A method for constructing spatial target pBRDF model based on improved SBA algorithm

Abstract

This application discloses a method for constructing a pBRDF model for a space target based on an improved SBA algorithm, relating to the technical field of space target polarization measurement and modeling. The method preliminarily establishes a pBRDF model for the target material corresponding to the sample to be tested based on pBRDF theory, elastic scattering theory of light, and blackbody radiation theory. The method obtains polarization characteristic measurement results of the sample to be tested to characterize the measured #imgabs0# values ​​of the sample to be tested at various angles under a simulated atmospheric environment. The improved SBA algorithm is used to perform parameter inversion based on the measured #imgabs1# values. The parameter inversion results are then introduced into the pBRDF model to calculate the error between the model simulation value and the measured value. The final pBRDF model is then selected based on the error accuracy to determine whether to output the pBRDF model. This application can improve the accuracy of polarization detection of space targets.

Description

Technical Field

[0001] The present application relates to the technical field of space target polarization measurement and modeling, and in particular to a method for constructing a space target pBRDF model based on an improved SBA algorithm. Background Art

[0002] Extensive research on polarization detection of space targets demonstrates that polarization detection technology offers unparalleled advantages over traditional optical detection methods, such as intensity and spectrum, in improving the detection and identification of faint space targets, diagnosing the target's state in the space environment, and mitigating the impact of atmospheric effects on space observations. This technology is highly likely to become a new means of effectively monitoring and identifying future spacecraft and space debris. Polarization detection of space targets requires pre-determining the polarization properties of the target material at all angles. The pBRDF (polarization bidirectional reflectance distribution function) is well-suited for studying the multi-angle polarization properties of target materials.

[0003] However, existing polarization measurement devices fail to fully consider the impact of the atmospheric environment on measurement accuracy. Furthermore, existing pBRDF-based parameter inversion methods are overly dependent on initial values ​​and suffer from poor robustness. Multi-angle degree of polarization (DOP) data and parameter inversion methods significantly impact the accuracy of pBRDF models, resulting in low accuracy in pBRDF models established using existing methods, which in turn affects the accuracy of polarization detection of space targets. Summary of the Invention

[0004] The purpose of this application is to provide a method for constructing a pBRDF model of a space target based on an improved SBA algorithm, which can improve the accuracy of measuring the polarization characteristics of space targets.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides a method for constructing a pBRDF model of a space target based on an improved SBA algorithm. The method for constructing a pBRDF model of a space target based on the improved SBA algorithm includes the following steps.

[0007] Initially construct a pBRDF model and obtain the polarization characteristic measurement results of the sample to be tested; the polarization characteristic measurement results include the polarization characteristic measurement results of the sample to be tested at various angles. value.

[0008] According to the polarization characteristic measurement results, an improved SBA algorithm is used to perform parameter inversion to obtain a parameter inversion result; in the improved SBA algorithm, a Tent chaotic mapping algorithm is used to initialize the mother plant position, and a simulated annealing algorithm is used to update the position of the mother plant's runner.

[0009] The parameter inversion result is brought into the pBRDF model to calculate the error between the model simulation value and the measured value, and whether to output the pBRDF model is selected according to the error accuracy to obtain the final pBRDF model; wherein, the final pBRDF model is used to analyze the infrared polarization characteristics of the sample to be tested at multiple angles.

[0010] Optionally, a pBRDF model is preliminarily constructed, specifically including the following steps.

[0011] According to the pBRDF theory, elastic scattering theory of light and blackbody radiation theory, the pBRDF model of the target material is preliminarily established and the Formula; the target material refers to the material corresponding to the sample to be tested.

[0012] Optionally, according to the polarization characteristic measurement result, an improved SBA algorithm is used to perform parameter inversion to obtain a parameter inversion result, which specifically includes the following steps.

[0013] The initial parameters of the SBA algorithm are defined; the initial parameters include the number of inversion parameters, the number of initial parent plant populations, the maximum number of iterations of the strawberry optimization algorithm and the Tent chaos mapping algorithm, and the maximum number of iterations of the simulated annealing algorithm.

[0014] According to the initial parameters, the mother plant position is initialized using the Tent chaotic mapping algorithm to obtain the initialized mother plant position.

[0015] According to the initialized mother plant position, the daughter roots and runners of the mother plant are randomly generated to obtain a plant propagation matrix; each mother plant in the plant propagation matrix randomly generates a daughter root and a runner.

[0016] According to the plant propagation matrix, the positions of the runners are updated by using a simulated annealing algorithm to obtain updated positions of the runners.

[0017] Based on the updated positions of the runners, fitness calculation is performed with the goal of minimizing the solution of the objective function, and the calculated fitness value and the roulette wheel algorithm are used to select the mother plant population for the next iteration.

[0018] Whether to output the parameter inversion result is determined based on whether any one of the termination conditions is met; the parameter inversion result includes the optimal inversion value of multiple parameters to be inverted, and the multiple parameters to be inverted include σ, n, k, k s 、km and k v , where σ is the roughness, n is the real part of the complex refractive index, k is the imaginary part of the complex refractive index, and k s is the mirror reflectivity, k m is the diffuse reflectivity, k v is the volume scattering rate.

[0019] The inversion accuracy is expressed according to the average value of the relative errors between the inverted values ​​and the measured values ​​of the parameters σ, n, and k to be inverted.

[0020] Optionally, the parameter inversion result is brought into the pBRDF model to calculate the error between the model simulation value and the measured value, and whether to output the pBRDF model is selected according to the error accuracy to obtain the final pBRDF model, which specifically includes the following steps.

[0021] Substitute the optimal inversion value corresponding to each parameter to be inverted in the parameter inversion result into the In the formula, the simulation of the pBRDF model is calculated value.

[0022] The simulation of the pBRDF model using an evaluation function The final pBRDF model is determined based on the evaluation results.

[0023] Optionally, the simulation of the pBRDF model is evaluated using an evaluation function The pBRDF model is determined based on the evaluation results, which specifically includes the following steps.

[0024] The simulation of the pBRDF model is calculated using the MAE function. The values ​​obtained from experimental measurements The MAE value between the values; the experimental measurement obtained The value refers to the polarization characteristic measurement result obtained by measurement. value.

[0025] The final pBRDF model is determined according to the MAE value and the preset threshold.

[0026] Optionally, determining a final pBRDF model according to the MAE value and a preset threshold value specifically includes the following steps.

[0027] When the MAE value is less than 5%, the simulation of the pBRDF model is determined to be If the value is within a reasonable threshold range, the current pBRDF model is used as the final pBRDF model.

[0028] When the MAE value is greater than or equal to 5%, the simulation of the pBRDF model is determined to be If the value is not within a reasonable threshold range, the pBRDF model is improved and the process returns to the step of "initializing the mother plant position according to the initial parameters using the Tent chaotic mapping algorithm to obtain the initialized mother plant position" until the MAE value is less than 5%.

[0029] Optionally, obtaining the polarization characteristic measurement result of the sample to be measured specifically includes the following steps.

[0030] The space target infrared polarization characteristic measuring device is used to measure the sample to be measured to obtain a polarization characteristic measurement result of the sample to be measured.

[0031] The device for measuring infrared polarization characteristics of space targets comprises a gas generation system, a closed moving system, an electric stage, a light source system, a polarization acquisition system, a control system and a computer.

[0032] The gas generating system is connected to the interior of the closed moving system through a pipeline, the electric stage is arranged inside the closed moving system, the light source system and the polarization collection system are arranged inside the closed moving system, the gas generating system, the electric stage, the light source system and the polarization collection system are all connected to the control system, and the control system is also connected to the computer.

[0033] The gas generating system is used to generate mixed gas and fill the mixed gas into the interior of the closed mobile system through the pipeline; the mixed gas includes aerosol and water vapor.

[0034] The electric stage is used to place the sample to be tested and rotate the sample to be tested.

[0035] The light source system is used to emit an infrared light beam to the sample to be tested.

[0036] The polarization acquisition system is used to acquire the polarization image of the sample to be tested.

[0037] The closed moving system is used to provide a closed environment for the mixed gas, and to move the light source system in the closed environment to achieve multi-angle emission of the infrared light beam, while also moving the polarization acquisition system in the closed environment to achieve multi-angle acquisition of the polarization image.

[0038] The control system is used to receive instructions issued by the computer, and control the working status of the gas generation system, the electric stage, the light source system and / or the polarization acquisition system according to the instructions; and obtain the polarization image and transmit it to the computer.

[0039] The computer is used to analyze and process the polarization image to obtain a measurement result of the polarization characteristics of the sample to be measured.

[0040] Optionally, the closed moving system includes a semicircular closed box and a semicircular guide rail.

[0041] The top center of the semicircular sealed box is connected to the gas generating system through the pipeline, and the electric loading platform is arranged just below the top center of the semicircular sealed box.

[0042] The semicircular guide rail is laid on the inner surface of the semicircular closed box, and the semicircular guide rail is connected to the control system.

[0043] The light source system and the polarization collection system are slidably arranged on the semicircular guide rail.

[0044] Optionally, the semicircular guide rail includes a first guide rail and a second guide rail.

[0045] The light source system is slidably arranged on the first guide rail, and the polarization collection system is slidably arranged on the second guide rail.

[0046] The first guide rail and the second guide rail are both provided with a first angle mark, the first angle mark is 0°~90°, and the graduation value of the first angle mark is 1°.

[0047] The first angle marks of 0° of the first guide rail and the second guide rail are both located at one end close to the top of the semicircular closed box.

[0048] Optionally, the electric loading platform is provided with a second angle mark, the second angle mark is 0°~360°, the graduation value of the second angle mark is 1°, and the second angle mark of 0° of the electric loading platform faces the first guide rail.

[0049] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0050] This application provides a method for constructing a pBRDF model of a space target based on an improved SBA algorithm. First, a pBRDF model is preliminarily constructed and the polarization characteristic measurement results of the sample to be tested are obtained, including the polarization characteristics of the sample to be tested at various angles. The improved SBA algorithm is then used to perform parameter inversion based on the polarization characteristic measurement results. The improved SBA algorithm uses the Tent Chaotic Map algorithm to initialize the mother plant position and the simulated annealing algorithm to update the position of the mother plant's runners. The parameter inversion results are then applied to a preliminarily constructed pBRDF model to calculate the error between the model simulation value and the measured value. Based on the error accuracy, the pBRDF model is then output to obtain the final pBRDF model. This final pBRDF model is used to analyze the long-wave infrared polarization characteristics of the sample under test at multiple angles, improving the accuracy of polarization detection of space targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A schematic structural diagram of a device for measuring infrared polarization characteristics of a space target provided in one embodiment of the present application.

[0053] Figure 2 A flowchart of a method for constructing a spatial target pBRDF model based on an improved SBA algorithm is provided in an embodiment of the present application.

[0054] Figure 3 A schematic diagram of the process of performing parameter inversion using the improved SBA algorithm provided in one embodiment of the present application.

[0055] Figure 4 A schematic diagram of the workflow of the model creation module provided in one embodiment of the present application.

[0056] Figure 5 A schematic diagram of the workflow of the image signal acquisition module provided in one embodiment of the present application.

[0057] Figure 6 A schematic diagram of the process of initializing the mother plant position provided in one embodiment of the present application.

[0058] Figure 7 A schematic diagram of the process of generating a new maternal population provided in one embodiment of the present application.

[0059] Figure 8 A three-dimensional coordinate diagram of DOLP, azimuth angle, and zenith angle generated by the pBRDF model provided in one embodiment of the present application.

[0060] Figure 9Polar coordinate diagram generated by the pBRDF model provided in one embodiment of the present application.

[0061] Reference numerals:

[0062] 1-Gas generation system; 2-Semicircular sealed box; 3-Semicircular guide rail; 4-Motorized stage; 5-Light source system; 6-Long-wave infrared polarization camera; 7-Control system; 8-Computer. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0064] At present, the existing polarization measurement devices do not take into account the influence of the atmospheric environment on the measurement accuracy, and the multi-angle The values ​​are the fundamental data for parameter inversion and constructing the pBRDF model. Traditional parameter inversion methods primarily include nonlinear least squares and particle swarm optimization. Nonlinear least squares is sensitive to initial values ​​and requires a large amount of experimental data, resulting in high computational complexity. Particle swarm optimization, on the other hand, can be inaccurate for certain problems, and its performance and results can be affected by random factors, resulting in low robustness.

[0065] Based on this, this embodiment aims to provide a method for constructing a pBRDF model for a space target based on an improved Strawberry Optimization (SBA) algorithm. This pBRDF model is based on pBRDF theory, elastic scattering theory, and blackbody radiation theory. As a model of the infrared polarization characteristics of a space target, this pBRDF model can be used to further analyze the infrared polarization characteristics of a space target at multiple angles. Infrared refers to the 8-14 μm wavelength band. First, a space target infrared polarization characteristic measurement device is used to accurately measure the infrared polarization characteristics of a space target under a simulated atmospheric environment, obtaining more accurate, reliable, and multi-angle polarization characteristic measurement results while simultaneously constructing a preliminary pBRDF model. These polarization characteristic measurement results are then used to refine the pBRDF model. The final pBRDF model is obtained through parameter inversion and evaluation verification. This method effectively addresses the initial value dependence and poor robustness of traditional parameter inversion methods. It also improves the slow convergence speed and susceptibility to local optimal solutions of the traditional SBA algorithm, thereby improving the accuracy of parameter inversion for metal objects.

[0066] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0067] like Figure 1 As shown, this embodiment provides a device for measuring the infrared polarization characteristics of a space target, which includes a gas generation system 1, a closed moving system, an electric stage 4, a light source system 5, a polarization acquisition system 6, a control system 7 and a computer 8.

[0068] In which, the gas generation system 1 is connected to the interior of the closed mobile system through a pipeline, the electric stage 4 is arranged inside the closed mobile system, the light source system 5 and the polarization collection system 6 are arranged inside the closed mobile system, the gas generation system 1, the electric stage 4, the light source system 5 and the polarization collection system 6 are all connected to the control system 7, and the control system 7 is also connected to the computer 8.

[0069] In this embodiment, the gas generation system 1 is used to generate a mixed gas and to fill the interior of the closed mobile system via the pipeline. The mixed gas includes aerosol and water vapor. The gas generation system 1 adopts the HRF-4B series gas generation system 1 of Suzhou Hongrui Purification Technology Co., Ltd., which is used to generate mixed gases of aerosol and water vapor in different proportions.

[0070] In this embodiment, the light source system 5 is used to emit an infrared beam toward the sample to be tested. The light source system 5 is a blackbody light source. The blackbody light source adopts the SLS303 series light source from Thorlabs, which can generate a 550nm-15μm beam with an output beam power of 4.5W and an output beam diameter of 50mm.

[0071] In this embodiment, the polarization acquisition system 6 is used to acquire the polarization image of the sample to be tested. The polarization acquisition system is a long-wave infrared polarization camera. The long-wave infrared polarization camera adopts the GWPL0318X2A polarization camera produced by Northern Guangwei. It is a focal plane polarization camera with a response band of 8-14 μm and can be used to obtain long-wave infrared polarization information of the object.

[0072] In this embodiment, the closed moving system is used to provide a closed environment for the mixed gas, and to move the light source system 5 in the closed environment to achieve multi-angle emission of the infrared light beam, and at the same time, to move the polarization acquisition system 6 in the closed environment to achieve multi-angle acquisition of the polarization image.

[0073] In this embodiment, the control system 7 is used to receive instructions issued by the computer 8, and control the working status of the gas generation system 1, the electric stage 4, the light source system 5 and / or the polarization acquisition system 6 according to the instructions; and obtain the polarization image and transmit it to the computer 8.

[0074] In this embodiment, the computer 8 is used to analyze and process the polarization image to obtain the polarization characteristic measurement results of the sample to be tested. The polarization characteristic measurement results include the polarization characteristics of the sample to be tested at various angles. In this embodiment, the polarization characteristic measurement results obtained by the space target infrared polarization characteristic measurement device can be used for parameter inversion and to construct and output a pBRDF model to further analyze the infrared polarization characteristics of the space target at multiple angles in detail.

[0075] In this embodiment, the computer 8 has built-in contrast software for extracting the polarization degree of the polarization image.

[0076] In this embodiment, the closed motion system includes a semicircular sealed box 2 and a semicircular guide rail 3. The top center of the semicircular sealed box 2 is connected to the gas generation system 1 via the pipeline, and the motorized stage 4 is located directly below the top center of the semicircular sealed box 2. The semicircular guide rail 3 is laid on the inner surface of the semicircular sealed box 2 and is connected to the control system 7. The light source system 5 and the polarization collection system 6 are slidably mounted on the semicircular guide rail 3.

[0077] In this embodiment, the semicircular guide rail 3 is a custom model and is divided into left and right sides. The left semicircular guide rail 3 is the first guide rail, which is used to dynamically measure angles between 0° and 90°. The right semicircular guide rail 3 is the second guide rail, which is used to dynamically measure angles between 0° and 90°.

[0078] The light source system 5 is slidably provided on the first guide rail, that is, the light source system 5 can slide along the first guide rail; the polarization collection system 6 is slidably provided on the second guide rail, that is, the polarization collection system 6 can slide along the second guide rail.

[0079] In this embodiment, the first guide rail and the second guide rail are both provided with a first angle mark, the first angle mark ranging from 0° to 90°, and the first angle mark having a graduation value of 1°. The first angle mark of 0° of the first guide rail and the second guide rail are both located at one end near the top of the semicircular sealed box 2.

[0080] In this embodiment, the motorized stage 4 is used to place and rotate the sample to be tested. The motorized stage 4 is a Thorlabs DDR100 series motorized stage, which dynamically measures angles between 0° and 360° and is provided with a second angle marker. The second angle marker ranges from 0° to 360°, with a graduation of 1°, and the second angle marker at 0° faces the first guide rail.

[0081] In this embodiment, the control system 7 includes a gas generation control system, a light source control system, an imaging device control system, and a motorized turntable control system. The gas generation control system is used to control the operating state and power of the gas generation system 1. The light source control system is used to control the operating state and power of the light source system 5. The imaging device control system is used to control the operating state and power of the long-wave infrared polarization camera 6. The motorized turntable control system is used to control the operating state and rotation angle of the motorized stage 4.

[0082] In this embodiment, the sample to be tested is a disc made of aluminum alloy, titanium alloy and stainless steel, three metal materials commonly used in spacecraft, and its diameter is equal to the beam aperture, both of which are 50 mm.

[0083] In an exemplary embodiment, a method for constructing a pBRDF model of a space target based on an improved SBA algorithm is provided. The method for constructing a pBRDF model of a space target based on the improved SBA algorithm can apply the above-mentioned space target infrared polarization characteristic measurement device to obtain polarization characteristic measurement results, and perform parameter inversion and verification based on the polarization characteristic measurement results to determine the final pBRDF model. Figure 2 As shown in FIG, the method for constructing a spatial target pBRDF model based on the improved SBA algorithm mainly includes the following steps.

[0084] Step S1: preliminarily construct a pBRDF model and obtain the polarization characteristic measurement results of the sample to be tested.

[0085] In this embodiment, the polarization characteristics measurement results of the sample to be tested can be measured using a space target infrared polarization characteristics measurement device to obtain the polarization characteristics of the sample to be tested at various angles. value.

[0086] Step S2: Based on the polarization characteristic measurement results, an improved SBA algorithm is used to perform parameter inversion to obtain a parameter inversion result.

[0087] In this embodiment, compared with the traditional SBA algorithm, the improved SBA algorithm mainly improves the position of the mother plant by using the Tent chaos mapping algorithm and the position of the runner of the mother plant by using the simulated annealing algorithm.

[0088] Step S3: Substitute the parameter inversion results into the pBRDF model to calculate the error between the model simulation value and the measured value, and select whether to output the pBRDF model based on the error accuracy to obtain a final pBRDF model. The final pBRDF model can be used to further comprehensively and in detail analyze the infrared polarization characteristics of the sample under test at multiple angles.

[0089] In this embodiment, step S1 preliminarily constructs a pBRDF model, which specifically includes the following steps.

[0090] According to the pBRDF theory, elastic scattering theory of light and blackbody radiation theory, the pBRDF model of the target material is preliminarily established and the Formula; the target material refers to the material corresponding to the sample to be tested.

[0091] In this embodiment, step S2 performs parameter inversion using an improved SBA algorithm based on the polarization characteristic measurement result to obtain a parameter inversion result, which specifically includes the following steps.

[0092] Step S21, defining the initial parameters of the SBA algorithm; the initial parameters include the number of inversion parameters, the initial maternal plant population, the maximum number of iterations of the strawberry optimization algorithm and the Tent chaos mapping algorithm, and the maximum number of iterations of the simulated annealing algorithm.

[0093] Step S22: Initialize the mother plant position using the Tent chaotic mapping algorithm according to the initial parameters to obtain the initialized mother plant position.

[0094] Step S23: randomly generating daughter roots and runners of the mother plant according to the initialized mother plant position to obtain a plant propagation matrix; each mother plant in the plant propagation matrix randomly generates a daughter root and a runner.

[0095] Step S24: According to the plant propagation matrix, a simulated annealing algorithm is used to update the position of the runner to obtain the updated position of the runner.

[0096] Step S25: Based on the updated positions of the runners, fitness calculation is performed with the goal of minimizing the objective function solution, and a new parent population for the next iteration is selected using the fitness value and the roulette wheel algorithm.

[0097] Step S26: Determine whether to output the parameter inversion result based on whether any one of the termination conditions is met. The parameter inversion result includes the optimal inversion values ​​of multiple parameters to be inverted. The multiple parameters to be inverted include σ, n, k, k s 、k m and k v , where σ is the roughness, n is the real part of the complex refractive index, k is the imaginary part of the complex refractive index, and ks is the mirror reflectivity, k m is the diffuse reflectivity, k v is the volume scattering rate. Wherein, the termination condition is as follows.

[0098] (1) Tent chaos mapping, strawberry optimization algorithm and simulated annealing algorithm have all reached the maximum number of iterations.

[0099] (2) A set of optimal solutions emerges so that the fitness value of the objective function is greater than 20.

[0100] Step S27: Inversion accuracy is expressed based on the average relative error between the inverted and measured values ​​of the parameters to be inverted σ, n, and k. The relative error is the ratio of the absolute error to the true value. The absolute error is the absolute value of the difference between the measured and inverted values ​​of the parameters to be inverted, and the true value is the measured value of the parameters to be inverted.

[0101] In this embodiment, step S3 brings the parameter inversion result into the pBRDF model to calculate the error between the model simulation value and the measured value, and selects whether to output the pBRDF model based on the error accuracy to obtain the final pBRDF model, which specifically includes the following steps.

[0102] Step S31: Substitute the optimal inversion value corresponding to each parameter to be inverted in the parameter inversion result into the In the formula, the simulation of the pBRDF model is calculated value.

[0103] Step S32: using an evaluation function to simulate the pBRDF model The final pBRDF model is determined based on the evaluation results.

[0104] In this embodiment, step S32 uses the evaluation function to evaluate the simulation of the pBRDF model. The pBRDF model is determined based on the evaluation results, which specifically includes the following steps.

[0105] Step S321: Use the MAE (mean absolute error) function to calculate the simulation of the pBRDF model. The values ​​obtained from experimental measurements The MAE value between the values. The value refers to the polarization characteristic measurement result obtained by measurement. For example, the polarization characteristic measurement result of the sample to be measured using the above-mentioned space target infrared polarization characteristic measurement device is value.

[0106] Step S322: Determine the final pBRDF model according to the MAE value and the preset threshold.

[0107] In this embodiment, step S322 determines the final pBRDF model according to the MAE value and the preset threshold, which specifically includes the following two cases.

[0108] (1) When the MAE value is less than 5%, the simulation of the pBRDF model is determined to be If the value is within a reasonable threshold range, the current pBRDF model is used as the final pBRDF model.

[0109] (2) When the MAE value is greater than or equal to 5%, the simulation of the pBRDF model is determined to be If the value is not within a reasonable threshold range, it is necessary to further improve the model and return to step S22 "according to the initial parameters, use the Tent chaotic mapping algorithm to initialize the mother plant position to obtain the initialized mother plant position", reinitialize the mother plant position and obtain the corresponding parameter inversion result, and according to the re-obtained parameter inversion result, re-submit the parameter inversion result into the pBRDF model to calculate the error between the model simulation value and the measured value, and choose whether to output the pBRDF model according to the error accuracy, until its corresponding MAE value is less than 5%.

[0110] In order to make the technical solution of this embodiment clearer, the specific structure of the device of this embodiment and the implementation steps of the method are described in detail below in the form of examples.

[0111] This embodiment proposes a method for constructing a spatial target pBRDF model based on an improved SBA algorithm, such as Figure 3 As shown in Figure 2, the method is divided into the polarization information acquisition stage, the parameter inversion stage and the verification stage. The polarization information acquisition stage uses the model creation module and the image signal acquisition module. Figure 4 As shown in the figure, the model creation module is mainly used to determine the pBRDF theory based on the specular reflection component, diffuse reflection component and body scattering component, and comprehensively consider the pBRDF theory, elastic scattering theory of light and blackbody radiation theory to preliminarily construct the pBRDF model and calculate Formula. Figure 5As shown, the image signal acquisition module is mainly used to use the gas generation system 1 to start inflation and use the blackbody light source to generate a light beam. The control system 7 is used to set the activity range and step angle of the long-wave infrared light source, the long-wave infrared polarization camera and the electric stage 4. The light source on the first guide rail slides in the range of 20°~60° according to the step angle of 10°. Every time the light source steps, the electric stage 4 rotates one circle at a step angle of 120°. Every time the light source and the electric stage 4 step, the long-wave infrared polarization camera on the second guide rail slides from 20° to 60° at a step angle of 2°, and at the same time, a photo of the sample to be tested is taken to realize the acquisition of polarization signals. Then, the contrast software is used to extract the polarization degree of the photo, and the polarization degree data is sorted out, that is, the polarization degree data obtained by experimental measurement. value.

[0112] In this embodiment, the polarization information acquisition stage specifically includes the following steps.

[0113] Step 1: The gas generating system 1 starts to inflate. The ratio of aerosol to water vapor in the mixed gas generated by the gas generating system 1 is set by the control system 7, and then the semicircular closed box 2 is started to be inflated.

[0114] Among them, the main components of the incoming gas are water vapor and a small amount of aerosol, because these two in the atmosphere have the greatest impact on the long-wave infrared polarization detection of space targets.

[0115] Step 2: A blackbody light source generates a light beam. The control system 7 sets the range of motion and step angles of the long-wave infrared light source, long-wave infrared polarization camera, and motorized stage 4. The long-wave infrared light source is located on the first guide rail, and the long-wave infrared polarization camera is located on the second guide rail. The light source on the first guide rail, i.e., the long-wave infrared light source, and the long-wave infrared polarization camera on the second guide rail, both slide within a zenith angle range of 20° to 60°, while the motorized stage 4 rotates within a range of 0° to 360°. The 0° angle of the motorized stage 4 is directly opposite the first guide rail. The step angle of the long-wave infrared light source is 10°, the step angle of the long-wave infrared polarization camera 6 is 2°, and the step angle of the motorized stage 4 is 120°.

[0116] Step 3: Control the long-wave infrared polarization camera 6 to start shooting. Each time the long-wave infrared light source steps forward, the motorized stage 4 rotates one circle. Each time the long-wave infrared light source and the motorized stage 4 step forward, the long-wave infrared polarization camera 6 steps from 20° to 60°. A cumulative round of acquisition can obtain 5×4×21=420 polarization images, numbered 001-420.

[0117] Step 4: Use contrast software to extract the polarization degree of the photo. Input all polarization images in the data set into Computer 8. Use contrast software to obtain the polarization degree of the sample under test in each polarization image. Ultimately, the polarization degree data of the sample under test at different zenith angles and azimuth angles under different simulated atmospheric environments are obtained.

[0118] Compared to traditional nonlinear least squares methods and particle swarm optimization algorithms, the improved SBA algorithm achieves higher accuracy and greater robustness for parameter inversion of metal objects using the same number of experimental data sets. This makes it ideal for parameter inversion of space targets, which are primarily made of metal and whose polarization properties must be affected by the atmospheric environment. The SBA algorithm is a heuristic algorithm that describes how strawberry plants migrate from one location to another and produce multiple daughter roots and runners. Strawberry plants use roots and runners to conduct local and global searches to discover vital resources. Whether a location is resource-rich determines the quality of the objective function solution. Moving roots and runners to new, resource-rich locations to produce daughter plants is considered to be finding an optimal solution. Through continuous iteration, the optimal solution to the objective function can be obtained. The basic concept of the SBA algorithm in this embodiment is that the process of each mother root producing new daughter roots and runners represents the inversion of a set of parameters, and the position of each new root and new runner represents a set of inverted parameter values. The mother plant required for the next iteration is selected by combining the fitness value of the objective function with the roulette wheel algorithm. This iteration is repeated until the predetermined termination condition is met and the final optimal parameter solution is obtained. At the same time, this embodiment introduces the Tent chaos mapping algorithm and simulated annealing algorithm to improve the traditional SBA algorithm, improving the traditional SBA algorithm's slow convergence speed and susceptibility to falling into local optimal solutions.

[0119] In this embodiment, a parameter inversion module is used in the parameter inversion stage, and the working process of the parameter inversion module specifically includes the following steps.

[0120] The parameters to be inverted in this embodiment include roughness σ, the real part of the complex refractive index n, the imaginary part of the complex refractive index k, and the mirror reflectivity k s , diffuse reflectivity k m and the volume scattering rate k v .

[0121] Among them, in the parameter inversion algorithm, the position solutions of the daughter roots and runners represent a set of unknown parameters to be inverted. Each mother plant inverts 6 parameters to be inverted at the same time, and the optimal solution obtained is the optimal inversion value of the parameters to be inverted obtained in the final inversion.

[0122] Step 1: First, construct a pBRDF model based on the pBRDF theory, elastic scattering theory of light, and blackbody radiation theory, and then calculate the polarization degree , The formula is as follows.

[0123] .

[0124] Among them, the known parameters are as follows: The Mueller matrix representing the mixture of aerosols and water vapor in the atmosphere, is the irradiance on the surface of the object, is the incident zenith angle, To detect the zenith angle, To detect the azimuth, is the Mueller matrix of the specular reflection component, 、 、 Both The elements in and denote the diffuse reflection component and volume scattering component respectively, is the blackbody radiation formula of the object, is the shadow masking function, is the probability distribution function of the surface normal, σ is the roughness, n is the real part of the complex refractive index, and k is the imaginary part of the complex refractive index. The unknown parameters are: mirror reflectivity k s , diffuse reflectivity k m , volume scattering rate k v Among them, σ, n, k, k s 、k m 、k v are the parameters to be inverted.

[0125] Among them, the pBRDF theory describes the reflection characteristics of the target material at various observation angles; the elastic scattering theory of light includes Rayleigh scattering and Mie scattering, which mainly describes the interaction between light and atmospheric particles; the blackbody radiation theory mainly describes the spontaneous radiation of objects.

[0126] Step 2: First, set the initial parameters of the SBA algorithm, including the number of inversion parameters m, the number of initial parent plant populations N, the maximum number of iterations of the Tent chaos mapping algorithm and the simulated annealing algorithm t max1 , the maximum number of iterations t of the Strawberry optimization algorithm max2 , the number of inversion parameters m=6, N is generally between 30 and 50. Since there are many inversion parameters, N=50. The number of iterations is generally between 50 and 500. Since N is large, more searches can be performed in each iteration, so the number of iterations can be appropriately reduced. t max1 =50, t max2 =200.

[0127] Step 3: Initialize the mother plant position using the Tent chaos mapping algorithm.

[0128] Because the traditional SBA algorithm uses a random operator to initialize the mother plant positions, it is prone to problems such as uneven mother plant position distribution, weak global search capabilities, and low population diversity, which can lead to falling into local optimality and affect the overall optimization efficiency. Therefore, this embodiment introduces a Tent chaotic mapping algorithm into the SBA algorithm to initialize the mother population. The Tent chaotic mapping algorithm incorporates the random number rand(0, 1) / N, which maintains the randomness, ergodicity, and regularity of the Tent chaotic mapping algorithm and can effectively prevent iterations from falling into small periodic points and unstable periodic points. The Tent chaotic mapping sequence is defined as follows.

[0129] .

[0130] .

[0131] in, represents the t+1th iteration value of the chaotic map, represents the t-th iteration value of the chaotic map, 0 <t<t max1 , rand(0,1) is a random number uniformly distributed between 0 and 1; N is the number of the initial mother plant population; X lb is the lower bound of the position variable, X ub is the upper bound of the position variable, represents the position of the i-th mother plant after the t-th iteration.

[0132] In this embodiment, when the Tent chaotic mapping algorithm is used to initialize the position of the mother plant, Figure 6 As shown in the figure, the position of the initial population is first calculated, and then a random number sequence rand(0, 1) in the interval [0, 1] is generated. Then, the chaotic distribution value of each individual in the interval [0, 1] is generated using the Tent mapping formula, and then the chaotic distribution value of each individual is converted into the actual position parameter to finally generate the initial population.

[0133] Step 4: Each mother plant randomly generates a daughter root and runner.

[0134] The initial population of mother plants is represented by an m×N matrix. Then, each mother plant randomly generates a closer root and a farther runner based on the initial population in each iteration, thereby generating new roots and runners. In order to obtain the optimal solution, the runners search everywhere. When the runners just reach the local minimum, the algorithm will achieve faster speed and better global search ability. Through continuous iterations, after the t+1th iteration, each variable can search for a possible optimal solution. The process can be expressed as follows:

[0135] .

[0136] in, represents the plant reproduction matrix at iteration t+1, 0 <t<t max2 , whose dimension is m × 2N, where m is the number of runners and N is the number of the initial mother plant population; and They represent the root position and the stolon position at iteration t+1, respectively, and their dimensions are both m×N; is the optimal solution for the root and runner positions at the tth iteration, and its dimension is also m×N; d root and d runner are two scalars, representing the distance from the root to the mother plant and the distance from the runner to the mother plant, usually d runner > d root ; r1 and r2 are random matrices whose members are uniformly distributed in the range [-0.5, 0.5].

[0137] Step 5: Use simulated annealing algorithm to update the position of the runners.

[0138] In this embodiment, the simulated annealing algorithm combines the Gaussian distribution with the Cauchy distribution, gradually shifting from a global search to a local search as the number of iterations increases. Initially, because global search dominates, the algorithm is able to explore a larger solution space; later, local search becomes dominant, helping to find a more accurate solution within a smaller range. In this way, the algorithm can more effectively avoid falling into local optimal solutions while ensuring that a better solution is found during the convergence phase. The following formula is used to calculate the position of the updated runner.

[0139] .

[0140] .

[0141] .

[0142] in, represents the stolon position obtained at the t+1th iteration, 0 <t<t max1 , and are the new positions generated by the Gaussian distribution and the Cauchy distribution at the t-th iteration, respectively. is the scale parameter of the Gaussian distribution, is the scale parameter of the Cauchy distribution; N(0,1) and C(0,1) are the standard normal distribution and the standard Cauchy distribution, respectively. Indicates the position of the stolon at the tth iteration. The Gaussian distribution is used for local search and requires a smaller step size, so the scale parameter of the Gaussian distribution is It is usually between 0.01 and 0.1, and the Cauchy distribution is used for global search and requires a larger step size, so the scale parameter of the Cauchy distribution is It is usually between 0.1 and 1.0. =0.05, = 0.5. This runner position update formula can avoid falling into a local optimal solution as much as possible, while ensuring that a better solution is found during the convergence stage.

[0143] Step 6: Calculation of fitness value.

[0144] In this embodiment, the fitness values ​​of all optimization solutions based on the objective function It can be expressed as the following formula.

[0145] .

[0146] in, a It is an adjustable parameter used to adjust the calculation method of the fitness value. It is usually set to 0. is the objective function, which is expressed as follows.

[0147] .

[0148] Among them, σ, n, k, k s 、k m and k v are all parameters to be inverted, σ is the roughness, n is the real part of the complex refractive index, k is the imaginary part of the complex refractive index, and k s is the mirror reflectivity, k m is the diffuse reflectivity, k v is the volume scattering rate, The experimental measurements value, To substitute the optimal solution value, is the incident zenith angle, To detect the zenith angle, is the difference between the incident zenith angle and the detection zenith angle.

[0149] Step 7: Select a new parent population for the next iteration.

[0150] First, the fitness values ​​of the calculated objective function are sorted in ascending order. Sorting is performed and the optimized solutions with the first N / 2 smaller fitness values ​​are selected. Then the roulette algorithm is used to select the remaining N / 2 optimal solutions. Finally, the N optimal solutions after the combination of the two selections are used as the new parent population for the next iteration.

[0151] In this embodiment, the expression of the roulette algorithm is as follows.

[0152] .

[0153] .

[0154] in, Represents an individual i The probability of an individual being selected, Represents an individual i The fitness of is the sum of all individual fitness, that is, the cumulative probability.

[0155] Then randomly generate an array m, the elements in the array range from 0 to 1, and sort them from small to large. F total If it is greater than the element m[i] in the array, the individual x(i) is selected. If it is less than m[i], the next individual x(i+1) is compared until an individual is selected. This step is repeated N / 2 times to obtain the remaining N / 2 optimal solutions.

[0156] This embodiment uses the fitness value and roulette algorithm to select the new parent population for the next iteration, such as Figure 7 As shown, the fitness value is first calculated, then the calculated fitness values ​​are sorted in ascending order, and the first N / 2 smaller fitness values ​​are selected. At the same time, the roulette algorithm is used to calculate the sum of the fitness values ​​of all individuals in the population, and the selection probability of each individual is calculated using the roulette algorithm, thereby generating a random number and selecting individuals. After repeating N / 2 times, N / 2 fitness values ​​can be obtained. Then, combined with the first N / 2 smaller fitness values ​​selected, a total of N fitness values ​​are obtained. Finally, the obtained N fitness values ​​are used as the mother plant position for the next iteration.

[0157] Step 8: Loop or output the final solution.

[0158] In this embodiment, when any one of the following two termination conditions occurs, the loop is terminated and the optimal inversion values ​​of the six parameters to be inverted are output.

[0159] (1) The Tent chaos mapping algorithm, the Strawberry optimization algorithm, and the simulated annealing algorithm all reached the maximum number of iterations.

[0160] (2) A set of optimal solutions emerges, making the fitness value of the objective function Greater than the fitness threshold. In this embodiment, the fitness threshold is set to 20.

[0161] Step 9: Calculate the accuracy of the inversion parameters.

[0162] In this embodiment, the relative errors of the parameters σ, n, and k are calculated respectively, and then the average value of the relative errors of the three parameters is used to represent the inversion result. The relative error expression is as follows.

[0163] .

[0164] in, δ is the relative error value; Δ is the absolute error value, that is, the absolute value of the difference between the measured value and the inverted value of the parameter to be inverted; L is the true value, that is, the measured value of the parameter to be inverted.

[0165] In this embodiment, a test module is used in the verification stage, and the workflow of the test module specifically includes the following steps.

[0166] First, we get σ, n, k, k s 、k m and k v After obtaining the optimal inversion values ​​of the six parameters to be inverted, substitute the optimal inversion values ​​of the six parameters to be inverted into The simulation of the pBRDF model is obtained from the formula The value represents the polarization data simulated by the pBRDF model. Then the simulation is judged by the evaluation function. Whether the value is within a reasonable threshold range, in this embodiment, the evaluation function adopts MAE, which is expressed as follows.

[0167] .

[0168] in, Simulation of the pBRDF model value, The experimental measurements value, n is the total number of iterations of the inversion algorithm, n=300.

[0169] Then judge the polarization data simulated by the model (the simulation of the pBRDF model value) and the experimentally measured polarization data (the experimentally measured If the MAE value of the data between the values ​​is less than 5%, it means that the pBRDF model simulated The value is within a reasonable threshold range, thereby obtaining the final pBRDF model. When the MAE value is greater than or equal to 5%, the pBRDF model needs to be further modified, and then jump to step S22 "according to the initial parameters, the mother plant position is initialized using the Tent chaos mapping algorithm to obtain the initialized mother plant position", reinitialize the mother plant position and obtain the corresponding parameter inversion results, and reconstruct the pBRDF model and simulation based on the re-obtained parameter inversion results. The calculation and evaluation of the value is continued until the corresponding MAE value is less than 5%.

[0170] Figure 8 The three-dimensional coordinate diagram of DOLP (degree of linear polarization) generated by the pBRDF model versus the detection azimuth and zenith angles shows that at an azimuth of 180°, the degree of polarization first increases and then decreases with the change in zenith angle, reaching its maximum at an observation zenith angle of 45°. Figure 9 Polar coordinate diagram generated for the pBRDF model, where the radial direction is the observation zenith angle and the circumference is the observation azimuth angle. It describes the polarization characteristics of the aluminum plate material in the entire hemisphere under the simulated atmospheric environment. The polarization characteristics distribution is symmetrical about the 0°-180° azimuth angle. Figure 8 and Figure 9 The darker the color, the lower the polarization degree, and the whiter the color, the higher the polarization degree; the azimuth angle of the light source is set to 0° and the zenith angle is set to 45°.

[0171] In view of the problems of the current pBRDF-based parameter inversion method being dependent on the initial value and having poor robustness, as well as the problems of the traditional SBA algorithm being slow to converge and prone to falling into local optimal solutions, this embodiment first establishes a pBRDF model of the target material based on the pBRDF theory, the elastic scattering theory of light, and the blackbody radiation theory, and obtains Formula, and then use the space target infrared polarization characteristic measurement device to collect polarization images and obtain multi-angle polarization characteristic measurement results of the sample to be tested; at the same time, couple the improved SBA algorithm to invert the roughness, complex refractive index and other inversion parameters, and finally substitute the optimal inversion value of the inversion parameter into After the formula is obtained, the simulation of the target material can be obtained The method can effectively solve the shortcomings of traditional parameter inversion methods. Compared with traditional parameter inversion methods such as particle swarm optimization, the inversion accuracy of metal objects can be improved by about 20%, thus obtaining a more accurate pBRDF model.

[0172] This pBRDF model is used to analyze the long-wave infrared polarization characteristics of the sample material under test at multiple angles. For long-wave infrared polarization detection, a detection method with low resolution and high background noise, the accuracy of polarization detection of space targets can be improved by comparing the polarization data obtained using this pBRDF model. It can also provide a reference for constructing polarization characteristic models in different environments in other fields.

[0173] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0174] This specification uses specific examples to illustrate the principles and implementation methods of this application. The above examples are only intended to help understand the method and core concept of this application. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of this application. In summary, the contents of this specification should not be construed as limiting this application.

Claims

1. A method for constructing a spatial target pBRDF model based on an improved SBA algorithm, characterized in that: The method for constructing a spatial target pBRDF model based on the improved SBA algorithm includes: Initially construct a pBRDF model and obtain the polarization characteristic measurement results of the sample to be tested; the polarization characteristic measurement results include the polarization characteristic measurement results of the sample to be tested at various angles. value; wherein, the polarization characteristic measurement result is obtained by a space target infrared polarization characteristic measurement device in a simulated atmospheric environment, wherein the simulated atmospheric environment generates a mixed gas containing aerosol and water vapor through a gas generation system and fills the mixed gas into the interior of a closed mobile system; preliminarily constructing a pBRDF model, specifically including: preliminarily establishing a pBRDF model of the target material based on the pBRDF theory, the elastic scattering theory of light and the blackbody radiation theory, and determining Formula; the target material refers to the material corresponding to the sample to be tested, and the pBRDF theory is used to describe the reflection characteristics of the target material at various observation angles; the elastic scattering theory of light includes Rayleigh scattering and Mie scattering, which are used to describe the interaction between light and atmospheric particles; the blackbody radiation theory is used to describe the spontaneous radiation of an object; The formula is: Among them, the known parameters are as follows: The Mueller matrix representing the mixture of aerosols and water vapor in the atmosphere, is the irradiance on the surface of the object, is the incident zenith angle, To detect the zenith angle, To detect the azimuth, is the Mueller matrix of the specular reflection component, 、 、 Both The elements in and denote the diffuse reflection component and the volume scattering component respectively, is the blackbody radiation formula of the object, is the shadow masking function, is the probability distribution function of the surface normal, σ is the roughness, n is the real part of the complex refractive index, and k is the imaginary part of the complex refractive index; the unknown parameters are: mirror reflectivity k s , diffuse reflectivity k m , volume scattering rate k v , where σ, n, k, k s 、k m 、k v is the parameter to be inverted; According to the polarization characteristic measurement results, an improved SBA algorithm is used to perform parameter inversion to obtain a parameter inversion result; in the improved SBA algorithm, a Tent chaotic mapping algorithm is used to initialize the mother plant position, and a simulated annealing algorithm is used to update the position of the mother plant's runner; according to the polarization characteristic measurement results, an improved SBA algorithm is used to perform parameter inversion to obtain a parameter inversion result, which specifically includes: defining initial parameters of the SBA algorithm; the initial parameters include the number of inversion parameters, the initial mother plant population number, the maximum number of iterations of the strawberry optimization algorithm and the Tent chaotic mapping algorithm, and the maximum number of iterations of the simulated annealing algorithm; according to the initial parameters, the Tent chaotic mapping algorithm is used to initialize the mother plant position to obtain the initialized mother plant position; according to The initialized mother plant position is used to randomly generate daughter roots and runners of the mother plant to obtain a plant reproduction matrix; each mother plant in the plant reproduction matrix randomly generates a daughter root and a runner; according to the plant reproduction matrix, a simulated annealing algorithm is used to update the position of the runner to obtain the position of the updated runner; based on the position of the updated runner, fitness calculation is performed with the goal of minimizing the solution of the objective function, and the mother plant population for the next iteration is selected using the calculated fitness value and a roulette algorithm; whether to output a parameter inversion result is determined based on whether any one of the termination conditions is met; the parameter inversion result includes optimal inversion values ​​of multiple parameters to be inverted, and the inversion accuracy is represented by the average value of the relative errors between the inversion values ​​of the parameters to be inverted σ, n, and k and the measured values; The parameter inversion result is brought into the pBRDF model to calculate the error between the model simulation value and the measured value, and whether to output the pBRDF model is selected according to the error accuracy to obtain the final pBRDF model; wherein, the final pBRDF model is used to analyze the infrared polarization characteristics of the sample to be tested at multiple angles.

2. The method for constructing a spatial target pBRDF model based on the improved SBA algorithm according to claim 1, wherein: The parameter inversion result is brought into the pBRDF model to calculate the error between the model simulation value and the measured value, and whether to output the pBRDF model is selected according to the error accuracy to obtain the final pBRDF model, specifically including: Substitute the optimal inversion value corresponding to each parameter to be inverted in the parameter inversion result into the In the formula, the simulation of the pBRDF model is calculated value; The simulation of the pBRDF model using an evaluation function The final pBRDF model is determined based on the evaluation results.

3. The method for constructing a spatial target pBRDF model based on the improved SBA algorithm according to claim 2, wherein: The simulation of the pBRDF model using an evaluation function The final pBRDF model is determined based on the evaluation results, including: The simulation of the pBRDF model is calculated using the MAE function. The values ​​obtained from experimental measurements The MAE value between the values; the experimental measurement obtained The value refers to the polarization characteristic measurement result obtained by measurement. value; The final pBRDF model is determined according to the MAE value and the preset threshold.

4. The method for constructing a spatial target pBRDF model based on the improved SBA algorithm according to claim 3, wherein: According to the MAE value and the preset threshold, the final pBRDF model is determined, specifically including: When the MAE value is less than 5%, the simulation of the pBRDF model is determined to be If the value is within a reasonable threshold range, the current pBRDF model is used as the final pBRDF model; When the MAE value is greater than or equal to 5%, the simulation of the pBRDF model is determined to be If the value is not within a reasonable threshold range, the pBRDF model is improved and the process returns to the step of "initializing the mother plant position according to the initial parameters using the Tent chaotic mapping algorithm to obtain the initialized mother plant position" until the MAE value is less than 5%.

5. The method for constructing a spatial target pBRDF model based on the improved SBA algorithm according to claim 1, wherein: Obtain the polarization characteristics measurement results of the sample to be tested, including: Measuring the sample to be measured using a space target infrared polarization characteristic measurement device to obtain a polarization characteristic measurement result of the sample to be measured; The device for measuring infrared polarization characteristics of space targets includes a gas generation system, a closed moving system, an electric stage, a light source system, a polarization acquisition system, a control system, and a computer; The gas generation system is connected to the interior of the closed moving system through a pipeline, the electric stage is arranged inside the closed moving system, the light source system and the polarization collection system are arranged inside the closed moving system, the gas generation system, the electric stage, the light source system and the polarization collection system are all connected to the control system, and the control system is also connected to the computer; The gas generating system is used to generate a mixed gas and fill the mixed gas into the interior of the closed mobile system through the pipeline; The electric stage is used to place the sample to be tested and rotate the sample to be tested; The light source system is used to emit an infrared beam to the sample to be tested; The polarization acquisition system is used to acquire the polarization image of the sample to be tested; The closed moving system is used to provide a closed environment for the mixed gas, and to move the light source system in the closed environment to achieve multi-angle emission of the infrared light beam, and at the same time to move the polarization acquisition system in the closed environment to achieve multi-angle acquisition of the polarization image; The control system is used to receive instructions from the computer and control the working states of the gas generation system, the electric stage, the light source system and / or the polarization acquisition system according to the instructions; and to acquire the polarization image and transmit it to the computer; The computer is used to analyze and process the polarization image to obtain a measurement result of the polarization characteristics of the sample to be measured.

6. The method for constructing a spatial target pBRDF model based on the improved SBA algorithm according to claim 5, characterized in that: The closed moving system includes a semicircular closed box and a semicircular guide rail; The top center of the semicircular sealed box is connected to the gas generating system through the pipeline, and the electric loading platform is provided just below the top center of the semicircular sealed box; The semicircular guide rail is laid on the inner surface of the semicircular closed box, and the semicircular guide rail is connected to the control system; The light source system and the polarization collection system are slidably arranged on the semicircular guide rail.

7. The method for constructing a spatial target pBRDF model based on the improved SBA algorithm according to claim 6, wherein: The semicircular guide rail includes a first guide rail and a second guide rail; The light source system is slidably provided on the first guide rail, and the polarization collection system is slidably provided on the second guide rail; The first guide rail and the second guide rail are both provided with a first angle mark, the first angle mark is 0° to 90°, and the graduation value of the first angle mark is 1°; The first angle marks of 0° of the first guide rail and the second guide rail are both located at one end close to the top of the semicircular closed box.

8. The method for constructing a spatial target pBRDF model based on the improved SBA algorithm according to claim 7, characterized in that: The electric loading platform is provided with a second angle mark, the second angle mark is 0°~360°, the graduation value of the second angle mark is 1°, and the second angle mark of 0° of the electric loading platform faces the first guide rail.

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