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Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar 被引量:2
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作者 JiaoJiao Zhang TianRan Sun +7 位作者 XiZheng Yu DaLin Li Hang Li JiaQi Guo ZongHua Ding Tao Chen Jian Wu Chi Wang 《Earth and Planetary Physics》 EI CSCD 2024年第1期299-306,共8页
The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology... The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research. 展开更多
关键词 Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite European Incoherent Scatter Sciences Association(EISCAT)-3D radar joint detection
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Overview of radar detection methods for low altitude targets in marine environments
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作者 YANG Yong YANG Boyu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2024年第1期1-13,共13页
In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance... In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments. 展开更多
关键词 radar sea clutter multipath scattering detection low altitude target
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HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation
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作者 Wenxia Wang Jinchen Xu +4 位作者 Xiaodong Ding Zhihui Song Yizhen Huang Xin Zhou Zheng Shan 《Computers, Materials & Continua》 SCIE EI 2024年第10期1363-1390,共28页
With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and ... With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals. 展开更多
关键词 Quantum computing hybrid quantum neural network drone detection using radar signals time domain features
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Detection method of forward-scatter signal based on Rényi entropy 被引量:1
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作者 ZHENG Yuqing AI Xiaofeng +2 位作者 YANG Yong ZHAO Feng XIAO Shunping 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第4期865-873,共9页
The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the targe... The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection. 展开更多
关键词 forward scatter radar(FSR) Global Navigation Satellite System(GNSS) time-frequency distribution Rényi entropy signal detection
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Short-time maritime target detection based on polarization scattering characteristics
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作者 CHEN Shichao LUO Feng +1 位作者 TIAN Min LYU Wanghan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2024年第1期55-64,共10页
In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar ... In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level.Due to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell.Then,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types.Finally,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection. 展开更多
关键词 sea clutter small target radar detection Cameron decomposition characteristics analysis
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Improved Small Target Detection Method for SAR Image Based on YOLOv7
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作者 YANG Ke SI Zhan-jun +1 位作者 ZHANG Ying-xue SHI Jin-yu 《印刷与数字媒体技术研究》 CAS 北大核心 2024年第5期53-62,共10页
In order to solve the problems that the current synthetic aperture radar(SAR)image target detection method cannot adapt to targets of different sizes,and the complex image background leads to low detection accuracy,an... In order to solve the problems that the current synthetic aperture radar(SAR)image target detection method cannot adapt to targets of different sizes,and the complex image background leads to low detection accuracy,an improved SAR image small target detection method based on YOLOv7 was proposed in this study.The proposed method improved the feature extraction network by using Switchable Around Convolution(SAConv)in the backbone network to help the model capture target information at different scales,thus improving the feature extraction ability for small targets.Based on the attention mechanism,the DyHead module was embedded in the target detection head to reduce the impact of complex background,and better focus on the small targets.In addition,the NWD loss function was introduced and combined with CIoU loss.Compared to the CIoU loss function typically used in YOLOv7,the NWD loss function pays more attention to the processing of small targets,so as to further improve the detection ability of small targets.The experimental results on the HRSID dataset indicate that the proposed method achieved mAP@0.5 and mAP@0.95 scores of 93.5%and 71.5%,respectively.Compared to the baseline model,this represents an increase of 7.2%and 7.6%,respectively.The proposed method can effectively complete the task of SAR image small target detection. 展开更多
关键词 Small target detection Synthetic aperture radar YOLOv7 DyHead module Switchable Around Convolution
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Deep convolutional neural network for meteorology target detection in airborne weather radar images 被引量:2
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作者 YU Chaopeng XIONG Wei +1 位作者 LI Xiaoqing DONG Lei 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第5期1147-1157,共11页
Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a de... Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes. 展开更多
关键词 meteorology target detection ground clutter sup-pression weather radar images convolutional neural network(CNN)
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Robust dual-channel correlation algorithm for complex weak target detection with wideband radar
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作者 DAI Yan LIU Dan +2 位作者 LI Chuanming WEI Shaopeng HU Qingrong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第5期1130-1146,共17页
In the scene of wideband radar,due to the spread of target scattering points,the attitude and angle of view of the target constantly change in the process of moving.It is difficult to predict,and the actual echo of mu... In the scene of wideband radar,due to the spread of target scattering points,the attitude and angle of view of the target constantly change in the process of moving.It is difficult to predict,and the actual echo of multiple scattered points is not fully matched with the transmitted signal.Therefore,it is challenging for the traditional matching filter method to achieve a complete matching effect in wideband echo detection.In addition,the energy dispersion of complex target echoes is still a problem in radar target detection under broadband conditions.Therefore,this paper proposes a wideband target detection method based on dualchannel correlation processing of range-extended targets.This method fully uses the spatial distribution characteristics of target scattering points of echo signal and the matching characteristics of the dual-channel point extension function itself.The radial accumulation of wideband target echo signal in the complex domain is realized through the adaptive correlation processing of a dual-channel echo signal.The accu-mulation effect of high matching degree is achieved to improve the detection probability and the performance of wideband detection.Finally,electromagnetic simulation experiments and measured data verify that the proposed method has the advan-tages of high signal to noise ratio(SNR)gain and high detection probability under low SNR conditions. 展开更多
关键词 wideband radar target detection DUAL-CHANNEL cor-relation
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A Practical Reinforcement Learning Framework for Automatic Radar Detection
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作者 YU Junpeng CHEN Yiyu 《ZTE Communications》 2023年第3期22-28,共7页
At present,the parameters of radar detection rely heavily on manual adjustment and empirical knowledge,resulting in low automation.Traditional manual adjustment methods cannot meet the requirements of modern radars fo... At present,the parameters of radar detection rely heavily on manual adjustment and empirical knowledge,resulting in low automation.Traditional manual adjustment methods cannot meet the requirements of modern radars for high efficiency,high precision,and high automation.Therefore,it is necessary to explore a new intelligent radar control learning framework and technology to improve the capability and automation of radar detection.Reinforcement learning is popular in decision task learning,but the shortage of samples in radar control tasks makes it difficult to meet the requirements of reinforcement learning.To address the above issues,we propose a practical radar operation reinforcement learning framework,and integrate offline reinforcement learning and meta-reinforcement learning methods to alleviate the sample requirements of reinforcement learning.Experimental results show that our method can automatically perform as humans in radar detection with real-world settings,thereby promoting the practical application of reinforcement learning in radar operation. 展开更多
关键词 meta-reinforcement learning radar detection reinforcement learning offline reinforcement learning
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A Hybrid Features Based Detection Method for Inshore Ship Targets in SAR Imagery 被引量:2
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作者 Tong ZHENG Peng LEI Jun WANG 《Journal of Geodesy and Geoinformation Science》 CSCD 2023年第1期95-107,共13页
Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,du... Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images. 展开更多
关键词 Convolutional Neural Network(CNN) Synthetic Aperture radar(SAR) inshore ship detection hybrid features high-energy point number amplitude spectrum
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Target detection using CDMA based passive bistatic radar 被引量:10
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作者 Haitao Wang Jun Wang Hongwei Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第6期858-865,共8页
Recently, the code division multiple access (CDMA) waveform exists in the large area across the world. However, when using the CDMA system as the illuminator of opportunity for the passive bistatic radar (PBR), th... Recently, the code division multiple access (CDMA) waveform exists in the large area across the world. However, when using the CDMA system as the illuminator of opportunity for the passive bistatic radar (PBR), there exists interference not only from the base station used as the illuminator of opportunity but also from other base stations with the same frequency. And be cause in the CDMA system, the signal transmitted by each base station is different, using the direct signal of one base station can not cancel the interference from other base stations. A CDMA based PBR using an element linear array antenna as both the reference antenna and surveillance antenna is introduced. To deal with the interference in this PBR system, an adaptive temporal cancellation algorithm is used to remove the interference from the base station used as the illuminator of opportunity firstly. And then a robust adaptive beamformer is used to suppress the interference from other base stations. Finally, the preliminary experiment re sults demonstrate the feasibility of using CDMA signals as a radar waveform. 展开更多
关键词 passive bistatic radar (PBR) code division multipleaccess (CDMA) signal interference suppression target detection.
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Method of moving target detection based on sub-image cancellation for single-antenna airborne synthetic aperture radar 被引量:4
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作者 Liu Shujun Yuan Yunneng Gao Fei Mao Shiyi 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第3期448-453,共6页
The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of ... The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of a stationary objects and moving object in the subimage based on the frequency division is analyzed from the fundamental principle. Then the developed method combines the shear averaging algorithm to focus on the moving target in the subimage, after the clutter suppression and the focusing position in each subimage is obtained. Next the observation model and the relative movement of the moving targets between the subimages estimate the moving targets. The theoretical analysis and simulation results demonstrate that the method is effective and can not only detect the moving targets, but also estimate their motion parameters precisely. 展开更多
关键词 synthetic aperture radar moving target detection sub-image cancellation parameter estimation.
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NONPARAMETRIC DETECTION OF RADAR TARGET IN NONHOMOGENEOUS ENVIRONMENTS 被引量:3
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作者 Mohamed B. El Mashade Abdel Aty Moussa(Electrical Engineering Dept., Faculty of Engineering, AI Azhar University. Nasr City, Cairo. Egypt) 《Journal of Electronics(China)》 1998年第4期302-313,共12页
This poaper is devoted to the performance evaluation of the Generalized Sigu(GS). Trimmed Generalized Sign(TGS), Modified Savage(MS). Mann-Whitney(MW) and a new proposed detector in multiple target situations. The ana... This poaper is devoted to the performance evaluation of the Generalized Sigu(GS). Trimmed Generalized Sign(TGS), Modified Savage(MS). Mann-Whitney(MW) and a new proposed detector in multiple target situations. The analysis is carried out for both fluctuating and nonfluctuating received signals. The simulation results show that the new proposed detector has the best detection performance in homogeneous as well as nonhomogeneous background conditions, while TGS procedure is better than the GS detector in distinguishing the primary target from the secondary interfering ones. 展开更多
关键词 radar SYSTEM NONPARAMETRIC detection: Nonhumogeneous environments
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A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection 被引量:3
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作者 LANG Haitao ZHANG Jie +2 位作者 WANG Yiduo ZHANG Xi MENG Junmin 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2016年第9期117-125,共9页
To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accura... To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR(CA-CFAR).To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset. 展开更多
关键词 Bézier curve nonparametric method ship detection sea surface distribution synthetic aperture radar
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Lira-YOLO: a lightweight model for ship detection in radar images 被引量:12
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作者 ZHOU Long WEI Suyuan +3 位作者 CUI Zhongma FANG Jiaqi YANG Xiaoting DING Wei 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第5期950-956,共7页
For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional ne... For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules.The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once(Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision(mAP) indicators on the mini-RD and SAR ship detection dataset(SSDD) reach 83.21% and 85.46%, respectively,which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost. 展开更多
关键词 LIGHTWEIGHT radar images ship detection you only look once(YOLO)
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Detection with High Resolution Radar: Advanced Topics & Potential Applications 被引量:2
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作者 Alfonso Farina Flavio A.Studer ALENIA S.p.A. 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1992年第1期21-34,共14页
In a previous companion paper [1], the potential advantages of high resolution radar for improved target detection were introduced. In particular, the concept of shaping both the transmitted waveform and the receiving... In a previous companion paper [1], the potential advantages of high resolution radar for improved target detection were introduced. In particular, the concept of shaping both the transmitted waveform and the receiving processor in accordance to the expected target down-range profile was highlighted and performance predictions were provided. In this paper, we present and evaluate an adaptive scheme devised to on-line estimate the target profile, in order to overcome a limited a-priori knowledge. In addition, we introduce a more general model of target impulse response, based on a statistical description, and we discuss the corresponding processing scheme and detection performance. 展开更多
关键词 High resolution radar radar detection Adaptive imaging Design criteria.
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Cued search algorithm with uncertain detection performance for phased array radars 被引量:2
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作者 Jianbin Lu Hui Xiao +1 位作者 Zemin Xi Mingmin Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第6期938-945,共8页
A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, a... A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section (RCS). Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability. 展开更多
关键词 phased array radar detection performance cued search information gain beta distribution
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Detection of weak target for MIMO radar based on Hough transform 被引量:2
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作者 Zeng Jiankui He Zishu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第1期76-80,共5页
An effective method of multiple input multiple output (MIMO) radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals ... An effective method of multiple input multiple output (MIMO) radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals (CPIs). Within each CPI, conventional methods such as fast Fourier transform (FFT) is exploit to coherent inte- grating in same range cell. Furthermore, noncoherent integration through several range cells can be implemented by Hough transform among all CPIs. Thus, higher integration gain can be obtained. Simulation results are also given to demonstrate that the detection performance of weak moving target can be dramatically improved. 展开更多
关键词 MIMO radar weak target detection Hough transform noncoherent integration.
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Target detection method in passive bistatic radar 被引量:5
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作者 CHEN Gang WANG Jun 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第3期510-519,共10页
Owing to the advantages in detecting the low altitude and stealth target,passive bistatic radar(PBR)has received much attention in surveillance purposes.Due to the uncontrollable characteristic of the transmitted sign... Owing to the advantages in detecting the low altitude and stealth target,passive bistatic radar(PBR)has received much attention in surveillance purposes.Due to the uncontrollable characteristic of the transmitted signal,a high level range or Doppler sidelobes may exist in the ambiguity function which will degrade the target detection performance.Mismatched filtering is a common method to deal with the ambiguity sidelobe problem.However,when mismatched filtering is applied,sidelobes cannot be eliminated completely.The residual sidelobes will cause false-alarm when the constant false alarm ratio(CFAR)is applied.To deal with this problem,a new target detection method based on preprocessing is proposed.In this new method,the ambiguity range and Doppler sidelobes are recognized and eliminated by the preprocessing method according to the prior information.CFAR is also employed to obtain the information of the target echo.Simulation results and results on real data illustrate the effectiveness of the proposed method. 展开更多
关键词 passive bistatic radar(PBR) mismatched filtering ambiguity sidelobe target detection
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Finite sensor selection algorithm in distributed MIMO radar for joint target tracking and detection 被引量:6
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作者 ZHANG Haowei XIE Junwei +2 位作者 GE Jiaang ZHANG Zhaojian LU Wenlong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第2期290-302,共13页
Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar sys... Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm. 展开更多
关键词 distributed MULTIPLE-INPUT multiple-output(MIMO)radar SUBARRAY selection TARGET tracking TARGET detection particle SWARM optimization(PSO)
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