of photon packets, which can realize accurate and efficient simulation of novel marine lidar signals in complex water environments.

[0032] The method for simulating lidar signals provided by the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than that shown here.

[0033] This embodiment provides a method for simulating lidar signals, which can be used in the aforementioned terminal devices, such as mobile phones and tablet computers. Figure 2 This is a flowchart illustrating a method for simulating lidar signals according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the hardware parameters of the lidar and the optical parameters of the target ocean water.

[0034] Specifically, a lidar system includes a transmitter and a detector. The transmitter emits a laser beam, which consists of multiple photon packets. The detector receives the laser beam emitted by the transmitter. The hardware parameters of a lidar system include the laser wavelength, detector height, detector field of view, and the aperture area of ​​the receiving telescope. The detector height refers to the height of the detector above the water surface (sea surface), and the detector field of view is the spatial angular range within which the receiving telescope in the detector can effectively collect echo photons.

[0035] The target ocean water body refers to the ocean water environment to be simulated. The optical parameters of the target ocean water body may include seabed depth, seabed albedo, and chlorophyll concentration at different seabed depths.

[0036] For example, the terminal device can acquire the hardware parameters of the lidar and the optical parameters of the target ocean water body based on user input. In one example, the laser wavelength... The detector wavelength can be 532 nm, the detector height H can be 150 m, the detector field of view (FOV) can be 0.1 rad, and the receiving telescope aperture area A can be 0.283 m². 2 The seabed depth d can be set to 100m, and the seabed albedo ρ b It can be set to 0.02, with the chlorophyll concentration remaining the same at different sea depths; the chlorophyll concentration can be set to 0.1 mg / m³. 3 .

[0037] It should be noted that this invention establishes a coordinate system with the plane formed by the horizontal X direction and the vertical Y direction as the sea level and the seabed depth as the Z-axis. The subsequent analytical model is established under this coordinate system.

[0038] Step S202: Establish an analytical model of elastic scattering corresponding to the target ocean water body.

[0039] The elastic scattering analytical model is used to characterize the return probability of photon packets emitted by the lidar after elastic scattering. The return probability refers to the relative energy value (signal strength) of the photon packet at the current position reaching the detector after elastic scattering. The elastic scattering analytical model established in this invention can be the same as the elastic scattering analytical model established in related technologies.

[0040] The target ocean water contains suspended particles. In particle scattering events, the main scattering signal is caused by Mie scattering, which is elastic scattering. The elastic scattering analytical model of the present invention can be a particle scattering model. The particle scattering analytical model can be established based on the scattering coefficient and scattering phase function of Mie scattering to obtain the photon return probability of elastic scattering events.

[0041] Step S203: Establish an inelastic scattering analytical model corresponding to the target ocean water body.

[0042] Among them, the inelastic scattering analytical model is used to characterize the return probability of a photon packet after inelastic scattering. The return probability refers to the relative energy value (signal strength) of the photon packet at the current position after inelastic scattering and reaching the detector.

[0043] The target ocean water body also includes substances such as water molecules, sediment, plankton, and bubbles. Inelastic scattering analytical models are used to characterize the return probability of photon packets after being scattered by these substances. Inelastic scattering analytical models can include water molecule scattering analytical models, fluorescence scattering analytical models, and Raman scattering analytical models, among others.

[0044] Specifically, with the development of lidar technology and the increasing demand for its applications, researchers have gradually realized the importance of inelastic scattering processes such as Raman scattering and fluorescence scattering, as well as polarization effects, in ocean detection. Inelastic scattering can provide information about dissolved substances and suspended particles in water, while polarization effects can reveal changes in the directionality of light as it propagates through water and information about particle morphology.

[0045] Step S204: Establish the polarization scattering analytical model corresponding to the target ocean water body.

[0046] The polarization scattering analytical model is used to characterize the changes in the polarization state and transmission direction of photon packets during transmission and scattering. If a photon packet undergoes scattering (elastic or inelastic scattering) during transmission, both its polarization state and transmission direction will change.

[0047] Step S205: Under the conditions of hardware and optical parameters, based on the semi-analytical Monte Carlo simulation method, analyze the elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water body.

[0048] Specifically, the created analytical models for elastic scattering, inelastic scattering, and polarization scattering are coupled to the echo photon expectation calculation module of the semi-analytical Monte Carlo simulation application. The semi-analytical Monte Carlo simulation application is launched, using hardware and optical parameters as boundary conditions. Based on photon tracking algorithms and polarization state tracking mechanisms, a semi-analytical Monte Carlo simulation is performed on the transmission process of photon packets emitted by the lidar in the target ocean water body. During the simulation, the analytical models for elastic scattering, inelastic scattering, and polarization scattering are called to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water body. The simulation results include the expected contribution value (contribution ratio) of the echo photons reaching the detector generated by each scattering event (elastic scattering, inelastic scattering).

[0049] The lidar signal simulation method provided in this embodiment, while acquiring the hardware parameters of the lidar and the optical parameters of the target ocean water, establishes an elastic scattering analytical model, an inelastic scattering analytical model, and a polarization scattering analytical model. Then, the elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model are coupled to a semi-analytical Monte Carlo simulation application. Under the boundary conditions of the hardware parameters and optical parameters, the transmission process of the photon packet emitted by the lidar in the target ocean water is simulated, and the simulation results of the echo photons generated by the scattering of the photon packet are obtained.

[0050] This embodiment unifies elastic and inelastic scattering processes within the same simulation framework, elevating the semi-analytical simulation technology of polarization lidar signals from a single perspective of elastic scattering to a multi-perspective combining inelastic and elastic scattering, as well as scattering polarization states. This enables the simulation results to fully cover all channels of a multi-channel marine lidar, achieving accurate and efficient simulation of novel marine lidar signals in complex aquatic environments. The simulation results are closer to actual observation data, and complete simulation data support is provided for the development and verification of multi-parameter joint inversion algorithms.

[0051] This embodiment also provides another method for simulating lidar signals, which can be used in the aforementioned terminal devices, such as mobile phones and tablet computers. Figure 3 This is a flowchart illustrating another method for simulating lidar signals according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain the hardware parameters of the lidar and the optical parameters of the target ocean water.

[0052] Please see details Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0053] Step S302: Based on the scattering coefficient and scattering phase function of Mie scattering, establish an analytical model for particle scattering.

[0054] Among them, the elastic scattering analytical model includes the particle scattering analytical model. Specifically, the elastic scattering analytical model corresponding to the target ocean water body can be established by: establishing the particle scattering analytical model based on the scattering coefficient and scattering phase function of Mie scattering.

[0055] The established analytical model for particle scattering can be represented by equation (1):

[0056] In formula (1), The wavelength of the laser. This represents the probability of a photon packet returning after being scattered by a particle. The scattering coefficient of Mie scattering is given by . Let be the scattering phase function of Mie scattering. For the first Each wavelength is The beam attenuation coefficient corresponding to the photon For the first The distance between a photon's current location along the receiving path and the sea surface, i.e., the photon collision depth. Atmospheric transmittance, Permeability at the air-sea interface This represents the number of strata in a non-homogeneous water body. The solid-state receiving angle of the receiving aperture (the receiving aperture of the receiving telescope) as seen at the photon collision depth. It can be calculated using formula (2):

[0057] In formula (2), To receive the telescope aperture area, H is the refractive index, and H is the detector height. The depth is the photon collision depth.

[0058] The laser wavelength was set to 532 nm, the detector height to 150 m, and the receiving telescope aperture area to 0.283 m². 2 In this case, the above formula (1) is specifically as follows:

[0059] The above formula (2) is as follows:

[0060] Step S303: Establish an inelastic scattering analytical model corresponding to the target ocean water body.

[0061] Specifically, the inelastic scattering analytical model includes the water molecule scattering analytical model, the fluorescence scattering analytical model, and the Raman scattering analytical model. Step S303 above may include: Step S3031: Based on the scattering coefficient and scattering phase function of Brillouin scattering, establish an analytical model for water molecule scattering.

[0062] Specifically, in water molecule scattering events, the main scattering signal is caused by Brillouin scattering, which is inelastic scattering. The analytical model of water molecule scattering based on the scattering coefficient and scattering phase function of Brillouin scattering can be shown in Equation (3):

[0063] In formula (3), This represents the probability of a photon packet returning after being scattered by water molecules. The scattering coefficient of Brillouin scattering. Let be the scattering phase function of Brillouin scattering. When the laser wavelength is set to 532 nm, the above formula (3) is specifically:

[0064] Step S3032: Based on the scattering coefficient and scattering phase function of chlorophyll fluorescence scattering, establish an analytical model for fluorescence scattering.

[0065] Specifically, the scattering coefficient and scattering phase function are first designed based on the chlorophyll fluorescence scattering mechanism. In most marine optical applications, especially when dealing with time-independent radiative transfer equations, fluorescence scattering can be considered as an inelastic scattering process, mathematically similar to Raman scattering. The Raman scattering function... It can be shown in formula (4):

[0066] In formula (4), It is the fluorescence scattering coefficient, with units of m. -1 , Indicates the wavelength of the scattered light. It is the fluorescence wavelength redistribution function, with units of nm. -1 , It is the fluorescence scattering phase function, with units of sr. -1 .

[0067] Before establishing the fluorescence scattering analytical model, the method also includes determining the scattering coefficient of chlorophyll fluorescence scattering based on the chlorophyll concentration and absorption spectrum of chlorophyll at different seabed depths.

[0068] Specifically, when chlorophyll fluorescence scattering is considered as inelastic scattering, the fluorescence scattering coefficient... The fluorescence scattering coefficient can be obtained using the absorption coefficient of chlorophyll, that is, it can be calculated using the following formula (5):

[0069] In formula (5), The photon collision depth is chlorophyll concentration at position, This refers to the characteristic absorption spectrum associated with chlorophyll.

[0070] Fluorescence scattering spectra are independent of excitation wavelength. Within the spectral range of 370 nm to 690 nm, the photons absorbed by the material will produce the same fluorescence output, and the fluorescence wavelength redistribution function... It can be as shown in formula (6):

[0071] in, Indicates the quantum efficiency of chlorophyll fluorescence. Indicates whether the incident light wavelength band can excite fluorescence. Let represent the fluorescence emission function. Subsequently, based on the above formulas (4) to (6), an analytical model of fluorescence scattering is established. The analytical model of fluorescence scattering established based on the chlorophyll fluorescence scattering mechanism, using the scattering coefficient and scattering phase function, can be shown in formula (7):

[0072] In formula (7), This represents the probability of a photon packet returning after being scattered by chlorophyll fluorescence.

[0073] The fluorescence emission spectrum is independent of the excitation wavelength, meaning that the absorption of photons in the 370nm to 690nm wavelength range will produce the same fluorescence output, thus the quantum efficiency of chlorophyll fluorescence is high. Take 0.07, Let's take 1. For any incident laser in the 370nm to 690nm band, the excitation peak of the fluorescence signal is at 685nm, with a wavelength range of approximately 660nm to 750nm. When the receiving bandwidth is set to 20nm, the intensity of the received fluorescence signal is approximately 3.5 times that of a 5nm bandwidth; when the receiving bandwidth exceeds 30nm, the intensity of the received fluorescence signal does not increase significantly and may even introduce additional background stray light. Therefore, a receiving bandwidth of 20nm to 30nm is more suitable for analytical fluorescence scattering models. Here, the receiving bandwidth refers to the range of spectral wavelengths that the receiving telescope can receive.

[0074] Meanwhile, since the fluorescence emission process is isotropic, the fluorescence scattering phase function Set to 1 / 4π, fluorescence emission function It can be approximated as the superposition of two Gaussian distributions, as shown in formula (8):

[0075] In formula (8), T The weights represent a Gaussian distribution. , These are the standard deviations of two Gaussian distributions. Combining these parameters, the probability of a photon returning after fluorescence scattering is... Specifically:

[0076] Step S3033: Based on the scattering coefficient and scattering phase function of Raman scattering in water, establish an analytical model of Raman scattering.

[0077] Specifically, firstly, based on the Raman scattering mechanism of water, the scattering coefficient and scattering phase function are designed, and the Raman scattering function is... It can be shown in formula (9):

[0078] in, The Raman scattering coefficient represents the value of the scattered light when the wavelength is... Greater than the excitation wavelength At that time, the scattered irradiance per unit wavelength, It is the Raman wavelength distribution function, which is constructed by its corresponding wavenumber distribution function. ,accomplish The best representation of .

[0079] Raman scattering phase function It can be shown in formula (10):

[0080] In formula (10), For Raman scattering angle, It is the depolarization factor, which varies with wavenumber shift.

[0081] Subsequently, based on the above formulas (9) and (10), the analytical model for Raman scattering can be established as shown in formula (11):

[0082] in, This represents the probability of a photon packet returning after being Raman scattered by the water body.

[0083] In this embodiment, when the incident laser wavelength is 532 nm, the center wavelength of the Raman scattering spectrum distribution function is approximately 650 nm, with a wavelength range of approximately 640 nm to 660 nm. Since the full width at half maximum (FWHM) of the characteristic peaks in the Raman scattering spectrum is narrower than that in the fluorescence spectrum, setting the receiving bandwidth of the lidar detector to 20 nm allows it to receive more than 87% of the energy of the Raman scattering spectrum signal. Therefore, a receiving bandwidth of 20 nm is very suitable for the analytical model of Raman scattering. At this point, the Raman scattering coefficient... Specifically:

[0084] Raman wavelength distribution function The wavenumber distribution function has been extended based on wavenumber migration for aquatic media. It can be represented as a set of four Gaussian functions, as shown in formula (12):

[0085] In formula (12), Representing the j The central wavenumber of a Gaussian function, in cm. -1 , Representing the j Full width at half maximum (FWHM) of a Gaussian function, in cm. -1 Each Gaussian function is composed of a dimensionless factor. The weighting factor takes into account the relative contribution of each Gaussian function to the overall distribution.

[0086] For pure water at 25°C, , , Specific values ​​are detailed in Table 1. In aqueous media, the wavenumber shift of Raman scattering is mainly concentrated around 3400 cm⁻¹. -1 Nearby.

[0087] Table 1 Values ​​of Raman wavelength distribution function coefficients for pure water

[0088] For Raman scattering phase function Depolarization factor It is a parameter that depends on the wavenumber offset; when the wavenumber offset is... hour, The value is approximately 0.18, Raman scattering phase function. Specifically:

[0089] Combining the above parameters, the contribution of Raman scattering events to the received signal of lidar can be obtained through formula (11).

[0090] Step S304: Establish an analytical model of polarization scattering corresponding to the target ocean water body.

[0091] For example, step S304 above may include: Step S3041: Determine the Mueller matrix based on the amplitude of Mie scattering.

[0092] The Mueller matrix is ​​used to characterize the change in photon polarization state during scattering.

[0093] Step S3042: Based on the Mueller matrix and the initial Stokes vector, determine the polarization scattering phase function and use the polarization scattering phase function as the analytical model for polarization scattering.

[0094] The initial Stokes vector is used to represent the polarization state of the photon packet before it undergoes scattering.

[0095] Specifically, the Mueller matrix and rotation matrix are used to calculate the change in the polarization state of the photon packet during the scattering process. The Mueller matrix describes the change law of the polarization state of the light wave during the scattering process, and the rotation matrix is ​​used to handle the coordinate transformation when the propagation direction of the photon packet changes.

[0096] The polarization of a photon can be represented by a Stokes vector. When a photon undergoes scattering, its propagation direction changes. This can be reflected by updating the Stokes vector, which indicates the new propagation direction of the scattered light relative to a reference meridional plane. When considering the scattering process of polarized light, the scattering angle of the photon packet after scattering is determined using a scattering phase function associated with the Stokes vector. .

[0097] After determining the Mueller matrix and the initial Stokes vector, the scattering phase function related to the Stokes vector can be obtained as shown in equation (13). And use formula (13) as the analytical model for polarization scattering.

[0098]

[0099] in, , and These represent the three initial components of the Stokes vector prior to the scattering event. and Taken from Mueller matrix , Indicates the total scattering intensity. This represents the conversion value of the degree of linear polarization (or the difference between the parallel polarization component and the perpendicular polarization component).

[0100] Mueller matrix elements in and The amplitude is determined based on Mie scattering, as shown in equation (14):

[0101] in, and The complex amplitude component of the Mie scattering. and Complex conjugation is used to eliminate phase terms and calculate observable intensity components. and Complex conjugate.

[0102] After constructing the scattering phase function under polarization conditions, the simulation further employs the rejection sampling method to accurately analyze the random azimuth angles during the scattering process. and scattering angle The calculation formula (15) is as follows:

[0103] In the formula, and All are random numbers between 0 and 1. Under polarization conditions, the azimuth angle and scattering angle Randomly generated from the ranges (0, 2π) and (0, π), the corresponding random scattering phase function The calculation formula (16) is:

[0104] In the formula, It is a random number between 0 and 1. If Greater than Current azimuth and scattering angle The scattering angle, accepted as a parameter of the scattering event, is the scattering angle of a linearly polarized photon packet after Mie scattering. Under the premise that the photon packet is linearly polarized light (considering changes in the polarization state of the photon packet), the propagation process of the photon packet after Mie scattering was simulated. If... Less than or equal to Then the random process is repeated until the requirement is met.

[0105] This embodiment is based on the scattering phase function. Formula 13 simulates the scattering signal of linearly polarized light and further separates the parallel and perpendicular components of the signal.

[0106] Step S305: Under the conditions of hardware and optical parameters, based on the semi-analytical Monte Carlo simulation method, analyze the elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water body.

[0107] Specifically, the simulation results of echo photons generated by the scattering of photon packets in the target ocean water body include the expected contribution value of echo photons generated by elastic scattering reaching the detector and the expected contribution value of echo photons generated by inelastic scattering reaching the detector. The above step S305 may include: Step a1: Under the conditions of hardware and optical parameters, simulate lidar to emit multiple photon packets in the target ocean water.

[0108] In one example, the number of emitted photon packets is set to 1 × 10. 7 indivual.

[0109] Specifically, in simulation applications, a certain number of photon packets are emitted by a simulated lidar, and the emission direction and photon step size of each photon packet are determined by random sampling.

[0110] Step a2: Track each photon packet and use simulation to determine whether the motion direction of the photon packet falls within the field of view.

[0111] Specifically, the process of absorption and scattering of each photon packet in the target ocean water is simulated and tracked. The weight and position of the photon packet after each scattering are recorded. The weight refers to the relative amount of energy remaining in the photon packet after a single scattering during transmission. When the weight is less than 1%, the photon packet has only a 1% probability of proceeding to the next transmission step (secondary scattering). Based on the position of the photon packet after scattering, it can be determined whether the photon packet's direction of motion falls within the field of view.

[0112] Step a3: If the photon packet falls within the field of view, the elastic scattering analytical model and the polarization scattering analytical model are invoked to determine the return probability of the photon packet after elastic scattering, and the inelastic scattering analytical model is invoked to determine the return probability of the photon packet after inelastic scattering.

[0113] Step a4: Based on the return probability and return time of multiple photon packets after elastic scattering, obtain the expected contribution value of the echo photons generated by elastic scattering reaching the detector.

[0114] Step a5: Based on the return probability and return time of multiple photon packets after inelastic scattering, obtain the expected contribution value of the echo photons generated by inelastic scattering reaching the detector.

[0115] Specifically, when a photon packet moves into the receiver's field of view after any number of scattering processes and its spatial position meets the receiver's set range, it is determined that the photon packet has been captured by the receiver, and the photon return probability is calculated based on the established analytical models (elastic scattering analytical model, polarization scattering analytical model, and inelastic scattering analytical model).

[0116] Photon packets at different ocean depths reach the detector at different times after scattering. The greater the return time, the deeper the seabed. By statistically analyzing the return probability of photon packets received at various times under the same scattering mechanism (Mie scattering, water molecule scattering, fluorescence scattering, or Raman scattering), and summing the return probabilities at the same time point, we can obtain the evolution law of laser echo signal intensity with ocean depth under this scattering mechanism. The ratio of the echo signal intensity corresponding to the ocean depth to the original signal intensity is the expected contribution value of the echo photons generated under this scattering mechanism to the detector.

[0117] Under the conditions of hardware and optical parameters, based on the semi-analytical Monte Carlo simulation method, the analytical elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model refer to the semi-analytical Monte Carlo simulation method performed on the transmission process of photon packets emitted by lidar in the target ocean water body based on photon tracking algorithms and polarization state tracking mechanisms. Specifically, it includes: 1) determining 1×10 through random sampling. 7The simulation process involves: 1) determining the emission direction and photon step size of each photon packet; 2) simulating and tracking the absorption and scattering process of each photon packet in seawater, recording the weight and position of the photon packets; 3) determining that a photon packet is captured by the receiver when its motion direction falls within the receiver's field of view after any number of scattering processes and its spatial position satisfies the receiver's geometric position. The photon return probability is calculated based on four analytical models: particle scattering, water molecule scattering, fluorescence scattering, and Raman scattering. The probability is accumulated according to the arrival time of the photon packets to obtain the received signals (echo signals) under the scattering mechanisms corresponding to the four analytical models. Finally, based on the simulation results, the evolution law of laser scattering signal intensity under different scattering mechanisms with water depth is analyzed, and the signal contribution ratio generated by different scattering mechanisms is evaluated. The echo signal intensity of the laser at different ocean depths can be the sum of the signal intensity corresponding to elastic scattering and the signal intensity corresponding to inelastic scattering at the corresponding depth.

[0118] It should be understood that, in order to simplify the calculation, this invention simulates a photon packet undergoing a single scattering to determine the expected contribution value of the echo photons generated by each scattering event (Mie scattering, water molecule scattering, fluorescence scattering, and Raman scattering) reaching the detector. In practical applications, a photon packet may undergo multiple scatterings. The weight of the photon packet after each scattering is recorded to determine whether the photon packet will undergo the next scattering. When the weight is less than a preset value, it indicates that the photon packet will not undergo the next scattering.

[0119] Simulation results of laser echo signals from particle scattering, water molecule scattering, fluorescence scattering, and Raman scattering channels of a High Spectral Resolution Lidar (HSRL) are as follows: Figure 4 As shown. Figure 4 The vertical axis represents ocean depth (m), and the horizontal axis represents the normalized signal strength of the received signal. The results show that the HSRL particle scattering channel has the highest signal strength and the deepest maximum detection depth among the four channels; the water molecule scattering channel ranks second in signal strength, but is one to two orders of magnitude weaker than the particle scattering channel; while the fluorescence and Raman scattering channels have relatively weaker signal strengths, two to five orders of magnitude weaker than the particle scattering channel, and their maximum detection depths are also significantly reduced.

[0120] The scattering signal of parallel linearly polarized light emitted by the HSRL was simulated, and the parallel and perpendicular components of the scattered signal were further separated. The results are as follows: Figure 5 As shown in the figure, the results indicate that the total polarized scattering signal and the parallel polarized scattering signal generally decrease exponentially with depth; while the perpendicular polarized scattering signal is zero at the sea surface, gradually strengthens with increasing detection depth, and rapidly decreases after the detection depth exceeds 6 m. This suggests that near the sea surface, most photons propagate in their initial polarization state, with a relatively small perpendicular component. As photons penetrate deeper into the water, multiple scattering causes some photons to undergo polarization state transitions, generating a perpendicular component, which leads to a gradual increase in the intensity of the perpendicular component signal. After a certain detection depth, due to the enhanced multiple scattering and absorption of photons, the perpendicular component signal begins to decrease rapidly.

[0121] This embodiment uses a semi-analytical Monte Carlo simulation method to simulate laser echo signals from particle scattering, water molecule scattering, fluorescence scattering, and Raman scattering channels of a marine lidar system. While retaining the flexibility of Monte Carlo simulation in numerically processing multiple scattering processes, it uses analytical models to explicitly calculate key physical processes in signal transmission, accurately describing the contribution ratio of different physical processes to the laser echo signal. This "analytical + numerical" simulation strategy concentrates computational resources on the numerical simulation of non-analytical solvable multiple scattering processes, reducing computational consumption on simulating physical processes with well-defined and analytically expressible mechanisms, thus significantly reducing computation time. Compared to the Monte Carlo simulation method, the semi-analytical Monte Carlo simulation method proposed in this invention shortens the actual operation time by three orders of magnitude. Furthermore, this invention further introduces inelastic scattering processes such as Raman scattering and fluorescence scattering, as well as polarization state changes, into the simulation of marine lidar laser echo signals, thereby obtaining more comprehensive and refined laser echo signal simulation results, improving the physical realism and information richness of the simulation results.

[0122] Taking the elastic scattering analytical model, including the particle scattering analytical model, and the inelastic scattering analytical model, including the water molecule scattering analytical model, the fluorescence scattering analytical model, and the Raman scattering analytical model, as examples, the simulation method of polarization lidar signal combining inelastic scattering provided by the present invention will be described in conjunction with the accompanying drawings.

[0123] This invention combines photon tracking algorithms and polarization state tracking mechanisms, and introduces simulation models for inelastic scattering and polarization scattering, enabling accurate and efficient simulation of novel marine lidar signals in complex aquatic environments, such as... Figure 6 As shown, the simulation method for lidar signals includes eight steps, which are described in detail below: S1. Determine the hardware system parameters of the marine lidar: including laser wavelength, detector height, field of view, and receiving telescope aperture.

[0124] S2. Determine the optical parameters of the ocean water: including seabed depth, seabed albedo, and chlorophyll concentration.

[0125] S3. Establish analytical models for particle scattering and water molecule scattering: In particle scattering events, the main scattering signal is caused by Mie scattering, which is elastic scattering. Based on the scattering coefficient and scattering phase function of Mie scattering, an analytical model for particle scattering is established, and the formula for calculating the photon return probability of particle scattering events is obtained. In water molecule scattering events, the main scattering signal is caused by Brillouin scattering, which is inelastic scattering. Based on the scattering coefficient and scattering phase function of Brillouin scattering, an analytical model for water molecule scattering is established, and the formula for calculating the photon return probability of water molecule scattering events is obtained.

[0126] S4. Establish a fluorescence scattering analytical model: Based on the chlorophyll fluorescence scattering mechanism, design the scattering coefficient and scattering phase function calculation formulas, establish a fluorescence scattering analytical model, and obtain the photon return probability calculation formula for fluorescence scattering events.

[0127] S5. Establish an analytical model for Raman scattering: Based on the Raman scattering mechanism of water, design the calculation formulas for the scattering coefficient and scattering phase function, establish an analytical model for Raman scattering, and obtain the calculation formula for the photon return probability of Raman scattering events.

[0128] S6. Establish an analytical model for polarization scattering: Calculate the change in the polarization state of light waves using the Mueller matrix and rotation matrix. The Mueller matrix describes in detail the change in the polarization state of light waves during scattering, while the rotation matrix is ​​specifically used to handle coordinate transformations when the direction of light wave propagation changes.

[0129] S7. Determine the semi-analytical Monte Carlo simulation parameters, including the number of emitted photons.

[0130] S8. Based on the photon tracking algorithm and polarization state tracking mechanism, a semi-analytical Monte Carlo simulation is performed on the transmission process of photon packets emitted by the lidar in a set marine water environment. During the simulation, the above-mentioned analytical model is called to record the expected contribution value of the echo photons generated by each scattering event to the detector.

[0131] Further, in step S8, a semi-analytical Monte Carlo simulation is performed on the transmission process of photon packets emitted by the lidar in a set marine environment, including: 1) simulating the emission of a certain number of photon packets, determining the emission direction and photon step size of each photon packet through random sampling; 2) simulating the absorption and scattering process of each photon packet in seawater, recording the weight and position of the photon packet; 3) when a photon packet's motion direction falls into the receiver's field of view after any number of scattering processes, and its spatial position satisfies the receiving geometry, it is determined that the photon packet has been captured by the receiver. The photon return probability is calculated based on the analytical model established in S3~S6, and the received signal at the corresponding time is increased according to its arrival time and weight. Finally, based on the simulation results, the evolution law of the laser backscattering signal intensity under different scattering mechanisms with water depth is analyzed, and the signal contribution ratio generated by different scattering mechanisms is evaluated.

[0132] Compared to analytical methods based on simplified radiative transfer physical models, this invention overcomes the limitations of simplified geometry and homogeneity assumptions, enabling flexible construction of photon transmission paths in non-uniform media and supporting arbitrary transmit / receive geometric configurations. This allows it to adapt to complex scenarios in actual detection, improving the matching degree between simulation results and measured data. Furthermore, this invention solves the problem that simplified radiative transfer physical models cannot accurately describe the changes in polarization state with scattering events in polarization lidar, and based on vector transfer equations, it fully tracks the evolution of photon polarization states.

[0133] Compared to Monte Carlo-based numerical simulation methods, this invention achieves explicit modeling of the process. By establishing analytical models for different scattering effects, it applies these models to analyze photon energy, direction of motion, and polarization state for each scattering process, accurately distinguishing the contribution ratio of various scattering effects to the signal. Furthermore, this invention only calculates scattering events falling within the field of view of the lidar receiver, significantly improving computational efficiency and convergence speed. Moreover, compared to Monte Carlo numerical simulation methods that use weighting to update the polarization state for each scattering event, this invention utilizes the Mueller matrix and rotation matrix to calculate the change in the polarization state of the light wave, achieving precise calculation of the polarization state.

[0134] Compared with the semi-analytical Monte Carlo method that only considers elastic scattering, this invention unifies the elastic and inelastic scattering processes in the same simulation framework, elevating the semi-analytical simulation technology of polarization lidar signals from a single perspective of elastic scattering to a multi-perspective combining inelastic and elastic scattering. This enables the simulation results to fully cover all channels of a multi-channel marine lidar and provides complete simulation data support for the development and verification of multi-parameter joint inversion algorithms.

[0135] In summary, this invention achieves accurate tracking of the photon scattering process by coupling analytical models of elastic and inelastic scattering into the Monte Carlo simulation method, and explicitly distinguishes the contributions of various scattering effects to the signal, achieving an efficient balance between analytical and numerical simulation strategies. This invention has significant advantages in physical rigor, multi-channel simulation capabilities, and computational efficiency, providing a powerful tool for signal simulation of novel multi-channel polarization lidar in complex aquatic environments.

[0136] This embodiment also provides a device for simulating lidar signals, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0137] This embodiment provides a device for simulating lidar signals, such as... Figure 7 As shown, it includes: The acquisition module 701 is used to acquire the hardware parameters of the lidar and the optical parameters of the target ocean water body; The elastic model building module 702 is used to build an elastic scattering analytical model corresponding to the target ocean water body. The elastic scattering analytical model is used to characterize the return probability of photon packets emitted by the lidar after elastic scattering. The inelastic model building module 703 is used to build an inelastic scattering analytical model corresponding to the target ocean water body. The inelastic scattering analytical model is used to characterize the return probability of photon packets after inelastic scattering. The polarization model establishment module 704 is used to establish a polarization scattering analytical model corresponding to the target ocean water body. The polarization scattering analytical model is used to characterize the changes in polarization state and transmission direction of photon packets during transmission and scattering. Simulation module 705 is used to obtain simulation results of echo photons generated by scattering of photon packets in the target ocean water body under the conditions of hardware parameters and optical parameters, based on the semi-analytical Monte Carlo simulation method, analytical elastic scattering model, analytical inelastic scattering model and analytical polarization scattering model.

[0138] In some optional implementations, the inelastic scattering analytical model includes a water molecule scattering analytical model, a fluorescence scattering analytical model, and a Raman scattering analytical model. The inelastic model building module 703 includes: A water molecule scattering establishment unit is used to establish an analytical model of water molecule scattering based on the scattering coefficient and scattering phase function of Brillouin scattering; A fluorescence scattering establishment unit is used to establish an analytical model of fluorescence scattering based on the scattering coefficient and scattering phase function of chlorophyll fluorescence scattering; Raman scattering establishment unit is used to establish an analytical model of Raman scattering based on the scattering coefficient and scattering phase function of Raman scattering in water.

[0139] In some alternative implementations, the optical parameters include chlorophyll concentration at different seabed depths, and the device includes: The coefficient determination module is used to determine the scattering coefficient of chlorophyll fluorescence scattering based on the chlorophyll concentration and absorption spectrum of chlorophyll at different seabed depths.

[0140] In some optional implementations, the elastic scattering analytical model includes a particle scattering analytical model, and the elastic model building module 702 includes: The particle scattering establishment unit is used to establish an analytical model of particle scattering based on the scattering coefficient and scattering phase function of Mie scattering.

[0141] In some alternative implementations, the polarization model building module 704 includes: The matrix determination unit is used to determine the Mueller matrix based on the amplitude of Mie scattering, where the Mueller matrix is ​​used to characterize the change of photon polarization state during scattering. The polarization establishment unit is used to determine the polarization scattering phase function based on the Mueller matrix and the initial Stokes vector, and uses the polarization scattering phase function as the analytical model of polarization scattering. The initial Stokes vector is used to represent the polarization state of the photon packet before it has undergone the scattering process.

[0142] In some optional implementations, the hardware parameters include the field of view of the detector in the lidar, and the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water include the expected contribution value of the echo photons generated by elastic scattering reaching the detector and the expected contribution value of the echo photons generated by inelastic scattering reaching the detector. The simulation module 705 includes: The transmitting unit is used to simulate a lidar transmitting multiple photon packets in a target ocean water body under the conditions of hardware and optical parameters. The tracking unit is used to track each photon packet and simulate whether the motion direction of the photon packet falls within the field of view. The calling unit is used to call the elastic scattering analytical model and the polarization scattering analytical model if the photon packet falls into the field of view, to determine the return probability of the photon packet after elastic scattering, and to call the inelastic scattering analytical model to determine the return probability of the photon packet after inelastic scattering. The first calculation unit is used to obtain the expected contribution value of the echo photons generated by elastic scattering to the detector based on the return probability and return time of multiple photon packets after elastic scattering. The second calculation unit is used to obtain the expected contribution value of the echo photons generated by inelastic scattering to the detector based on the return probability and return time of multiple photon packets after inelastic scattering.

[0143] The lidar signal simulation device provided in this embodiment of the invention can execute the lidar signal simulation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0144] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0145] The following is a detailed reference. Figure 8 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 801, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 802 or a program loaded from memory 808 into random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device. The processor 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0146] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 8 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0147] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a memory 808, or installed from a ROM 802. When the computer program is executed by the processor 801, it performs the functions defined in the method for simulating lidar signals according to embodiments of the present invention.

[0148] Figure 8 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0149] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the method for simulating lidar signals shown in the above embodiments is implemented.

[0150] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0151] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method of simulating a lidar signal, the method comprising: include: Acquire the hardware parameters of the lidar and the optical parameters of the target ocean water body; An analytical model of elastic scattering corresponding to the target ocean water body is established, wherein the analytical model of elastic scattering is used to characterize the return probability of the photon packet emitted by the lidar after elastic scattering; An inelastic scattering analytical model is established for the target ocean water body, wherein the inelastic scattering analytical model is used to characterize the return probability of the photon packet after inelastic scattering; A polarization scattering analytical model is established for the target ocean water body, wherein the polarization scattering analytical model is used to characterize the changes in polarization state and transmission direction of the photon packet during transmission and scattering. Under the conditions of the hardware parameters and the optical parameters, the semi-analytical Monte Carlo simulation method is used to analyze the elastic scattering analytical model, the inelastic scattering analytical model and the polarization scattering analytical model to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water body.

2. The method of claim 1, wherein, The inelastic scattering analytical model includes a water molecule scattering analytical model, a fluorescence scattering analytical model, and a Raman scattering analytical model. Establishing the inelastic scattering analytical model corresponding to the target ocean water body includes: An analytical model for water molecule scattering is established based on the scattering coefficient and scattering phase function of Brillouin scattering. An analytical model for fluorescence scattering is established based on the scattering coefficient and scattering phase function of chlorophyll fluorescence scattering. An analytical model for Raman scattering is established based on the scattering coefficient and scattering phase function of Raman scattering in water.

3. The method of claim 2, wherein, The optical parameters include chlorophyll concentration at different seabed depths. Before establishing the analytical model of fluorescence scattering based on the scattering coefficient and scattering phase function of chlorophyll fluorescence scattering, the method includes: The scattering coefficient of chlorophyll fluorescence scattering was determined based on the chlorophyll concentration and absorption spectrum at different seabed depths.

4. The method of claim 2, wherein, The elastic scattering analytical model includes a particle scattering analytical model. Establishing the elastic scattering analytical model corresponding to the target ocean water body includes: An analytical model for particle scattering is established based on the scattering coefficient and scattering phase function of Mie scattering.

5. The method of claim 4, wherein, The establishment of the polarization scattering analytical model corresponding to the target ocean water body includes: Based on the amplitude of Mie scattering, the Mueller matrix is ​​determined, wherein the Mueller matrix is ​​used to characterize the change of photon polarization state during scattering; Based on the Mueller matrix and the initial Stokes vector, the polarization scattering phase function is determined, and the polarization scattering phase function is used as the polarization scattering analytical model, wherein the initial Stokes vector is used to represent the polarization state of the photon packet before it has undergone the scattering process.

6. The method according to any one of claims 1 to 5, characterized in that, The hardware parameters include the field of view of the detector in the lidar. The simulation results of the echo photons generated by the scattering of photon packets in the target ocean water include the expected contribution value of the echo photons generated by elastic scattering reaching the detector and the expected contribution value of the echo photons generated by inelastic scattering reaching the detector. Under the conditions of the hardware parameters and the optical parameters, the analytical models of elastic scattering, inelastic scattering, and polarization scattering are analyzed based on the semi-analytical Monte Carlo simulation method, including: Under the conditions of the hardware parameters and the optical parameters, the lidar is simulated to emit multiple photon packets in the target ocean water body; Track each photon packet and use simulation to determine whether the motion direction of the photon packet falls within the field of view. If the photon packet falls within the field of view, the elastic scattering analytical model and the polarization scattering analytical model are invoked to determine the return probability of the photon packet after elastic scattering, and the inelastic scattering analytical model is invoked to determine the return probability of the photon packet after inelastic scattering. Based on the return probability and return time of multiple photon packets after elastic scattering, the expected contribution value of the echo photons generated by elastic scattering to the detector is obtained. Based on the return probability and return time of multiple photon packets after inelastic scattering, the expected contribution value of the echo photons generated by inelastic scattering to the detector is obtained.

7. An apparatus for simulating a lidar signal, the apparatus comprising: The device includes: The acquisition module is used to acquire the hardware parameters of the lidar and the optical parameters of the target ocean water. The elastic model building module is used to build an elastic scattering analytical model corresponding to the target ocean water body, wherein the elastic scattering analytical model is used to characterize the return probability of the photon packet emitted by the lidar after elastic scattering; An inelastic model building module is used to build an inelastic scattering analytical model corresponding to the target ocean water body, wherein the inelastic scattering analytical model is used to characterize the return probability of the photon packet after inelastic scattering; The polarization model establishment module is used to establish a polarization scattering analytical model corresponding to the target ocean water body, wherein the polarization scattering analytical model is used to characterize the changes in polarization state and transmission direction of the photon packet during transmission and scattering. The simulation module is used to analyze the elastic scattering analytical model, the inelastic scattering analytical model, and the polarization scattering analytical model based on the semi-analytical Monte Carlo simulation method under the conditions of the hardware parameters and the optical parameters, so as to obtain the simulation results of the echo photons generated by the scattering of photon packets in the target ocean water body.

8. An electronic device, comprising: include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for simulating the lidar signal according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the simulation method of the lidar signal according to any one of claims 1 to 6.

10. A computer program product, characterised in that, Includes computer instructions for causing a computer to perform a method for simulating the lidar signal according to any one of claims 1 to 6.

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