Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104286
Haodong Li, G. Liao, Jingwei Xu, Jun Zhang
ISAR imaging for a marine moving target faces a series of challenges in the airborne radar system, especially the strong clutter interference with Doppler frequency spreading. To alleviate such problems, this paper proposes an efficient ISAR imaging method based on sliding window STAP. In the method, the whole CPI is divided into a series of coherent processing sub-intervals (CPSIs). Those CPSIs are generated with sliding window technique and they have identical length. In each CPSI, sub-CPI STAP is adopted to suppress the clutter. After that, the target signal is enhanced in terms of signal-to-clutter-plus-noise ratio (SCNR). Meanwhile, the Doppler frequency linear differences with respect to the azimuth dimension is still maintained, which contributes to the further ISAR imaging by Range-Doppler (RD) algorithm after range migration correction. Comparing with existing full-CPI STAP based method, the proposed method improves ISAR imaging performance while requiring less computational complexity. The simulation experiments are carried out to verify the effectiveness of the proposed method.
{"title":"An efficient ISAR imaging method based on sliding window STAP","authors":"Haodong Li, G. Liao, Jingwei Xu, Jun Zhang","doi":"10.1109/SAM48682.2020.9104286","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104286","url":null,"abstract":"ISAR imaging for a marine moving target faces a series of challenges in the airborne radar system, especially the strong clutter interference with Doppler frequency spreading. To alleviate such problems, this paper proposes an efficient ISAR imaging method based on sliding window STAP. In the method, the whole CPI is divided into a series of coherent processing sub-intervals (CPSIs). Those CPSIs are generated with sliding window technique and they have identical length. In each CPSI, sub-CPI STAP is adopted to suppress the clutter. After that, the target signal is enhanced in terms of signal-to-clutter-plus-noise ratio (SCNR). Meanwhile, the Doppler frequency linear differences with respect to the azimuth dimension is still maintained, which contributes to the further ISAR imaging by Range-Doppler (RD) algorithm after range migration correction. Comparing with existing full-CPI STAP based method, the proposed method improves ISAR imaging performance while requiring less computational complexity. The simulation experiments are carried out to verify the effectiveness of the proposed method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"14 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79503616","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104273
Xiaolin Du, A. Aubry, A. Maio, G. Cui
Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance structure. The resulting constrained optimization problem is solved globally resorting to the Dykstra’ projection framework. Each step of the procedure involves the solution of two convex sub-problems, whose minimizers are available in closed form. Simulation results related to typical radar environments highlight the effectiveness of the devised method.
{"title":"Toeplitz Structured Covariance Matrix Estimation for Radar Applications","authors":"Xiaolin Du, A. Aubry, A. Maio, G. Cui","doi":"10.1109/SAM48682.2020.9104273","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104273","url":null,"abstract":"Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance structure. The resulting constrained optimization problem is solved globally resorting to the Dykstra’ projection framework. Each step of the procedure involves the solution of two convex sub-problems, whose minimizers are available in closed form. Simulation results related to typical radar environments highlight the effectiveness of the devised method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"31 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76469519","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104378
B. Tang, Hai Wang, Lilong Qin, Longxiang Li
This paper addresses the design of constant-modulus waveforms for a dual-function multiple-input-multiple-output (MIMO) radar-communication system. The purpose of the design is to match a desired beampattern for radar sensing, and minimize the transmission distortion of communication signals. To tackle the non-convex waveform design problem, we develop an iterative algorithm based on cyclic optimization and majorization-minimization (MM). We show that the proposed algorithm has guaranteed convergence of the objective values. Numerical results demonstrate that the transmit beampattern of the synthesized waveforms can well approximate the desired one, and the emitted communication signals from the dual-function system has little distortions.
{"title":"Waveform Design for Dual-function MIMO Radar-communication Systems","authors":"B. Tang, Hai Wang, Lilong Qin, Longxiang Li","doi":"10.1109/SAM48682.2020.9104378","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104378","url":null,"abstract":"This paper addresses the design of constant-modulus waveforms for a dual-function multiple-input-multiple-output (MIMO) radar-communication system. The purpose of the design is to match a desired beampattern for radar sensing, and minimize the transmission distortion of communication signals. To tackle the non-convex waveform design problem, we develop an iterative algorithm based on cyclic optimization and majorization-minimization (MM). We show that the proposed algorithm has guaranteed convergence of the objective values. Numerical results demonstrate that the transmit beampattern of the synthesized waveforms can well approximate the desired one, and the emitted communication signals from the dual-function system has little distortions.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"278 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83075532","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}
This paper develops a time reversal moving target detector based on array antennas. Time reversal utilizes the multipath effect by retransmitting the received signal containing the Doppler shift of the moving target. Two detectors are derived in both conventional and time reversal scenarios, respectively. The Monte Carlo results show that the time reversal detector significantly improves the target detection probability in multi-path environments compared with the conventional detector. The increase of multipath number is helpful to improve the detection probability of time reversal detector.
{"title":"Moving target detection of array antennas based on time reversal","authors":"Zhaoming Zhang, Baixiao Chen, Minglei Yang, Hui Xu","doi":"10.1109/SAM48682.2020.9104342","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104342","url":null,"abstract":"This paper develops a time reversal moving target detector based on array antennas. Time reversal utilizes the multipath effect by retransmitting the received signal containing the Doppler shift of the moving target. Two detectors are derived in both conventional and time reversal scenarios, respectively. The Monte Carlo results show that the time reversal detector significantly improves the target detection probability in multi-path environments compared with the conventional detector. The increase of multipath number is helpful to improve the detection probability of time reversal detector.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"9 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85965089","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104281
Chengcheng Wang, Wee Peng Tay, Ye Wei, Yuan Wang
We develop a resilient distributed strategy over multitask networks, where individual tasks are linearly related within each neighborhood, and information exchanges between neighboring agents are noisy. In the proposed strategy, each agent follows an adapt-then-project procedure to iteratively update its local estimate. In particular, weighted projection operators are utilized in the projection step in order to attenuate the negative effect of noisy exchanges on the cooperative inference performance. We motivate a strategy for computing the weights in a distributed and adaptive manner. Simulation results demonstrate that the proposed scheme shows good resilience against noise in the information exchange between agents.
{"title":"Resilient Multitask Distributed Adaptation Over Networks With Noisy Exchanges","authors":"Chengcheng Wang, Wee Peng Tay, Ye Wei, Yuan Wang","doi":"10.1109/SAM48682.2020.9104281","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104281","url":null,"abstract":"We develop a resilient distributed strategy over multitask networks, where individual tasks are linearly related within each neighborhood, and information exchanges between neighboring agents are noisy. In the proposed strategy, each agent follows an adapt-then-project procedure to iteratively update its local estimate. In particular, weighted projection operators are utilized in the projection step in order to attenuate the negative effect of noisy exchanges on the cooperative inference performance. We motivate a strategy for computing the weights in a distributed and adaptive manner. Simulation results demonstrate that the proposed scheme shows good resilience against noise in the information exchange between agents.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89472830","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104298
Yang Xiong, Zeyang Li, Fang-qing Wen
Direction-of-arrival (DOA) estimation is an interesting research topic with various applications. Existing algorithms provide superior estimation performance, at the cost of accurate quantified measurements. In this paper, we stress the problem of 2D DOA estimation for uniform rectangular array using one-bit measurements. The relationship between the covariance matrices of one-bit measurement and that of the accurately quantified measurement is analyzed in detail, from which we find the existing tensor algorithm can be directly applied. As a result, a one-bit parallel factor analysis (PARAFAC) estimator is proposed. Simulation results show the effectiveness of the proposed method.
{"title":"2D DOA Estimation for Uniform Rectangular Array With One-bit Measurement","authors":"Yang Xiong, Zeyang Li, Fang-qing Wen","doi":"10.1109/SAM48682.2020.9104298","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104298","url":null,"abstract":"Direction-of-arrival (DOA) estimation is an interesting research topic with various applications. Existing algorithms provide superior estimation performance, at the cost of accurate quantified measurements. In this paper, we stress the problem of 2D DOA estimation for uniform rectangular array using one-bit measurements. The relationship between the covariance matrices of one-bit measurement and that of the accurately quantified measurement is analyzed in detail, from which we find the existing tensor algorithm can be directly applied. As a result, a one-bit parallel factor analysis (PARAFAC) estimator is proposed. Simulation results show the effectiveness of the proposed method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"46 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87786307","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104377
Chengwei Zhou, Yujie Gu, Zhiguo Shi, M. Haardt
In this paper, we consider the problem of underdetermined direction-of-arrival (DOA) estimation using coprime arrays from a ont-bit perspective, where the coarray correlations of the quantized sparse measurements are explored for augmented covariance matrix reconstruction. To fully utilize the coarray signals calculated from the one-bit coprime array measurements for DOA estimation, a correlation reconstruction problem is formulated to obtain the quantized covariance matrix corresponding to a filled coarray containing the discontiguous one, where the one-bit quantization transforms the possibilities of correlations from an infinite to a finite number. The performance of the proposed method is validated from the aspects of degrees- of-freedom (DOFs), estimation accuracy, as well as the resolution performance. Simulation results demonstrate that the proposed method not only retains full achievable DOFs of the coprime array, but is also capable of presenting a better DOA estimation performance than the non-quantization approaches.
{"title":"Direction-of-Arrival Estimation for Coprime Arrays via Coarray Correlation Reconstruction: A One-Bit Perspective","authors":"Chengwei Zhou, Yujie Gu, Zhiguo Shi, M. Haardt","doi":"10.1109/SAM48682.2020.9104377","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104377","url":null,"abstract":"In this paper, we consider the problem of underdetermined direction-of-arrival (DOA) estimation using coprime arrays from a ont-bit perspective, where the coarray correlations of the quantized sparse measurements are explored for augmented covariance matrix reconstruction. To fully utilize the coarray signals calculated from the one-bit coprime array measurements for DOA estimation, a correlation reconstruction problem is formulated to obtain the quantized covariance matrix corresponding to a filled coarray containing the discontiguous one, where the one-bit quantization transforms the possibilities of correlations from an infinite to a finite number. The performance of the proposed method is validated from the aspects of degrees- of-freedom (DOFs), estimation accuracy, as well as the resolution performance. Simulation results demonstrate that the proposed method not only retains full achievable DOFs of the coprime array, but is also capable of presenting a better DOA estimation performance than the non-quantization approaches.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"38 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80344784","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104293
Kan Tang, Xin Guo, Xiaowei Liang, Zhongshan Lin
This paper presents a synthetic aperture radar (SAR) system and processing algorithm for automotive applications using a short range vehicle-mounted Frequency Modulation Continuous Wave (FMCW) radar. Aiming at real-time high resolution vehicle-borne SAR imaging, the algorithm combines the advantages of the high accurate focusing of the wavenumber domain algorithms with high precision motion compensation by utilizing only the knowledge of the vehicle's velocity and angular velocity. We present measurement results collected during various driving tests with an experimental 79GHz synthetic aperture radar. The results indicate that the proposed method could produce SAR imagery of high resolution (0.0825 m0.0825 m) with detection range of 16m in real time, which is suitable for automotive applications.
{"title":"Implementation of Real-time Automotive SAR Imaging","authors":"Kan Tang, Xin Guo, Xiaowei Liang, Zhongshan Lin","doi":"10.1109/SAM48682.2020.9104293","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104293","url":null,"abstract":"This paper presents a synthetic aperture radar (SAR) system and processing algorithm for automotive applications using a short range vehicle-mounted Frequency Modulation Continuous Wave (FMCW) radar. Aiming at real-time high resolution vehicle-borne SAR imaging, the algorithm combines the advantages of the high accurate focusing of the wavenumber domain algorithms with high precision motion compensation by utilizing only the knowledge of the vehicle's velocity and angular velocity. We present measurement results collected during various driving tests with an experimental 79GHz synthetic aperture radar. The results indicate that the proposed method could produce SAR imagery of high resolution (0.0825 m0.0825 m) with detection range of 16m in real time, which is suitable for automotive applications.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"29 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88923374","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104333
Gongping Huang, I. Cohen, J. Benesty, Jingdong Chen
Differential microphone arrays (DMAs) are very attractive because of their high directional gains and frequency-invariant beampatterns. However, it is generally required that the array aperture is small, such that the DMA can respond to acoustic pressure differentials. In this paper, we propose a method to design differential beamformers with larger arrays consisting of multiple DMAs. In our study, conventional DMAs are considered as elementary units. The beamforming process consists of elementary differential beamformers and an additional beamformer that combines the multiple DMAs’ outputs. The steering vector of the global array is written as a Kronecker product of the steering vectors of an elementary DMA unit and the virtual array constructed from all the DMA units. This enables to design the global beamformer as a Kronecker product of the differential beamformer and the beamformer that corresponds to the virtual array. With the proposed method, one can take advantage of the good properties of DMAs for the design of beamformers with any size of microphone array.
{"title":"Kronecker Product Beamforming with Multiple Differential Microphone Arrays","authors":"Gongping Huang, I. Cohen, J. Benesty, Jingdong Chen","doi":"10.1109/SAM48682.2020.9104333","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104333","url":null,"abstract":"Differential microphone arrays (DMAs) are very attractive because of their high directional gains and frequency-invariant beampatterns. However, it is generally required that the array aperture is small, such that the DMA can respond to acoustic pressure differentials. In this paper, we propose a method to design differential beamformers with larger arrays consisting of multiple DMAs. In our study, conventional DMAs are considered as elementary units. The beamforming process consists of elementary differential beamformers and an additional beamformer that combines the multiple DMAs’ outputs. The steering vector of the global array is written as a Kronecker product of the steering vectors of an elementary DMA unit and the virtual array constructed from all the DMA units. This enables to design the global beamformer as a Kronecker product of the differential beamformer and the beamformer that corresponds to the virtual array. With the proposed method, one can take advantage of the good properties of DMAs for the design of beamformers with any size of microphone array.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"25 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77939659","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104350
A. Hassanien, E. Aboutanios
In this paper, we consider the problem of adaptive beamforming for sample-starved scenarios. A beamforming design method is developed for the case when the only available data is a single-snapshot that is contaminated by the signal-of-interest. Instead of using the non-invertible rank-one sample covariance matrix, the proposed method reconstructs the interference-plus-noise covariance matrix via estimating the interference signal component(s) and the noise variance. The computationally-efficient fast iterative interpolated beamforming (FIIB) algorithm is used to estimate the spatial frequencies and complex amplitudes associated with the interference. The reconstructed covariance matrix is used for adaptive beamforming design. Unlike existing sparsity-based covariance reconstruction techniques, the proposed method is able to reconstruct off-grid interference components and its performance is shown to not suffer from estimation bias. Simulation examples are used to demonstrate the performance superiority of the proposed method over other adaptive single-snapshot beamforming techniques.
{"title":"Single-Snapshot Beamforming using Fast Iterative Adaptive Techniques","authors":"A. Hassanien, E. Aboutanios","doi":"10.1109/SAM48682.2020.9104350","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104350","url":null,"abstract":"In this paper, we consider the problem of adaptive beamforming for sample-starved scenarios. A beamforming design method is developed for the case when the only available data is a single-snapshot that is contaminated by the signal-of-interest. Instead of using the non-invertible rank-one sample covariance matrix, the proposed method reconstructs the interference-plus-noise covariance matrix via estimating the interference signal component(s) and the noise variance. The computationally-efficient fast iterative interpolated beamforming (FIIB) algorithm is used to estimate the spatial frequencies and complex amplitudes associated with the interference. The reconstructed covariance matrix is used for adaptive beamforming design. Unlike existing sparsity-based covariance reconstruction techniques, the proposed method is able to reconstruct off-grid interference components and its performance is shown to not suffer from estimation bias. Simulation examples are used to demonstrate the performance superiority of the proposed method over other adaptive single-snapshot beamforming techniques.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"57 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78641818","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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