Rock-imitating camouflage apparatus based on multi-spectral matching and design method of rock-imitating camouflage apparatus

Abstract

The invention relates to the technical field of military camouflage, and provides a rock-imitating camouflage apparatus based on multispectral matching and a design method thereof. The equipment comprises a camouflage surface layer, a first structural layer, a second structural layer and a third structural layer, the camouflage surface layer comprises an electromagnetic loss material, a binding material and a pigment body; phase change microcapsules are arranged in the first structural layer, the second structural layer and the third structural layer, and the content of electromagnetic loss substances is increased layer by layer; the design method is mainly a design method for disguising surface layer color matching. According to the invention, by constructing a layered impedance matching structure, a dynamic temperature field regulation and control system and a composite spectral feature cooperation mechanism, global spectral feature matching of an artificial structure and a natural rock mass in a millimeter wave / centimeter wave radar detection wave band, a middle and far infrared thermal imaging wave band and a visible light reconnaissance wave band is realized; and the concealment efficiency of strategic engineering under multi-spectrum reconnaissance can be obviously improved.

Description

Technical Field

[0001] The present application relates to the field of military camouflage technology, and in particular to a rock-like camouflage equipment based on multi-spectral matching and a design method thereof. Background Art

[0002] Key military infrastructure within modern defense engineering systems, such as command and protection facilities and strategic missile bunkers, serves as the core support for a nation's strategic deterrent. Their concealment effectiveness is directly linked to battlefield survivability and combat system stability. With the coordinated evolution of space-based reconnaissance systems and intelligent data analysis technologies, the automated recognition capabilities of high-resolution satellite imagery have enabled precise location of standardized construction projects. For example, the construction of strategic missile launch sites, such as bunker excavation and shaft construction, results in landform changes that exhibit distinct engineering characteristics: irreversible deformation of the original terrain structure, stripping of surface soil, exposing large areas of bedrock, accumulation of debris, and the formation of unnatural geological formations. This disrupts the continuity of vegetation communities. These artificially modified areas exhibit anomalous electromagnetic scattering characteristics in radar imagery, sudden changes in thermal inertia parameters in infrared remote sensing data, and visible light reconnaissance reveals engineered texture features, resulting in multi-spectral composite exposure sources.

[0003] Current battlefield reconnaissance systems have formed a multi-spectral, three-dimensional detection network, posing a multi-dimensional threat to engineering camouflage. Synthetic aperture radar (SAR) uses centimeter-wave (X / Ku band) penetrating detection to resolve structural contours at depths of 5-10 meters underground. Hyperspectral infrared imaging systems have achieved 0.1°C temperature anomaly detection accuracy, identifying daytime heat accumulation differences of 3-5°C in concrete structures. Visible light super-resolution imaging (GSD ≤ 5cm) can discern millimeter-level differences in texture features. More critically, intelligent fusion of multi-source reconnaissance data allows for spatiotemporal correlation analysis of radar scattering, infrared thermal, and visible light texture features, significantly increasing the probability of revealing camouflage flaws. For example, a certain type of multi-spectral reconnaissance satellite can detect surface deformation as small as 0.5mm using SAR phase interferometry. Combined with infrared hotspot location and visible light texture analysis, this technology can accurately identify the entrances and exits of underground engineering projects.

[0004] Traditional rock restoration techniques primarily target visual similarity through surface treatment, making it difficult to address the mismatch of multi-spectral features. Strong radar reflections from the metal framework within the project create significant electromagnetic signatures. The difference in dielectric constant between building materials (ε = 6-8) and natural rock (ε = 3-5) leads to a sudden change in electromagnetic parameters. The thermal diffusion coefficient of concrete (approximately 0.8 × 10 -6 m² / s) is higher than natural rock (about 1.2×10 -6m² / s) decreases by 33%, triggering a daytime heat accumulation effect; insufficient control of the spectral reflectance characteristics of surface pigments leads to metamerism. Existing camouflage technologies face multiple technical bottlenecks: First, the monolithic spectral adaptation. Radar-absorbing coatings exacerbate infrared radiation differences when optimizing electromagnetic parameters. Infrared camouflage nets improve thermal properties but destroy visible light textures. Second, the discrete nature of the material system. Layered and stacked designs lead to mutual constraints between thermal, electro-optical properties. Dielectric relaxation and lattice vibration coupling between the millimeter-wave and infrared spectrums lead to cross-spectral compatibility failures. Third, parameter optimization is localized. Traditional color difference models ignore the influence of pigment dielectric properties, making single-objective optimization strategies difficult to achieve dynamic balancing of multi-spectral parameters. Fourth, current multi-spectral composite camouflage materials are mostly composed of one or more functional coatings applied to the surface of a substrate. These functional coatings are susceptible to corrosion and damage, resulting in poor durability.

[0005] Therefore, it is necessary to provide an improved technical solution to the above-mentioned deficiencies in the prior art. Summary of the Invention

[0006] The purpose of this application is to provide a rock-like camouflage equipment based on multi-spectral matching and a design method thereof to solve or alleviate the problems existing in the above-mentioned prior art.

[0007] In order to achieve the above objectives, this application provides the following technical solutions: A rock-like camouflage device based on multi-spectral matching includes a skeleton as a mechanical support structure and a camouflage surface layer, a first structural layer, a second structural layer, and a third structural layer sequentially stacked on the skeleton from the outside to the inside; The camouflage surface layer is a low-emissivity coating, including an electromagnetic loss material, a binder, and a pigment. The color of the camouflage surface layer is matched by the pigment to achieve visible light and infrared spectrum feature matching and rock background matching. The first structural layer, the second structural layer, and the third structural layer include concrete and electromagnetic loss materials and phase change microcapsules mixed in the concrete; the content of the electromagnetic loss materials in the first structural layer, the second structural layer, and the third structural layer increases layer by layer to achieve continuous matching of the air-material interface impedance and attenuate radar wave reflection; the phase change microcapsules are used to automatically control the internal and surface temperatures of the rock-like camouflage equipment to achieve infrared thermal imaging spectrum feature matching.

[0008] Furthermore, the thickness of the camouflage surface layer is 2-3 mm, the thickness of the first structural layer is 1-5 cm, and the thicknesses of the first structural layer, the second structural layer, and the third structural layer are equal.

[0009] Furthermore, the electromagnetic loss material is graphite powder and ferroferric oxide powder, and the mass ratio of the graphite powder to the ferroferric oxide powder is 1:(3-4).

[0010] Furthermore, the radar reflectivity of the first structural layer is below -5.0dB; the mass of the electromagnetic loss material in the first structural layer is A% of the mass of the concrete of this layer, the mass of the electromagnetic loss material in the second structural layer is 2A% of the mass of the concrete of this layer, and the mass of the electromagnetic loss material in the third structural layer is 3A% of the mass of the concrete of this layer, and A is 4.8-5.2.

[0011] Furthermore, the phase change microcapsules are 18°C ​​phase change microcapsules with a particle size of 50-100 μm; the amount of the phase change microcapsules used is 16-20% of the mass of each layer of concrete.

[0012] Furthermore, the adhesive is epoxy resin, fluorocarbon resin, aliphatic polyurethane resin, modified silicone, UV curing acrylate or acrylic resin.

[0013] Furthermore, the pigment body includes at least six colors: white, black, blue, green, red, and yellow; the white pigment body uses aluminum oxide powder or rutile titanium dioxide powder, and the black pigment body uses ferrosoferric oxide powder; some of the pigment bodies have low emissivity characteristics and electromagnetic loss characteristics.

[0014] Furthermore, the white pigment body is used to reduce thermal infrared radiation, and its usage is more than 20% of the total mass of the pigment.

[0015] The present invention also proposes a design method for rock-like camouflage equipment based on multi-spectral matching. The color matching design steps of the six pigment bodies in the camouflage surface layer are: measuring the spectral reflectance R∞ of the target stone sample and calculating its K / S ratio; selecting a pigment body combination and using the GA-LM algorithm to collaboratively optimize and calculate the pigment concentration so that the K / S ratio of the mixed pigment body matches the target value; the K / S ratio is the ratio of the absorption coefficient to the scattering coefficient of the target stone sample.

[0016] Furthermore, when the GA-LM algorithm is used for collaborative optimization calculation of pigment concentration, a genetic algorithm is first used to perform a global search for the ratio of the six pigment concentrations through group iteration until the algorithm converges or reaches the maximum evolutionary generation, and the iteration is terminated. Ten optimal results are output, and then the LM algorithm is used to balance the convergence speed and accuracy through a dynamic damping factor to perform local fine optimization. When the convergence conditions are met or the maximum number of iterations is reached, and the spectral root mean square error RMSE ≤ 0.01 and the spectral matching probability SMP ≥ 95% are met, the calculation is terminated and the results are output.

[0017] The technical concept of this application is: The present invention realizes the design of bionic rock camouflage equipment that integrates radar, infrared and visible light multi-spectral characteristics: 1. Radar Stealth: High-conductivity materials (such as graphite powder) and high-magnetic permeability materials (such as ferroferric oxide powder) were used as electromagnetic loss materials in the absorbing structure. Based on Maxwell's laws, a layered radar absorbing structure with progressive impedance was designed. With the goal of optimizing the electromagnetic shielding performance of the absorbing structure, electromagnetic simulation was used to determine the optimal ratio of the electromagnetic loss materials (graphite and ferroferric oxide), and the optimal structural layering was determined to be three layers. Based on this, a concrete-based rock-like camouflage device was fabricated.

[0018] 2. Infrared Stealth: A thermal radiation model was established using the Stefan-Boltzmann law. A camouflage surface layer (low-emissivity coating) was prepared using an alumina powder-graphite powder mixture as the functional material and epoxy resin as the binder. This reduces the emissivity of the rock-like camouflage equipment. Phase-change microcapsules were added to the rock to control its temperature. Simulations were used to determine the optimal ratio of alumina powder to graphite powder and the amount of phase-change material used in the stealth coating.

[0019] 3. Visible light matching: A spectral intelligent color matching method for multi-spectral camouflage engineering based on the Kubelka-Munk theory mainly includes: (1) constructing a multifunctional composite pigment system with a camouflage surface layer to achieve coordinated regulation of electromagnetic loss, low infrared emissivity and high scattering characteristics; (2) establishing a multi-objective optimization model based on a hybrid intelligent algorithm (a collaborative algorithm of genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm) to break through the pathological problem of slow calculation caused by too many repeated iterations in traditional parameter inversion.

[0020] The technical solution of this application has the following beneficial effects: This application is mainly used for rock mass geomorphology reproduction and multi-spectral stealth camouflage of strategic targets such as military facilities and underground fortifications. It aims to solve the single-spectral band adaptation defects of existing stone-like camouflage materials in complex electromagnetic environments and multi-spectral reconnaissance systems, as well as multi-spectral exposure problems such as strong radar reflection echoes, abnormal infrared thermal characteristics, and visible light texture distortion in traditional camouflage technology. It realizes the full-domain spectral feature matching of artificial structures and natural rock masses in millimeter wave / centimeter wave radar detection bands, medium and far infrared thermal imaging bands, and visible light reconnaissance bands by constructing a layered impedance matching structure, a dynamic temperature field control system, and a composite spectral feature coordination mechanism. It is particularly suitable for tactical engineering camouflage, strategic landform restoration, and other scenarios in battlefield environments, providing a multi-physical field coupled camouflage solution for modern reconnaissance system confrontation. Specifically, it has the following significant advantages: 1. The multi-spectral material-structure collaborative innovation system breaks through the limitations of traditional camouflage materials in adapting to a single spectral band, and pioneers a surface multifunctional pigment system to achieve the triple characteristics of electromagnetic loss, low infrared emission, and high visible light scattering. Through the three-layer impedance gradient structure design: Fe3O4 and graphite are added to the concrete matrix layer, which not only explains durability but also reduces the dielectric constant to 3.2-4.1; the three structural layers gradually control the Fe3O4 content to achieve continuous air-material impedance matching; the surface camouflage layer simultaneously completes radar terminal matching, with a reflectivity of ≤-10dB, and at the same time achieves infrared emissivity suppression to the range of 0.65-0.75. The concrete matrix is ​​embedded with phase change microcapsules, which increases the thermal diffusion coefficient to 0.5×10 -6 m² / s-1.1×10 -6 Dynamically matching the thermal inertia of rock within a range of m² / s. This design reduces the RCS of rock-like camouflage equipment by 12-15dB in the 1-18GHz band and reduces the temperature rise deviation by ≤1.2°C in the 3-14μm band, thus solving the problems of exposed metal frames and thermal mismatch.

[0021] 2. This invention innovatively proposes a GA-LM collaborative optimization algorithm, using radar reflectivity ≤ -10dB, infrared ΔT ≤ 1.2°C, and visible light ΔE ≤ 1.5NBS as parallel targets. Through a nested mechanism combining global search with the local optimization of the LM algorithm, rapid multi-target matching is achieved. A spectral intelligent color matching model is constructed based on the Kubelka-Munk theory. Pigment ion doping is used to simultaneously control electromagnetic loss and optical properties, resulting in a camouflaged surface scattering coefficient ≥ 0.85. Combined with algorithmic optimization, the spectral matching degree is ≥ 90% and the color difference ΔE ≤ 1.5NBS. Compared to traditional single-target optimization strategies, this invention improves color parameter matching efficiency by over 40%, completely resolving the phenomenon of metamerism and avoiding cross-spectral performance conflicts caused by layered designs.

[0022] 3. The three-layer composite structure and phase-change material can be directly integrated into concrete-based equipment manufacturing: the graded impedance absorbing layer suppresses radar echoes from the metal frame; the phase-change microcapsule matrix enhances thermal dynamic response; and the camouflaged surface meets the first-level color difference standard of the "GB / T15608-2006 Chinese Color System." This design overcomes the bottleneck of material system discretization, achieving high-fidelity reproduction of natural rock textures in the 380-780nm visible light band, eliminating dielectric constant abrupt changes in the millimeter / centimeter wave radar bands, and maintaining thermal stability in the infrared band. Field tests have shown that the system maintains consistent concealment under SAR / X-Ku band penetration detection, 0.1°C infrared recognition, and 5cm resolution visible light reconnaissance, significantly reducing the risk of exposure in multi-source data correlation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings and descriptions that constitute part of this application are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. Among them: Figure 1 Schematic diagram of the structure of the rock-like camouflage equipment according to an embodiment of the present invention.

[0024] Figure 2 This is a surface spectrum test diagram of the rock-like camouflage equipment according to an embodiment of the present invention.

[0025] Figure 3 This is a radar reflectivity test chart of the rock-like camouflage equipment according to an embodiment of the present invention.

[0026] Figure 4 This is the pigment body spectrum test data of the camouflage surface layer of an embodiment of the present invention.

[0027] Figure 5 This is a pigment contribution graph for calculating the camouflage surface color matching according to an embodiment of the present invention.

[0028] Figure 6 This is a radar chart of the pigment concentration of the camouflaged surface layer according to an embodiment of the present invention.

[0029] Figure 7 This is a cross-sectional view of the actual object of the present invention (without the camouflage surface layer).

[0030] Figure 8 Schematic diagrams of different types of rocks used in the rock-imitation camouflage equipment of the present invention; (a), (b), and (c) respectively imitate granite, limestone, and seaside sandstone.

[0031] Figure 9 These are the appearance pictures of the test blocks of the rock-imitation camouflage equipment of the present invention; among them, (a), (b), and (c) are natural basalt blocks, concrete test blocks, and imitation basalt test blocks, respectively.

[0032] Figure 10 These are test diagrams of the radar absorbing performance of natural basalt blocks, concrete test blocks, and the simulated basalt test blocks of the present invention in an outdoor environment; wherein, the test times of (a)-(d) are 08:00, 09:00, 10:00, and 11:00, respectively.

[0033] Figure 11 These are test diagrams of the radar absorbing performance of natural basalt blocks, concrete test blocks, and the simulated basalt test blocks of the present invention in an outdoor environment; wherein, the test times of (a)-(d) are 17:00, 18:00, 19:00, and 20:00, respectively.

[0034] Figure 12 This is a simulation result diagram of the electromagnetic loss material of the present invention at different mass ratios.

[0035] Explanation of the accompanying drawings: 1-camouflage surface layer, 2-first structural layer, 3-second structural layer, 4-third structural layer, 5-filling concrete, 6-surface interface, 7-first interface, 8-second interface, 9-third interface, 10-fourth interface, 11-epoxy resin, 12-concrete substrate, 13-first pigment body, 14-second pigment body, 15-third pigment body, 16-fourth pigment body, 17-fifth pigment body, 18-sixth pigment body, 19-graphite powder, 20-phase change microcapsules, 21-skeleton. DETAILED DESCRIPTION

[0036] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as part of one embodiment can be used in another embodiment to produce yet another embodiment. Therefore, it is expected that the present application includes such modifications and variations within the scope of the appended claims and their equivalents.

[0037] In the following description, the terms "first / second / third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that "first / second / third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of the present disclosure. The terms used herein are only for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0039] like Figure 1 、 Figure 7 As shown, a rock-like camouflage device based on multi-spectral matching includes a skeleton 21 as a mechanical support structure, and a camouflage surface layer 1, a first structural layer 2, a second structural layer 3, and a third structural layer 4 stacked on the skeleton 21 from the outside to the inside. The skeleton 21 is filled with filling concrete 5, or the surface of the skeleton 21 is covered with a metal mesh. A surface interface 6, a first interface 7, a second interface 8, a third interface 9, and a fourth interface 10 are formed between the air, the camouflage surface, the first structural layer 2, the second structural layer 3, the third structural layer 4, and the filling concrete 5 / metal mesh, respectively. The camouflage surface layer 1 is a low-emissivity coating, including electromagnetic loss material, binder, and pigment. The pigment concentration ratio of the camouflage surface layer is calculated based on the color of the rock background (target rock), and the ratio is fixed for different target colors. The thickness of the camouflage surface layer 1 is 2-3 mm. The first structural layer 2, the second structural layer 3, and the third structural layer 4 (when there is no need to distinguish between the three, the three are collectively referred to as structural layers in this document) include concrete and electromagnetic loss materials and phase change microcapsules 20 mixed in the concrete; the content of electromagnetic loss materials in the first structural layer 2, the second structural layer 3, and the third structural layer 4 increases layer by layer to achieve continuous matching of the air-material interface impedance and attenuate radar wave reflection; the thickness of the first structural layer 2 is 1-5 cm, and the thickness of the first structural layer 2, the second structural layer 3, and the third structural layer 4 is equal; the phase change microcapsules 20 are used to automatically control the internal and surface temperatures of the rock-like camouflage equipment to achieve infrared thermal imaging spectrum feature matching.

[0040] The rock-like camouflage device of the present application comprises a three-layer stepped impedance distribution structure consisting of a first structural layer 2, a second structural layer 3, and a third structural layer 4. This achieves continuous and gradual impedance matching of the incident radar wave at each interface, effectively suppressing interface reflections. Radar waves penetrating the interface experience layer-by-layer dielectric and magnetic losses within the first, second, and third structural layers 2, 3, and 4, significantly attenuating their energy and thereby reducing the target's radar cross section (RCS). The camouflaged surface layer 1 acts as a first-level barrier to reduce the infrared emissivity of the target surface. The phase-change microcapsules 20 in the first, second, and third structural layers 2, 3, and 4 act as a second-level control mechanism through latent heat absorption and release during phase change, stabilizing the temperature of the rock-like camouflage device itself. This dual-stage temperature synergy significantly suppresses the characteristic signal intensity of the rock-like camouflage device in the thermal infrared spectrum.

[0041] The three-layer radar-absorbing structure designed in this paper effectively absorbs radar electromagnetic waves and is suitable for all environments. Regarding infrared, the internal temperature control system designed in this paper adjusts the temperature of the simulated stone to match that of natural rock. Since infrared characteristics are primarily influenced by temperature, the primary factor affecting infrared properties is temperature. Therefore, when mimicking different types of natural rock, only the color of the pigment in the camouflage surface layer 1 needs to be adjusted; the material ratios in the three structural layers do not need to be adjusted.

[0042] Furthermore, the electromagnetic loss material is graphite powder 19 and ferroferric oxide powder, and the mass ratio of the graphite powder 19 to ferroferric oxide powder is 1:(3-4). The mass of the electromagnetic loss material in the first structural layer 2 is A% of the mass of the concrete in this layer, the mass of the electromagnetic loss material in the second structural layer 3 is 2A% of the mass of the concrete in this layer, and the mass of the electromagnetic loss material in the third structural layer 4 is 3A% of the mass of the concrete in this layer, and A is 4.8-5.2. The mass of the electromagnetic loss material in the first structural layer 2 is 4.8-5.2% of the mass of the concrete in this layer. When the volumes of the first structural layer 2, the second structural layer 3, and the third structural layer 4 are equal, the mass of the electromagnetic loss material in the second structural layer 3 is twice that of the electromagnetic loss material in the first structural layer 2; the mass of the electromagnetic loss material in the third structural layer 4 is three times that of the electromagnetic loss material in the first structural layer 2. According to the stealth material camouflage specification, the emissivity must be ≤-5dB. Simulation data shows that when the electromagnetic loss material content is 5%, the maximum radar reflectivity is around -5dB. Therefore, the electromagnetic loss material content of the first structural layer 2 of the three-layer absorbing structure is 5%. In order to meet the impedance matching, the electromagnetic loss material content of the remaining structural layers increases.

[0043] Furthermore, the phase-change microcapsules 20 are 18°C ​​phase-change microcapsules 20 with a particle size of 50-100 μm. The dosage of the phase-change microcapsules 20 is 16-20% of the mass of each concrete layer. The core material of the 18°C ​​phase-change microcapsules 20 is n-octadecane, and the shell material is PMMA (polymethyl methacrylate). The 18°C ​​phase-change microcapsules 20 are produced using interfacial polymerization. COMSOL software can be used to simulate the optimal dosage of phase-change microcapsules 20 for different rock types. Pigment spectrum testing shows that the spectral information of the pigments selected in this paper closely matches that of the rock, thus avoiding the phenomenon of metamerism when matching colors.

[0044] Furthermore, the pigment body includes at least six colors: white, black, blue, green, red, and yellow. Figure 1The figure schematically illustrates a concrete substrate 12 formed by the solidification of concrete, along with six pigments within it: a first pigment 13 (black), a second pigment 14 (red), a third pigment 15 (white), a fourth pigment 16 (green), a fifth pigment 17 (yellow), and a sixth pigment 18 (blue). The white pigment is made of aluminum oxide powder or rutile titanium dioxide powder, while the black pigment is made of ferroferric oxide powder. Aluminum oxide powder or rutile titanium dioxide powder has low infrared emissivity, while ferroferric oxide powder exhibits electromagnetic loss. The remaining pigments are used to adjust color tone and brightness for spectral matching, thus avoiding metamerism. The infrared emissivity of aluminum oxide at room temperature is 0.7-0.9. The inventors' measured infrared emissivity of aluminum oxide powder at room temperature is 0.76. The infrared emissivity of natural rock surfaces is 0.85-0.95. Therefore, using aluminum oxide powder as a surface pigment can reduce the infrared emissivity of rock-like camouflage equipment. The infrared emissivity of concrete surfaces is 0.9-0.95, while that of rutile TiO2 powder is 0.75-0.9. The addition of TiO2 powder can reduce the emissivity of concrete surfaces. However, it's important to note that a significant reduction in the infrared emissivity of concrete surfaces is not necessary; if it were, dark spots would appear in infrared imaging. Therefore, an appropriate amount of TiO2 can bring the emissivity of concrete surfaces close to that of natural rock surfaces. The color of camouflage layer 1 is adjusted based on the color of the rock background (target rock), using a calculated amount of each pigment.

[0045] The binder can be epoxy resin 11, fluorocarbon resin, aliphatic polyurethane resin, modified silicone, UV-curable acrylate, or acrylic resin. Epoxy resin 11 can be used for short-term applications. It offers excellent adhesion, high hardness, good chemical resistance, and good pigment dispersion and load-bearing capacity—properties crucial for the initial performance of camouflage coatings. However, epoxy resin 11 has poor UV resistance and cannot meet the long-term service requirements of camouflage coatings. Therefore, epoxy resin 11 can be used as a binder during experimental research, but alternative binders are recommended for practical use.

[0046] The present invention also proposes a design method for rock-like camouflage equipment based on multi-spectral matching. The color matching design steps for the six pigments in the camouflage surface layer 1 are as follows: measuring the spectral reflectance R∞ of the target stone sample and calculating its K / S ratio; selecting a pigment combination and using the GA-LM algorithm to collaboratively optimize and calculate the pigment concentration so that the K / S ratio of the mixed pigments matches the target value. The K / S ratio is the ratio of the absorption coefficient to the scattering coefficient of the target stone sample. Matching the K / S ratio to the target value is a color matching method, and this method can theoretically calculate the pigment concentration ratio.

[0047] Furthermore, when using the GA-LM algorithm for collaborative optimization to calculate pigment concentration, a genetic algorithm is first used through group iteration to perform a global search for the ratio of six pigment concentrations. The algorithm is terminated when it converges or reaches the maximum evolutionary number, and the ten optimal results are output. The LM algorithm is then used to balance convergence speed and accuracy through a dynamic damping factor, performing local fine optimization. When the convergence conditions are met or the maximum number of iterations is reached, and the spectral root mean square error (RMSE) is ≤ 0.01 and the spectral matching probability (SMP) is ≥ 95%, the calculation is terminated and the results are output. The GA-LM hybrid optimization algorithm is a pigment concentration calculation algorithm based on the K / S ratio and target value matching method. Conventional algorithms are prone to falling into local optimal solutions. This algorithm can perform a global search to avoid local optimal solutions and is fast to calculate.

[0048] Example 1 This application first finds the optimal ratio of graphite powder, Fe3O4 powder, addition ratio of phase change microcapsules, etc. through preliminary experiments or simulation experiments, and determines the optimal amount of each substance; then the actual production and related tests of Example 1 are carried out based on the optimal amount of each substance.

[0049] The preliminary experiment mainly determines the optimal ratio of electromagnetic loss materials (graphite and ferroferric oxide) through electromagnetic simulation, such as Figure 12 This is the simulation result diagram of different mass ratios of electromagnetic loss materials generated by electromagnetic simulation software CST. Figure 12 The RCS in the middle vertical axis represents the radar cross section. The smaller the RCS value, the weaker the electromagnetic waves scattered back to the radar by the target, and the more difficult it is to be detected by the radar. Figure 12 The two bottom curves show the best graphite powder:Fe₃O₄ ratio of 1:3, followed by a graphite powder:Fe₃O₄ ratio of 1:4. COMSOL simulations revealed the optimal phase change microcapsule dosage, which ranges from 5% to 25% of the concrete mass, with a gradient of 5%. Marble takes 488 seconds to heat up by 1°C. When the phase change microcapsule dosage is 5%, 10%, 15%, 20%, and 25% of the concrete mass, the rock-like camouflage material takes 227 seconds, 362 seconds, 428 seconds, 508 seconds, and 613 seconds, respectively. At a 20% dosage, the heating time is closest to that of basalt. Therefore, the optimal dosage of phase change microcapsules in rock-like camouflage material should be between 16% and 20%, with the specific value possibly being 18.75%.

[0050] Next, this embodiment will be described in detail.

[0051] A rock-like camouflage device based on multi-spectral matching includes a skeleton as a mechanical support structure and a camouflage surface layer, a first structural layer, a second structural layer, and a third structural layer sequentially stacked on the skeleton from the outside to the inside; The camouflage surface layer comprises a binder and pigments. Six pigments are used: TiO2, Fe3O4, Fe2O3, CuPc, Cr2O3, and PbCrO4. TiO2, Fe3O4, and CuPc are also electromagnetic loss materials. The mass ratio of binder to pigment in the camouflage surface layer is 1:4. In this example, a small-scale test block was produced. Given the excellent adhesion, high hardness, good chemical resistance, and good pigment dispersion and load-bearing capacity of epoxy resin, the binder used in this example was 0.12 kg. The total amount of the six pigments was 0.03 kg. This example simulated natural basalt, and the resulting pigment composition is shown in Table 1.

[0052] Table 1 The proportion of pigment bodies in the camouflage surface layer

[0053] Table 2 Mass ratio of each material in the raw materials of the three structural layers

[0054] The raw materials for the first, second, and third structural layers include concrete, electromagnetic loss material, phase change microcapsules, polypropylene fiber, and a water reducer. The concrete is composed of medium sand, cement, water, and crushed stone fine aggregate (particle size 2.38-4.75 mm). The mass ratios of medium sand, cement, water, and aggregate in the concrete are approximately 34.2%, 18.4%, 7.3%, and 40.1% (e.g., 0.81 kg of medium sand, 0.435 kg of cement, 0.172 kg of water, and 0.95 kg of crushed stone fine aggregate). The polypropylene fiber is approximately 0.063% of the concrete mass. The water reducer in each of the three structural layers is 1% of the concrete mass. The mass of the electromagnetic loss material (graphite powder: Fe₃O₄ powder = 1:3) in the first, second, and third structural layers is approximately 5%, 10%, and 15% of the concrete mass, respectively. The phase change microcapsules in each structural layer are 20% of the concrete mass. The mass ratios of the various materials in the three structural layers are shown in Table 2.

[0055] In this embodiment, the test block for the multi-spectral matching rock-like camouflage equipment (hereinafter referred to as the camouflage test block) omitted the metal frame. The specific steps for implementing the three-layer camouflage test block were as follows: First, prepare the materials for the third structural layer according to the proportions in Tables 1 and 2. Pour the prepared materials into a 15cm×15cm×3cm mold. After oscillation and stabilization, the height of the third structural layer was 1cm. After the concrete of the third structural layer reached the initial setting period, prepare and pour the materials for the second structural layer. After stabilization, the height of the second structural layer was 1cm. Similarly, after the concrete of the second structural layer reached the initial setting period, pour the materials for the first structural layer. The thickness of the first structural layer was 1cm. The camouflage surface layer material was prepared according to the aforementioned proportions and applied to the surface of the test block using a glass rod scraper method. The coating layer was 1mm thick.

[0056] Figure 8 This is a rock-like camouflage device made of different camouflage surfaces and shaped like rocks. Figure 8 (a), (b), and (c) simulate granite, limestone, and coastal sandstone, respectively. The steps for making this unusual rock-like camouflage are as follows: First, a metal frame model suitable for the specific project is created and a metal mesh is attached. The mesh is tightly attached to the frame to achieve the shape of the frame. Then, a 1-2 cm thick third layer of material is applied to the mesh. As the third layer is about to set, a 1-2 cm thick second layer of material is applied. As the second layer is about to set, a 1-2 cm thick first layer of material is applied. After curing for 28 days, a 1 mm thick camouflage top layer of material is applied. Due to the irregular shape of the rock-like camouflage, the thickness of each layer is approximately the same. Some areas require protrusions, and the thickness of these protrusions is close to the average thickness. However, for special terrain, the thickness of each layer may need to be adjusted, depending on the project.

[0057] Test and result analysis: The camouflaged surface of the test block in Example 1 simulates natural basalt (i.e., the camouflaged test block in Example 1 is an imitation basalt test block). The spectrum information of the surface of the imitation basalt test block is measured by a spectrometer and compared with the spectrum of the natural rock surface. Figure 2 ,Depend on Figure 2 The results show that the calculated fit of the two spectral reflectance curves is 91.2%, indicating that the color matching of the imitation rock camouflage equipment is highly matched with the natural basalt rock surface in the visible light range of 400-700nm.

[0058] Radar band test: Use bow radar test system to test the radar reflectivity of the rock camouflage test block and compare it with the radar reflectivity of ordinary concrete test block. The test results are shown in Figure 3As shown. Analysis of the test results shows that at an incident angle of 60°, the overall reflectivity of the rock-like camouflage equipment is less than -5dB, with a minimum reflectivity of -31.23dB. The bandwidth with a reflectivity less than -10dB reaches 8.1GHz, meaning that 45% of the bandwidth has a radar absorptivity greater than 90%. The average radar reflectivity is -12.34dB, a 7.13dB reduction compared to ordinary concrete. At an incident angle of 30°, the overall reflectivity of the absorbing structure is less than -2.50dB, with a minimum reflectivity of -47.15dB. The bandwidth with a reflectivity less than -10dB reaches 6.8GHz, meaning that 37.8% of the bandwidth has a radar absorptivity greater than 90%. The average radar reflectivity is -10.54dB, a 5.08dB reduction compared to ordinary concrete.

[0059] Figure 4 This is the pigment body spectrum test data of the camouflaged surface layer of the embodiment of the present invention. The pigment ferrosoferric oxide in the coating is an electromagnetic loss material that can dissipate electromagnetic waves and reduce the impedance of electromagnetic waves at the first interface. The pigment titanium dioxide in the layer is a low-emissivity material that can reduce the surface emissivity of the equipment, thereby reducing the thermal infrared radiation of the imitation stone. The radar performance camouflage consists of three layers of stepped layered impedance structure: the first structural layer, the second structural layer, and the third structural layer. The first structural layer has the largest content of electromagnetic loss materials, and the remaining layers of electromagnetic loss materials decrease layer by layer. When electromagnetic waves enter the interface, reflection and refraction will occur. The stepped layered impedance structure can reduce the reflection of electromagnetic waves at the interface, thereby weakening the signal received by the radar receiver. The electromagnetic waves reach the skeleton and reflect. The electromagnetic waves reflected from the fourth interface are consumed again by the three structural layers, and finally the extremely weak electromagnetic waves reach the radar receiver. Infrared performance camouflage is achieved through the dual effects of low emissivity of the surface layer and temperature control of internal phase change microcapsules. As Figure 3 The surface shown is the camouflage surface layer with low emissivity material titanium dioxide TiO2 added to reduce the surface emissivity, and the interior is the concrete layer with 18°C ​​phase change microcapsules added to control the internal temperature of the rock-like camouflage equipment structure through the action of the phase change material.

[0060] Infrared performance test: Figure 10 The 12-hour thermal infrared images of the simulated basalt test block, ordinary concrete test block, and natural basalt test block. Figure 10 (a)-(d) are thermal infrared images from 8:00 AM to 11:00 AM. This period represents the daily temperature rise. The temperature of the ordinary concrete specimens rises rapidly, resulting in strong surface thermal radiation. The temperature of the natural basalt and artificial basalt specimens rises more slowly, resulting in weak surface radiation. During this period, the thermal infrared images of the artificial and natural basalt specimens are dark, similar, and difficult to distinguish. The thermal infrared image of the ordinary concrete specimen is brighter and significantly different from the two. This is because the low-emissivity aluminum oxide powder on the artificial basalt specimens reduces the thermal infrared radiation from the surface. Figure 11(a)-(d) are thermal infrared images from 5:00 PM to 8:00 PM. This period represents the cooling phase of the day. The temperature of the ordinary concrete specimens drops rapidly, resulting in low surface thermal radiation. However, the surface temperatures of the natural basalt and simulated basalt specimens drop more slowly, resulting in higher surface thermal radiation. During this period, the thermal infrared images of the natural and simulated basalt specimens appear brighter, making them similar and difficult to distinguish. This is because the camouflage coating, made with low-infrared-emissivity pigments like titanium dioxide (TiO2), effectively reduces the exposure of the simulated basalt specimens in the thermal infrared band.

[0061] The above test results confirm that the camouflage test block of Example 1 (simulated basalt test block) has high stealth performance in the three levels of visible light, radar, and thermal infrared, and meets the camouflage requirements.

[0062] To enhance radar stealth, the embodiment utilizes a three-layer, gradient impedance absorbing structure (three structural layers). Magnetic loss material is incorporated into the concrete matrix of each structural layer to reduce the dielectric constant, attenuating transmitted electromagnetic waves through dielectric loss. The transition layer gradiently adjusts the Fe₃O₄ content to achieve continuous impedance matching between the air and material interfaces, minimizing the surface radar reflectivity to ≤ -10dB. The camouflage surface layer utilizes a 2-3mm composite path, combining low infrared emission with impedance termination matching. The rock-like camouflage reduces the radar cross section by 12-15dB.

[0063] In terms of infrared stealth, the emissivity is controlled at 0.65-0.75 by camouflaging the surface to reduce thermal radiation; 50-100 μm 18°C ​​phase change microcapsules (phase change enthalpy ≥ 180 J / g) are embedded in the concrete matrix of each structural layer to increase the thermal diffusion coefficient to 0.9×10 -6 m² / s-1.1×10 -6 m² / s, achieving dynamic thermal matching with a daytime surface temperature rise deviation of ≤1.2°C.

[0064] For visible light camouflage, six pigments are formulated and ion doped to simultaneously achieve electromagnetic loss, low infrared emission, and high visible light scattering (scattering coefficient ≥ 0.85). An innovative GA-LM collaborative optimization algorithm is developed, with parallel objectives of achieving spectral matching ≥ 90% and color difference ΔE ≤ 1.5 NBS. This nested mechanism of global genetic algorithm search and local LM optimization addresses the problem of heterochromatic color, improving parameter matching efficiency by over 40%. This design overcomes the limitations of single-band optimization, achieving an RCS reduction of ≥ 12 dB in the 1-18 GHz radar band, maintaining thermal stability of ΔT ≤ 1.2°C in the 3-14 μm infrared band, and meeting the first-level standard of the "GB / T15608-2006 Chinese Color System" (ΔE ≤ 1.5) in the visible light band of 380-780 nm. This demonstrates that the color of the simulated basalt specimens is highly consistent with the natural basalt.

[0065] Example 2 This embodiment 2 provides details of a design method for rock-like camouflage equipment based on multi-spectral matching.

[0066] Six pigments were used in this example: white aluminum oxide (TiO2), black ferroferric oxide (Fe3O4), blue copper phthalocyanine (CuPc), green chromium oxide (Cr2O3), red ferric oxide (Fe2O3), and yellow lead chromate (PbCrO4). White aluminum oxide, black ferroferric oxide, and red ferric oxide are the primary raw materials. Aluminum oxide contributes to low emissivity, while black ferroferric oxide and red ferric oxide contribute to electromagnetic loss and are therefore used in large quantities. The other three pigments are used to adjust the color tone and are therefore used in smaller quantities. See the spectral test graphs for these six pigments. Figure 4 .

[0067] The Kubelka-Munk theory describes the quantitative relationship between light scattering (S) and absorption (K) in opaque media. Its core formula is shown in Equation (1): (1); Where: R ∞ It represents the reflectivity of the material at infinite thickness (400–700nm wavelength range); K represents the absorption coefficient, which characterizes the material's ability to absorb light; S represents the scattering coefficient, which characterizes the material's ability to scatter light.

[0068] Based on the above theory, the present invention also proposes a design method for rock-like camouflage equipment based on multi-spectral matching. The color matching design steps of the six pigment bodies in the camouflage surface are as follows: Measure the spectral reflectance R of the target stone sample ∞ , calculate its K / S ratio; select the pigment body combination, use the GA-LM algorithm to collaboratively optimize and calculate the pigment concentration, so that the K / S ratio of the mixed pigment body matches the target value; the K / S ratio is the ratio of the absorption coefficient to the scattering coefficient of the target stone sample.

[0069] GA-LM algorithm collaborative optimization principle: The GA algorithm has global search capabilities. Through selection, crossover, and mutation operations, it can explore candidate solutions across the entire search space, avoiding local optima. However, its convergence speed is slow, especially when approaching the optimal solution due to a potential loss of population diversity. The LM algorithm combines the advantages of gradient descent and the Gauss-Newton method, utilizing second-order derivative information (approximate Hessian matrix) to accelerate convergence. It is suitable for solving nonlinear least squares problems. However, it is prone to local optima and is sensitive to initial solutions.

[0070] When approaching the optimal solution, the convergence speed is much faster than that of heuristic algorithms. The GA algorithm generates a diverse set of candidate solutions globally, covering the potential optimal region. Through selection, crossover, and mutation operations, the population is gradually optimized, approaching the neighborhood of the global optimal solution. Initial points are selected from the elite individuals (e.g., the solution with the highest fitness) obtained by the GA algorithm and fed into the LM algorithm for a local, refined search. The LM algorithm leverages gradient information to quickly converge to the local optimal solution, avoiding the local optimality problem caused by sensitivity to initial values. The GA algorithm explores the unknown region and provides multiple local optimal candidate solutions through population diversity, avoiding premature convergence. The LM algorithm leverages local information for efficient development and further optimization, reducing the risk of falling into suboptimal solutions. The combination of the two overcomes the limitations of a single algorithm. The GA algorithm narrows the search range through coarse-grained screening, while the LM algorithm only needs to perform detailed calculations within a smaller neighborhood, significantly reducing computational cost and improving accuracy.

[0071] The steps for collaborative optimization calculation of pigment concentration using GA-LM algorithm are as follows: A genetic algorithm was used to perform a global search for the six pigment concentration ratios through population iteration. The ten best results were output and then locally refined using the LM algorithm. The genetic algorithm used Latin hypercube sampling to generate 100 uniformly distributed initial concentration matrices as the initial population. Fitness was evaluated by calculating the spectral root mean square error (RMSE) based on spectral matching, as shown in Equation (2). The definitions and methods of the genetic algorithm parameters are shown in Table 3.

[0072] (2); Where: λ is the wavelength point (or spectral channel), N is the number of wavelength points; y pred is the K / S spectrum of each individual pigment formula (calculated from the pigment concentration ratio); exp is the K / S spectrum of the target rock.

[0073] Table 3 Definition and method of genetic algorithm related parameters

[0074] Perform a global search based on the parameters in Table 3, merge elite individuals with newly generated individuals, maintain the population size, select the 10 individuals with the highest fitness from the previous generation (TOP10), merge the 90 new individuals generated by crossover mutation with the 10 elite individuals, and form a new generation of 100 individuals. Repeat the above steps until the algorithm converges or reaches the maximum number of evolutionary generations. This means that G≥G max =50 or RMSE change of the optimal solution for 20 consecutive generations |ΔRMSE|<10 -4 The solution terminates when . Output the top 10 solutions with the highest fitness.

[0075] Based on the GA global search for the top 10 solutions with the highest fitness, the dynamic damping factor is used to balance the convergence speed and accuracy. The input data for the LM algorithm local optimization are: ① the top 10 formulas output by GA, ② the target reflectance spectrum R target (λ), ③6 kinds of pigment K / S database. The residual calculation is based on the Kubelka-Munk model to calculate the difference between the predicted spectrum and the target, see formula (3). By a small perturbation δ = 10 -6 The sensitivity matrix J is calculated to construct the Jacobian matrix, as shown in Equation (4). Singular value decomposition (SVD) is used to solve the incremental equation, as shown in Equation (5). The SMP spectral matching accuracy is quantified in Equation (6). The SMP spectral matching accuracy quantifies the degree of match between the actual spectrum and the target spectrum. The relevant parameters for the LM algorithm local optimization are shown in Table 4.

[0076] (3); (4); (5); (6); In formulas (3) and (6), r(λ) is the reflectivity function or spectral response value, R target (λ) is the target reflectivity function, K / S prod (λ) is the spectral product coefficient, λ is the wavelength, and the unit is nanometer (nm).

[0077] In formula (4), J i,j is the loss function gradient or sensitivity coefficient, which indicates the influence of the jth parameter on the i-th band, r(λ i ) is the wavelength λ i The reflectance or spectral response value at wj is the change of the j-th weight parameter.

[0078] In formula (5), Δw is the weight update amount, V is the right singular vector matrix, μ is the regularization parameter, diag(1 / (s i +μ)) is a diagonal matrix with elements 1 / (s i +μ); U T is the transpose of matrix U, and r represents the residual vector.

[0079] Table 4 Definition of parameters related to local optimization of LM algorithm

[0080] Ensure that the parameter adjustment amount or error change is small enough to reach the local optimum, and the absolute convergence requirement is met: |RMSEnew−RMSEold|<10 -6Relative convergence: |Δw|<10 -6 When the convergence condition is met or the maximum number of iterations (20) is reached, the calculation is terminated. The RMSE ≤ 0.01 and the SMP ≥ 95% are calculated according to Equations (2) and (6). If so, the six pigment concentration ratios are output. If not, the genetic algorithm population range is expanded, the pigment concentration range is expanded, and the maximum number of iterations is adjusted to continue optimization (the population range is increased by 20 samples each time, the pigment concentration range is increased by 0.1 each time, and the maximum number of iterations is increased by 10 each time).

[0081] According to the above theory and parameters, the MATLAB calculation is used to calculate the pigment spectrum contribution diagram calculated by MATLAB. Figure 5 , pigment concentration radar chart see Figure 6 , Figure 6 The concentrations of the six pigment bodies obtained in the experiment are consistent with those in Table 1.

[0082] from Figure 5 As can be seen from the figure, different pigments contribute differently to reflection (reflection contribution value) at different wavelengths (400-700nm visible light band). Pigments have a higher reflection contribution in specific wavelength ranges (such as short wavelength or long wavelength), showing a distinct peak, reflecting their dominant role in the optical properties of the corresponding light band. Figure 5 The spectral response characteristics of different pigments in the visible light band (400-700nm) are intuitively presented through three-dimensional modeling technology. The horizontal axis (400-700nm) corresponds to the visible spectrum range of the human eye, the vertical axis (0-1) represents the reflectivity of the pigment to the incident light, and the concentration value of the surface color mapping (0.1-0.9) reveals the quantitative relationship between the pigment concentration and the spectral characteristics. Cr2O3 (chromium oxide) with the highest reflectivity may appear bright green in the green band (about 550nm), and CuPc (copper phthalocyanine) with the lowest reflectivity absorbs strongly in the blue band (about 450nm) and appears dark blue. 400-450nm (purple-blue light region): A general dip in reflectivity occurs, which may be related to the π→π* electron transition of the pigment molecule. 550-600nm (yellow-green light region): The reflectivity of most pigments increases, corresponding to the maximum energy reflection range of 650-700nm (red light region): Fe3O4 maintains high reflectivity, while Cr2O3 experiences a secondary decrease. From Figure 6 The pigment concentration radar chart, shown in the figure, uses multi-dimensional data visualization technology to systematically reveal the contribution of different base pigments to the total pigment count. It can be seen that Fe3O4 accounts for the largest proportion, while Fe2O3, CuPc, Cr2O3, and PbCrO4 have smaller and similar concentrations.

[0083] To address the exposure risks of defense projects in multi-spectral reconnaissance environments, this method aims to address the limitations of traditional camouflage in radar, infrared, and visible spectrum bands through collaborative innovation in materials, structures, and camouflage surfaces. Its core innovation lies in the construction of a collaborative optimization system of materials, structures, and algorithms, specifically integrating three layers of innovation mechanisms: At the material level, the camouflage surface design adopted achieves the multifunctional compatibility of electromagnetic loss, low infrared emission and visible light scattering; at the structural level, an impedance gradient absorbing framework is designed (Fe3O4 / graphite is doped into the matrix layer to reduce the dielectric constant, the transition layer is gradient-controlled to achieve impedance matching, and the surface layer is composited with Fe3O4-Al2O3 to optimize the terminal reflection), combined with phase change microcapsules to enhance the thermal dynamic response, and a multi-physics field coupling model of radar scattering field, infrared radiation field and visible light reflection field is established; in terms of camouflage surface construction, six pigment bodies are used, and an innovative GA-LM collaborative optimization mechanism (genetic algorithm global search and LM algorithm local optimization nested) is used to efficiently solve the problem of homochromatic spectrum matching. This method unifies the impedance gradient structure, phase change heat storage matrix and spectral control pigment into a single system for the first time, breaking through the bottleneck of single spectral band optimization. The measured results show that it can achieve RCS reduction of ≥12dB in the radar band (1-18GHz), dynamic thermal stability of temperature rise deviation ≤1.2℃ in the infrared band (3-14μm), spectral matching of ≥90% (ΔE≤1.5) in the visible light band (380-780nm) and color difference reaching the first-level standard of "GB / T15608-2006 China Color System". It can significantly improve the concealment effectiveness of strategic projects under multi-spectral reconnaissance, reduce the risks of metal skeleton exposure, thermal inertia mismatch and spectral distortion.

[0084] This invention can be directly applied to military facility camouflage projects, particularly for recreating rock mass and geomorphological features such as strategic missile silo entrances and exits, command and protection fortifications, and other locations. Through an integrated concrete base casting process, a three-layer composite structure is rapidly constructed: an impedance gradient layer eliminates the radar scattering characteristics of metal frames 5-10 meters below ground; a phase-change microcapsule matrix dynamically balances the 33% thermal diffusivity difference between concrete and natural rock; and a spectrally optimized coating mitigates the exposure of standardized engineering textures. In actual combat deployments, this technology can ensure that strategic targets maintain full-spectral feature matching under multi-spectral fusion detection using SAR phase interferometry, hyperspectral infrared imaging (3-5μm / 8-14μm), and super-resolution visible light reconnaissance (GSD ≤ 5cm), enhancing concealment effectiveness and providing core technical support for cross-spectral countermeasures in defense projects.

[0085] The foregoing description is merely a preferred embodiment of the present application and is not intended to limit the present application. Persons skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A rock-like camouflage device based on multi-spectral matching, characterized by: It comprises a skeleton (21) as a mechanical support structure, and a camouflage surface layer (1), a first structural layer (2), a second structural layer (3), and a third structural layer (4) stacked on the skeleton (21) from the outside to the inside. The camouflage surface layer (1) is a low-emissivity coating, comprising an electromagnetic loss material, a binder, and a pigment body; the color of the camouflage surface layer (1) is matched by the pigment body, and is used to achieve visible light and infrared spectrum feature matching and rock background matching; The first structural layer (2), the second structural layer (3), and the third structural layer (4) comprise concrete, and electromagnetic loss material and phase change microcapsules (20) mixed in the concrete; the content of the electromagnetic loss material in the first structural layer (2), the second structural layer (3), and the third structural layer (4) increases layer by layer, so as to achieve continuous matching of air-material interface impedance and attenuate radar wave reflection; the phase change microcapsules (20) are used to automatically control the internal and surface temperatures of the rock-like camouflage equipment to achieve infrared thermal imaging spectrum feature matching.

2. The rock-like camouflage equipment based on multi-spectral matching according to claim 1 is characterized in that: The camouflage surface layer (1) has a thickness of 2-3 mm, the first structural layer (2) has a thickness of 1-5 cm, and the first structural layer (2), the second structural layer (3), and the third structural layer (4) have the same thickness.

3. The rock-like camouflage equipment based on multi-spectral matching according to claim 1 is characterized in that: The electromagnetic loss material is graphite powder (19) and ferroferric oxide powder, and the mass ratio of the graphite powder (19) to the ferroferric oxide powder is 1:(3-4).

4. The rock-like camouflage equipment based on multi-spectral matching according to claim 1 is characterized in that: The radar reflectivity of the first structural layer (2) is below -5.0 dB; the mass of the electromagnetic loss material in the first structural layer (2) is A% of the mass of the concrete in this layer; the mass of the electromagnetic loss material in the second structural layer (3) is 2A% of the mass of the concrete in this layer; the mass of the electromagnetic loss material in the third structural layer (4) is 3A% of the mass of the concrete in this layer, and A is 4.8-5.

2.

5. The rock-like camouflage equipment based on multi-spectral matching according to claim 1 is characterized in that: The phase change microcapsules (20) are 18°C ​​phase change microcapsules with a particle size of 50-100 μm; the amount of the phase change microcapsules (20) used is 16-20% of the mass of each layer of concrete.

6. The rock-like camouflage equipment based on multi-spectral matching according to claim 1 is characterized in that: The adhesive is epoxy resin (11), fluorocarbon resin, aliphatic polyurethane resin, modified silicone, UV curing acrylate or acrylic resin.

7. The rock-like camouflage equipment based on multi-spectral matching according to claim 1 is characterized in that: The pigment bodies include at least six colors: white, black, blue, green, red, and yellow; the white pigment body uses aluminum oxide powder or rutile titanium dioxide powder, and the black pigment body uses ferrosoferric oxide powder; some of the pigment bodies have low emissivity characteristics and electromagnetic loss characteristics.

8. The rock-like camouflage equipment based on multi-spectral matching according to claim 7 is characterized in that: The white pigment body is used to reduce thermal infrared radiation, and its usage is more than 20% of the total mass of the pigment.

9. A method for designing a rock-like camouflage device based on multi-spectral matching according to any one of claims 1 to 8, characterized in that: The color matching design steps of the six pigment bodies in the camouflage surface layer (1) are as follows: measuring the spectral reflectance R∞ of the target stone sample and calculating its K / S ratio; selecting a pigment body combination and using the GA-LM algorithm to collaboratively optimize and calculate the pigment concentration so that the K / S ratio of the mixed pigment body matches the target value; the K / S ratio is the ratio of the absorption coefficient to the scattering coefficient of the target stone sample.

10. The design method of rock-like camouflage equipment based on multi-spectral matching according to claim 9, characterized in that: When using the GA-LM algorithm for collaborative optimization calculation of pigment concentration, the genetic algorithm is first used to perform a global search for the ratio of the six pigment concentrations through group iteration until the algorithm converges or reaches the maximum evolutionary generation. The iteration is terminated and ten optimal results are output. The LM algorithm is then used to balance the convergence speed and accuracy through a dynamic damping factor to perform local fine optimization. When the convergence conditions are met or the maximum number of iterations is reached, and the spectral root mean square error (RMSE) is ≤ 0.01 and the spectral matching probability (SMP) is ≥ 95%, the calculation is terminated and the results are output.

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