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
Passive target detection through shipping-radiated noise is a key technology in current underwater operations and is of great research value in civil and military fields.In this study,the stable spectral line componen...Passive target detection through shipping-radiated noise is a key technology in current underwater operations and is of great research value in civil and military fields.In this study,the stable spectral line component of shipping-radiated noise is used as the research object,and the classification of multisource targets is studied from the perspective of underwater channels.We utilize the channel impulse response function as the classification basis of different targets.First,the underwater channel is estimated by the cepstrum.Then,the channel cepstral features carried by different spectral line components are extracted in turn.Finally,the spectral line components belonging to the same target are clustered by the cepstral feature distance to realize the classification of different targets.The simulation and experimental results verify the effectiveness of the proposed method in this research.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
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
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This study was supported by the National Natural Sci-ence Foundation of China(No.11774073)the State Key Laboratory of Acoustics(No.SKLA201904).
文摘Passive target detection through shipping-radiated noise is a key technology in current underwater operations and is of great research value in civil and military fields.In this study,the stable spectral line component of shipping-radiated noise is used as the research object,and the classification of multisource targets is studied from the perspective of underwater channels.We utilize the channel impulse response function as the classification basis of different targets.First,the underwater channel is estimated by the cepstrum.Then,the channel cepstral features carried by different spectral line components are extracted in turn.Finally,the spectral line components belonging to the same target are clustered by the cepstral feature distance to realize the classification of different targets.The simulation and experimental results verify the effectiveness of the proposed method in this research.
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.