Pub Date : 2021-06-29DOI: 10.15918/J.JBIT1004-0579.2021.003
Z. Fang, Xinrong Wang, W. Ji, Meng Xu, Yinan Zhang, Yan Li, Longfei Li
The evaluation of geometric calibration accuracy of high resolution satellite images has been increasingly recognized in recent years. In order to evaluate geometric accuracy for dual-camera satellite images based on the ground control points (GCP), a rigorous geometric imaging model, which was based on the collinear equation of the probe directional angle and the optimized tri-axial attitude determination (TRIAD) algorithm, is presented. Two reliable test fields in Tianjin and Jinan (China) were utilized for geometric accuracy validation of Pakistan Remote Sensing Satellite-1. The experimental results demonstrate a certain deviation of the on-orbit calibration result from the initial design values of the calibration parameters. Therefore, on-orbit geometric calibration is necessary for optical satellite imagery. Within this research, the geometrical performances including positioning accuracy without/with GCP and band registration of the dual-camera satellite were analyzed in detail, and the results of geometric image quality are assessed and discussed. As a result, it is feasible and necessary to establish such a geometric calibration model to evaluate the geometric quality of dual-camera satellite.
{"title":"Geometric Calibration and Image Quality Assessment of High Resolution Dual-Camera Satellite","authors":"Z. Fang, Xinrong Wang, W. Ji, Meng Xu, Yinan Zhang, Yan Li, Longfei Li","doi":"10.15918/J.JBIT1004-0579.2021.003","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.003","url":null,"abstract":"The evaluation of geometric calibration accuracy of high resolution satellite images has been increasingly recognized in recent years. In order to evaluate geometric accuracy for dual-camera satellite images based on the ground control points (GCP), a rigorous geometric imaging model, which was based on the collinear equation of the probe directional angle and the optimized tri-axial attitude determination (TRIAD) algorithm, is presented. Two reliable test fields in Tianjin and Jinan (China) were utilized for geometric accuracy validation of Pakistan Remote Sensing Satellite-1. The experimental results demonstrate a certain deviation of the on-orbit calibration result from the initial design values of the calibration parameters. Therefore, on-orbit geometric calibration is necessary for optical satellite imagery. Within this research, the geometrical performances including positioning accuracy without/with GCP and band registration of the dual-camera satellite were analyzed in detail, and the results of geometric image quality are assessed and discussed. As a result, it is feasible and necessary to establish such a geometric calibration model to evaluate the geometric quality of dual-camera satellite.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"125-138"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41608819","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 : 2021-06-29DOI: 10.15918/J.JBIT1004-0579.2021.013
Zhen Ye, Shihao Shi, Tao Sun, Lin Bai
As a key technique in hyperspectral image pre-processing, dimensionality reduction has received a lot of attention. However, most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures. In this paper, we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information. These two methods explore local information into the collaborative graph through competing constraints, thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm. By classifying four benchmark hyperspectral data, the proposed methods are proved to be superior to several advanced algorithms, even under small-sample-size conditions.
{"title":"Local Preserving Graphs Using Intra-Class Competitive Representation for Dimensionality Reduction of Hyperspectral Image","authors":"Zhen Ye, Shihao Shi, Tao Sun, Lin Bai","doi":"10.15918/J.JBIT1004-0579.2021.013","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.013","url":null,"abstract":"As a key technique in hyperspectral image pre-processing, dimensionality reduction has received a lot of attention. However, most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures. In this paper, we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information. These two methods explore local information into the collaborative graph through competing constraints, thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm. By classifying four benchmark hyperspectral data, the proposed methods are proved to be superior to several advanced algorithms, even under small-sample-size conditions.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"139-158"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43728054","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}
With the deterioration of the environment, it is imperative to protect coastal wetlands. Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method. The object-based hierarchical classification using remote sensing indices (OBH-RSI) for coastal wetland is proposed to achieve fine classification of coastal wetland. First, the original categories are divided into four groups according to the category characteristics. Second, the training and test maps of each group are extracted according to the remote sensing indices. Third, four groups are passed through the classifier in order. Finally, the results of the four groups are combined to get the final classification result map. The experimental results demonstrate that the overall accuracy, average accuracy and kappa coefficient of the proposed strategy are over 94% using the Yellow River Delta dataset.
{"title":"OBH-RSI: Object-Based Hierarchical Classification Using Remote Sensing Indices for Coastal Wetland","authors":"Zhaoyang Lin, Jianbu Wang, Wei Li, Xian-shu Jiang, Wenbo Zhu, Yuanqing Ma, Andong Wang","doi":"10.15918/J.JBIT1004-0579.2021.014","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.014","url":null,"abstract":"With the deterioration of the environment, it is imperative to protect coastal wetlands. Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method. The object-based hierarchical classification using remote sensing indices (OBH-RSI) for coastal wetland is proposed to achieve fine classification of coastal wetland. First, the original categories are divided into four groups according to the category characteristics. Second, the training and test maps of each group are extracted according to the remote sensing indices. Third, four groups are passed through the classifier in order. Finally, the results of the four groups are combined to get the final classification result map. The experimental results demonstrate that the overall accuracy, average accuracy and kappa coefficient of the proposed strategy are over 94% using the Yellow River Delta dataset.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"159-171"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42733757","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}
Industrial Internet of Things (IoT) connecting society and industrial systems represents a tremendous and promising paradigm shift. With IoT, multimodal and heterogeneous data from industrial devices can be easily collected, and further analyzed to discover device maintenance and health related potential knowledge behind. IoT data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT ecosystem. But how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a challenge. In this paper, a novel Deep Multimodal Learning and Fusion (DMLF) based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices coexist. First, a DMLF model is designed by combining a Convolution Neural Network (CNN) and Stacked Denoising Autoencoder (SDAE) together to capture more comprehensive fault knowledge and extract features from different modal data. Second, these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential faults. Third, a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models. A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis method. The experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.
{"title":"Deep Multimodal Learning and Fusion Based Intelligent Fault Diagnosis Approach","authors":"Huifang Li, Huang Jianghang, Huang Jingwei, Chai Senchun, Leilei Zhao, Xia Yuanqing","doi":"10.15918/J.JBIT1004-0579.2021.017","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.017","url":null,"abstract":"Industrial Internet of Things (IoT) connecting society and industrial systems represents a tremendous and promising paradigm shift. With IoT, multimodal and heterogeneous data from industrial devices can be easily collected, and further analyzed to discover device maintenance and health related potential knowledge behind. IoT data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT ecosystem. But how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a challenge. In this paper, a novel Deep Multimodal Learning and Fusion (DMLF) based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices coexist. First, a DMLF model is designed by combining a Convolution Neural Network (CNN) and Stacked Denoising Autoencoder (SDAE) together to capture more comprehensive fault knowledge and extract features from different modal data. Second, these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential faults. Third, a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models. A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis method. The experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"172-185"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48192483","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 : 2021-04-26DOI: 10.15918/J.JBIT1004-0579.2021.016
Chen Gao, Wei Li
This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion. Focusing on the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm is implemented. The key technologies include image scale-space for implementing multi-scale properties, Harris corner detection for keypoints extraction, and partial intensity invariant feature descriptor (PIIFD) for keypoints description. Eventually, a multi-scale Harris-PIIFD image registration algorithm framework is proposed. The experimental results of fifteen sets of representative real data show that the algorithm has excellent, stable performance in multi-source remote sensing image registration, and can achieve accurate spatial alignment, which has strong practical application value and certain generalization ability.
{"title":"Multi-Scale PIIFD for Registration of Multi-Source Remote Sensing Images","authors":"Chen Gao, Wei Li","doi":"10.15918/J.JBIT1004-0579.2021.016","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.016","url":null,"abstract":"This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion. Focusing on the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm is implemented. The key technologies include image scale-space for implementing multi-scale properties, Harris corner detection for keypoints extraction, and partial intensity invariant feature descriptor (PIIFD) for keypoints description. Eventually, a multi-scale Harris-PIIFD image registration algorithm framework is proposed. The experimental results of fifteen sets of representative real data show that the algorithm has excellent, stable performance in multi-source remote sensing image registration, and can achieve accurate spatial alignment, which has strong practical application value and certain generalization ability.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"113-124"},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43540016","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 : 2021-03-19DOI: 10.15918/J.JBIT1004-0579.2021.009
Yu Lin, Yi Bu, Hui Qiu, Xue Yao, Xianxiang Yu, G. Cui
This paper proposes a new information modulation resorting to orthogonal signal and its phase for dual-function radar communication (DFRC) systems. Focusing on the standardized linear frequency modulation (LFM) signal by additional phase, a bank of signals enjoying satisfactory autocorrelation and cross-correlation characteristics, are generated. Then, these signals map the different information as well as their phases are also modulated to increase the communication bit rate, thus yielding a series of dual-use signals. Finally, the radar detection and communication performance of dual-use signals are also provided through numerical simulation and half-physical platform verification, confirming the effectiveness of the designed signals compared with the existing design strategy.
{"title":"Dual-Use Signal Design for Radar and Communication via Joint Orthogonal Signal and Phase Modulation","authors":"Yu Lin, Yi Bu, Hui Qiu, Xue Yao, Xianxiang Yu, G. Cui","doi":"10.15918/J.JBIT1004-0579.2021.009","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.009","url":null,"abstract":"This paper proposes a new information modulation resorting to orthogonal signal and its phase for dual-function radar communication (DFRC) systems. Focusing on the standardized linear frequency modulation (LFM) signal by additional phase, a bank of signals enjoying satisfactory autocorrelation and cross-correlation characteristics, are generated. Then, these signals map the different information as well as their phases are also modulated to increase the communication bit rate, thus yielding a series of dual-use signals. Finally, the radar detection and communication performance of dual-use signals are also provided through numerical simulation and half-physical platform verification, confirming the effectiveness of the designed signals compared with the existing design strategy.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"20-30"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49528227","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 : 2021-03-19DOI: 10.15918/J.JBIT1004-0579.2021.004
Jingwei Xu, Lan Lan, Xiongpeng He, Shengqi Zhu, Cao Zeng, G. Liao, Yuhong Zhang
Frequency diverse array (FDA) radar has been studied for more than 15 years and has attracted a lot of attention due to its potential advantages over the well-known phased array radar. The representative feature of FDA is range-angle-time-dependent transmit beampattern and its underlying properties are continuously revealed in the research. The formulation and exploitation of the transmit diversity with a frequency increment is the fundamental principle, which brings extra degrees-of-freedom (DOFs) in the transmit dimension. As the FDA radar carries additional information in range, it provides more flexibility in signal processing and also brings in new technical issues. This article overviews the state-of-the-art in FDA radar area and its applications, mainly based on the progress in our group. There are two main catalogs in FDA radar area, namely coherent FDA and FDA-MIMO(multiple-input multiple-output) radars. Potential applications including target parameter estimation, ambiguous clutter suppression, and deceptive jammer suppression are discussed.
{"title":"System Design and Signal Processing for Frequency Diverse Array Radar","authors":"Jingwei Xu, Lan Lan, Xiongpeng He, Shengqi Zhu, Cao Zeng, G. Liao, Yuhong Zhang","doi":"10.15918/J.JBIT1004-0579.2021.004","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.004","url":null,"abstract":"Frequency diverse array (FDA) radar has been studied for more than 15 years and has attracted a lot of attention due to its potential advantages over the well-known phased array radar. The representative feature of FDA is range-angle-time-dependent transmit beampattern and its underlying properties are continuously revealed in the research. The formulation and exploitation of the transmit diversity with a frequency increment is the fundamental principle, which brings extra degrees-of-freedom (DOFs) in the transmit dimension. As the FDA radar carries additional information in range, it provides more flexibility in signal processing and also brings in new technical issues. This article overviews the state-of-the-art in FDA radar area and its applications, mainly based on the progress in our group. There are two main catalogs in FDA radar area, namely coherent FDA and FDA-MIMO(multiple-input multiple-output) radars. Potential applications including target parameter estimation, ambiguous clutter suppression, and deceptive jammer suppression are discussed.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49551045","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}
Joint radar and communication (JRC) technology is gradually becoming an essential approach to alleviating spectral congestion. Radar and communications systems were designed with common spectral and hardware resources to reduce size, improve performance, reduce cost, and decongest the spectrum. Various approaches have been proposed to achieve the coexistence of radar and communication systems. This paper mainly focuses on the research directions of radar communication coexistence (RCC) and dual-function radar communication systems (DFRC) in JRC technology. We summarize and analyze the existing research problems in the JRC era. According to the characteristics and advantages of JRC technology, we highlight several potentials in military and commercial applications.
{"title":"A Brief Introduction on Joint Radar and Communication Systems","authors":"Jia Zhu, Fangpei Zhang, Yuanhao Cui, Junsheng Mu, Ronghui Zhang, Xiaojun Jing","doi":"10.15918/J.JBIT1004-0579.2021.010","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.010","url":null,"abstract":"Joint radar and communication (JRC) technology is gradually becoming an essential approach to alleviating spectral congestion. Radar and communications systems were designed with common spectral and hardware resources to reduce size, improve performance, reduce cost, and decongest the spectrum. Various approaches have been proposed to achieve the coexistence of radar and communication systems. This paper mainly focuses on the research directions of radar communication coexistence (RCC) and dual-function radar communication systems (DFRC) in JRC technology. We summarize and analyze the existing research problems in the JRC era. According to the characteristics and advantages of JRC technology, we highlight several potentials in military and commercial applications.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"60-68"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48521838","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 : 2021-03-19DOI: 10.15918/J.JBIT1004-0579.2021.008
Xue Yao, Yu Liu, Zaichen Zhang, Xianxiang Yu, G. Cui
This paper develops a new transmit beamforming for an integrated mechanical and electrical scanning dual-function radar-communication (DFRC) system. Differing from the related some works using beampattern sidelobe level to communication, we exploit the fact that transmit beamforming weight vector begin{document}$ {{u}}_{k}$end{document} in direction begin{document}$ theta$end{document} and weight vector begin{document}$ {{u}}_{k}^{*}$end{document} in direction begin{document}$ -theta$end{document} can achieve the same spatial power distribution, and formulate a new transmit beamforming vector design problem accounting for some extra sidelobe level constraints. By doing so, the number of the transmit beamforming weight vectors and the computing demand in the multi-user communication (MUC) scenario can be reduced. Finally, the numerical examples are designed to verify the effectiveness of the proposed design strategy in comparison with the existing method.
This paper develops a new transmit beamforming for an integrated mechanical and electrical scanning dual-function radar-communication (DFRC) system. Differing from the related some works using beampattern sidelobe level to communication, we exploit the fact that transmit beamforming weight vector begin{document}$ {{u}}_{k}$end{document} in direction begin{document}$ theta$end{document} and weight vector begin{document}$ {{u}}_{k}^{*}$end{document} in direction begin{document}$ -theta$end{document} can achieve the same spatial power distribution, and formulate a new transmit beamforming vector design problem accounting for some extra sidelobe level constraints. By doing so, the number of the transmit beamforming weight vectors and the computing demand in the multi-user communication (MUC) scenario can be reduced. Finally, the numerical examples are designed to verify the effectiveness of the proposed design strategy in comparison with the existing method.
{"title":"A New Transmit Beamforming Method for Multi-User Communication in Dual-Function Radar-Communication","authors":"Xue Yao, Yu Liu, Zaichen Zhang, Xianxiang Yu, G. Cui","doi":"10.15918/J.JBIT1004-0579.2021.008","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.008","url":null,"abstract":"This paper develops a new transmit beamforming for an integrated mechanical and electrical scanning dual-function radar-communication (DFRC) system. Differing from the related some works using beampattern sidelobe level to communication, we exploit the fact that transmit beamforming weight vector begin{document}$ {{u}}_{k}$end{document} in direction begin{document}$ theta$end{document} and weight vector begin{document}$ {{u}}_{k}^{*}$end{document} in direction begin{document}$ -theta$end{document} can achieve the same spatial power distribution, and formulate a new transmit beamforming vector design problem accounting for some extra sidelobe level constraints. By doing so, the number of the transmit beamforming weight vectors and the computing demand in the multi-user communication (MUC) scenario can be reduced. Finally, the numerical examples are designed to verify the effectiveness of the proposed design strategy in comparison with the existing method.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"31-43"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43944086","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 : 2021-03-19DOI: 10.15918/J.JBIT1004-0579.2021.006
Tianyuan Gu, Weilai Peng, Huiyong Li
Array pattern synthesis is an important research direction in array processing. It is a signal processing technology that uses sensor arrays to send and receive signals directionally. Pattern design and synthesis play an important role in the high performance of the array system. In this paper, we give an overview about the recently developed pattern synthesis algorithms with the concept of accurate array response control theory.
{"title":"Pattern Synthesis via Accurate Array Response Control: An Overview","authors":"Tianyuan Gu, Weilai Peng, Huiyong Li","doi":"10.15918/J.JBIT1004-0579.2021.006","DOIUrl":"https://doi.org/10.15918/J.JBIT1004-0579.2021.006","url":null,"abstract":"Array pattern synthesis is an important research direction in array processing. It is a signal processing technology that uses sensor arrays to send and receive signals directionally. Pattern design and synthesis play an important role in the high performance of the array system. In this paper, we give an overview about the recently developed pattern synthesis algorithms with the concept of accurate array response control theory.","PeriodicalId":39252,"journal":{"name":"Journal of Beijing Institute of Technology (English Edition)","volume":"30 1","pages":"69-81"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47990601","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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