Pub Date : 2016-10-01DOI: 10.1109/RADAR.2016.8059252
Yi Wang, Baixiao Chen, Minglei Yang, Yan Ma
Distributed array can obtain a higher aperture than conventional uniform linear array, thus obtain high direction of arrival (DOA) estimation accuracy. However, the degree of freedom of the difference co-array of the distributed array has not been fully used. In this paper, the multi frequency is used to complete the missing elements of the co-array of the distributed array. The wideband sensor output is first transformed to the desired additional frequency via the discrete Fourier transform, and the holes of the covariance vector at the center frequency can be completed using the covariance matrix at the additional frequency. Then the high-resolution DOA estimation method SSMUSIC can be employed to the whole co-array. The proposed method has higher estimation accuracy than the single frequency distributed array in the underdetermined case. Simulation results show the effectiveness of the proposed method.
{"title":"Multi-frequency distributed arrays for underdetermined direction-of-arrival estimation","authors":"Yi Wang, Baixiao Chen, Minglei Yang, Yan Ma","doi":"10.1109/RADAR.2016.8059252","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059252","url":null,"abstract":"Distributed array can obtain a higher aperture than conventional uniform linear array, thus obtain high direction of arrival (DOA) estimation accuracy. However, the degree of freedom of the difference co-array of the distributed array has not been fully used. In this paper, the multi frequency is used to complete the missing elements of the co-array of the distributed array. The wideband sensor output is first transformed to the desired additional frequency via the discrete Fourier transform, and the holes of the covariance vector at the center frequency can be completed using the covariance matrix at the additional frequency. Then the high-resolution DOA estimation method SSMUSIC can be employed to the whole co-array. The proposed method has higher estimation accuracy than the single frequency distributed array in the underdetermined case. Simulation results show the effectiveness of the proposed method.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"5 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":"116330449","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.2016.8059295
Qingyu Li, Yu Zhang, Changyong Pan, Jian Song
The integration of radar and communication is a good way to achieving miniaturization of the equipment, easing the tension of spectrum resource and reducing the interference between the radar and communication equipment. Recent research mainly concentrates on realizing the function of transmission and detection in one integrated equipment and improving the performance of radar with the random communication symbols, while improving the spectrum efficiency in communication is lack of study, which is also very important with the rapid growth of the need for multimedia transmission in either vehicular networking or battlefield. In this paper, based on multicarrier chirp signal (OFD-LFM), a spectrum effective integrated waveform with multiple symbols on each pulse is proposed and demodulation algorithm is work out. Simulation results show that optimal demodulation can be achieved simply by addition, subtraction and matching filtering, and the ambiguity function won't be influenced by the random communication symbols.
{"title":"Waveform design for high speed radar-communication integration","authors":"Qingyu Li, Yu Zhang, Changyong Pan, Jian Song","doi":"10.1109/RADAR.2016.8059295","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059295","url":null,"abstract":"The integration of radar and communication is a good way to achieving miniaturization of the equipment, easing the tension of spectrum resource and reducing the interference between the radar and communication equipment. Recent research mainly concentrates on realizing the function of transmission and detection in one integrated equipment and improving the performance of radar with the random communication symbols, while improving the spectrum efficiency in communication is lack of study, which is also very important with the rapid growth of the need for multimedia transmission in either vehicular networking or battlefield. In this paper, based on multicarrier chirp signal (OFD-LFM), a spectrum effective integrated waveform with multiple symbols on each pulse is proposed and demodulation algorithm is work out. Simulation results show that optimal demodulation can be achieved simply by addition, subtraction and matching filtering, and the ambiguity function won't be influenced by the random communication symbols.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"1 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":"126564368","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.2016.8059218
Y. Liao, H. Shao, Hui Chen, Wen-qin Wang
A modified phase gradient autofocus (PGA) method tailored for circular trace scanning synthetic aperture radar (CTSSAR) is provided in this paper to handle the motion compensation problem. In CTSSAR, the circular orbit makes the imaging and the motion compensation hard to perform, and the traditional algorithms cannot accomplish precise imaging. Different from the classical PGA method, the modified PGA algorithm takes the residual high order phase errors into account. The precise phase estimation can be achieved after the residual phase errors are accurately estimated and compensated. Simulation results are presented to demonstrate the validity of the proposed approach.
{"title":"A modefied PGA motion compensation method for circular trace scanning SAR","authors":"Y. Liao, H. Shao, Hui Chen, Wen-qin Wang","doi":"10.1109/RADAR.2016.8059218","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059218","url":null,"abstract":"A modified phase gradient autofocus (PGA) method tailored for circular trace scanning synthetic aperture radar (CTSSAR) is provided in this paper to handle the motion compensation problem. In CTSSAR, the circular orbit makes the imaging and the motion compensation hard to perform, and the traditional algorithms cannot accomplish precise imaging. Different from the classical PGA method, the modified PGA algorithm takes the residual high order phase errors into account. The precise phase estimation can be achieved after the residual phase errors are accurately estimated and compensated. Simulation results are presented to demonstrate the validity of the proposed approach.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"51 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":"121953490","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.2016.8059450
Zhe-ran Shang, Xiansi Tan, Zhiguo Qu, Hong Wang
Radon Fourier Transform (RFT) is a kind of generalized MTD, which can integrate along the track of targets. However, it is not easy for RFT to be engineered due to the calculating burden. Aiming at this problem, a kind of RFT parallelization strategy is put forward based on GPU and CUDA. Through experimental verification, the execution time of RFT on GPU platform proved a great speedup compared with that of RFT and fast RFT on CPU. In addition, it suggests in the results that the execution time can be as fast as MTD when RFT results are saved in global memory.
{"title":"A parallel implementation of RFT on GPU","authors":"Zhe-ran Shang, Xiansi Tan, Zhiguo Qu, Hong Wang","doi":"10.1109/RADAR.2016.8059450","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059450","url":null,"abstract":"Radon Fourier Transform (RFT) is a kind of generalized MTD, which can integrate along the track of targets. However, it is not easy for RFT to be engineered due to the calculating burden. Aiming at this problem, a kind of RFT parallelization strategy is put forward based on GPU and CUDA. Through experimental verification, the execution time of RFT on GPU platform proved a great speedup compared with that of RFT and fast RFT on CPU. In addition, it suggests in the results that the execution time can be as fast as MTD when RFT results are saved in global memory.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"161 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":"121789901","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.2016.8059512
Hongbo Li, Wei Jing, Yang Bai
With the increasing complexity of electromagnetic environment and the rising of operating patterns of new radars, emitter recognition is becoming more and more difficult. This paper presents a deep learning architecture (DLA) based on the deep belief network (DBN) and logistic regression (LR) for radar emitter recognition. A multilayer structure of DBN is established to learn emitter feature, and LR is devoted to identify a specific type of radar. Compared experiments with conventional methods are conducted, and the results show that the proposed model outperforms other existing techniques. Moreover, simulation experiments in different noise and loss pulse environment show that DLA is effective and robust in solving problems of radar emitter recognition.
{"title":"Radar emitter recognition based on deep learning architecture","authors":"Hongbo Li, Wei Jing, Yang Bai","doi":"10.1109/RADAR.2016.8059512","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059512","url":null,"abstract":"With the increasing complexity of electromagnetic environment and the rising of operating patterns of new radars, emitter recognition is becoming more and more difficult. This paper presents a deep learning architecture (DLA) based on the deep belief network (DBN) and logistic regression (LR) for radar emitter recognition. A multilayer structure of DBN is established to learn emitter feature, and LR is devoted to identify a specific type of radar. Compared experiments with conventional methods are conducted, and the results show that the proposed model outperforms other existing techniques. Moreover, simulation experiments in different noise and loss pulse environment show that DLA is effective and robust in solving problems of radar emitter recognition.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"120 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":"115837843","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.2016.8059514
Jiejun Yin, Gong Zhang, Su Liu, Xiuxia Ji
Weak relatedness among tasks leads to failure of regularized multi-task sparse representation (RMTSR) model to handle target recognition in synthetic aperture radar (SAR) imagery. Therefore, it is vital to measure task relationship not only in order to obtain desired model but shrink the size of dictionary and the training time. In this paper, sparse representation under each feature modality is considered as a single task in RMTSR. A nonlinear sparsity correlation index (NSCI) is presented. Furthermore, nonlinear correlation information entropy (NCIE) deduced from NSCI is utilized to quantify the relatedness among tasks from view of information theory. Experiments conducted on MSTAR demonstrate the outperformance and effectiveness of RMTSR even in the case of limited training resource. Moreover, NCIE is efficient to measure the generalization of model and select appropriate feature set to reduce complexity.
{"title":"Target recognition with information entropy based multi-task sparse representation in SAR imagery","authors":"Jiejun Yin, Gong Zhang, Su Liu, Xiuxia Ji","doi":"10.1109/RADAR.2016.8059514","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059514","url":null,"abstract":"Weak relatedness among tasks leads to failure of regularized multi-task sparse representation (RMTSR) model to handle target recognition in synthetic aperture radar (SAR) imagery. Therefore, it is vital to measure task relationship not only in order to obtain desired model but shrink the size of dictionary and the training time. In this paper, sparse representation under each feature modality is considered as a single task in RMTSR. A nonlinear sparsity correlation index (NSCI) is presented. Furthermore, nonlinear correlation information entropy (NCIE) deduced from NSCI is utilized to quantify the relatedness among tasks from view of information theory. Experiments conducted on MSTAR demonstrate the outperformance and effectiveness of RMTSR even in the case of limited training resource. Moreover, NCIE is efficient to measure the generalization of model and select appropriate feature set to reduce complexity.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"200 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":"132505140","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.2016.8059498
Bo Liu, C. Tian, Jianlin Zhang, Dongjin Wang
Based on the temporal-spatial stochastic radiation field, microwave staring correlated imaging (MSCI) can achieve high resolution reconstruction of the target. In MSCI, the traditional processing method has a bad real-time performance, which need to wait for all the echo signals and require matrix inversions. In this paper, we introduce the sequential least squares (SLS) method to improve the real-time performance. SLS imaging result can be computed recursively, no longer need to wait for all the echo signals received. Moreover, no matrix inversions are required by SLS method. That can effectively reduce the computation cost, especially when the imaging equations scale is large. The effectiveness of the SLS method is demonstrated via the simulation results.
{"title":"A real-time processing method for microwave staring correlated imaging based on sequential LS","authors":"Bo Liu, C. Tian, Jianlin Zhang, Dongjin Wang","doi":"10.1109/RADAR.2016.8059498","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059498","url":null,"abstract":"Based on the temporal-spatial stochastic radiation field, microwave staring correlated imaging (MSCI) can achieve high resolution reconstruction of the target. In MSCI, the traditional processing method has a bad real-time performance, which need to wait for all the echo signals and require matrix inversions. In this paper, we introduce the sequential least squares (SLS) method to improve the real-time performance. SLS imaging result can be computed recursively, no longer need to wait for all the echo signals received. Moreover, no matrix inversions are required by SLS method. That can effectively reduce the computation cost, especially when the imaging equations scale is large. The effectiveness of the SLS method is demonstrated via the simulation results.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"1 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":"130140885","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.2016.8059207
Penghui Huang, G. Liao, Zhiwei Yang, X. Xia, Jingtao Ma
When the synthetic aperture radar (SAR) imaging is applied to observe a ground scene containing a ground moving target, the moving target image will be typically smeared due to the range cell migration and Doppler spectrum broadening caused by target motions. To eliminate these motion effects, a novel algorithm for ground moving target imaging, which is based on an improved axis rotation-time reversal transform (IAR-TRT), is proposed in this paper. In this method, the linear range migration is corrected by an improved axis rotation (IAR) operation and then the coherent integration is accomplished by a time reversal transform (TRT). The proposed method has low computational complexity since the exhaustive searching for the Doppler chirp rate estimation is avoided, which is suitable for real-time imaging. In addition, the defocusing influence of Doppler ambiguity can be eliminated. The effectiveness of the proposed algorithm is demonstrated by the simulation results in a single-channel airborne SAR system.
{"title":"A new method for ground moving target imaging with single-antenna SAR","authors":"Penghui Huang, G. Liao, Zhiwei Yang, X. Xia, Jingtao Ma","doi":"10.1109/RADAR.2016.8059207","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059207","url":null,"abstract":"When the synthetic aperture radar (SAR) imaging is applied to observe a ground scene containing a ground moving target, the moving target image will be typically smeared due to the range cell migration and Doppler spectrum broadening caused by target motions. To eliminate these motion effects, a novel algorithm for ground moving target imaging, which is based on an improved axis rotation-time reversal transform (IAR-TRT), is proposed in this paper. In this method, the linear range migration is corrected by an improved axis rotation (IAR) operation and then the coherent integration is accomplished by a time reversal transform (TRT). The proposed method has low computational complexity since the exhaustive searching for the Doppler chirp rate estimation is avoided, which is suitable for real-time imaging. In addition, the defocusing influence of Doppler ambiguity can be eliminated. The effectiveness of the proposed algorithm is demonstrated by the simulation results in a single-channel airborne SAR system.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"4 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":"134110373","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.2016.8059135
S. Zhao, C. Feng, W. H. Lu
Micro-motion feature is one of the crucial features used for ballistic target recognition. Aiming at the problem that single-view observation can't extract the nutation parameters, a novel algorithm based on the radar network is proposed to extract the target features. Firstly, the nutation model of cone-shaped target is built, the micro-Range modulation trait caused by nutation is analyzed in detail. Then under the precondition of considering the invisible problem of scattering centers, each scattering center in different perspectives is matched based on the scattering center' microRange difference, and the periodicity of target nutation is proved by Bessel function expansion, what' more, the target nutation and configuration parameters are estimated by utilizing the change rule of nutation angle combined with curve fitting method. Finally, Simulation results are given for validating the proposed algorithms.
{"title":"Feature extraction of space nutation cone-shaped targets based on radar network","authors":"S. Zhao, C. Feng, W. H. Lu","doi":"10.1109/RADAR.2016.8059135","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059135","url":null,"abstract":"Micro-motion feature is one of the crucial features used for ballistic target recognition. Aiming at the problem that single-view observation can't extract the nutation parameters, a novel algorithm based on the radar network is proposed to extract the target features. Firstly, the nutation model of cone-shaped target is built, the micro-Range modulation trait caused by nutation is analyzed in detail. Then under the precondition of considering the invisible problem of scattering centers, each scattering center in different perspectives is matched based on the scattering center' microRange difference, and the periodicity of target nutation is proved by Bessel function expansion, what' more, the target nutation and configuration parameters are estimated by utilizing the change rule of nutation angle combined with curve fitting method. Finally, Simulation results are given for validating the proposed algorithms.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"22 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":"134584992","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.2016.8059479
L. Zhai, Yu Li, Yi Su
In this paper, we present a novel method to detect the ship in harbor based on image segmentation for SAR imagery. According to the position of the ship in harbor, we divide them into the offshore ship and inshore ship, and different strategies are implemented for ship detection. First, we use the sea-land segmentation method to separate SAR image into land region and sea region, and then extract buffer region according to the coastline. Second, we employ the TS-CFAR detector which has a state-of-the-art performance in multiple-target situations to achieve offshore ship detection. Finally, for the inshore ship, we propose a region-based saliency detection to complete the ship detection. The region-based saliency detection method can tolerate a certain degree of speckle noise. Experimental results show that the proposed method is robust, efficient and can detect different kinds of ship in the harbor.
{"title":"Segmentation-based ship detection in harbor for SAR images","authors":"L. Zhai, Yu Li, Yi Su","doi":"10.1109/RADAR.2016.8059479","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059479","url":null,"abstract":"In this paper, we present a novel method to detect the ship in harbor based on image segmentation for SAR imagery. According to the position of the ship in harbor, we divide them into the offshore ship and inshore ship, and different strategies are implemented for ship detection. First, we use the sea-land segmentation method to separate SAR image into land region and sea region, and then extract buffer region according to the coastline. Second, we employ the TS-CFAR detector which has a state-of-the-art performance in multiple-target situations to achieve offshore ship detection. Finally, for the inshore ship, we propose a region-based saliency detection to complete the ship detection. The region-based saliency detection method can tolerate a certain degree of speckle noise. Experimental results show that the proposed method is robust, efficient and can detect different kinds of ship in the harbor.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"40 3 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":"134091732","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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