Space-time joint simulation method based on near-field target scattering model and dynamic cloud environment

Abstract

The invention belongs to the technical field of laser detector signal processing, and particularly relates to a space-time joint simulation method based on a near-field target scattering model and dynamic cloud environment simulation, which comprises the following steps of: analyzing a target three-dimensional model by utilizing near-field target LRCS simulation software; setting a cloud and mist environment according to a space-time joint simulation method for dynamic cloud and mist environment simulation; calculating near-field target LRCS simulation data according to the model analysis data; and performing signal processing simulation calculation according to the LRCS simulation data to complete target identification in the cloud environment. According to the method, the visibility model of the cloud and mist particles is dynamically established based on the Mie scattering theory and the Monte Carlo method in combination with the time dimension. According to the method, the adaptability of the laser detector simulation system under variable meteorological conditions can be remarkably improved, and reliable data support is provided for design and optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of laser detector signal processing, and in particular relates to a space-time joint simulation method based on a near-field target scattering model and a dynamic cloud and fog environment. Background Art

[0002] As an important component of modern intelligent detection systems, the reliability and accuracy of laser detectors have an important impact on the overall performance of the system. The basic principle of laser detectors is to detect the distance and position of target objects through laser emission and echo reception. However, laser signals are often affected by environmental factors in practical applications, especially under complex meteorological conditions (such as clouds, fog, smoke, rain and snow, etc.), laser signals will be significantly attenuated and scattered, resulting in distortion of echo signals or even undetectable signals, which seriously affects the working stability and reliability of the detector.

[0003] In a dynamic fog environment, the density, distribution, shape and other characteristics of fog particles change over time and space, causing the propagation path and intensity of the laser signal to change continuously. Therefore, how to accurately simulate the impact of the dynamic fog environment on the laser detector signal becomes the key to improving the performance of the laser detector. Traditional laser detector simulation methods mostly focus on static environments, ignoring the dynamic characteristics of fog particles in actual environments, and cannot truly reflect the attenuation and scattering characteristics of signals in complex environments, resulting in large differences between simulation results and actual applications.

[0004] At present, some laser simulation technologies on the market have attempted to describe the scattering and attenuation of lasers by clouds and fog through simple mathematical models, but these models are usually based on static or uniform cloud fields and fail to reflect the spatiotemporal dynamic characteristics of the cloud environment. In addition, existing technologies mostly rely on the observation of a single sensor, making it difficult to obtain complete distribution information of clouds and fog in three-dimensional space, thus affecting the accuracy of the simulation results. With the increasing demand for the application of laser detectors in complex environments, a more accurate spatiotemporal joint simulation method for dynamic cloud environments is urgently needed to improve the anti-interference ability and detection accuracy of laser detectors under complex meteorological conditions.

[0005] Based on the above requirements, it is necessary to propose a dynamic space-time joint simulation method for cloud and fog environment. Through data collection and dynamic modeling in the time and space dimensions, the scattering, absorption and attenuation characteristics of the cloud and fog environment on the laser signal can be accurately simulated, thereby providing reliable simulation data support for the design and optimization of the laser detector system, and more realistically simulating the actual situation of the laser detector passing through the cloud and fog during the intersection with the target. Summary of the invention

[0006] The purpose of the present invention is to solve the problems raised in the background technology, and to propose a method for time-space joint simulation based on a near-field target scattering model and a dynamic foggy environment. The effect of simulating foggy environment on laser signals under complex background conditions is solved, especially the scattering, absorption, attenuation and echo characteristics of laser signals are accurately modeled. By combining multi-channel data acquisition and simulation in time and space dimensions, the anti-interference ability and detection accuracy of laser detectors in dynamic foggy environments can be effectively improved. When passing through complex environments such as foggy environments, traditional laser detector systems are easily affected by scattering and absorption, resulting in unstable or even lost signals, affecting their working reliability. The present invention uses a time-space joint simulation method for dynamic foggy environments to achieve accurate simulation of foggy distribution characteristics and their effects on laser signal propagation, thereby solving the problem of inaccurate detection of laser detectors in complex foggy environments.

[0007] In order to achieve the purpose of the present invention, the present invention discloses a space-time joint simulation method based on a near-field target scattering model and a dynamic cloud and fog environment, comprising the following steps:

[0008] Step 1: Analyze and import the target three-dimensional model using the near-field target LRCS simulation software;

[0009] Step 2, setting the cloud and fog environment according to the dynamic cloud and fog environment spatiotemporal joint simulation method;

[0010] Step 3, calculating the near-field target LRCS simulation data according to the model analysis data;

[0011] Step 4: Perform signal processing simulation calculations based on the LRCS simulation data to complete target recognition in a cloud and fog environment.

[0012] Furthermore, in step 1, a 3D model analysis method is used, which specifically includes the following steps:

[0013] Step 1-1, read the relevant data of the target model, mainly including the vertex coordinates and material properties of each face element, and then obtain the area, center point, and external normal information of the face element according to the vertex coordinates of the face element;

[0014] Step 1-2, process and store the data obtained in step 1-1, combine the initial position of the target geometric center in the detector coordinate system, perform coordinate transformation on the vertex coordinates and the external normal vectors of all small facets on the target surface, and transform them from the target coordinate system to the detector coordinate system;

[0015] Step 1-3, in the detector coordinate system, judging whether the facet is simultaneously in the meridian field of view of the detector transceiver system by judging whether the facet center is simultaneously in the width of the transmitting and receiving beams;

[0016] Step 1-4: whether the center of the cross-section element is simultaneously within the equatorial field of view of the detector transceiver system and whether the cross-section element is self-occluded or mutually-occluded;

[0017] Step 1-5: Store the facets that meet steps 1-3 and 1-4 into a valid facet list.

[0018] Furthermore, in step 2, setting the foggy environment requires using a foggy visibility linear representation method, and the definition method specifically includes the following steps:

[0019] Step 2-1: According to Mie scattering theory, for cloud particles with a particle size close to the laser wavelength, use the dimensionless scale parameter To determine the scattering model; according to the Mie scattering equation, calculate the scattering and absorption characteristics of a single spherical particle to the laser signal. The specific formula is as follows: Where I0 is the incident light intensity, λ is the wavelength, and r is the particle radius; the scattering coefficient K is calculated as follows: Where α is the scale parameter;

[0020] Scattering coefficient a n and b n Solve using the continued fraction method,

[0021] About D n (mα) is obtained by backward recursion method:

[0022] For the n value, select the following empirical formula:

[0023]

[0024] When the particles in the cloud are uniform, the laser radar scattering coefficient has the following formula: or Where N is the number of particles in the cloud, and the scale distribution function C(r) = ar α exp(-br β ), where r is the cloud particle size; a, b, α, β are distribution parameters;

[0025] The calculation formula of laser radar scattering cross-sectional area LRCS is as follows:

[0026] Step 2-2: Visibility changes dynamically with cloud density and particle distribution. A linear function model is used to describe the attenuation of cloud concentration at a certain moment. The simulation time is divided into multiple time points. The changes of cloud parameters over time are collected and recorded at each time step to form time series data. The data of each time step is used to simulate the changes of the cloud environment of the laser detector at different time points, thereby reflecting the dynamic characteristics of the cloud environment over time. The visibility model is based on the real-time update of cloud density and distribution, which is expressed as: I total =I0·y(t), where y(t) is a linear function related to time.

[0027] Further, in step 3, the method for calculating the near-field target LRCS simulation data is defined to specifically include the following steps:

[0028] Step 3-1: In the microwave scattering theory, the energy flux density of the incident plane wave is In the formula, E i is the incident electric field intensity, H i is the incident magnetic field intensity;

[0029] When the laser radar scattering cross section is σ, the total intercepted power is Then the scattered power density at a distance R from the target is The scattered power density is also expressed as Know From the above formula we can deduce:

[0030]

[0031] The target must satisfy the far-field condition, i.e. λ is the wavelength of the incident wave, D is the target geometry, the target is considered as a point target, and the laser is considered as a plane incident wave; LRCS is expressed as Then the target unit area LRCS is In the formula, A is the area of ​​the target illuminated by the light beam;

[0032] Step 3-2: When the receiving aperture area of ​​the receiving system is A r When the scattered power ΔP s With the incident power P i The ratio is Combining the above formula, the unit laser radar scattering cross section is: In the formula, At the same time, the bidirectional reflectance distribution function is expressed as:

[0033]

[0034] From the above formula, we can get σ 0 =4πf r cosθ i cosθ r , which is the mathematical relationship between the unit area lidar scattering cross section and the bidirectional reflectance distribution function, and is the theoretical model used to calculate the target LRCS;

[0035] Step 3-3, after obtaining the laser radar scattering cross section of each effective surface element using the mathematical model of the unit laser radar scattering cross section in the above formula, the laser radar scattering cross sections of all effective surface elements are mathematically superimposed to obtain the total LRCS of the target, that is:

[0036] Furthermore, in step 4, target recognition in a foggy environment is completed, wherein the target recognition method is as follows:

[0037] When the echo power value exceeds the threshold, the signal is likely to be the echo signal of the target; during the simulation process, the simulation step size is set to be synchronized with the period of the laser detector emitting pulses. When the eight channels simultaneously emit laser pulses of the same frequency and pulse width, the receiving systems of the eight channels simultaneously detect. When the echo power received by any channel is continuous and stable, that is, when N consecutive echo signals exceed the set power threshold, the signal is considered to be the target signal, and a startup prompt is given in the software; during the judgment process, once the echo signal of the channel is discontinuous, it is necessary to recount until the end of the simulation. If there is no continuous and stable signal, it is considered that the detector has not detected the target, and then a prompt is given in the software that the detector has not been started.

[0038] Compared with the prior art, the significant progress of the present invention lies in: compared with the traditional static or simplified simulation methods, the dynamic cloud and fog environment spatiotemporal joint simulation method of the present invention has significant advantages. By combining data collection in time and space dimensions, a cloud and fog distribution model is dynamically constructed, ensuring the real perception of the laser detector in a complex cloud and fog environment, making the simulation results closer to reality; the scattering modeling based on Mie scattering theory and the real-time updated visibility model can accurately describe the dynamic influence of cloud and fog particles on laser signals; the spatiotemporal joint simulation makes the visibility and particle distribution change over time, effectively reflecting the signal changes in the real environment.

[0039] In order to more clearly illustrate the functional characteristics and structural parameters of the present invention, further description is given below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0041] Figure 1 This is a schematic diagram of the Monte Carlo method for cloud and fog particle systems;

[0042] Figure 2 This is a schematic diagram of the multiple scattering process of cloud particles;

[0043] Figure 3 This is a schematic diagram of the change of cloud visibility over time;

[0044] Figure 4 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0046] like Figure 4 As shown, a space-time joint simulation method based on a near-field target scattering model and a dynamic cloud and fog environment includes the following steps:

[0047] Step 1: Analyze and import the target three-dimensional model using the near-field target LRCS simulation software;

[0048] Step 2, setting the cloud and fog environment according to the dynamic cloud and fog environment spatiotemporal joint simulation method;

[0049] Step 3, calculating the near-field target LRCS simulation data according to the model analysis data;

[0050] Step 4: Perform signal processing simulation calculations based on the LRCS simulation data to complete target recognition in a cloud and fog environment.

[0051] Specifically, in step 1, a 3D model analysis method is used, which specifically includes the following steps:

[0052] Step 1-1, read the relevant data of the target model, mainly including the vertex coordinates and material properties of each face element, and then obtain the area, center point, external normal and other information of the face element according to the vertex coordinates of the face element;

[0053] Step 1-2, process and store the data obtained in step 1-1, combine the initial position of the target geometric center in the detector coordinate system, perform coordinate transformation on the vertex coordinates and the external normal vectors of all small facets on the target surface, and transform them from the target coordinate system to the detector coordinate system;

[0054] Step 1-3, in the detector coordinate system, judging whether the facet is simultaneously in the meridian field of view of the detector transceiver system by judging whether the facet center is simultaneously in the width of the transmitting and receiving beams;

[0055] Step 1-4: whether the center of the cross-section element is simultaneously within the equatorial field of view of the detector transceiver system and whether the cross-section element is self-occluded or mutually-occluded;

[0056] Step 1-5: Store the facets that meet steps 1-3 and 1-4 into a valid facet list.

[0057] Specifically, in step 2, setting the fog environment requires using a fog visibility linear representation method, and the definition method specifically includes the following steps:

[0058] Step 2-1: According to Mie scattering theory, for cloud particles with a particle size close to the laser wavelength, use the dimensionless scale parameter To determine the scattering model. According to the Mie scattering equation, the scattering and absorption characteristics of a single spherical particle to the laser signal are calculated. The specific formula is as follows: Where I0 is the incident light intensity, λ is the wavelength, and r is the particle radius. The scattering coefficient K is calculated as follows: Where α is the scale parameter.

[0059] Scattering coefficient a n and b n Solve using the continued fraction method,

[0060] About D n (mα) can be obtained by backward recursion:

[0061] For the selection of n value, researchers have given corresponding empirical formulas. The present invention selects the following empirical formula:

[0062] When the particles in the cloud are uniform, the laser radar scattering coefficient has the following formula: or Where N is the number of particles in the cloud. Generally speaking, the sizes of cloud particles in nature are generally different. Therefore, in order to make the simulation more realistic, the scale distribution function C(r)=ar is selected. α exp(-br β ), where r is the cloud particle size; a, b, α, and β are distribution parameters.

[0063] The calculation formula of laser radar scattering cross-sectional area LRCS is as follows:

[0064] Step 2-2: For the laser short-range detector detection system, the laser beam it emits will be reflected after irradiating the cloud particles. After the first reflection, it may be reflected back into the receiving range of the receiver. In this way, this laser beam is emitted in other directions, and it does not contribute to the intensity of the receiver. But on the other hand, this laser beam may hit other cloud particles in this direction and reflect for the second time. In this way, it is possible to return the beam to the receiver, thus generating multiple scattering. The multiple scattering path is complex, the required calculation amount is high, and its uncertainty fluctuates greatly with the actual shape of the cloud particles.

[0065] like Figure 1 As shown in the figure, in order to allow photons to collide with cloud particles, the cloud particles that make up the cloud cluster are regarded as multiple small cloud clusters. After the photon is emitted by the transmitter, if the position after moving L is within a certain range of the small cloud cluster, then it is considered that the photon collides with the cloud particles of the small cloud cluster and is scattered. The photon is scattered multiple times in the cloud cluster along the direction of vector D. The position after the first movement and the position after the third movement are in the receiving field of view of the receiver, but the position after the first movement is not within a certain range of the small cloud cluster. Therefore, only the photon scattering after the third movement contributes to the echo power, while the second scattering is not in the receiver's field of view, and the fourth scattering is not in the entire cloud cluster, so they have no contribution to the echo. The flow chart is as follows Figure 2 shown.

[0066] Step 2-3, such as Figure 3 The visibility shown changes dynamically with the cloud density and particle distribution, and a linear function model is used to describe the attenuation of the cloud concentration at a certain time. The simulation time is divided into multiple time points, and the changes of the cloud parameters over time are collected and recorded at each time step to form time series data. The data of each time step can be used to simulate the changes of the cloud environment of the laser detector at different time points, thereby reflecting the dynamic characteristics of the cloud environment over time. The visibility model is based on the real-time update of the cloud density and distribution, and can be expressed as: total =I0·y(t), where y(t) is a linear function related to time.

[0067] Specifically, in step 3, the method for calculating the near-field target LRCS simulation data, the definition method specifically includes the following steps:

[0068] Step 3-1: In the microwave scattering theory, the energy flux density of the incident plane wave is In the formula, E i is the incident electric field intensity, H i is the incident magnetic field intensity.

[0069] When the laser radar scattering cross section is σ, the total intercepted power is Then the scattered power density at a distance R from the target is The scattered power density can also be expressed as Knowable From the above formula, we can deduce:

[0070]

[0071] The target must satisfy the far-field condition, i.e. λ is the wavelength of the incident wave, D is the target geometry, the target is considered as a point target, and the laser is considered as a plane incident wave. LRCS can be expressed as Then the target unit area LRCS is Where A is the area of ​​the target illuminated by the light beam.

[0072] Step 3-2: When the receiving aperture area of ​​the receiving system is A r When the scattered power ΔP s With the incident power P i The ratio is Combining the above formula, the unit laser radar scattering cross section can be obtained as follows: In the formula, At the same time, the bidirectional reflectance distribution function can be expressed as:

[0073]

[0074] From the above formula, we can get σ 0 =4πf r cosθ i cosθ r , which is the mathematical relationship between the laser radar scattering cross section per unit area and the bidirectional reflectance distribution function, and is the theoretical model used to calculate the target LRCS.

[0075] Step 3-3: After obtaining the laser radar scattering cross section of each effective bin using the mathematical model of the unit laser radar scattering cross section in the above formula, the total LRCS of the target can be obtained by mathematically superimposing the laser radar scattering cross sections of all effective bins, that is:

[0076] Specifically, in step 4, target recognition in a foggy environment is completed, wherein the target recognition method is as follows:

[0077] When the echo power value exceeds the threshold, the signal is likely to be the echo signal of the target. During the simulation process, the simulation step size is set to be synchronized with the cycle of the laser detector's emission pulse. When the eight channels simultaneously emit laser pulses of the same frequency and pulse width, the receiving systems of the eight channels simultaneously detect. When the echo power received by any channel is continuous and stable, that is, when N consecutive echo signals exceed the set power threshold, the signal is considered to be the target signal, and a startup prompt is given in the software. During the judgment process, once the echo signal of the channel is discontinuous, it is necessary to recount until the end of the simulation. If there is no continuous and stable signal, it is considered that the detector has not detected the target, and then a prompt is given in the software that the detector has not been started.

[0078] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A space-time joint simulation method based on near-field target scattering model and dynamic cloud and fog environment, characterized in that: The following steps are involved: Step 1: Analyze and import the target three-dimensional model using the near-field target LRCS simulation software; Step 2, setting the cloud and fog environment according to the dynamic cloud and fog environment spatiotemporal joint simulation method; Step 3, calculating the near-field target LRCS simulation data according to the model analysis data; Step 4: Perform signal processing simulation calculations based on the LRCS simulation data to complete target recognition in a cloud and fog environment.

2. The method of spatiotemporal joint simulation based on near-field target scattering model and dynamic cloud and fog environment according to claim 1 is characterized in that: In step 1, a 3D model analysis method is used, which specifically includes the following steps: Step 1-1, read the relevant data of the target model, mainly including the vertex coordinates and material properties of each face element, and then obtain the area, center point, and external normal information of the face element according to the vertex coordinates of the face element; Step 1-2, process and store the data obtained in step 1-1, combine the initial position of the target geometric center in the detector coordinate system, perform coordinate transformation on the vertex coordinates and the external normal vectors of all small facets on the target surface, and transform them from the target coordinate system to the detector coordinate system; Step 1-3, in the detector coordinate system, judging whether the facet is simultaneously in the meridian field of view of the detector transceiver system by judging whether the facet center is simultaneously in the width of the transmitting and receiving beams; Step 1-4: whether the center of the cross-section element is simultaneously within the equatorial field of view of the detector transceiver system and whether the cross-section element is self-occluded or mutually-occluded; Step 1-5: Store the facets that meet steps 1-3 and 1-4 into a valid facet list.

3. The method of spatiotemporal joint simulation based on near-field target scattering model and dynamic cloud and fog environment according to claim 1 is characterized in that: In step 2, setting the fog environment requires using the fog visibility linear representation method, and the definition method specifically includes the following steps: Step 2-1: According to Mie scattering theory, for cloud particles with a particle size close to the laser wavelength, use the dimensionless scale parameter To determine the scattering model; according to the Mie scattering equation, calculate the scattering and absorption characteristics of a single spherical particle to the laser signal. The specific formula is as follows: Where I0 is the incident light intensity, λ is the wavelength, and r is the particle radius; the scattering coefficient K is calculated as follows: Where α is the scale parameter; Scattering coefficient a n and b n Solve using the continued fraction method, About D n (mα) is obtained by backward recursion: For the n value, select the following empirical formula: When the particles in the cloud are uniform, the laser radar scattering coefficient has the following formula: or Where N is the number of particles in the cloud, and the scale distribution function C(r) = ar α exp(-br β ), where r is the cloud particle size; a, b, α, β are distribution parameters; The calculation formula of laser radar scattering cross-sectional area LRCS is as follows: Step 2-2: Visibility changes dynamically with cloud density and particle distribution. A linear function model is used to describe the attenuation of cloud concentration at a certain moment. The simulation time is divided into multiple time points. The changes of cloud parameters over time are collected and recorded at each time step to form time series data. The data of each time step is used to simulate the changes of the cloud environment of the laser detector at different time points, thereby reflecting the dynamic characteristics of the cloud environment over time. The visibility model is based on the real-time update of cloud density and distribution, which is expressed as: I total =I0·y(t), where y(t) is a linear function related to time.

4. The method of spatiotemporal joint simulation based on near-field target scattering model and dynamic cloud and fog environment according to claim 1 is characterized in that: In step 3, the method for calculating the near-field target LRCS simulation data is defined to specifically include the following steps: Step 3-1: In the microwave scattering theory, the energy flux density of the incident plane wave is In the formula, E i is the incident electric field intensity, H i is the incident magnetic field intensity; When the laser radar scattering cross section is σ, the total intercepted power is Then the scattered power density at a distance R from the target is The scattered power density is also expressed as Know From the above formula we can deduce: The target must satisfy the far-field condition, i.e. λ is the wavelength of the incident wave, D is the target geometry, the target is considered as a point target, and the laser is considered as a plane incident wave; LRCS is expressed as Then the target unit area LRCS is In the formula, A is the area of ​​the target illuminated by the light beam; Step 3-2: When the receiving aperture area of ​​the receiving system is A r When the scattered power ΔP s With the incident power P i The ratio is Combining the above formula, the unit laser radar scattering cross section is σ 0 = In the formula, At the same time, the bidirectional reflectance distribution function is expressed as: From the above formula, we can get σ 0 =4πf r cosθ i cosθ r , which is the mathematical relationship between the unit area lidar scattering cross section and the bidirectional reflectance distribution function, and is the theoretical model used to calculate the target LRCS; Step 3-3, after obtaining the laser radar scattering cross section of each effective surface element using the mathematical model of the unit laser radar scattering cross section in the above formula, the laser radar scattering cross sections of all effective surface elements are mathematically superimposed to obtain the total LRCS of the target, that is:

5. The method of spatiotemporal joint simulation based on near-field target scattering model and dynamic cloud and fog environment according to claim 1 is characterized in that: In step 4, target recognition in a foggy environment is completed, wherein the target recognition method is as follows: When the echo power value exceeds the threshold, the signal is likely to be the echo signal of the target; during the simulation process, the simulation step size is set to be synchronized with the period of the laser detector emitting pulses. When the eight channels simultaneously emit laser pulses of the same frequency and pulse width, the receiving systems of the eight channels simultaneously detect. When the echo power received by any channel is continuous and stable, that is, when N consecutive echo signals exceed the set power threshold, the signal is considered to be the target signal, and a startup prompt is given in the software; during the judgment process, once the echo signal of the channel is discontinuous, it is necessary to recount until the end of the simulation. If there is no continuous and stable signal, it is considered that the detector has not detected the target, and then a prompt is given in the software that the detector has not been started.

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