Low earth orbit (LEO) satellites are capable of conducting rapid geolocation of global navigation satellite system (GNSS) interference sources with Doppler measurement data. However, the highly dynamic characteristics of LEO satellites limit the quantity of acquired Doppler observations, consequently restricting the localization accuracy achievable through conventional least squares algorithms. To enhance the accuracy, a weighted least squares localization algorithm based on Doppler measurement Cramér-Rao bound (CRB) is proposed in this paper. This method incorporates a signal-to-noise ratio (SNR) lookup table for Doppler sampling instances to ascertain the Cramér-Rao bound (CRB), thereby standardizing the noise across various Doppler observation moments through weighting. This adjustment ensures that the least squares solution for the interference source location is made close to the optimal estimation. The effectiveness of the proposed algorithm was verified by simulation under different sampling rates, SNRs and sampling durations. The FPGA-based GNSS interference acquisition and localization system was also constructed and tested in real scenarios. The simulation and experiment results show that the proposed algorithm significantly improves the localization accuracy by 17.43%, compared with the least squares algorithm.

The HAPNet-CD, a new change detection method, is proposed in this paper to solve the problems of noise misalignment, object boundary ambiguity and low change detection rate of small targets in the processes of encoding and decoding with the existing methods. On the one hand, the encoder of HAPNet-CD adopts siamese branches, in which HRNetV2 is used as the backbone network, and the alignment-and-perturbation-aided difference module is embedded to extract the variation features and difference information. As a result, the high-resolution feature representation can always be maintained in the process of feature extraction, so that the obtained features are more accurate in space. On the other hand, the decoder of HAPNet-CD uses the change features and difference information to construct a hybrid decoder and a differential decoder for decoding. By designing a loss function based on label smoothing, the network pays more attention to the variations of object boundaries and small targets, so that the change detection accuracy of object boundaries and small targets can be improved. Tests were carried out on the public data sets DSIFN-CD and LEVIR-CD, and the experimental results are as follows. Compared with the other 9 mainstream methods, the HAPNet-CD has improved the metrics of Precision, Recall, F1, and IoU by 2.55%,4.58%, 3.59%, and 5.9%, respectively, on the DSIFN-CD dataset. On the LEVIR-CD dataset, the Precision metric is improved by 0.54%, while the metrics of Recall, F1, and IoU are all close to the most advanced level.

To address the problems of low registration accuracy and nonlinear radiometric differences in the alignment of visible light and Synthetic Aperture Radar (SAR) images, a novel two-stage registration algorithm for visible light and SAR imagery based on structural information is proposed. In the coarse registration stage, the improved maximal self-dissimilarity detector (MSD) is used to detect feature points in visible light and SAR images; then, based on the local self-similarity (LSS), the maximum self-similarity index map (MSSIM) is constructed; finally, the correct matched point pairs are identified using the nearest neighbor to second nearest neighbor distance ratio combined with a rapid consensus sampling approach to estimate the transformation model. In the fine matching stage, the pixelwise structural feature representations of visible light and SAR images are established respectively; then, in visible light images, the template windows centered on feature points are selected, and the transformation model is used to estimate the corresponding search areas in SAR images; finally, Euclidean distance is employed as a similarity measure for template matching, and the Fast Fourier Transform (FFT) is utilized to accelerate the template matching process. The experimental results on nine pairs of real visible light and SAR images show that the proposed method has significant advantages in the registration of visible light and SAR images, compared with the other methods, such as RIFT, HAPCG, OSS, LNIFT, and ASS methods. The number of correct matches (NCM) has been increased by 7.95, 10.86, 29.32, 14.75, and 9.84 times, respectively, and the positioning accuracy has been improved by 0.44, 0.42, 0.35, 0.41, and 0.40 pixels.

The application of metal organic frameworks (MOFs) to the technology of adsorption-based atmospheric water harvesting (ABAWH) is an effective means to solve the problem of atmosphere water harvesting under low humidity conditions. However, there are few systematic reviews on MOFs, and there is a lack of studies on the thermal stability and toxicology of MOFs. In this paper, a multi-dimensional evaluation system (physical properties, adsorption-desorption isotherm, water stability, thermal stability and toxicity) is established based on the research background of low humidity and high temperature climate in a certain desert. The Analytic hierarchy process (AHP) is used to compare and study four MOFs materials (MOF-801, Aluminum fumarate, MIL-100 and CAU-10H). The main conclusions are as follows. (1) MIL-100 has obvious advantages in terms of specific surface area and adsorption-desorption performance. (2) Aluminum fumarate is best in terms of thermal stability, CAU-10H is better than MOF-801 and MIL-100. (3) In terms of safety, MIL-100 and MOF-801 are more reliable. It is concluded that MIL-100 is more suitable as adsorbent for ABAWH devices.

Existing code similarity detection models primarily focus on constructing encoders, with limited research on loss functions in deep learning. To address the overlooked issue of evaluating embedded binary function vectors, this paper proposes an angular-margin-based binary code contrastive learning framework (AngCLF). By optimizing the objective function of contrastive learning, the model's accuracy and convergence speed are enhanced. Besides, the study analyzes the reasons for the model's effectiveness and introduces multiple metrics for evaluating binary code vector spaces. The experimental results validate the accuracy of the AngCLF. The AngCLF surpasses six models including the jTrans model in accuracy, and has faster convergence speed and obvious advantages in alignment and uniformity metrics.

Aiming at the challenges of limited data, insufficient knowledge, and difficulty in learning in adversarial simulation environments where intelligent agents cannot easily improve their strategies and overcome difficulties. an agent self-play (SP) training method based on the Rule-N/MSP system is proposed, combined with the thought of curriculum learning. The hierarchical courses are designed. To begin with, the expert experience is used to design the agent opponent coupled with Rule, and the agent is guided to carry out hot start, and the decision-making ability is preliminarily mastered. Then, the agent opponent is trained for naive SP(NSP) to enrich the battle data and steadily improve the ability. Finally, the mature SP(MSP) training is conducted for the agent opponent to improve strengths and address weaknesses, and seek for breakthrough in strategies. The Rule-N/MSP training method is formed to improve the decision-making abilities of agents and further improve the training efficiencies of agents. The adversarial simulation environment was constructed to verify the effects. The experiment results show the winning rate of the agent trained by the proposed method is about 12% higher than that of the agent trained only by rules, which can provide references for intelligent decision making, especially for agent training.

Light source is the engine of the microwave photonic (MWP) system, which fundamentally determines the upper limitation of the performance of the system. Optically-injected semiconductor lasers have many advantages, but the low integration of discrete devices, complex structure, large volume and poor stability greatly limit the engineering application of the MWP systems. Integration is the key to solving the current bottleneck problems of practical application of microwave photon technology. Lightweight, high-performance and multifunctional laser sources are the keys to integrated MWP systems. Based on the above considerations, this paper reviews the recent innovative researches of the integrated mutually-injected semiconductor lasers in MWP applications, covering from high-efficiency electrical-optical conversion, microwave signal generation, MWP frequency conversion, and weak signal detection to optical frequency comb. It is expected to provide a feasible high-performance integrated light source for integrated MWP systems and promote the realization of the engineering application of the integrated MWP system as soon as possible.

The metal airbag has become a research hot issue in the design of dynamic deformation and shock reduction structures of various spacecraft because of its high strength, good air tightness and environmental adaptability, as well as better shape stability, This paper mainly studies the key parameters that lead to fracture or fold instability during the inflation process of airbags, and also analyzes the influences of the location of the air inlet and the thickness of the balloon edge on the swelling effect. With the finite element simulation method, the five groups of commonly used metal material parameters are selected as the basic data to carry out the orthogonal experiments on material density, Young's modulus, Poisson's ratio and yield limit. The simulation results show that the maximum expansion height of the metal airbag is most affected by the material density, and the range is 8.26 mm. The maximum fold depth is most affected by the yield limit, with a range of 5.48 mm. The number of folds is least affected by the yield limit, and the range is only 0.8. The location of the air inlet will affect the uniformity of the balloon expansion, but will provide a larger expansion height in a specific area. Increasing the edge thickness will enhance the strength of the airbag, but reduce the maximum expansion height of the airbag. This conclusion can provide theoretical reference for engineering design of metal airbag.

Low earth orbit (LEO) satellite communication（SATCOM）is one of the most promising systems to be incorporated with future 6G and beyond wireless network. This paper explores how to apply massive multiple-input multiple-output (mMIMO) technology to LEO SATCOM. Starting from the mMIMO LEO satellite channel model, the focus is on the channel handover issue within a single mMIMO LEO satellite. Based on the quality of service (QoS) requirements of different user terminals (UTs), three different channel handover scenarios are constructed. By fully utilizing the strong beamforming capability of mMIMO, with beam tracking and dynamic adjustment of beam cells as the technological core, a channel handover strategy based on hybrid beamforming is proposed. Simulation results show that the proposed channel handover strategy has more advantages, compared with the typical Iridium system.

The parallel computing has been widely used in the analysis of geospatial big data, and among them, to represent computational intensity with geospatial domain to achieve load balancing is currently a hot spot in parallel geocomputing. However, the existing geospatial computational intensity modelling methods are overly dependent on expert knowledge, resulting in a complex modelling process and poor applicability. In order to address this problem, this paper proposes a method for predicting the computational intensity of geospatial domain based on machine learning from the perspective of data science. Taking the intersection of vector spaces as an example, this paper analyzes and extracts candidate feature spaces for vector intersection, combines machine learning feature selection and regression fitting algorithms, automatically searches for the optimal feature space in the geospatial domain, thereby achieving accurate prediction of computational intensity of the geospatial domain. Based on this, a balanced decomposition of the geospatial domain is accomplished. The performance of the machine learning optimization method is compared with that of the traditional domain decomposition method by experiments, and the results demonstrate the feasibility and efficiency of the proposed method. This method provides a reference for using machine learning to build prediction models of computational intensity of the geospatial domain and optimize the decomposition of the geospatial domain.

High frequency (HF) communication, which has the advantages of long distance transmission, quick starting, high flexibility and convenient networking, is an important means of military and emergency communications. Focusing on the basic problems of effectiveness and reliability of information transmission in time-varying dispersion wireless channel, the research advances of data transmission, link establishment, anti-jamming communication and networking were first reviewed. Then, considering the development requirements of the next generation intelligent HF communication system, some key technologies of HF communication were discussed, such as HF channel modeling and prediction with big data, autonomous communication under complex electromagnetic environment, active defense in the strong confrontation scenario, and multi-domain integrated communication for wide-area heterogeneous scenarios. It is expected that this research can promote the further development of HF communication system.

The state vector input-based reinforcement learning approach is currently a fundamental research direction in the field of reinforcement learning with broad application prospects. However, the low data efficiency of current reinforcement learning methods leads to prolonged learning times, making it difficult to apply in real-world environments. To address these issues, an environment-adaptive Gaussian noise augmentation(EAGNA) method is proposed, which is integrated as a module into soft actor-critic (SAC) and proximal policy optimization (PPO) methods. This study focuses on the distribution range of each element in the state vector of the task environment and adds Gaussian noise with different means and standard deviations to each element for data augmentation. Across three state-vector-based control tasks in the OpenAI Gym benchmark, EAGNA achieved a higher average return than the original algorithm, enhancing data efficiency. Notably, in the Lunar Lander control task with complex state inputs, EAGNA outperformed the SAC and PPO methods by 30.52 and 26.09 average returns, respectively.

As the core operator of the orthogonal frequency division multiplexing（OFDM) system, the fast Fourier transform （FFT） algorithm occupies most of the system processing time．In order to improve the data transmission speed of the OFDM system, a modified multi-path parallel-pipelined radix 2² FFT implementation architecture is proposed. During implementation, the on-chip cache optimization is focused on the storage of the twiddle factor, which reduces the number of multiplications and the overall computational complexity. The designed data integration module is used to control the timing, so as to ensure the correct implementation of the P-channel parallel pipeline architecture, with the throughput rate of data operation increased by P times. The RTL simulation results show that, compared with the similar architectures in three dimensions, the proposed one has a performance advantage by 1.27%，2.04% and 50.88% respectively with the moderate hardware overheads; with the great scalability of the FFT points, the proposed architecture can meet the practical application requirements for gradually increasing FFT points, with the upgrade of the communication standards.

In order to improve the flexibility of photonic UWB generation system, a novel method to photonically generate multiparameter modulated UWB signals was proposed in this paper. The proposed method can generate desired UWB Doublet signals based on nonlinear electrooptic intensity modulation. Moreover, the multiparameter modulation of the generated UWB Doublet signals can be realized by using a dual parallel system structure. This proposed approach was analyzed and verified by theoretical derivation and computer simulations. The results show that the proposed approach can generate the Doublet impulse up to the international standards and realize the combined modulation of the 3 key signal parameters, namely signal polarity, amplitude and time pulse position, which greatly improves the flexibility of the photonic UWB signal generation and can further boost the communication capacity of the system.

In response to the safety issues in tunnel construction projects, a hierarchical linear model (HLM) was established from the perspective of tunnel workers' behavior, combined with questionnaire survey data, microscopically explaining how factors related to safety behavior affect the process. The reliability of the 309 valid data was tested using criterion-related validity evaluation, and the hypothesis testing was used to analyze the impact of group safety behavior on individual safety behavior, safety climate, and team effectiveness among tunnel workers successively. The results show that group safety behavior has a significant positive effect on individual safety behavior; safety climate has a mediating effect between group safety behavior and individual safety behavior; team effectiveness has a positive moderating effect between safety climate and safety behavior. The results above provide guidance for better utilizing safety behavior in the management of tunnel construction safety.

As the communications network of large-scale small UAVs has frequent link failures, effective topology control is the current research focus of flying ad-hoc networks (FANET). To meet the diverse scenarios of large-scale UAV missions, a random waypoint-flight information prediction mobility model is proposed in this paper, by which UAV nodes can predict the link duration through the flight information of neighboring nodes. On this basis, an N-UCDS algorithm is proposed. Compared with the UCDS algorithm, the N-UCDS algorithm improves the selection method of members of connected dominating sets, the construction and maintenance mechanism of virtual backbone networks, the calculation method of dominant factors and the transmission interval of HELLO packets. The simulation results show that the robustness of the algorithm in the proposed method is greatly improved, the time to construct and maintain the network is reduced by one cycle respectively, the network survival time is increased by 5%, and the HELLO packet overheads decrease with the increase of the maximum communication radius of nodes.

Vertical cylindrical steel storage tanks, which are usually used as oil storage facilities, are prone to overall deformation or local damage failure in the event of blast or other strong dynamic loads, which will result in significant losses. In order to address this issue, this paper proposes a rapid processing method in which the polyurea spraying is applied to enhance the blast resistance of existing steel tanks, and some blast resistance experiments on sprayed composite tanks have been conducted. The experimental results demonstrate that the polyurea layer can effectively absorb a portion of the energy of the blast products under the action of repeated explosion loads, resulting in a reduction of 12.3% in the transverse width of the concave yield surface of the coated tank and a reduction of 12.5% in the maximum deflection value. A numerical simulation with LS-DYNA was employed to reproduce the experiment. The numerical simulation results were then compared with the experiment in terms of the shape of the concave yield surface and the depth of the tank wall collapse. The result of this comparison demonstrates that the numerical simulation is able to accurately reproduce the experiment, thereby providing evidence of its reliability. The further investigations reveals that the polyurea layer decreases and diffuses the localized strong impact of the repeated blast loads, reduces the local impact compression capacity of the shock wave on the tank wall, and attenuates the stress concentration on the tank wall and weld joints, which can prevent parts of the tank wall from entering the plastic phase and producing local failure. Furthermore, the polyurea layer can reduce the peak kinetic energy of the tank by 27.8% under the action of repeated explosive loads, reduce the overall degree of collapse of the tank, and protect the structural integrity of the tank.

Communication while jamming is an important function for the integration of network information system with electronic attack and defense. In electronic warfare, the spectrum environment is complex, the communication quality can not be guaranteed, and the jamming signal is difficult to control flexibly. Focused on these problems, a method of designing the jamming system under communication constraints was proposed. By exploiting multi-antenna spatial division multiplex to conduct communication while jamming at the same time, the impact of jamming signals on own communication signals can be avoided by the reasonable design of beamforming vectors, so as to improve the jamming effect and ensure the efficiency of communication. Considering the threat level of the jammed target and jamming beamforming based on the pattern of null space, the efficiency optimization problem of jamming while communicating was established. The optimization problem was solved under conditions of three different pieces of apriori information. The simulation results show that the scheme, making full use of the spatial division multiplex and various apriori information of the jammed target, avoids the impact of the jamming signal on own communication signal in confrontational scenarios, and achieves better jamming effect with the same energy consumption.

In order to solve the problem that single attribute handoff between satellites in low earth orbit (LEO) satellite communication systems is likely to cause the current attribute to be optimal and other attributes to be poor, this paper proposed a multi-attribute handoff path selection algorithm between LEO satellites. According to the movement of the terminal and the satellite ephemeris information, the algorithm predicted all the service satellites available to the terminal handoff in a future period of time. At the same time, considering the influence of service duration, elevation angle and idle channel on inter-satellite handoff, a handover path that was optimal in all these three attributes was found. The simulation results show that the proposed algorithm can not only reduce the handoff failure rate, but also reduce the average load on the satellite and the blocking rate of new calls.

Insufficient data resource and limited response time in electromagnetic interference are the main challenges faced by non-cooperative communication behavior recognition. However, most existing studies rely on large amounts of data or cumbersome data preprocessing, which apparently cannot meet the demands of non-cooperative communication behavior recognition. A non-cooperative communication behavior recognition method fused with multi-dimensional-squeeze-and-excitation network (MSENeT) is proposed in this paper. The method enhances the global view of the attention module on the relationship between channels by expanding the channel-level dimension so as to increase the precision of the weights of attention mechanism. Therefore, the feature extraction ability of the deep convolutional neural network (CNN) will be improved from limited data resource while the computational complexity of the algorithm is still within a bearable range. Meanwhile, the communication behavior simulation scenes are built after considering the actual environmental influences, which provide subsequent research on communication behavior recognition with data support. At last, the comparative experiments show that the proposed MSENet-based method has 14.9% and 8.5% higher recognition accuracy than the original CNN and SENeT methods respectively under the non-cooperative condition.

This paper proposes a hybrid active-passive intelligent-reflecting-surface-assisted downlink secure transmission method in cell-free massive MIMO networks in the presence of malicious eavesdroppers. Firstly, subject to the total transmit power of the base station, the power of the hybrid active-passive intelligent reflective surface and the reflective phase modulus, the optimization approach to maximize the secrecy rate of the system is discussed, which is a multivariate coupled non-convex optimization problem in nature. Then, the successive convex approximation-based active beamforming and reflection phase optimization algorithm is proposed. The original problem is firstly transformed into two univariate subproblems with the alternating optimization algorithm. After that, the optimal active beamforming and reflection phase for each subproblem are solved separately with a successive convex approximation or a semi-definite relaxation algorithm. The simulation results show that the proposed scheme can effectively utilize the technical advantages of the high gain of the hybrid active-passive intelligent reflecting surface to further improve the channel gain of the cascaded link and obtain higher secrecy rate, compared with the traditional passive intelligent-reflecting-surface-assisted secure transmission scheme.

It is of practical significance to study the parameter estimation of the frequency hopping (FH) signalsinthecaseofrandom missingobservations. Inviewofthefailureofthelineartimefrequency analysisinthe case of missing observations, a frequency hopping signal parameter estimation methodis proposedin this paper, based on orthogonal matching pursuit (OMP) and Kalman filter (KF) . By this method, a sliding windowis addedto the signals, andthe random missing observationsinthe windowis modeled as a signal sparse representation. As an overcomplete dictionary, the Fourier orthonormal bases makes use of OMPtoestimatethefrequencyofthesignalinthe window withoutrestoringthesignals. KF performssmoothlyontheestimatedsignalfrequency. Whenthefrequencychanges, thefrequencyprediction value of KF willseriouslydeviatefromthehistoricalvalueandthefrequencyestimatedvalue, andthedegreeofdeviation will offer the support for hopping time estimation. The simulation results and comparative experiments showthattheparameterestimationoftheproposed methodoutperformsthatofother methods, whentheobservationsare not missing. Withthesufficientsliding windowlengthandtheeligiblesignalsparsityrequirement, effective FH signal parameterestimationresultscanalso be obtainedeveninthecase of missing observations.

Aiming at the challenges such as hyperdense distribution of users, limited spectrum resources and distributed decision-making on 6G network, a spectrum resource sharing method based on multi-dimensional hypergraph game is proposed. Firstly, according to the characteristics of the terahertz communications on 6G network, a multi-dimensional hypergraph interference model was designed, including co-channel direct interference, co-channel accumulated interference and adjacent channel interference. The multi-dimensional interference value was reduced to improve the throughput of the network. In order to realize distributed decision making, the problem was modeled as a hypergraph game, which was proved to be a potential game and had at least one Nash equilibrium. Then, a distributed spectrum decision method based on concurrent highest response was designed to solve the optimal spectrum allocation strategy. The simulation results show that the proposed multi-dimensional hypergraph game has achieved the distributed spectrum sharing in 6G environment. Compared with the traditional hypergraph game methods, the interference level between users is further reduced and the network throughput is greatly improved.

Aiming at the scenarios where UAVs are used as relay nodes to collect the data of internet of things （IoT） devices when human-to-human (H2H) and machine-to-machine (M2M) users coexist, an uplink user scheduling scheme based on the cumulative distribution function is proposed. Multi-user diversity and network load balancing are considered in the modeling process of uplink joint user association and user resource allocation with the optimization goal of maximizing the energy efficiency of nodes in the entire network. In order to solve the problem, the scheme utilizes the characteristics of independence and identical distribution of user throughput, relaxes integer variables, combines the augmented Lagrangian method and the standard subgradient method, transforms the difficult non-convex problem into a convex function, then uses the alternating direction multiplier method to decompose the convex problem and alternately iteratively optimizes it, and finally realizes the maximum energy efficiency of the system and obtains the optimal scheduling scheme. Simulation results indicate that the scheme is superior to existing schemes in terms of network performance and load balancing.

Aiming at the low efficiency of rescue robot path planning in the current emergency rescue process, an improved genetic algorithm and quadratic optimization method is adopted to optimize the rescue path. First, the traditional genetic algorithm is improved in the following way. In the initialization process, a heuristic feasible point insertion method is used to establish the initial population, a path smoothing evaluation function is added to the fitness function, and the method of "focusing on the big and releasing the small" is adopted in the retention strategy, which greatly improves the overall convergence ability and convergence speed of the algorithm. Secondly, on the basis of the initial route, the path is optimized section by section from the starting point to the end point to further reduce the length of the planned path and the number of inflection points. Finally, the method is simulated. The simulation results show that compared with the suboptimal method, the path length of this method is reduced by 2.61%, 2.21%, 3.52% and 1.22 ‰ respectively, and the average convergence times and search time are reduced, which can effectively solve the defects of traditional genetic algorithms and improve the rescue ability of rescue robots.

Camouflage primarily encompasses concealment, deformation, interference and reduction of saliency, etc. With the advancement and progress of camouflage technology, the technical requirements for detecting concealed camouflage targets have gradually increased. Regarding concealed camouflage, this article reviews the current research status of camouflage technology; discusses the traditional methods for detecting concealed camouflage targets, especially those based on deep learning in recent years; further analyzes the commonly used datasets for concealed camouflage target detection, including animal camouflage datasets and military camouflage datasets, and summarizes four frequently employed evaluation metrics; summarizes the challenges faced by concealed camouflage target detection in aspects such as overlapping occlusion, scale variation, dynamic changes, and limited device resources; and explores the development prospects of concealed camouflage target detection in the military field from three different perspectives.

The large heat dissipation of information equipment in protective engineering will lead to the increase of internal temperature, equipment failure and even paralysis. Combined with the advantages of high efficiency of vertical buried pipe operation and low cost of horizontal buried pipe, a composite buried pipe ground source heat pump system is used to deal with the waste heat and moisture problems at the entrance of the project. The annual hourly load of the communications rooms and office hall in the project is calculated; with the TRNSYS simulation software, a model of composite buried pipe and vertical buried pipe heat pump system is constructed to compare the thermal performance, economic benefits and moisture-proof effect of composite buried pipe heat pump system with those of the traditional vertical buried pipe heat pump system. The results show that the relative humidity of the air at the entrance can be reduced effectively by setting the horizontal buried pipe at the entrance of the project, and horizontal buried pipes play the role of moisture-proof. Under the premise that the total coefficient of performance(COP) of the composite buried pipe heat pump system is higher, the total cost of the composite buried pipe heat pump system is reduced by 21.8%, compared with that of the vertical buried pipe heat pump system.

Hyperspectral imaging technology enables the comprehensive utilization of spectral and spatial information, providing a viable solution for precise target identification. It is widely used in areas such as military target characteristic analysis and classification of camouflaged targets. In order to solve the problems of high spectral similarity and low classification and detection accuracy between camouflaged objects and real targets, a camouflage target classification method based on hyperspectral imaging and extreme learning machine (ELM) is proposed. This method first searches for the "window band" for identifying camouflage by studying the spectral characteristics between the camouflage object and the real target. Then, the spectral dimensionality reduction method is used to find the most suitable features for camouflage target classification, and the extracted features are used as input to the ELM multi-classification model to complete the classification task. Taking the classification of camouflage materials in grassland backgrounds as an example, experiments were conducted using hyperspectral images captured in the field. The experimental results show that the classification accuracy of the proposed method reaches 97.27%, which is superior to the classification performance of other comparative classification algorithms. The proposed method provides a valuable reference for future camouflage target classification.