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
The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper devel...The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper develops a variational Bayesian mixture of factor analyzers （VBMFA） model. This procedure can obtain a lower bound on the Bayesian integral using the Jensen＇s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent. During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model au- tomatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.展开更多
Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical ...Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.展开更多
For radar high resolution range profile (HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for f...For radar high resolution range profile (HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for feature space. To tackle these issues, a novel target recognition method is designed, denoted by the multiple support vectors (multi-SV) method. With the proposed method, a special framework is constructed by a treble correlate support vector model to segment the feature space to two regions with the distribution of density, and then the description and classification hyperplane for each region are achieved. Based on the support vector framework, this method needs less memory and computation complexity to fit practical radar HRRP recognition. Finally, the experiment based on the measured data verifies the excellent performance of this method.展开更多
Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting...Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.展开更多
Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is ba...Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.展开更多
This paper deals with the detection performance evaluation of a new nonparametric procedure. The test statistics of the proposed processor have a simple statistical form which is derived from that of the suboptimum ra...This paper deals with the detection performance evaluation of a new nonparametric procedure. The test statistics of the proposed processor have a simple statistical form which is derived from that of the suboptimum rank test procedure [1]. The performance of the new distributionfree detection technique is obtained through Monte-Carlo simulations and compared with that of the suboptimum scheme when detecting nonfluctuating target embedded in Gaussian noise and in the presence of interfering target returns among the reference samples. The new nonparametric detector is shown to give a relative improvement over the suboptimum processor, especially, when the background environment contains a numerous-number of extraneous targets.展开更多
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 ima...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.展开更多
针对冲击脉冲超宽带雷达(Impulse Radio Ultra-Wideband Radar,IR-UWBR)在小样本条件及探测场景复杂等挑战下导致目标识别能力不足的问题,提出基于距离-多普勒图与自适应特征选择网络(Range-Doppler Map and Adaptive Feature Selection...针对冲击脉冲超宽带雷达(Impulse Radio Ultra-Wideband Radar,IR-UWBR)在小样本条件及探测场景复杂等挑战下导致目标识别能力不足的问题,提出基于距离-多普勒图与自适应特征选择网络(Range-Doppler Map and Adaptive Feature Selection Network,RDM-AFSN)的运动目标识别方法。在分析IR-UWBR在慢时间维接收回波信号规律的基础上,建立了IR-UWBR多普勒信息提取模型。同时,深入分析运动目标距离-多普勒图由于背景信息复杂、目标种类多导致图像空间特征差异大的特性,构建基于坐标软阈值去噪模块与空间自适应下采样层的RDM-AFSN目标识别模型。实验结果表明,所提模型能够有效提高小样本条件下对运动目标的分类能力,对不同场景下的同类目标均有较好的识别效果,与常用于地面目标识别的卷积-循环深度网络和图像编码深度网络相比,所提出的RDM-AFSN在识别准确率上分别提高了3.64%和7.53%。展开更多
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.
基金supported in part by the National Natural Science Foundation of China(60772140)the Program for Cheung Kong Scholarsand Innovative Research Team in University(IRT0645)
文摘The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper develops a variational Bayesian mixture of factor analyzers （VBMFA） model. This procedure can obtain a lower bound on the Bayesian integral using the Jensen＇s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent. During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model au- tomatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.
基金This work was supported in part by National Defence Science and Technology Foundation (413220402)
文摘Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.
文摘For radar high resolution range profile (HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for feature space. To tackle these issues, a novel target recognition method is designed, denoted by the multiple support vectors (multi-SV) method. With the proposed method, a special framework is constructed by a treble correlate support vector model to segment the feature space to two regions with the distribution of density, and then the description and classification hyperplane for each region are achieved. Based on the support vector framework, this method needs less memory and computation complexity to fit practical radar HRRP recognition. Finally, the experiment based on the measured data verifies the excellent performance of this method.
基金supported by the National Natural Science Foundation of China(6107113961471019+5 种基金61171122)the Aeronautical Science Foundation of China(20142051022)the Foundation of ATR Key Lab(C80264)the National Natural Science Foundation of China(NNSFC)under the RSE-NNSFC Joint Project(2012-2014)(61211130210)with Beihang Universitythe RSE-NNSFC Joint Project(2012-2014)(61211130309)with Anhui Universitythe"Sino-UK Higher Education Research Partnership for Ph D Studies"Joint Project(2013-2015)
文摘Current research on target detection and recognition from synthetic aperture radar （SAR） images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition （ATR）, es- pecially for the detection and recognition of vehicles, an algorithm based on kernel fisher discdminant analysis （KFDA） is proposed. First, in order to make a better description of the difference be- tween the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED （image Euclidean distance） combined with a support vector machine （SVM） to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recogni- tion rate.
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.
文摘This paper deals with the detection performance evaluation of a new nonparametric procedure. The test statistics of the proposed processor have a simple statistical form which is derived from that of the suboptimum rank test procedure [1]. The performance of the new distributionfree detection technique is obtained through Monte-Carlo simulations and compared with that of the suboptimum scheme when detecting nonfluctuating target embedded in Gaussian noise and in the presence of interfering target returns among the reference samples. The new nonparametric detector is shown to give a relative improvement over the suboptimum processor, especially, when the background environment contains a numerous-number of extraneous targets.
基金supported by the National Natural Science Foundation of China(62231001).
文摘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.
文摘针对冲击脉冲超宽带雷达(Impulse Radio Ultra-Wideband Radar,IR-UWBR)在小样本条件及探测场景复杂等挑战下导致目标识别能力不足的问题,提出基于距离-多普勒图与自适应特征选择网络(Range-Doppler Map and Adaptive Feature Selection Network,RDM-AFSN)的运动目标识别方法。在分析IR-UWBR在慢时间维接收回波信号规律的基础上,建立了IR-UWBR多普勒信息提取模型。同时,深入分析运动目标距离-多普勒图由于背景信息复杂、目标种类多导致图像空间特征差异大的特性,构建基于坐标软阈值去噪模块与空间自适应下采样层的RDM-AFSN目标识别模型。实验结果表明,所提模型能够有效提高小样本条件下对运动目标的分类能力,对不同场景下的同类目标均有较好的识别效果,与常用于地面目标识别的卷积-循环深度网络和图像编码深度网络相比,所提出的RDM-AFSN在识别准确率上分别提高了3.64%和7.53%。
