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Classification of birds and drones by exploiting periodical motions in Doppler spectrum series 被引量:1
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作者 DUAN Jia ZHANG Lei +3 位作者 WU Yifeng ZHANG Yue ZHAO Zeya GUO Xinrong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第1期19-27,共9页
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
关键词 target classification long-to-short memory(LSTM) drone discrimination Doppler spectrum series
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A Classification Algorithm for Ground Moving Targets Based on Magnetic Sensors
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作者 崔逊学 刘綦 刘坤 《Defence Technology(防务技术)》 SCIE EI CAS 2011年第1期52-58,共7页
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
关键词 information processing magnetic sensor abnormal magnetic signal target detection target classification classification algorithm
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Analysis and inversion of target polarization characteristics based on pBRDF
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作者 GU Xiansong FU Qiang +4 位作者 WANG Liya LIU Xuanwei FAN Xinyu DUAN Jin LI Yingchao 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第5期1073-1083,共11页
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
关键词 polarization detection polarization degree zenith angle azimuth angle target classification.
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MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK
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作者 GeGuangying ChenLili XuJianjian 《Journal of Electronics(China)》 2005年第3期321-328,共8页
Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving tar... Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively. 展开更多
关键词 Moving targets detection Pattern recognition Wavelet neural network targets classification
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Target classification using SIFT sequence scale invariants 被引量:5
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作者 Xufeng Zhu Caiwen Ma +1 位作者 Bo Liu Xiaoqian Cao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第5期633-639,共7页
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
关键词 target classification scale invariant feature transform descriptors sequence scale support vector machine
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Data association based on target signal classification information 被引量:3
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作者 Guo Lei Tang Bin Liu Gang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第2期246-251,共6页
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
关键词 passive tracking joint probabilistic data association target signal classification information.
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New algorithm of target classification in polarimetric SAR
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作者 Wang Yang Lu Jiaguo Wu Xianliang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第2期273-279,共7页
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
关键词 polarimetric synthetic aperture radar target decomposition support vector machine target classification kernel function.
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Multisource Target Classification Based on Underwater Channel Cepstral Features
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作者 LI Xiukun JIA Hongjian +1 位作者 DONG Jianwei QIN Jixing 《Journal of Ocean University of China》 SCIE CAS CSCD 2022年第4期917-925,共9页
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
关键词 shipping-radiated noise underwater channel cepstral features target classification
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Generalization Capabilities of Feedforward Neural Networks for Pattern Recognition
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作者 黄德双 《Journal of Beijing Institute of Technology》 EI CAS 1996年第2期192+184-192,共10页
This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that th... This paper studies the generalization capability of feedforward neural networks (FNN).The mechanism of FNNs for classification is investigated from the geometric and probabilistic viewpoints. It is pointed out that the outputs of the output layer in the FNNs for classification correspond to the estimates of posteriori probability of the input pattern samples with desired outputs 1 or 0. The theorem for the generalized kernel function in the radial basis function networks (RBFN) is given. For an 2-layer perceptron network (2-LPN). an idea of using extended samples to improve generalization capability is proposed. Finally. the experimental results of radar target classification are given to verify the generaliztion capability of the RBFNs. 展开更多
关键词 feedforward neural networks radial basis function networks multilayer perceptronnetworks generalization capability radar target classification
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