UWOC light intensity distribution visualization method and system oriented to complex environment

Abstract

The invention belongs to the technical field of underwater optical communication, and discloses a UWOC light intensity distribution visualization method and system oriented to a complex environment. The method comprises the following steps: initializing modeling parameters, and carrying out light source modeling and water body parameter modeling according to an initialization result; carrying out TTHG scattering modeling on the basis of light source modeling and water body parameter modeling results; based on the calculation result of the TTHG scattering modeling, simulating the propagation process of all photons based on a Monte Carlo method, and judging whether the photons fall into the aperture range of a receiver or not at a receiving end; and receiving plane statistics is carried out to obtain a channel impulse response curve, and three-dimensional visual display is carried out. According to the method, the characteristics of the underwater optical communication channel in a complex environment can be reflected more truly, and the method is of great significance to system design and anti-interference optimization.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of underwater wireless optical communication (UWOC), and particularly relates to a complex environment-oriented UWOC light intensity distribution visualization method and system. BACKGROUND

[0002] With the development of underwater unmanned underwater vehicle (UUV), ocean monitoring network and other applications, the demand for underwater high-speed data transmission is increasing. Underwater optical communication has become an important research direction due to its high bandwidth, low delay and other advantages. However, the absorption and scattering effects of water medium will seriously affect the light propagation characteristics, and the light intensity distribution law under different water quality is difficult to accurately predict.

[0003] Most of the existing UWOC modeling adopts a single parameter Henyey-Greenstein (HG) phase function, which cannot fully characterize the difference between forward and backward scattering in seawater, and lacks modeling of the receiving end aperture, field of view (FOV) and multi-dimensional visualization of the light spot distribution. At the same time, the existing underwater optical communication research mainly focuses on hardware implementation and performance optimization, and the influence of environmental factors on channel characteristics is insufficient. In actual application, external background light interference such as sunlight exists in shallow water area, which will have a significant impact on the signal-to-noise ratio, bit error rate and anti-interference performance of the communication system. Most of the existing channel modeling methods only consider a single laser light source, ignoring the existence of external light interference, resulting in deviation between the modeling results and the actual application scene. SUMMARY

[0004] In order to overcome the problems in the related art, the application discloses a complex environment-oriented UWOC light intensity distribution visualization method and system, in particular to a complex environment-oriented received plane light intensity distribution modeling and visualization method and system based on a Monte Carlo simulation method. The application introduces a double light source model, considers signal light and interference light at the same time, and models the behavior of photons in the propagation, scattering, absorption and reception process based on a Monte Carlo simulation framework, so as to more truly reflect the influence of external light interference on system performance, and solve the problems of insufficient precision and limited visualization capability of the existing modeling method.

[0005] The technical solution is as follows: a complex environment-oriented UWOC light intensity distribution visualization method, comprising the following steps:

[0006] S1, initializing modeling parameters, including water body parameters, light source parameters and receiver parameters;

[0007] S2, modeling the light source and water body parameters according to the initialization results;

[0008] S3, based on the modeling results, TTHG scattering modeling is performed;

[0009] S4, according to the calculation results of the TTHG scattering modeling, the propagation process of all photons is simulated based on the Monte Carlo method, and it is determined at the receiving end whether the photons fall within the receiver aperture range;

[0010] S5, according to the determination result, the receiving plane statistics are performed to obtain the channel impulse response curve, and three-dimensional visualization is performed.

[0011] In step S1, the receiver parameters include: aperture diameter D, receiving surface position / height Z, field of view FOV, sensitivity, background light power, spatial sampling grid resolution and integration time constant;

[0012] The initialization is completed by the following process: first, load or interpolate the water optical parameters according to the selected water type, and calculate the extinction coefficient and phase function parameters; then, according to the light source type, initialize the transverse position and exit direction of each photon on the emitting surface by random sampling; then, according to the receiver geometry and photoelectric parameters, establish the receiving plane grid, set the aperture and FOV determination threshold, and convert the photon weight falling on the grid into an electrical signal.

[0013] In step S2, the light source modeling includes: using a Gaussian beam to simulate the initial state of the laser light source; the initial position of the laser light source photon obeys a Gaussian distribution, and the direction satisfies the laser divergence angle constraint; the number of laser light source photons and the number of external light interference light source photons are both taken as 10 7 levels;

[0014] A spherical beam model is used to simulate the initial state of the external light interference light source, and the initial direction of the external light interference light source photon is uniformly distributed in a hemisphere space.

[0015] Further, the distance r of the initial position of the photon of the Gaussian beam from the origin is:

[0016]

[0017] In the formula, σ is the standard deviation of the Gaussian distribution, ζ is a random variable obeying a uniform distribution between 0 and 1, and w0 is the beam radius of the laser;

[0018] The azimuth angle of the initial position of the photon of the Gaussian beam obeys a uniform distribution random variable between 0 and 2π, by polar coordinates the initial position of the photon of the Gaussian beam is:

[0019]

[0020] The photon motion direction of the Gaussian beam is determined by the direction cosine of each coordinate axis of the photon position (u x ,u y ,u z ) ; the initial motion direction of the tube of the Gaussian beam is the initial scattering angle; θ is the initial scattering angle, is the initial position azimuth angle of the photon;

[0021] The external light interference light source function is:

[0022]

[0023] The PDF and CDF of the initial scattering angle θ are:

[0024] P PDF (θ) = 2πI(θ)sinθ

[0025]

[0026] The initial scattering angle θ of the external light interference light source is obtained by the following formula:

[0027] cosθ = 1-ζ.

[0028] In step S2, the water body parameter modeling includes: according to different water quality types, obtaining the absorption coefficient a, the scattering coefficient b, the backscattering coefficient bb, and calculating the attenuation coefficient c.

[0029] In step S3, the TTHG scattering modeling includes: using a two-term TTHG phase function to model the photon scattering, calculating the forward asymmetry factor g fwd , the back asymmetry factor g bkwd , and the mixing weight α respectively;

[0030] The TTHG phase function is defined as a linear combination of two HG phase functions:

[0031] P TTHG (θ) = α*P HG (θ,g fwd )+(1-α)*P HG (θ,g bkwd )

[0032] Wherein, P HG (θ,g) is a standard HG phase function;

[0033] P HG (θ,g) = (1-g 2 ) / 4π*(1+g 2 -2gcosθ) 3 / 2

[0034] g fwd is the forward asymmetry factor, 0.9 < g fwd <0.99, dominant strong forward scattering; g bkwd is the backward asymmetry factor, -0.5 < g bkwd <-0.1, describes weaker backward scattering; 0 < a < 1, controls the proportion of forward scattering term in the total scattering;

[0035] When a photon scatters, a scattering angle θ needs to be sampled from the P TTHG (θ) distribution, the specific steps are as follows:

[0036] Select scattering type: generate a uniform random number ξ1; if ξ1< a, it is determined that the photon scatters forward, and g fwd is used; otherwise, it is determined to be backward scattering, and g bkwd is used;

[0037] Calculate scattering angle: according to the selected g value, use the standard sampling formula of HG phase function to calculate the scattering angle θ, the expression is as follows:

[0038]

[0039] In the formula, ξ2 is another uniform random number.

[0040] In step S4, the propagation process of all photons is simulated based on the Monte Carlo method, including:

[0041] Initialize the position and exit angle of the photon;

[0042] The initial position is r0= (x0, y0, 0), and the exit angle is

[0043] Iteratively calculate the photon propagation step, scattering angle, and direction vector;

[0044] Calculate step: the distance that the photon freely propagates before the next interaction, which is calculated by sampling a random number ξ1 using the formula s = -ln(ξ1) / μ t ; μ t is the total attenuation coefficient of the medium, and s is the step;

[0045] Update position: the photon moves the calculated step in the current direction; the new position is: new position = old position + s × current direction vector;

[0046] Scattering and updating direction:

[0047] Absorption / weight update: the energy weight of the photon is attenuated in proportion to simulate partial absorption;

[0048] Determine new direction: if photon survives, determine scattering angle by random sampling: azimuthal angle Uniformly random sampling between 0 and 2pi, polar angle is sampled from scattering phase function;

[0049] Update direction vector: according to old direction vector and sampled angle, calculate new 3D direction vector by coordinate transformation;

[0050] The process of moving, absorbing / scattering, and updating direction is repeated until the photon escapes, is completely absorbed, or its energy is negligible.

[0051] Thus, the propagation direction and weight are updated.

[0052] In step S4, determining whether the photon falls within the receiver aperture range at the receiving end includes:

[0053] During the propagation of all photons, the absorption attenuation and scattering process are combined; it is determined whether the photon enters the receiver aperture range and meets the receiver field of view FOV constraint.

[0054] In step S5, receiving plane statistics include: mapping the received photon landing point to a two-dimensional grid; statistical light intensity distribution and Gaussian smoothing; and normalizing the light intensity;

[0055] The channel impulse response curve includes a three-dimensional surface distribution map, an isometric map, a three-dimensional grid surface map, and a polar coordinate distribution map; in three-dimensional visualization, interactive control based on GUI is performed to adjust the transmission distance, beam width, aperture size, and divergence angle parameters.

[0056] Another object of the present application is to provide a complex environment-oriented UWOC light intensity distribution visualization system, which implements the complex environment-oriented UWOC light intensity distribution visualization method, and the system comprises:

[0057] An initialization module is configured to initialize modeling parameters, including water body parameters, light source parameters, and receiver parameters.

[0058] A light source and water body parameter modeling module is configured to model the light source and the water body parameters based on the initialization results.

[0059] A scattering modeling module is configured to perform TTHG scattering modeling based on the modeling results of the light source and the water body parameters.

[0060] A propagation process simulation module is configured to simulate the propagation process of all photons based on the calculation results of the TTHG scattering modeling and to determine whether the photon falls within the receiver aperture range at the receiving end based on the Monte Carlo method.

[0061] A receiving plane statistics and display module is configured to perform receiving plane statistics, obtain a channel impulse response curve, and perform three-dimensional visualization display.

[0062] In combination with all the above technical solutions, the present application has the beneficial effects that: the existing technology mainly focuses on system design and performance optimization, ignoring the interference influence of background light noise such as sunlight in the shallow water environment. The present application builds a double light source model, introduces an external interference light source on the basis of a traditional laser signal light source, and uses the Monte Carlo method to track the propagation, scattering and absorption process of photons underwater.

[0063] The present application can simulate the coupling effect of signal light and external light in a complex environment, and obtain a channel impulse response (CIR) curve by statistically receiving the arrival time and weight of photons. The simulation results show that the present application can accurately depict the influence of external light interference on the communication system, including the increase of peak power, the widening of waveform and the interference effect with water quality changes. Compared with the existing method, the present application can more realistically reflect the underwater optical communication channel characteristics in a complex environment, and has important significance for system design and anti-interference optimization. The precision of channel modeling is significantly improved, and high-precision channel modeling simulation is realized. BRIEF DESCRIPTION OF DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure;

[0065] Figure 1 is a UWOC light intensity distribution visualization method flowchart for a complex environment provided by the embodiment of the present application;

[0066] Figure 2 is a UWOC light intensity distribution visualization method principle diagram for a complex environment provided by the embodiment of the present application;

[0067] Figure 3 is a 3D light intensity distribution under different distances between the receiving end and the transmitting end in a software scenario provided by the embodiment of the present application Figure 1 ;

[0068] Figure 4 is a 3D light intensity distribution under different distances between the receiving end and the transmitting end in a software scenario provided by the embodiment of the present application Figure 2 ;

[0069] Figure 5 is a 3D light intensity distribution under different distances between the receiving end and the transmitting end in a software scenario provided by the embodiment of the present application Figure 3 ;

[0070] Figure 6 is a 3D light intensity distribution under different distances between the receiving end and the transmitting end in a software scenario provided by the embodiment of the present application Figure 4. DETAILED DESCRIPTION

[0071] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the drawings. In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the concept of the present application, so the present application is not limited to the specific implementation disclosed below.

[0072] The innovation of the present application is that: the present application can simultaneously consider various typical water types, beam divergence characteristics and background light interference by introducing Monte Carlo random photon propagation modeling and visualization technology, realize dynamic simulation and three-dimensional display of light intensity distribution in complex environment. This method not only improves the accuracy and intuitiveness of channel modeling, but also provides support for subsequent system design and performance optimization. In the process of modeling the channel of the underwater wireless optical communication system, the present application considers the interference of external light and improves the accuracy of modeling.

[0073] Embodiment 1, as shown in the figure, the light intensity distribution visualization method for complex environment provided by the embodiment of the present application includes the following steps: Figure 1

[0074] S1, initialize the modeling parameters, including water parameters, light source parameters, and receiver parameters;

[0075] S2, according to the initialization result, perform light source modeling and water parameter modeling;

[0076] Exemplarily, the light source modeling includes:

[0077] The initial state of the laser light source (signal light source) is simulated using a Gaussian beam. The initial position of the photon of the laser light source obeys Gaussian distribution, and the direction satisfies the laser divergence angle constraint. The initial state of the external light interference light source is simulated using a spherical beam model, and the initial direction of the photon of the external light interference light source is uniformly distributed in a hemisphere space;

[0078] Exemplarily, for a Gaussian beam, the initial position distribution of the photon is a Gaussian distribution with the origin as the center. The distance r of the initial position of the photon from the origin is:

[0079]

[0080] In the formula, σ is the standard deviation of the Gaussian distribution, ζ is a random variable obeying uniform distribution between 0 and 1, and w0 is the beam radius of the laser;

[0081] The azimuth angle of the initial position of the photon of the Gaussian beam is ​a uniform distribution random variable between [0, 2π], by polar coordinates The initial position of the photon of the Gaussian beam is obtained as a rectangular coordinate system (x0, y0, z0):

[0082]

[0083] The motion direction of the photon of the Gaussian beam is determined by the directional cosine of each coordinate axis of the photon position (u x , u y , u z ); the initial motion direction of the tube of the Gaussian beam is the initial scattering angle (zenith angle); θ is the initial scattering angle, is the azimuth angle of the initial position of the photon;

[0084] Exemplarily, for an external light interference light source, the spherical beam model considers that all photons come from the origin, and only the photon radiation facing the positive direction of the Z axis is considered. The external light interference light source is modeled using the spherical beam model, the external light interference light source simulates sunlight and other external background light, and the external light interference light source is regarded as isotropic light, and the external light interference beam source function is: θ represents the initial scattering angle, and represents the azimuth angle of the initial position of the photon.

[0085] The PDF and CDF of the initial scattering angle θ are:

[0086] P PDF (θ) = 2πI(θ)sinθ

[0087]

[0088] The initial scattering angle θ at the external light interference light source is obtained by:

[0089] cosθ = 1-ζ.

[0090] The initial motion direction and the initial azimuth angle are the same as the definition of the Gaussian beam.

[0091] Exemplarily, the number of photons of the laser light source and the number of photons of the external light interference light source are both taken as 10 7 levels to ensure the simulation accuracy.

[0092] Exemplarily, the water body parameter modeling includes: according to different water quality types (pure seawater, clear seawater, nearshore seawater, turbid port water), the absorption coefficient a, the scattering coefficient b, and the backscattering coefficient bb are obtained, and the attenuation coefficient c is calculated; wherein c = a + b, and different water quality data are shown in Table 1:

[0093] Table 1 Different water quality data

[0094]

[0095] S3, based on the modeling results, TTHG scattering modeling is carried out;

[0096] The two-term Henyey-Greenstein (TTHG) phase function is used to model photon scattering, and the forward asymmetry factor g fwd , the backward asymmetry factor g bkwd , and the mixing weight a are calculated respectively;

[0097] The TTHG phase function is defined as a linear combination of two HG phase functions as follows:

[0098] P TTHG (θ)=α*P HG (θ,g fwd )+(1-α)*P HG (θ,g bkwd )

[0099] Where P HG (θ,g) is the standard HG phase function;

[0100] P HG (θ,g)=(1-g 2 ) / 4π*(1+g 2 -2gcosθ) 3 / 2

[0101] g fwd is the forward asymmetry factor, 0.9<g fwd <0.99, which dominates the strong forward scattering; g bkwd is the backward asymmetry factor, -0.5<g bkwd <-0.1, which describes weaker backward scattering; 0<α<1, which controls the proportion of the forward scattering term in the total scattering;

[0102] Sampling process: when a photon scatters, a scattering angle θ needs to be randomly sampled according to the P TTHG (θ) distribution, and the specific steps are as follows:

[0103] Select the scattering type: generate a uniform random number ξ1; if ξ1<α, it is determined that the photon has forward scattering, and g fwd is used; otherwise, it is determined to be backward scattering, and g bkwd is used;

[0104] Calculate the scattering angle: according to the selected g value (g fwd or g bkwd), the scattering angle θ is calculated using the standard sampling formula for HG phase functions, which is:

[0105]

[0106] where ξ2 is another uniform random number.

[0107] This procedure gives each scattering event more realistic physical properties of the water body, significantly improving the simulation accuracy.

[0108] Based on the optical properties of the water body (especially the measurement or model data of the volume scattering function β(θ)) through fitting.

[0109] Process description:

[0110] Target data acquisition: Obtain the experimental measurement data of the volume scattering function β(θ) of the target water body, or generate it from its main components (such as the concentrations of phytoplankton, mineral suspended matter, and colored dissolved organic matter) through a theoretical model.

[0111] Parameter fitting:

[0112] Multiply P TTHG (θ) by the total scattering coefficient μ s , to get the simulated volume scattering function: μ s × P TTHG (θ);

[0113] Using optimization algorithms such as nonlinear least squares, adjust the parameters g fwd , g bkwd , and α so that μ s × P TTHG (θ) most closely approximates the target β(θ) data at all angles.

[0114] S4, based on the calculation results of the TTHG scattering modeling, simulate the propagation process of all photons based on the Monte Carlo method, and determine at the receiving end whether the photons fall within the receiver aperture range;

[0115] For example, the photons experience scattering and absorption events in each step of propagation, and the propagation direction and weight are updated;

[0116] Specifically, it includes:

[0117] Initialize the photon position and exit angle; iteratively calculate the photon propagation step, scattering angle, and direction vector; and complete the update of the propagation direction and weight;

[0118] For example, determining at the receiving end whether the photons fall within the receiver aperture range includes:

[0119] Combining absorption attenuation and scattering process in the propagation of all photons; judging whether the photon is in the aperture of the receiver and meets the field of view (FOV) constraint.

[0120] Two conditions need to be met simultaneously for successful reception:

[0121] Condition 1: Geometric aperture judgment (whether the photon hits the receiver surface?)

[0122] Calculate the intersection: the path of the photon is a line segment. Judge whether the line segment intersects with the plane where the receiver is located (such as z=z r ), and calculate the intersection coordinates (x i ,y i ,z r ).

[0123] Judge whether it is in the aperture:

[0124] Circular aperture: calculate the distance between the intersection point and the center of the receiver (x c ,y c ): d=sqrt((x i -x c ) 2 +(y i -y c ) 2 ), if d≤R, it meets the condition, R is the radius of the receiver.

[0125] Square aperture: judge whether the intersection coordinates meet |x i -x c |≤L / 2 and |y i -y c |≤L / 2 at the same time, L is the side length.

[0126] Condition 2: FOV judgment (whether the incident angle of the photon is in the "field of view" of the receiver?)

[0127] Calculate the incident angle: calculate the angle θ inc between the direction vector u of the photon and the normal vector n (such as (0,0,1)) of the receiver plane. The cosine value is: cos(θ inc )=|u·n|.

[0128] Make a judgment: if θ inc ≤FOV / 2 (i.e. cos(θ inc )≥cos(FOV / 2), it meets the FOV condition.

[0129] Only when the photon meets both condition 1 and condition 2 at the same time, it is judged as "successful reception".

[0130] Exemplarily, the receiver can be PIN type or APD type, and the receiving area and the field of view angle can be set according to application scenarios.

[0131] S5, according to the determination result, receiving plane statistics are performed to obtain a channel impulse response curve, and three-dimensional visualization is performed;

[0132] The received photon landing points are mapped to a two-dimensional grid; the light intensity distribution (time domain distribution of all received photons) is counted and Gaussian smoothing is performed; the light intensity is normalized to obtain a channel impulse response curve, and three-dimensional visualization is performed;

[0133] In the process of photon transmission, if the photon finally reaches the receiving surface, the spatial coordinates will be projected to the receiving plane, and according to the grid division rule of the receiving plane, the landing points are mapped to the corresponding two-dimensional grid unit, and the weight of the photon is accumulated at the grid position as the light intensity distribution

[0134] Gaussian smoothing: purpose: the original Monte Carlo simulation result will have statistical fluctuations (noise) due to randomness. Gaussian smoothing is a kind of low-pass filtering, which can eliminate these random fluctuations and reveal the smooth and physically real waveform without changing its essential characteristics.

[0135] Process:

[0136] a. Select a Gaussian function as the smoothing kernel, and the width is controlled by the standard deviation σ. The larger σ is, the higher the smoothing degree is.

[0137] b. Convolution operation is performed between the original discrete impulse response h[i] and the Gaussian kernel:

[0138] h smooth [i]=∑ j (h[j]*G[i-j])

[0139] Where G[k] = exp(-k 2 / (2σ 2 )) is the Gaussian kernel function, and is normalized to keep the total energy unchanged.

[0140] c. The convolution result h smooth [t] is the smoothed light intensity time domain distribution.

[0141] The process of normalizing the light intensity distribution usually includes the following steps:

[0142] Statistical receiving light intensity: in the photon Monte Carlo simulation, first, the photon energy or weight falling into each sampling point on the receiving plane is accumulated to obtain a two-dimensional light intensity matrix;

[0143] Determine the reference value: calculate the maximum value or total energy sum in the matrix as the normalization reference;

[0144] Normalization calculation: scale the light intensity matrix by a selected reference so that the maximum light intensity after normalization is 1;

[0145] Three-dimensional visualization display includes: output channel impulse response curve, including three-dimensional surface distribution map, contour map, three-dimensional grid surface map, polar coordinate distribution map; support based on GUI interactive control, can adjust transmission distance, light beam width, aperture size, divergence angle and other parameters.

[0146] The user can adjust the distance between the transmitting end and the receiving end, the light beam width, the aperture size and the divergence angle through the slider or the input box. The whole interactive process is completed by triggering the update_plot callback function through the GUI: whenever the parameters are adjusted, the system will automatically re-run the photon initialization, propagation and reception calculation, and update the visualization results in real time in the three-dimensional coordinate graph. In this way, the user can intuitively compare the influence of different parameters on the light intensity distribution of the UWOC channel without modifying the underlying code.

[0147] As can be seen from the above embodiments, the present application has high authenticity: by introducing a spherical beam interference light source, the present application can more realistically simulate the complex environment of shallow water, making up for the shortcomings of traditional single light source models.

[0148] Quantifiable analysis: this method can accurately quantify the influence of external light interference on system performance, which is specifically manifested as the increase of peak power of CIR curve (noise superposition) and the intensification of waveform broadening (enhancement of multipath effect).

[0149] Strong guidance: the simulation results can provide important theoretical basis and data support for the optimization design of UWOC system (such as optical filter selection, reception field angle optimization, equalization algorithm design).

[0150] Flexible and universal: the method is suitable for different water quality, different communication distance and different system parameters, and has high flexibility and universality.

[0151] Embodiment 2, a UWOC light intensity distribution visualization system for complex environment, comprising:

[0152] An initialization module for initializing modeling parameters, including water body parameters, light source parameters and receiver parameters;

[0153] A light source and water body parameter modeling module for modeling the light source and water body parameters based on the initialization results;

[0154] A scattering modeling module for TTHG scattering modeling based on the light source modeling and water body parameter modeling results;

[0155] A propagation process simulation module is configured to simulate the propagation process of all photons based on the Monte Carlo method based on the calculation result of the TTHG scattering modeling, and determine whether the photons fall into the receiver aperture range at the receiving end;

[0156] A receiving plane statistics and display module is configured to perform receiving plane statistics, obtain a channel impulse response curve, and perform three-dimensional visual display.

[0157] Application example.

[0158] Figure 2 The method is a UWOC light intensity distribution visualization method for complex environments, and a specific implementation process of the UWOC light intensity distribution visualization method for complex environments provided by the embodiment of the application in combination with a MATLAB / GUI environment is as follows:

[0159] (1) initialization step: set the number of photons N = 1 x 10 7 , and the water quality parameters include an absorption coefficient a, a scattering coefficient b, and an attenuation coefficient c. The laser wavelength is set to 532 nm, the receiver aperture D = 5 cm, and the field of view angle FDV = 40°.

[0160] (2) signal light modeling. A Gaussian beam model is used, the initial position of the photon is subject to a Gaussian distribution with the origin as the center, and the direction is subject to a set divergence angle range, so as to ensure that the actual laser output characteristics are met.

[0161] (3) interference light modeling. A spherical light source model is used, all photons start from (0, 0, 0), and the direction is limited to the positive hemisphere space of the Z axis. The distribution of the zenith angle theta satisfies cos theta = 1-zeta, wherein zeta is a uniformly distributed random number, so as to ensure that the direction distribution is uniform.

[0162] (4) propagation cycle. In the propagation process of the photon, the length of each step is determined by an exponential distribution. If the photon is scattered, the direction vector is updated; if it is absorbed, the tracking of the photon is terminated. The cycle continues until the photon leaves the system or is received by the receiver.

[0163] Technical means: inverse transform sampling method.

[0164] Russian roulette combined with weight threshold method.

[0165] 1. Continuous absorption (weight attenuation): after each scattering event, the photon is not directly “killed”, but its weight is reduced in proportion, and the reduced part is the energy absorbed at the position.

[0166] 2. Weight threshold determination: a very small weight threshold is set. When the weight of the photon is lower than the threshold, it is considered that the energy of the photon can be ignored, and it is a waste of computing resources to continue tracking it.

[0167] 3. Russian roulette: For photons with weight below a threshold, instead of being terminated directly, a round of "gambling" is performed to maintain statistical unbiasedness.

[0168] (5) Reception decision: When a photon arrives at the reception plane, if its lateral position falls within the receiver aperture range and its propagation direction is within the receiver field of view angle, it is determined as "reception success", and the photon's arrival time and weight are recorded.

[0169] The principle of determining whether the lateral position falls within the receiver aperture range is to calculate the Euclidean distance or coordinate difference in the two-dimensional plane.

[0170] Circular aperture: Distance principle. Calculate the distance from the point (photon landing point) to the center of the circle (receiver center) and compare it with the radius.

[0171] Rectangular / square aperture: Boundary principle. Respectively check whether the X coordinate and Y coordinate of the point are within the X direction boundary and Y direction boundary.

[0172] Arrival time: calculated by the cumulative path length of the photon and the speed of light; weight: equal to the energy share remaining after the photon undergoes multiple absorption attenuations during propagation.

[0173] (6) Result output, statistics of the time delay distribution of all received photons, to obtain the channel impulse response (CIR). By comparing with the single light source model, the phenomena of peak power increase and waveform broadening under complex environment can be directly revealed.

[0174] During the reception process, the arrival time and corresponding weight of all photons determined as reception success are stored in an array; then the time axis is binned and the weight is accumulated in each time interval to obtain the time delay distribution of the received photons

[0175] Scattering model: TTHG phase function and its two-term sampling process are mainly used to more accurately fit the scattering characteristics of real water bodies, which is a significant improvement over the simple HG phase function implementation.

[0176] Parameter determination: The process of determining key parameters such as g_fwd, g_bkwd, and α through fitting water optical data emphasizes the physical accuracy and customizability of the model.

[0177] Post-processing technique: A post-processing step of Gaussian smoothing on the original data is performed to improve the readability and smoothness of the results and suppress the inherent statistical noise of the Monte Carlo method.

[0178] Focus of comparative analysis: comparison with the single light source model aims to quantitatively reveal the peak power change and waveform broadening effect brought by the multi-light source system, highlighting the simulation's ability to analyze the performance of specific system architecture.

[0179] wherein, Figure 3 is the 3D light intensity distribution under different distances between the two ends of the software scenario provided by the embodiment of the application Figure 1 ; Figure 4 is the 3D light intensity distribution under different distances between the two ends of the software scenario provided by the embodiment of the application Figure 2 ; Figure 5 is the 3D light intensity distribution under different distances between the two ends of the software scenario provided by the embodiment of the application Figure 3 ; Figure 6 is the 3D light intensity distribution under different distances between the two ends of the software scenario provided by the embodiment of the application Figure 4 .

[0180] Compared with the prior art, the embodiment of the application indeed has significant advantages:

[0181] High precision and clear physical mechanism: Monte Carlo method is adopted to strictly solve the transport equation of photons. Combined with the TTHG phase function, the anisotropic scattering behavior in complex water bodies can be more realistically simulated. Compared with the prior art: better than the analytical model based on small-angle approximation and diffusion approximation, and the error of these models is larger under strong absorption or specific scattering conditions.

[0182] Wide applicability and strong flexibility of modeling capability: any complex system can be easily simulated, including non-uniform water bodies, strong turbulent channels, receivers of any shape, and mobile platforms. Compared with the prior art: traditional models usually can only handle point light sources, uniform media, and weakly disturbed idealized scenes. Advantage: not only can the final light intensity distribution be given, but also the path length distribution of photons, the multiple scattering contribution degree and other information can be provided, providing full-fledged data support for system optimization.

[0183] The above describes only the preferred specific embodiments of the application, but the protection scope of the application is not limited thereto, and any modification, equivalent replacement and improvement made by those skilled in the art within the technical range disclosed by the application, within the spirit and principles of the application, should be covered within the protection scope of the application.

Claims

1. A method for visualizing light intensity distribution of UWOC towards complex environment, characterized in that, The method comprises the following steps: S1, initializing modeling parameters, including water body parameters, light source parameters, and receiver parameters; S2, performing light source modeling and water body parameter modeling according to the initialization results; S3, performing TTHG scattering modeling based on the modeling results; S4, simulating the propagation process of all photons based on the Monte Carlo method according to the calculation results of the TTHG scattering modeling, and determining whether the photons fall within the receiver aperture range at the receiving end; S5, performing receiver plane statistics according to the determination results to obtain a channel impulse response curve and perform three-dimensional visualization display.

2. The complex environment oriented UWOC light intensity distribution visualization method of claim 1, wherein, In step S1, the receiver parameters include: an aperture diameter D, a receiving surface position / height Z, a field of view FOV, a sensitivity, a background light power, a spatial sampling grid resolution, and an integration time constant. The initialization is completed by the following process: first, the water optical parameters are loaded or interpolated according to the selected water type, and the extinction coefficient and phase function parameters are calculated; then, the transverse position and exit direction of each photon are initialized on the emission surface according to the light source type by random sampling; and then, the receiving plane grid is established according to the receiver geometry and photoelectric parameters, the aperture and FOV determination threshold are set, and the photon weight falling on the grid is converted into an electrical signal.

3. The complex environment oriented UWOC light intensity distribution visualization method of claim 1, wherein, In step S2, the light source modeling includes: simulating the initial state of the laser light source using a Gaussian light beam; the initial position of the photons of the laser light source obeys a Gaussian distribution, and the direction satisfies the laser divergence angle constraint; the number of photons of the laser light source and the number of photons of the external light interference light source are both taken as 10 7 levels; The initial state of the external light interference light source is simulated using a spherical beam model, and the initial direction of the photons of the external light interference light source is uniformly distributed in a hemisphere space.

4. The complex environment oriented UWOC light intensity distribution visualization method of claim 3, wherein, The distance r of the initial position of the photon of the Gaussian beam from the origin is: where σ is the standard deviation of the Gaussian distribution, ζ is a random variable uniformly distributed between 0 and 1, and w0 is the beam radius of the laser; azimuthal angle of the initial position of the photon of the gaussian beam subject to a uniform distribution random variable between [0, 2π], obtained by polar coordinates obtaining the initial position of the photon of the gaussian beam in the cartesian coordinate system (x0, y0, z0) The photon motion direction of the Gaussian beam is determined by the directional cosines (u x ,u y ,u z ) of the photon position coordinate axes; the initial motion direction of the Gaussian beam tube is the initial scattering angle; θ is the initial scattering angle, is the initial position azimuth angle of the photon; The external light interference light source function is: The PDF and CDF of the initial scattering angle θ are: P PDF (θ) = 2πI(θ)sinθ The initial scattering angle θ at the external light interference light source is obtained by: cosθ=1-ζ.

5. The complex environment oriented UWOC light intensity distribution visualization method of claim 1, wherein, In step S2, the water body parameter modeling includes: obtaining the absorption coefficient a, the scattering coefficient b, and the backscattering coefficient bb according to different water quality types, and calculating the attenuation coefficient c.

6. The complex environment oriented UWOC light intensity distribution visualization method of claim 1, wherein, In step S3, performing TTHG scattering modeling includes: modeling photon scattering by using a two-term TTHG phase function, respectively calculating a forward asymmetry factor g fwd , a backward asymmetry factor g bkwd , and a mixing weight α; The TTHG phase function is defined as a linear combination of two HG phase functions: P TTHG (θ) = a * P HG (θ, g fwd ) + (1 - a) * P HG (θ, g bkwd ) where P HG (0,g) is the standard HG phase function; P HG (θ,g) = (1 - g) 2 ) / 4π*1+g 2 -2gcosθ) 3 / 2 g fwd is the forward asymmetry factor, 0.9 < g fwd < 0.99, dominant strong forward scattering; g bkwd is the backward asymmetry factor, -0.5 < g bkwd < -0.1, describing a relatively weak backscattering; 0 < a < 1, controlling the proportion of the forward scattering term in the total scattering; When the photon is scattered, a scattering angle θ is randomly sampled according to P TTHG (θ) distribution, and the specific steps are as follows: Selecting scattering type: generate a uniform random number ξ1; if ξ1<α, then decide that the photon undergoes forward scattering, use g fwd ; otherwise, decide that it undergoes backward scattering, use g bkwd ; The scattering angle is calculated according to the selected g value using the standard sampling formula of the HG phase function, and the expression is: where ξ2 is another uniform random number.

7. The complex environment oriented UWOC light intensity distribution visualization method of claim 1, wherein, In step S4, simulating the propagation process of all photons based on the Monte Carlo method includes: Initializing the photon position and exit angle; Initial position, the initial position of the Gaussian beam is r0=(x0, y0, 0), the exit angle Iteratively calculating the photon propagation step, scattering angle, and direction vector; Step size: distance a photon travels freely before the next interaction, calculated by sampling a random number, ξ1, using the formula s = -ln(ξ1) / μ t calculated; μ t μ is the total attenuation coefficient of the medium, and s is the step size. Updating the position: the photon moves the calculated step in the current direction; the new position is: new position = old position + s × current direction vector; Scattering and updating the direction: Absorption / weight update: the photon energy weight is attenuated in proportion to simulate partial absorption; Determine new direction: if photon survives, scatter angle is determined by random sampling: azimuthal angle Uniformly random between 0 and 2π, polar angle is drawn from scattering phase function; updating the direction vector: based on the old direction vector and the sample angle, a new three-dimensional direction vector is calculated by coordinate transformation; The processes of moving, absorbing / scattering, and updating the direction are repeated until the photon escapes, is completely absorbed, or its energy is negligible; Thus, the propagation direction and weight are updated.

8. The complex environment oriented UWOC light intensity distribution visualization method of claim 1, wherein, In step S4, determining whether the photons fall within the receiver aperture range at the receiving end includes: Combining the absorption attenuation and scattering processes during the propagation process of all photons; determining whether the photons enter the receiver aperture range and satisfy the receiver field of view FOV constraint.

9. The complex environment oriented UWOC light intensity distribution visualization method of claim 1, wherein, In step S5, the receiving plane statistics include: mapping the received photon landing points to a two-dimensional grid; counting the light intensity distribution and performing Gaussian smoothing; normalizing the light intensity; The channel impulse response curve includes a three-dimensional surface distribution map, an isometric line map, a three-dimensional grid surface map, and a polar coordinate distribution map; in the three-dimensional visualization, GUI-based interactive control is performed to adjust the transmission distance, the light beam width, the aperture size, and the divergence angle parameters.

10. A complex environment oriented UWOC light intensity distribution visualization system, characterized by, The system implements the complex environment-oriented UWOC light intensity distribution visualization method according to any one of claims 1-9, and the system comprises: An initialization module configured to initialize modeling parameters, including water body parameters, light source parameters, and receiver parameters; A light source and water body parameter modeling module configured to perform light source modeling and water body parameter modeling according to the initialization results; A scattering modeling module configured to perform TTHG scattering modeling based on the light source modeling and water body parameter modeling results; A propagation process simulation module configured to simulate the propagation process of all photons based on the calculation results of the TTHG scattering modeling based on a Monte Carlo method, and determine whether the photons fall within the receiver aperture range at the receiving end; A receiving plane statistics and display module configured to perform receiving plane statistics to obtain a channel impulse response curve, and perform three-dimensional visualization display.

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