Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059406
Y. Zhu, Zhaocheng Yang, Jianjun Huang
This paper introduces a novel sparsity-based spacetime adaptive processing (STAP) considering array gain/phase er-ror (AGPE-STAP) for airborne radar. The proposed AGPE-STAP algorithm combines a conventional sparsity-based STAP method and a conventional array gain/phase error calibration method. The proposed method first models the received returns considering array gain/phase error, estimates the array gain/phase error, calibrates the space-time steering dictionary, and at last designs the filter using the conventional sparsity-based STAP algorithm. Simulation results show that the proposed algorithm outperforms the existing sparsity-based STAP algorithm without calibration in presence of array gain/phase error.
{"title":"Sparsity-based space-time adaptive processing considering array gain/phase error","authors":"Y. Zhu, Zhaocheng Yang, Jianjun Huang","doi":"10.1109/RADAR.2016.8059406","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059406","url":null,"abstract":"This paper introduces a novel sparsity-based spacetime adaptive processing (STAP) considering array gain/phase er-ror (AGPE-STAP) for airborne radar. The proposed AGPE-STAP algorithm combines a conventional sparsity-based STAP method and a conventional array gain/phase error calibration method. The proposed method first models the received returns considering array gain/phase error, estimates the array gain/phase error, calibrates the space-time steering dictionary, and at last designs the filter using the conventional sparsity-based STAP algorithm. Simulation results show that the proposed algorithm outperforms the existing sparsity-based STAP algorithm without calibration in presence of array gain/phase error.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116107203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059505
Yi-xiao Xiong, Jinming Ding, Wei Wang
A new unsupervised segmentation algorithm of SAR(Synthetic aperture radar) imageries based on multiscale Stochastic Models is proposed, considering non-gaussian statistical property of SAR image data and Markov property of neighboring scales. Since EM(expectation maximum) algorithm can not get the parameter estimation of non-gauss distribution, MAR(Multiscale Autoregressive) model is used for extracting image Feature data which obeys gauss distribution. HMT(Hidden Markov Tree) model can be used to model image consisting of multi-scale feature data, which can be approximated by mixed gauss distribution and its parameters can be straightly trained by EM algorithm. Then we propose a context model to fuse feature information of multiscale. Finally, we obtain a new unsupervised segmentation approach for SAR imageries. Simulations on SAR imagery indicate that the new approach improves segmentation accuracy in some degree.
{"title":"Application of unsupervised segmentation for SAR imageries based on multiscale stochastic models","authors":"Yi-xiao Xiong, Jinming Ding, Wei Wang","doi":"10.1109/RADAR.2016.8059505","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059505","url":null,"abstract":"A new unsupervised segmentation algorithm of SAR(Synthetic aperture radar) imageries based on multiscale Stochastic Models is proposed, considering non-gaussian statistical property of SAR image data and Markov property of neighboring scales. Since EM(expectation maximum) algorithm can not get the parameter estimation of non-gauss distribution, MAR(Multiscale Autoregressive) model is used for extracting image Feature data which obeys gauss distribution. HMT(Hidden Markov Tree) model can be used to model image consisting of multi-scale feature data, which can be approximated by mixed gauss distribution and its parameters can be straightly trained by EM algorithm. Then we propose a context model to fuse feature information of multiscale. Finally, we obtain a new unsupervised segmentation approach for SAR imageries. Simulations on SAR imagery indicate that the new approach improves segmentation accuracy in some degree.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115299477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059550
Jian-Qi Wu, Kai Wang, Y. Gu
Prospective characteristics of the next generation of radar is investigated in this paper, combined with future requirement. The demands of the next generation radar for the wide band, high speed, parallelism and high integration are precisely the four prominent respects of photonics. The technological path of future radar could hopefully be interdisciplinary fusion to solve bottleneck problems in the microwave field by means of optical methods, ideas and technical features, namely constructing microwave photonics technology based radar system. One system architecture of multifunctional radar based on the technology of microwave photonics is proposed. Meanwhile, representative techniques such as photonic frequency conversion and photonic beamforming are presented. Some stages of experiment and test results are reported.
{"title":"Research on technology of microwave-photonic-based multifunctional radar","authors":"Jian-Qi Wu, Kai Wang, Y. Gu","doi":"10.1109/RADAR.2016.8059550","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059550","url":null,"abstract":"Prospective characteristics of the next generation of radar is investigated in this paper, combined with future requirement. The demands of the next generation radar for the wide band, high speed, parallelism and high integration are precisely the four prominent respects of photonics. The technological path of future radar could hopefully be interdisciplinary fusion to solve bottleneck problems in the microwave field by means of optical methods, ideas and technical features, namely constructing microwave photonics technology based radar system. One system architecture of multifunctional radar based on the technology of microwave photonics is proposed. Meanwhile, representative techniques such as photonic frequency conversion and photonic beamforming are presented. Some stages of experiment and test results are reported.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115309761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059257
Yaotian Zhang, Yifeng Yang, Shaoming Wei, Jun Wang
Gaussian-Mixture Probability Hypothesis Density (GM-PHD) filter is one of the implementation of PHD filter based on Random Finite Set (RFS). The algorithm performs well in jointly estimating the number of targets and their states with low computation demanding. However, the GM-PHD filter can't provide trajectories of individual targets. This paper proposes two approaches to combine the GM-PHD filter with the Multiple Hypothesis Tracking (MHT). On the one hand, GM-PHD filter effectively reduce the computation complexity of MHT; On the other hand, the data association problem is successfully solved by MHT. The simulation shows that the calculation cost is decreased remarkably and the association accuracy is improved at the same time compared with MHT.
{"title":"Fast data association approaches for multi-target tracking","authors":"Yaotian Zhang, Yifeng Yang, Shaoming Wei, Jun Wang","doi":"10.1109/RADAR.2016.8059257","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059257","url":null,"abstract":"Gaussian-Mixture Probability Hypothesis Density (GM-PHD) filter is one of the implementation of PHD filter based on Random Finite Set (RFS). The algorithm performs well in jointly estimating the number of targets and their states with low computation demanding. However, the GM-PHD filter can't provide trajectories of individual targets. This paper proposes two approaches to combine the GM-PHD filter with the Multiple Hypothesis Tracking (MHT). On the one hand, GM-PHD filter effectively reduce the computation complexity of MHT; On the other hand, the data association problem is successfully solved by MHT. The simulation shows that the calculation cost is decreased remarkably and the association accuracy is improved at the same time compared with MHT.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123095137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059145
M. Radmard, M. M. Chitgarha, M. N. Majd, M. Nayebi
It has been shown that using multiple antennas in a radar system improves the performance considerably, since multiple target echoes are received from different aspect angles of the target. In this way, the target detection is improved. However, when using multiple antennas, some problems, such as designing the transmit signals, synchronization, etc. emerge that should be solved. One of such problems is the receiver placement. Receiver placement deals with choosing a proper position for the receive antenna in order to optimize the whole system's performance. In this paper, a receiver placement procedure based on improving the radar ambiguity function is proposed for the case of a multisite radar with multiple transmit antennas and a single receiver.
{"title":"Ambiguity function based receiver placement in multi-site radar","authors":"M. Radmard, M. M. Chitgarha, M. N. Majd, M. Nayebi","doi":"10.1109/RADAR.2016.8059145","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059145","url":null,"abstract":"It has been shown that using multiple antennas in a radar system improves the performance considerably, since multiple target echoes are received from different aspect angles of the target. In this way, the target detection is improved. However, when using multiple antennas, some problems, such as designing the transmit signals, synchronization, etc. emerge that should be solved. One of such problems is the receiver placement. Receiver placement deals with choosing a proper position for the receive antenna in order to optimize the whole system's performance. In this paper, a receiver placement procedure based on improving the radar ambiguity function is proposed for the case of a multisite radar with multiple transmit antennas and a single receiver.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123318162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059394
Xueyao Hu, Teng Yu, Xinyu Zhang, Yanhua Wang, Hongyu Wang, Yang Li
The performance of adaptive beamforming degrades severely when the strong desired signal is present in training snapshots with model mismatch. A robust adaptive beamforming is proposed using interference covariance matrix reconstruction in this paper. In the proposed method, the eigenvalue and eigenvector of desired signal is determined by calculating the correlation coefficients between eigenvectors of sample covariance matrix and the presumed array steering vector. Subsequently, the covariance matrix is reconstructed after removing the desired signal component from signal subspace. Finally, the average noise power is computed by estimating the noise subspace dimensions indirectly, and added to the reconstructed matrix in order to prevent the matrix from being singular. Compared with the conventional robust adaptive beamforming methods, the proposed method has improved performance and less computational complexity. Simulation results demonstrate the robustness and effectiveness of the proposed method.
{"title":"Robust adaptive beamforming using interference covariance matrix reconstruction","authors":"Xueyao Hu, Teng Yu, Xinyu Zhang, Yanhua Wang, Hongyu Wang, Yang Li","doi":"10.1109/RADAR.2016.8059394","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059394","url":null,"abstract":"The performance of adaptive beamforming degrades severely when the strong desired signal is present in training snapshots with model mismatch. A robust adaptive beamforming is proposed using interference covariance matrix reconstruction in this paper. In the proposed method, the eigenvalue and eigenvector of desired signal is determined by calculating the correlation coefficients between eigenvectors of sample covariance matrix and the presumed array steering vector. Subsequently, the covariance matrix is reconstructed after removing the desired signal component from signal subspace. Finally, the average noise power is computed by estimating the noise subspace dimensions indirectly, and added to the reconstructed matrix in order to prevent the matrix from being singular. Compared with the conventional robust adaptive beamforming methods, the proposed method has improved performance and less computational complexity. Simulation results demonstrate the robustness and effectiveness of the proposed method.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123597640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059442
Liang Guo, Qiang Yang, Weibo Deng
The spread of the dominant first order Bragg lines in shipborne high frequency surface wave radar (HFSWR) severely obscures targets within the spreading domain. Joint domain localized (JDL) algorithm is one of reduced-dimension STAP methods to suppress the sea clutter. Its performance depends on the accuracy of the estimation of the covariance matrix. Conventional JDL utilizes the secondary training data directly to calculate the covariance matrix. A modified calculation of the covariance matrix based on the distribution and characteristics of sea clutter is proposed to fit for sea clutter better and make maximum use of the few secondary training data. Simulation based on real data shows that the modified JDL algorithm is effective on improving the anti-clutter performance in shipborne HFSWR.
{"title":"Suppression of sea clutter with modified joint domain localized algorithm in shipborne HFSWR","authors":"Liang Guo, Qiang Yang, Weibo Deng","doi":"10.1109/RADAR.2016.8059442","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059442","url":null,"abstract":"The spread of the dominant first order Bragg lines in shipborne high frequency surface wave radar (HFSWR) severely obscures targets within the spreading domain. Joint domain localized (JDL) algorithm is one of reduced-dimension STAP methods to suppress the sea clutter. Its performance depends on the accuracy of the estimation of the covariance matrix. Conventional JDL utilizes the secondary training data directly to calculate the covariance matrix. A modified calculation of the covariance matrix based on the distribution and characteristics of sea clutter is proposed to fit for sea clutter better and make maximum use of the few secondary training data. Simulation based on real data shows that the modified JDL algorithm is effective on improving the anti-clutter performance in shipborne HFSWR.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123063008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059501
Dongling Xiao, Ling Wang, Xudong Wang, Chang Li
Since the trajectories of space targets can be actually tracked, we can apply the Backprojection (BP) method to Inverse Synthetic aperture radar (ISAR) imaging of satellite. Due to the limitation of an accuracy of the tracking data in some scenarios, the motion errors of the target cannot be avoided. We cannot obtain good reconstructed-images. In this paper, we present an analysis of the motion errors of the targets in ISAR imaging. Our analysis provides an explicit quantitative relationship between the motion errors of the target and the position errors in the reconstructed ISAR image. We provide simulation results to demonstrate the performance of analysis. This analysis is helpful for developing autofocus methods for ISAR imaging of space targets.
{"title":"Motion error analysis for ISAR imaging of space targets","authors":"Dongling Xiao, Ling Wang, Xudong Wang, Chang Li","doi":"10.1109/RADAR.2016.8059501","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059501","url":null,"abstract":"Since the trajectories of space targets can be actually tracked, we can apply the Backprojection (BP) method to Inverse Synthetic aperture radar (ISAR) imaging of satellite. Due to the limitation of an accuracy of the tracking data in some scenarios, the motion errors of the target cannot be avoided. We cannot obtain good reconstructed-images. In this paper, we present an analysis of the motion errors of the targets in ISAR imaging. Our analysis provides an explicit quantitative relationship between the motion errors of the target and the position errors in the reconstructed ISAR image. We provide simulation results to demonstrate the performance of analysis. This analysis is helpful for developing autofocus methods for ISAR imaging of space targets.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"480 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123557187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059293
Sun Wei, He Kunxi, Ye Zhenyu, Sun Jinping
Terahertz Video Synthetic Aperture Radar (ViSAR) system can indicate target information continuously and actively with an image frame rate as high as infrared video, having advantages such as high resolution and good sensitivity in target motion detection. However, terahertz wavelength is rather short, and as a result, traditional dual-channel SAR motion target indication method gets a tiny blind velocity period which goes against target detection. Aimed at the problem, this paper provides a multi-channel terahertz ViSAR motion target detection method based on along track interferometry (ATI) technique. It selects several baselines of varying length and utilizes algebra coprime theory to enlarge blind velocity period so that the range of non-ambiguity velocity can be extended. Simulation results verify the effectiveness of the method.
{"title":"Multi-channel terahertz ViSAR motion target indication based on ATI technique","authors":"Sun Wei, He Kunxi, Ye Zhenyu, Sun Jinping","doi":"10.1109/RADAR.2016.8059293","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059293","url":null,"abstract":"Terahertz Video Synthetic Aperture Radar (ViSAR) system can indicate target information continuously and actively with an image frame rate as high as infrared video, having advantages such as high resolution and good sensitivity in target motion detection. However, terahertz wavelength is rather short, and as a result, traditional dual-channel SAR motion target indication method gets a tiny blind velocity period which goes against target detection. Aimed at the problem, this paper provides a multi-channel terahertz ViSAR motion target detection method based on along track interferometry (ATI) technique. It selects several baselines of varying length and utilizes algebra coprime theory to enlarge blind velocity period so that the range of non-ambiguity velocity can be extended. Simulation results verify the effectiveness of the method.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122084326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059558
Ziqian Wang, Jun Sun
Owing to a high detection possibility and a simple kinetic model, track-before-detect (TBD) processorsare capable to detect low signal-to-noise ratio (SNR) targets uniformly moving with constant velocities. However, when a target with weak echo is accelerating, redirecting or decelerating, conventional TBD method might be ineffective for two reasons: heavier computational cost and higher possibility of forming false trajectories. In order to solve the problem of poor capability in tracking with the maneuvering targets, we propose a dynamic programming based TBD algorithm. In this TBD procedure, higher threshold is selected in order to reduce the possibility of forming false tracks during multi-frame processing. Additionally, resulted lower detection probability can be tolerated. The performance of tracking maneuvering objects based on this TBD processor is also exhibited.
{"title":"Maneuvering target tracking via dynamic-programming based Track-Before-Detect algorithm","authors":"Ziqian Wang, Jun Sun","doi":"10.1109/RADAR.2016.8059558","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059558","url":null,"abstract":"Owing to a high detection possibility and a simple kinetic model, track-before-detect (TBD) processorsare capable to detect low signal-to-noise ratio (SNR) targets uniformly moving with constant velocities. However, when a target with weak echo is accelerating, redirecting or decelerating, conventional TBD method might be ineffective for two reasons: heavier computational cost and higher possibility of forming false trajectories. In order to solve the problem of poor capability in tracking with the maneuvering targets, we propose a dynamic programming based TBD algorithm. In this TBD procedure, higher threshold is selected in order to reduce the possibility of forming false tracks during multi-frame processing. Additionally, resulted lower detection probability can be tolerated. The performance of tracking maneuvering objects based on this TBD processor is also exhibited.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122104292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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