查看更多>>摘要:Deep learning has achieved excellent results in various tasks in the field of computer vision,especially in fine-grained visual categorization.It aims to distinguish the subordinate categories of the label-level categories.Due to high intra-class variances and high inter-class similarity,the fine-grained visual categorization is extremely challenging.This paper first briefly introduces and analyzes the related public datasets.After that,some of the latest methods are reviewed.Based on the feature types,the feature processing methods,and the overall structure used in the model,we divide them into three types of methods:methods based on general convolutional neural network(CNN)and strong supervision of parts,methods based on single feature processing,and meth-ods based on multiple feature processing.Most methods of the first type have a relatively simple structure,which is the result of the initial research.The methods of the other two types include models that have special structures and training processes,which are helpful to obtain discriminative features.We conduct a specific analysis on several methods with high accuracy on pub-lic datasets.In addition,we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power.In terms of tech-nology,the extraction of the subtle feature information with the burgeoning vision transformer(ViT)network is also an important research direction.
查看更多>>摘要:In challenging situations,such as low illumination,rain,and background clutter,the stability of the thermal infrared(TIR)spectrum can help red,green,blue(RGB)visible spectrum to improve tracking performance.However,the high-level image information and the modality-specific features have not been sufficiently studied.The proposed correlation filter uses the fused saliency content map to improve filter training and extracts different features of modalities.The fused content map is intro-duced into the spatial regularization term of correlation filter to highlight the training samples in the content region.Furthermore,the fused content map can avoid the incompleteness of the con-tent region caused by challenging situations.Additionally,differ-ent features are extracted according to the modality characteris-tics and are fused by the designed response-level fusion stra-tegy.The alternating direction method of multipliers(ADMM)algorithm is used to solve the tracker training efficiently.Experi-ments on the large-scale benchmark datasets show the effec-tiveness of the proposed tracker compared to the state-of-the-art traditional trackers and the deep learning based trackers.
查看更多>>摘要:The direction of ground-based interference reaching the satellite is generally very close to the spot beam of the satel-lite.The traditional array anti-jamming method may cause signifi-cant loss to the uplink signal while suppressing the interference.In this paper,an aperiodic multistage array is used,and a sub-array aperiodic distribution optimization scheme based on paral-lel differential evolution is proposed,which effectively improves the beam resolution and suppresses the grating lobe.On this basis,a two-stage signal processing method is used to sup-press interference.Finally,the comprehensive performance of the proposed scheme is evaluated and verified.
查看更多>>摘要:This paper considers an intelligent reflecting surface(IRS)-assisted multiple-input multiple-output(MIMO)system.To maximize the average achievable rate(AAR)under outdated channel state information(CSI),we propose a twin-timescale passive beamforming(PBF)and power allocation protocol which can reduce the IRS configuration and training overhead.Specifi-cally,the short-timescale power allocation is designed with the outdated precoder and fixed PBF.A new particle swarm opti-mization(PSO)-based long-timescale PBF optimization is pro-posed,where mini-batch channel samples are utilized to update the fitness function.Finally,simulation results demonstrate the effectiveness of the proposed method.
查看更多>>摘要:Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.
查看更多>>摘要:During high-speed flight,both thermal and mechani-cal loads can degrade the electrical performance of the antenna-radome system,which can subsequently affect the performance of the guidance system.This paper presents a method for evalu-ating the electrical performance of the radome when subjected to thermo-mechanical-electrical(TME)coupling.The method involves establishing a TME coupling model(TME-CM)based on the TME sharing mesh model(TME-SMM)generated by the tetrahedral mesh partitioning of the radome structure.The effects of dielectric temperature drift and structural deformation on the radome's electrical performance are also considered.Firstly,the temperature field of the radome is obtained by tran-sient thermal analysis while the deformation field of the radome is obtained by static analysis.Subsequently,the dielectric varia-tion and structural deformation of the radome are accurately incorporated into the electrical simulation model based on the TME-SMM.The three-dimensional(3D)ray tracing method with the aperture integration technique is used to calculate the radome's electrical performance.A representative example is provided to illustrate the superiority and necessity of the pro-posed method.This is achieved by calculating and analyzing the changes in the radome's electrical performance over time dur-ing high-speed flight.
查看更多>>摘要:The parametric scattering center model of radar tar-get has the advantages of simplicity,sparsity and mechanism relevant,making it widely applied in fields such as radar data compression and rapid generation,radar imaging,feature extraction and recognition.This paper summarizes and analyzes the research situation,development trend,and difficult prob-lems on scattering center(SC)parametric modeling from three aspects:parametric representation,determination method of model parameters,and application.
查看更多>>摘要:This paper considers the problem of sea clutter sup-pression.We propose the cuttable encoder-decoder-augmenta-tion network(CEDAN)to improve clutter suppression perfor-mance by enriching the contrast information between the target and clutter.Specifically,the plug-and-play residual U-block(ResUblock)is proposed to augment the feature representation ability of the clutter suppression model.The CEDAN first extracts and fuses the multi-scale features using the encoder and the decoder composed of the ResUblocks.Then,the fused features are processed by the contrast information augmenta-tion module(CIAM)to enhance the diversity of target and clutter,resulting in encouraging sea clutter suppression results.In addi-tion,we propose the result-consistency loss to further improve the suppression performance.The result-consistency loss enables CEDAN to cut some blocks of decoder and CIAM to reduce the inference time without significantly degrading the suppression performance.Experimental results on measured and simulated data show that the CEDAN outperforms state-of-the-art sea clutter suppression methods in sea clutter suppres-sion performance and computation efficiency.
查看更多>>摘要:Separation and recognition of radar signals is the key function of modern radar reconnaissance,which is of great sig-nificance for electronic countermeasures and anti-countermea-sures.In order to improve the ability of separating mixed signals in complex electromagnetic environment,a blind source separa-tion algorithm based on degree of cyclostationarity(DCS)crite-rion is constructed in this paper.Firstly,the DCS criterion is con-structed by using the cyclic spectrum theory.Then the algo-rithm flow of blind source separation is designed based on DCS criterion.At the same time,Givens matrix is constructed to make the blind source separation algorithm suitable for multiple sig-nals with different cyclostationary frequencies.The feasibility of this method is further proved.The theoretical and simulation results show that the algorithm can effectively separate and re-cognize common multi-radar signals.
查看更多>>摘要:Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster's rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.