On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits o...On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.展开更多
The different approaches used for target decomposition （TD） theory in radar polarimetry are reviewed and three main types of theorems are introduced： those based on Mueller matrix, those using an eigenvector analys...The different approaches used for target decomposition （TD） theory in radar polarimetry are reviewed and three main types of theorems are introduced： those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine （SVM）, as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is proposed. To conduct the experiment, the polarimetric synthetic aperture radar （SAR） data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.展开更多
The physical architecture of optical convolution restricts its capacity to capture multi-scale features from targets,thus impeding the precision of network recognition.In this work,we propose a multi-scale optical con...The physical architecture of optical convolution restricts its capacity to capture multi-scale features from targets,thus impeding the precision of network recognition.In this work,we propose a multi-scale optical convolutional neural network(MS-OCNN),which uses convolution kernels with different resolutions in the convolution layer to extract different scale features,along with attention mechanism and residual structure to analyze features.By separating the training and inference platforms of the network,we facilitate the electronic training of model parameters on a computer and the optical deployment on a system equipped with a spatial light modulator,enabling efficient target classification.The proposed MS-OCNN exhibits 1% to 3% improvement in classification performance on the modified national institute of standards and technology(MNIST)and Fashion-MNIST datasets compared to single-scale optical inference models.Online experimental systems in real-world scenarios have validated the target recognition capabilities of this method,which yielded classification accuracies of 97% and 87% on the MNIST and Fashion-MNIST datasets,respectively.This work enhances the feature acquisition capabilities of optical convolutional networks,elevates network recognition accuracy,and significantly propels the application of optical computing in domains such as guidance systems,autonomous driving,and robotics.展开更多
Inverse synthetic aperture radar(ISAR),as the core technology of high-resolution radar imaging,has significant application value in fields of marine monitoring,air reconnaissance and space target recognition due to it...Inverse synthetic aperture radar(ISAR),as the core technology of high-resolution radar imaging,has significant application value in fields of marine monitoring,air reconnaissance and space target recognition due to its all-weather and long-range advantages.However,the current research on ISAR target classification mainly focuses on the low-frequency band,and the target classification methods for terahertz ISAR images is still in its infancy.Compared with low-frequency band ISAR images,terahertz ISAR images not only have richer pixel-level details,but also have more complex scattering characteristics,which poses new challenges to the feature extraction and classification capabilities of existing methods.To address these challenges,this paper proposes a terahertz ISAR image target classification method based on deep learning and designs a multi-scale space-frequency dual-branch features fusion(MSFF)network.The network consists of two key blocks:the multi-scale spatial feature extraction(MSFE)block and the frequency domain convolution awareness(FDCA)block.The MSFE block extracts local spatial features of different scales through multi-scale convolution kernels,thereby enhancing the model's perception ability of the local features of target.The FDCA block extracts the global features of the target through frequency domain transformation,enhancing the model's ability to capture global structural information.This method achieves feature fusion between spatial and frequency-domain branches through feature concatenation,effectively integrating the multi-domain information and significantly improving the performance of feature extraction.Furthermore,the MSFF network provides fewer parameters and floating-point operations per second(FLOPs).Experimental results on the selfconstructed terahertz ISAR dataset demonstrate that the MSFF network achieves a minimum classification accuracy of 99.49%,with only 0.49 M parameters and 0.37 G FLOPs.Furthermore,comparative experiments with the low-frequency ISAR datasets further validate the superior performance of the MSFF network.展开更多
A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,t...A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.展开更多
In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too...In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.展开更多
With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually ...With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.展开更多
Imaging detection is an important means to obtain target information.The traditional imaging detection technology mainly collects the intensity information and spectral information of the target to realize the classif...Imaging detection is an important means to obtain target information.The traditional imaging detection technology mainly collects the intensity information and spectral information of the target to realize the classification of the target.In practical applications,due to the mixed scenario,it is difficult to meet the needs of target recognition.Compared with intensity detection,the method of polarization detection can effectively enhance the accuracy of ground object target recognition(such as the camouflage target).In this paper,the reflection mechanism of the target surface is studied from the microscopic point of view,and the polarization characteristic model is established to express the relationship between the polarization state of the reflected signal and the target surface parameters.The polarization characteristic test experiment is carried out,and the target surface parameters are retrieved using the experimental data.The results show that the degree of polarization(DOP)is closely related to the detection zenith angle and azimuth angle.The(DOP)of the target is the smallest in the direction of light source incidence and the largest in the direction of specular reflection.Different materials have different polarization characteristics.By comparing their DOP,target classification can be achieved.展开更多
基金supported by the National High Technology Research and Development Program (863 Program) (2010AA7080302)
文摘On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.
文摘The different approaches used for target decomposition （TD） theory in radar polarimetry are reviewed and three main types of theorems are introduced： those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine （SVM）, as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is proposed. To conduct the experiment, the polarimetric synthetic aperture radar （SAR） data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.
基金supported by the National Natural Science Foundation of China(62201025)the Fundamental Research Funds for the Central Universities(YWF-23-L-1225)the Chinese Aeronautical Establishment(2022Z037051001)。
文摘The physical architecture of optical convolution restricts its capacity to capture multi-scale features from targets,thus impeding the precision of network recognition.In this work,we propose a multi-scale optical convolutional neural network(MS-OCNN),which uses convolution kernels with different resolutions in the convolution layer to extract different scale features,along with attention mechanism and residual structure to analyze features.By separating the training and inference platforms of the network,we facilitate the electronic training of model parameters on a computer and the optical deployment on a system equipped with a spatial light modulator,enabling efficient target classification.The proposed MS-OCNN exhibits 1% to 3% improvement in classification performance on the modified national institute of standards and technology(MNIST)and Fashion-MNIST datasets compared to single-scale optical inference models.Online experimental systems in real-world scenarios have validated the target recognition capabilities of this method,which yielded classification accuracies of 97% and 87% on the MNIST and Fashion-MNIST datasets,respectively.This work enhances the feature acquisition capabilities of optical convolutional networks,elevates network recognition accuracy,and significantly propels the application of optical computing in domains such as guidance systems,autonomous driving,and robotics.
基金supported in part by the National Natural Science Foundation of China under Grant No.62588201in part by the Artificial Intelligence Promotes Scientific Research Paradigm Reform and Empowers Discipline Advancement Planin part by the Natural Science Foundation of Shanghai under Grant No.21ZR1444300。
文摘Inverse synthetic aperture radar(ISAR),as the core technology of high-resolution radar imaging,has significant application value in fields of marine monitoring,air reconnaissance and space target recognition due to its all-weather and long-range advantages.However,the current research on ISAR target classification mainly focuses on the low-frequency band,and the target classification methods for terahertz ISAR images is still in its infancy.Compared with low-frequency band ISAR images,terahertz ISAR images not only have richer pixel-level details,but also have more complex scattering characteristics,which poses new challenges to the feature extraction and classification capabilities of existing methods.To address these challenges,this paper proposes a terahertz ISAR image target classification method based on deep learning and designs a multi-scale space-frequency dual-branch features fusion(MSFF)network.The network consists of two key blocks:the multi-scale spatial feature extraction(MSFE)block and the frequency domain convolution awareness(FDCA)block.The MSFE block extracts local spatial features of different scales through multi-scale convolution kernels,thereby enhancing the model's perception ability of the local features of target.The FDCA block extracts the global features of the target through frequency domain transformation,enhancing the model's ability to capture global structural information.This method achieves feature fusion between spatial and frequency-domain branches through feature concatenation,effectively integrating the multi-domain information and significantly improving the performance of feature extraction.Furthermore,the MSFF network provides fewer parameters and floating-point operations per second(FLOPs).Experimental results on the selfconstructed terahertz ISAR dataset demonstrate that the MSFF network achieves a minimum classification accuracy of 99.49%,with only 0.49 M parameters and 0.37 G FLOPs.Furthermore,comparative experiments with the low-frequency ISAR datasets further validate the superior performance of the MSFF network.
基金Sponsored by the National Natural Science Foundation of China (60773129)the Excellent Youth Science and Technology Foundation of Anhui Province of China ( 08040106808)
文摘A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.
基金the Youth Science and Technology Foundection of University of Electronic Science andTechnology of China (JX0622).
文摘In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.
基金supported by the National Natural Science Foundation of China (62101603)the Shenzhen Science and Technology Program(KQTD20190929172704911)+3 种基金the Aeronautical Science Foundation of China (2019200M1001)the National Nature Science Foundation of Guangdong (2021A1515011979)the Guangdong Key Laboratory of Advanced IntelliSense Technology (2019B121203006)the Pearl R iver Talent Recruitment Program (2019ZT08X751)。
文摘With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.
基金supported by the National Key Laboratory of Electromagnetic Space Security(JCKY2023230C009).
文摘Imaging detection is an important means to obtain target information.The traditional imaging detection technology mainly collects the intensity information and spectral information of the target to realize the classification of the target.In practical applications,due to the mixed scenario,it is difficult to meet the needs of target recognition.Compared with intensity detection,the method of polarization detection can effectively enhance the accuracy of ground object target recognition(such as the camouflage target).In this paper,the reflection mechanism of the target surface is studied from the microscopic point of view,and the polarization characteristic model is established to express the relationship between the polarization state of the reflected signal and the target surface parameters.The polarization characteristic test experiment is carried out,and the target surface parameters are retrieved using the experimental data.The results show that the degree of polarization(DOP)is closely related to the detection zenith angle and azimuth angle.The(DOP)of the target is the smallest in the direction of light source incidence and the largest in the direction of specular reflection.Different materials have different polarization characteristics.By comparing their DOP,target classification can be achieved.
