Radar coincidence imaging (RCI) is a high-resolution staring imaging technique, which is fit for sparse recovery natively. The performance of RCI depends on the coherence property of dictionary matrix. In this paper, RCI using frequency-hopping (FH) waveforms is considered, and a FH code design method optimizing the dictionary matrix is introduced. First, the waveform optimization object function minimizing the difference between the correlation matrix and identity matrix is derived. Then, the quantum simulated annealing (QSA) is employed to optimize the FH code. Numerical simulations show that the optimized FH code could improve the imaging performance of RCI.
{"title":"Frequency-hopping code optimization for radar coincidence imaging by exploiting the dictionary matrix","authors":"Xiaoli Zhou, Hongqiang Wang, Yongqiang Cheng, Yuliang Qin, Xianwu Xu","doi":"10.1109/RADAR.2016.8059304","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059304","url":null,"abstract":"Radar coincidence imaging (RCI) is a high-resolution staring imaging technique, which is fit for sparse recovery natively. The performance of RCI depends on the coherence property of dictionary matrix. In this paper, RCI using frequency-hopping (FH) waveforms is considered, and a FH code design method optimizing the dictionary matrix is introduced. First, the waveform optimization object function minimizing the difference between the correlation matrix and identity matrix is derived. Then, the quantum simulated annealing (QSA) is employed to optimize the FH code. Numerical simulations show that the optimized FH code could improve the imaging performance of RCI.","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":"129977937","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.2016.8059220
Yingzhi Kan, Wei Qiu, Yongfeng Zhu, Q. Fu
In this paper, a new Inverse Synthetic Aperture Radar (ISAR) imaging method via matrix completion theory by far less measurements than that traditional imaging method required is proposed. In this method, signal with only a few selected frequencies instead of wideband signal is transmitted, and then the complete received data matrix can be recovered by the matrix completion due to its low-rank property; after that, range-Doppler ISAR imaging algorithm is applied on the reconstructed data to generate the ISAR image with reduced sidelobes. Finally, simulation results are shown to demonstrate the validity of the proposed method.
{"title":"ISAR imaging with randomly sampled data via matrix completion","authors":"Yingzhi Kan, Wei Qiu, Yongfeng Zhu, Q. Fu","doi":"10.1109/RADAR.2016.8059220","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059220","url":null,"abstract":"In this paper, a new Inverse Synthetic Aperture Radar (ISAR) imaging method via matrix completion theory by far less measurements than that traditional imaging method required is proposed. In this method, signal with only a few selected frequencies instead of wideband signal is transmitted, and then the complete received data matrix can be recovered by the matrix completion due to its low-rank property; after that, range-Doppler ISAR imaging algorithm is applied on the reconstructed data to generate the ISAR image with reduced sidelobes. Finally, simulation results are shown to demonstrate the validity of the proposed method.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"78 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":"130100578","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.2016.8059427
Guo Qixun, Zhang Linbing, Tao Xuefeng
In passive time difference location system, localization always requires auxiliary measurement information such as target azimuth. This paper analyzes the singular value of target azimuth aided time-difference-of-arrival (TODA) under normal station layouts. The localization distance of target azimuth can be derived by analyzing the relationship between target azimuth and localization distance, then the height information can be obtained through that localization distance. In addition, the localization singular value point can be acquired by analyzing the relationship of target azimuth and localization distance and singular value point can be verified by simulation test.
{"title":"Research on singular value point of target azimuth aided passive time-difference-of-arrival","authors":"Guo Qixun, Zhang Linbing, Tao Xuefeng","doi":"10.1109/RADAR.2016.8059427","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059427","url":null,"abstract":"In passive time difference location system, localization always requires auxiliary measurement information such as target azimuth. This paper analyzes the singular value of target azimuth aided time-difference-of-arrival (TODA) under normal station layouts. The localization distance of target azimuth can be derived by analyzing the relationship between target azimuth and localization distance, then the height information can be obtained through that localization distance. In addition, the localization singular value point can be acquired by analyzing the relationship of target azimuth and localization distance and singular value point can be verified by simulation test.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"27 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":"129131152","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.2016.8059452
Guangna Zhang, Mingxi Guo, Yue-hong Shen
FTN signaling is seen as a promising technique to improve the bandwidth efficiency, and it uses faster symbol rate than Nyquist rate. However the inter-symbol interference (ISI) is inevitable in the receiver, which results in high computational complexity in the receiver. In order to reduce the interference effectively, low complexity receiver technologies and the improved algorithms are proposed, including: Windows Chase Equalization (WCE) and FTN-MWCE, Iterative Decision Feedback Equalization (IBDFE) and LC-IBDFE, Decision Feedback Equalization (DFE) and Partial Decision Feedback Equalization (PDFE). The analysis and comparison of these low complexity algorithms were given from computational complexity and BER performance. Advantages and disadvantages as well as applications are also presented.
{"title":"Comparison of low complexity receiver techniques for faster-than-nyquist signaling","authors":"Guangna Zhang, Mingxi Guo, Yue-hong Shen","doi":"10.1109/RADAR.2016.8059452","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059452","url":null,"abstract":"FTN signaling is seen as a promising technique to improve the bandwidth efficiency, and it uses faster symbol rate than Nyquist rate. However the inter-symbol interference (ISI) is inevitable in the receiver, which results in high computational complexity in the receiver. In order to reduce the interference effectively, low complexity receiver technologies and the improved algorithms are proposed, including: Windows Chase Equalization (WCE) and FTN-MWCE, Iterative Decision Feedback Equalization (IBDFE) and LC-IBDFE, Decision Feedback Equalization (DFE) and Partial Decision Feedback Equalization (PDFE). The analysis and comparison of these low complexity algorithms were given from computational complexity and BER performance. Advantages and disadvantages as well as applications are also presented.","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":"129295104","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.2016.8059538
Runqing Cao, Ming Li, Lei Zuo, Zeyu Wang
This paper looks into G4 function, which helps to estimate the third order phase coefficient parameter of a polynomial phase signal (PPS). However, the use of the lag parameter of G4 function has not yet been fully exploited. Therefore, in this paper, we theoretically derive the optimal lag with respect to the ratio of mean square error (MSE) to the Cramer-Rao low bound (CRB). When the signal-to-noise ratio (SNR) is high, the resulting ratio of the MSE to the CRB can be as low as 0.4dB. Simulation results show that the estimated lag is optimal and G4 function with the optimal lag achieves a much more accurate parameter estimation than its competitors.
{"title":"Lag-optimized G4 function for the third order phase parameter estimation of polynomial phase signals","authors":"Runqing Cao, Ming Li, Lei Zuo, Zeyu Wang","doi":"10.1109/RADAR.2016.8059538","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059538","url":null,"abstract":"This paper looks into G4 function, which helps to estimate the third order phase coefficient parameter of a polynomial phase signal (PPS). However, the use of the lag parameter of G4 function has not yet been fully exploited. Therefore, in this paper, we theoretically derive the optimal lag with respect to the ratio of mean square error (MSE) to the Cramer-Rao low bound (CRB). When the signal-to-noise ratio (SNR) is high, the resulting ratio of the MSE to the CRB can be as low as 0.4dB. Simulation results show that the estimated lag is optimal and G4 function with the optimal lag achieves a much more accurate parameter estimation than its competitors.","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":"130578846","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.2016.8059231
Xiaohan Yu, Xiaolong Chen, Wenchao Hu, J. Guan
Robust and effective detection of marine moving targets in the sea clutter is one of the fundamental and difficult problems in both military and civil fields. There are both advantages and limitations of classical radar detection methods. Sparsity has been proved as a promising tool to solve inverse problems for a high-resolution solution which mathematically may be nonunique. The main purpose of this paper is to provide ideas for marine moving target detection from the view of sparse representation, which utilizes the merits of compressed sensing (CS). First, a brief introduction of sparse representation is given. Then the research status of sparse representation for marine moving target detection is described, the morphological component analysis (MCA) based detection method is introduced, and the feasibility of sparse time-frequency representation used for radar target detection is analyzed. Moreover, we give an example of sparse representation-based marine target detection using real data, which indicates the effectiveness of the detection method. Finally, the future research direction of the detection method is presented.
{"title":"An overview of marine moving target detection via high-resolution sparse representation","authors":"Xiaohan Yu, Xiaolong Chen, Wenchao Hu, J. Guan","doi":"10.1109/RADAR.2016.8059231","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059231","url":null,"abstract":"Robust and effective detection of marine moving targets in the sea clutter is one of the fundamental and difficult problems in both military and civil fields. There are both advantages and limitations of classical radar detection methods. Sparsity has been proved as a promising tool to solve inverse problems for a high-resolution solution which mathematically may be nonunique. The main purpose of this paper is to provide ideas for marine moving target detection from the view of sparse representation, which utilizes the merits of compressed sensing (CS). First, a brief introduction of sparse representation is given. Then the research status of sparse representation for marine moving target detection is described, the morphological component analysis (MCA) based detection method is introduced, and the feasibility of sparse time-frequency representation used for radar target detection is analyzed. Moreover, we give an example of sparse representation-based marine target detection using real data, which indicates the effectiveness of the detection method. Finally, the future research direction of the detection method is presented.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"8 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":"130639724","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.2016.8059343
Changhai Shi, Zhenxing Wu
Low probability of intercept (LPI) radars have developed into important tactical requirements due to its RF stealth. One of the important features of LPI radars is power management. The acceptance of a power management strategy is evaluated by leveraging numerical evaluation. In this paper, a novel metric evaluation including stability of target tracking and LPI performance is proposed, and simulation results demonstrate the effectiveness of this evaluation method.
{"title":"A metric evaluation of the power management for LPI radar","authors":"Changhai Shi, Zhenxing Wu","doi":"10.1109/RADAR.2016.8059343","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059343","url":null,"abstract":"Low probability of intercept (LPI) radars have developed into important tactical requirements due to its RF stealth. One of the important features of LPI radars is power management. The acceptance of a power management strategy is evaluated by leveraging numerical evaluation. In this paper, a novel metric evaluation including stability of target tracking and LPI performance is proposed, and simulation results demonstrate the effectiveness of this evaluation method.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"22 36","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132274637","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.2016.8059584
P. Tong, Rongqing Xu, Yinsheng Wei
In High Frequency (HF) hybrid sky-surface wave radars, the spread-Doppler clutter (SDC) severely deteriorates the detection performance of low-velocity surface vessels. To suppress SDC, a large number of training samples are usually needed which can be hardly satisfied due to the heterogeneous clutter environment. In this paper, a space-frequency cascaded approach is proposed to suppress SDC using training samples from only one range cell. The proposed method involves two components. First, a frequency domain minimum variance distortionless response (MVDR) weight is designed to cancel the SDC. Second, the spatial orthogonal projection matrix is constructed to estimate clutter covariance matrix, where the signal of interest has been excluded from the training data. According to the experimental results, the proposed method can obtain a more accurate estimation of the clutter characteristics with limited training data and thus achieve better clutter suppression performance.
{"title":"Spread-Doppler clutter cancellation in high frequency hybrid sky-surface wave radar","authors":"P. Tong, Rongqing Xu, Yinsheng Wei","doi":"10.1109/RADAR.2016.8059584","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059584","url":null,"abstract":"In High Frequency (HF) hybrid sky-surface wave radars, the spread-Doppler clutter (SDC) severely deteriorates the detection performance of low-velocity surface vessels. To suppress SDC, a large number of training samples are usually needed which can be hardly satisfied due to the heterogeneous clutter environment. In this paper, a space-frequency cascaded approach is proposed to suppress SDC using training samples from only one range cell. The proposed method involves two components. First, a frequency domain minimum variance distortionless response (MVDR) weight is designed to cancel the SDC. Second, the spatial orthogonal projection matrix is constructed to estimate clutter covariance matrix, where the signal of interest has been excluded from the training data. According to the experimental results, the proposed method can obtain a more accurate estimation of the clutter characteristics with limited training data and thus achieve better clutter suppression performance.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"123 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":"130234566","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.2016.8059458
Weijian Liu, Xiufeng Zha, F. Xu, Z. Hu, Tai Zeng, Yongliang Wang
Besides the noise and possible target signal, the data received by radar often contains interference, which can significantly degrade the detection performance. This paper considers the problem of detecting a target in partially-known interference and unknown Gaussian noise. The interference is unknown, but it is constrained in a subspace orthogonal to the signal in the whitened space. According to the generalized likelihood ratio test (GLRT) criterion, we proposed an effective detector, which has improved detection performance than the corresponding Rao and Wald tests. Remarkably, the GLRT in the presence of interference has the same form as that in the absence of interference. However, the interference affects the detection performance of the detector. The effectiveness of the proposed detector is demonstrated by Monte Carlo simulations.
{"title":"Adaptive signal detection in partially-known interference and unknown Gaussian noise","authors":"Weijian Liu, Xiufeng Zha, F. Xu, Z. Hu, Tai Zeng, Yongliang Wang","doi":"10.1109/RADAR.2016.8059458","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059458","url":null,"abstract":"Besides the noise and possible target signal, the data received by radar often contains interference, which can significantly degrade the detection performance. This paper considers the problem of detecting a target in partially-known interference and unknown Gaussian noise. The interference is unknown, but it is constrained in a subspace orthogonal to the signal in the whitened space. According to the generalized likelihood ratio test (GLRT) criterion, we proposed an effective detector, which has improved detection performance than the corresponding Rao and Wald tests. Remarkably, the GLRT in the presence of interference has the same form as that in the absence of interference. However, the interference affects the detection performance of the detector. The effectiveness of the proposed detector is demonstrated by Monte Carlo simulations.","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":"130428269","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.2016.8059158
Rongqiang Zhu, Jianxiong Zhou, Q. Fu
Multiple-input-multiple-output (MIMO) array imaging is more challenging than the monostatic configuration because the incident and reflected path are different. In FFT-based MIMO array imaging algorithms, rearrangement in wavenumber domain to reduce dimension is implemented which has low efficiency and restricts the sampling steps in spatial frequencies. This paper proposes a novel wavenumber domain imaging algorithm based on spherical wave decomposition. This algorithm uses FFT to transform the measurements into wavenumber domain for compensation, and then the spectrum is retransformed to reconstruct image by FFT and coherent accumulation. It avoids the rearrangement operation and preserves the high efficiency of the FFT-based method, and can be implemented for transmitting and receiving arrays of different length. The imaging performance is demonstrated by simulation.
{"title":"Near field MIMO array imaging algorithm based on spherical wave decomposition","authors":"Rongqiang Zhu, Jianxiong Zhou, Q. Fu","doi":"10.1109/RADAR.2016.8059158","DOIUrl":"https://doi.org/10.1109/RADAR.2016.8059158","url":null,"abstract":"Multiple-input-multiple-output (MIMO) array imaging is more challenging than the monostatic configuration because the incident and reflected path are different. In FFT-based MIMO array imaging algorithms, rearrangement in wavenumber domain to reduce dimension is implemented which has low efficiency and restricts the sampling steps in spatial frequencies. This paper proposes a novel wavenumber domain imaging algorithm based on spherical wave decomposition. This algorithm uses FFT to transform the measurements into wavenumber domain for compensation, and then the spectrum is retransformed to reconstruct image by FFT and coherent accumulation. It avoids the rearrangement operation and preserves the high efficiency of the FFT-based method, and can be implemented for transmitting and receiving arrays of different length. The imaging performance is demonstrated by simulation.","PeriodicalId":245387,"journal":{"name":"2016 CIE International Conference on Radar (RADAR)","volume":"34 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":"131703567","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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