This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual ca...This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.展开更多
In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numer...In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numerous challenges,primarily due to substantial variations in the amplitude and distribution of HRRP scattering points because of slight azimuthal changes.To alleviate the effect of aspect sensitivity,a novel multi-frame attention network(MFA-Net)comprising a range deformable convolution module(RDCM),multi-frame attention module(MFAM),and global-local Transformer module(GLTM)is proposed.The RDCM is designed to adaptively learn the distance of scattering center migration.Subsequently,the MFAM extracts consistent features across different frames to alleviate the influence of power fluctuation.Finally,the GLTM allocates attention between global and local fea-tures.The feasibility and effectiveness of the proposed method are validated through simulation and experimental datasets,and the recognition rate is enhanced by more than 3%compared to the state-of-the-art methods.展开更多
A two-period duopoly model is developed to examine the competitive effects of targeted advertising with customer recognition （TACR）. In the model, two competing firms sell goods to end consumers in the first period,...A two-period duopoly model is developed to examine the competitive effects of targeted advertising with customer recognition （TACR）. In the model, two competing firms sell goods to end consumers in the first period, during which customer recognition is obtained. In the second period, advertising can be targeted toward different consumer types. Advertising is assumed to be persuasive in the way that consumer valuation is increased. Equilibrium decisions and profits in each period are derived, showing that the firm who loses the current competition will win in the future. As a result, forward-looking firms price less aggressively so that their long-term profits can be enhanced with the help of TACR. Particularly, TACR improves profits through three important effects： valuation increasing, customer poaching, and anti-competition. Finally, this paper investigates the welfare issues, showing that firms enhance profits at the expense of consumer surplus. It is, therefore, suggested that public sectors take a step to protect consumers with the rapid development of targeting technology.展开更多
By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Com...By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.展开更多
Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for gro...Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed. The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features. Numerous experiments with the target type number ranging from 2 to 6 have been implemented. Results show that the proposed scheme not only provides higher recognition preciseness in real time, but also achieves more robust performance as the target type number increases.展开更多
Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise ...Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise signals Through state analysis and inference of state transition probability,both the reliability and early burst probability of the system were obtained in theory展开更多
For the recognition of high-resolution range profile （HRRP） in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize th...For the recognition of high-resolution range profile （HRRP） in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize the weighted HRRP. In the approach, the separability of weighted HRRPs in different targets is measured by de- signing an objective function, and the weighted coefficients are computed by using the gradient descent method, thus enhancing the influence of stable range cells. Simulation results based on five aircraft models show that the approach can effectively optimize the weighted HRRP and improve the recognition accuracy.展开更多
Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rul...Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target reeognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable reeognition results rapidly.展开更多
With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on ...With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.展开更多
This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear featu...This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.展开更多
Underwater target recognition is a key technology for underwater acoustic countermeasure.How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic i...Underwater target recognition is a key technology for underwater acoustic countermeasure.How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals.In this paper,the deep learning model is applied to underwater target recognition.Improved anti-noise Power-Normalized Cepstral Coefficients(ia-PNCC)is proposed,based on PNCC applied to underwater noises.Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity.The method is combined with a convolutional neural network in order to recognize the underwater target.Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are wellsuited to underwater target recognition using a convolutional neural network.Compared with the combination of convolutional neural network with single acoustic feature,such as MFCC(Mel-scale Frequency Cepstral Coefficients)or LPCC(Linear Prediction Cepstral Coefficients),the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition.展开更多
Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent r...Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.展开更多
Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approac...Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approach which consists of the algorithms of Bark-wavelet analysis, Hilbert-Huang transform and support vector machine is proposed. The performance of the proposed method is validated by comparing with traditional method and evaluated by the recognition experiments for SNRs of 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.The results show that the average recognition rate of the method is above 88% and can be increased by 0.75 % to 6.25% under various SNR conditions compared to the baseline system.展开更多
We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar （Ladar） using the algorithm of spin-image surface matching. The method used to recognize the target in the ran...We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar （Ladar） using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coeff^cient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one.展开更多
When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performanc...When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.展开更多
Target recognition is a significant part of a Ballistic Missile Defense System(BMDS).However,most existing ballistic target recognition methods overlook the impact of data representation on recognition outcomes.This p...Target recognition is a significant part of a Ballistic Missile Defense System(BMDS).However,most existing ballistic target recognition methods overlook the impact of data representation on recognition outcomes.This paper focuses on systematically investigating the influences of three novel data representations in the Range-Doppler(RD)domain.Initially,the Radar Cross Section(RCS)and micro-Doppler(m-D)characteristics of a cone-shaped ballistic target are analyzed.Then,three different data representations are proposed:RD data,RD sequence tensor data,and RD trajectory data.To accommodate various data inputs,deep-learning models are designed,including a two-Dimensional Residual Dense Network(2D RDN),a three-Dimensional Residual Dense Network-Gated Recurrent Unit(3D RDN-GRU),and a Dynamic Trajectory Recognition Network(DTRN).Finally,an Electromagnetic(EM)computation dataset is collected to verify the performances of the networks.A broad range of experimental results demonstrates the effectiveness of the proposed framework.Moreover,several key parameters of the proposed networks and datasets are extensively studied in this research.展开更多
This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed...This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.展开更多
In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculat...In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculate the probability of tactical intention.Then the sequence intention probability is obtained by applying Dempster-Shafer rule of combination.To verify the accuracy of recognition results,we compare the experimental results of this paper with the results in the literatures.The experiment shows that the probability of tactical intention recognition through this method is improved,so this method is feasible.展开更多
This paper proposes a novel and comprehensive method of automatic target recognition based on real ISAR images with the aim to recognize the non-cooperative ship targets. The special characteristics of the ISAR images...This paper proposes a novel and comprehensive method of automatic target recognition based on real ISAR images with the aim to recognize the non-cooperative ship targets. The special characteristics of the ISAR images for the real data compared with the simulated ISAR images are analyzed firstly. Then,the novel technique for the target recognition is proposed,and it consists of three steps,including the preprocessing,feature extraction and classification. Some segmentation and morphological methods are used in the preprocessing to obtain the clear target images. Then,six different features for the ISAR images are extracted.By estimating the features' conditional probability, the effectiveness and robustness of these features are demonstrated. Finally,Fisher's linear classifier is applied in the classification step. The results for the allfeature space are provided to illustrate the effectiveness of the proposed method.展开更多
Abstract: A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixesare constructed iwth many frames of the high resolution range profiles which result from radar...Abstract: A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixesare constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Takingthe methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Where-upon, a template library for target recognition is built by the projection of a class-mean vector made from the radar dataonto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and eachtemplate in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar dataarte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noisewhen SNR≥5dB.展开更多
基金funded by the China Postdoctoral Science Foundation(No.2023M730337)。
文摘This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.
基金The National Natural Science Foundation of China(No.62388102)the Natural Science Foundation of Shandong Province(No.ZR2021MF134).
文摘In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numerous challenges,primarily due to substantial variations in the amplitude and distribution of HRRP scattering points because of slight azimuthal changes.To alleviate the effect of aspect sensitivity,a novel multi-frame attention network(MFA-Net)comprising a range deformable convolution module(RDCM),multi-frame attention module(MFAM),and global-local Transformer module(GLTM)is proposed.The RDCM is designed to adaptively learn the distance of scattering center migration.Subsequently,the MFAM extracts consistent features across different frames to alleviate the influence of power fluctuation.Finally,the GLTM allocates attention between global and local fea-tures.The feasibility and effectiveness of the proposed method are validated through simulation and experimental datasets,and the recognition rate is enhanced by more than 3%compared to the state-of-the-art methods.
基金The National Natural Science Foundation of China(No.71071033)the Research and Innovation Project for College Graduates of Jiangsu Province(No.CXZZ-0186)
文摘A two-period duopoly model is developed to examine the competitive effects of targeted advertising with customer recognition （TACR）. In the model, two competing firms sell goods to end consumers in the first period, during which customer recognition is obtained. In the second period, advertising can be targeted toward different consumer types. Advertising is assumed to be persuasive in the way that consumer valuation is increased. Equilibrium decisions and profits in each period are derived, showing that the firm who loses the current competition will win in the future. As a result, forward-looking firms price less aggressively so that their long-term profits can be enhanced with the help of TACR. Particularly, TACR improves profits through three important effects： valuation increasing, customer poaching, and anti-competition. Finally, this paper investigates the welfare issues, showing that firms enhance profits at the expense of consumer surplus. It is, therefore, suggested that public sectors take a step to protect consumers with the rapid development of targeting technology.
文摘By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.
文摘Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed. The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features. Numerous experiments with the target type number ranging from 2 to 6 have been implemented. Results show that the proposed scheme not only provides higher recognition preciseness in real time, but also achieves more robust performance as the target type number increases.
文摘Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise signals Through state analysis and inference of state transition probability,both the reliability and early burst probability of the system were obtained in theory
基金Supported by the Academician Foundation of the 14th Research Institute of China Electronics Technology Group Corporation(2008041001)~~
文摘For the recognition of high-resolution range profile （HRRP） in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize the weighted HRRP. In the approach, the separability of weighted HRRPs in different targets is measured by de- signing an objective function, and the weighted coefficients are computed by using the gradient descent method, thus enhancing the influence of stable range cells. Simulation results based on five aircraft models show that the approach can effectively optimize the weighted HRRP and improve the recognition accuracy.
基金This project was supported by the National "863" High Technology Research and Development Program of China(2001AA602021)
文摘Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target reeognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable reeognition results rapidly.
基金supported by the National Natural Science Foundation of China(61471191)the Aeronautical Science Foundation of China(20152052026)the Foundation of Graduate Innovation Center in NUAA(kfjj20170313)
文摘With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.
基金supported in part by the National Natural Science Foundation of China under Grant No. 61033012, No. 611003177, and No. 61070181Fundamental Research Funds for the Central Universities under Grant No.1600-852016 and No. DUT12JR07
文摘This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.
基金This work was funded by the National Natural Science Foundation of China under Grant(Nos.61772152,61502037)the Basic Research Project(Nos.JCKY2016206B001,JCKY2014206C002,JCKY2017604C010)and the Technical Foundation Project(No.JSQB2017206C002).
文摘Underwater target recognition is a key technology for underwater acoustic countermeasure.How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals.In this paper,the deep learning model is applied to underwater target recognition.Improved anti-noise Power-Normalized Cepstral Coefficients(ia-PNCC)is proposed,based on PNCC applied to underwater noises.Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity.The method is combined with a convolutional neural network in order to recognize the underwater target.Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are wellsuited to underwater target recognition using a convolutional neural network.Compared with the combination of convolutional neural network with single acoustic feature,such as MFCC(Mel-scale Frequency Cepstral Coefficients)or LPCC(Linear Prediction Cepstral Coefficients),the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition.
基金partially supported by the National Natural Science Foundation of China(No.62173272)。
文摘Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.
基金Sponsored by Program for New Century Excellent Talents in University ( NCET-08-0459)
文摘Recognizing the underwater targets by the radiated noise information is one of the most significant subjects in the area of underwater acoustics. Based on the theory of auditory perception, a novel recognition approach which consists of the algorithms of Bark-wavelet analysis, Hilbert-Huang transform and support vector machine is proposed. The performance of the proposed method is validated by comparing with traditional method and evaluated by the recognition experiments for SNRs of 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.The results show that the average recognition rate of the method is above 88% and can be increased by 0.75 % to 6.25% under various SNR conditions compared to the baseline system.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60901046)
文摘We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar （Ladar） using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coeff^cient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one.
文摘When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.
基金supported by the Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JCYB-491).
文摘Target recognition is a significant part of a Ballistic Missile Defense System(BMDS).However,most existing ballistic target recognition methods overlook the impact of data representation on recognition outcomes.This paper focuses on systematically investigating the influences of three novel data representations in the Range-Doppler(RD)domain.Initially,the Radar Cross Section(RCS)and micro-Doppler(m-D)characteristics of a cone-shaped ballistic target are analyzed.Then,three different data representations are proposed:RD data,RD sequence tensor data,and RD trajectory data.To accommodate various data inputs,deep-learning models are designed,including a two-Dimensional Residual Dense Network(2D RDN),a three-Dimensional Residual Dense Network-Gated Recurrent Unit(3D RDN-GRU),and a Dynamic Trajectory Recognition Network(DTRN).Finally,an Electromagnetic(EM)computation dataset is collected to verify the performances of the networks.A broad range of experimental results demonstrates the effectiveness of the proposed framework.Moreover,several key parameters of the proposed networks and datasets are extensively studied in this research.
基金supported by the National Natural Science Foundation of China(Grant No.61973037 and No.61673066).
文摘This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.
文摘In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculate the probability of tactical intention.Then the sequence intention probability is obtained by applying Dempster-Shafer rule of combination.To verify the accuracy of recognition results,we compare the experimental results of this paper with the results in the literatures.The experiment shows that the probability of tactical intention recognition through this method is improved,so this method is feasible.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.61622107 and 61471149)
文摘This paper proposes a novel and comprehensive method of automatic target recognition based on real ISAR images with the aim to recognize the non-cooperative ship targets. The special characteristics of the ISAR images for the real data compared with the simulated ISAR images are analyzed firstly. Then,the novel technique for the target recognition is proposed,and it consists of three steps,including the preprocessing,feature extraction and classification. Some segmentation and morphological methods are used in the preprocessing to obtain the clear target images. Then,six different features for the ISAR images are extracted.By estimating the features' conditional probability, the effectiveness and robustness of these features are demonstrated. Finally,Fisher's linear classifier is applied in the classification step. The results for the allfeature space are provided to illustrate the effectiveness of the proposed method.
基金This project was supported by Advanced National Defence Program of China(41307050103)Advanced National Defence Found of China(00JS24.3.2DZ0117).
文摘Abstract: A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixesare constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Takingthe methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Where-upon, a template library for target recognition is built by the projection of a class-mean vector made from the radar dataonto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and eachtemplate in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar dataarte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noisewhen SNR≥5dB.
