Pub Date : 2018-11-01DOI: 10.1109/ICDSP.2018.8631551
Zhongxia Shang, Yang Zhao, Y. Lian
Different frequency bands in an electroencephalogram (EEG) signal contain different information. It is very helpful to divide an EEG signal by its sub-bands before applying further classification. FIR filter is one of the best choices for processing EEG signal because of its linear phase property. However, the implementation of an FIR filter requires more multipliers compared to its IIR counterpart. With frequency-response masking (FRM) technique, the multipliers needed to implement FIR filter can be reduced dramatically leading to a low power design. This paper proposes a filter bank structure for processing EEG signal based on the FRM technique. The design equations for all the sub-filters are derived and the condition for applying the proposed structure is given. A design example is included to illustrate the effectiveness of the proposed filter. It shows that the filter can fulfill the design objectives with 77% less multipliers comparing to the conventional FIR filter synthesizing technique.
{"title":"Low Power FIR Filter Bank for EEG Processing Using Frequency-Response Masking Technique","authors":"Zhongxia Shang, Yang Zhao, Y. Lian","doi":"10.1109/ICDSP.2018.8631551","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631551","url":null,"abstract":"Different frequency bands in an electroencephalogram (EEG) signal contain different information. It is very helpful to divide an EEG signal by its sub-bands before applying further classification. FIR filter is one of the best choices for processing EEG signal because of its linear phase property. However, the implementation of an FIR filter requires more multipliers compared to its IIR counterpart. With frequency-response masking (FRM) technique, the multipliers needed to implement FIR filter can be reduced dramatically leading to a low power design. This paper proposes a filter bank structure for processing EEG signal based on the FRM technique. The design equations for all the sub-filters are derived and the condition for applying the proposed structure is given. A design example is included to illustrate the effectiveness of the proposed filter. It shows that the filter can fulfill the design objectives with 77% less multipliers comparing to the conventional FIR filter synthesizing technique.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"253 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133355924","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631792
Qidi Wu, Yibing Li, Yun Lin
Compressed sensing(CS) is a significant technology in MRI reconstruction, which can reconstruct the image with few undersampled data and speed up the imaging. The conventional CS-based MRI is implemented on the global image, which not only loss many local structures but also fails in preserving the detail information. To improve the reconstruction quality, we proposed a novel CS-based reconstruction model, which is incorporated with nonlocal technology to gain extra details preservation. The proposed model grouped the similar patches within the nonlocal area, and stacked them to form a 3D array. Then, to process the array in a realistic 3D way, a tensor-based sparsity constraint is developed as the regularization on the reconstructed image. Experimental results show that the proposed method is more effectiveness and efficiency than the conventional ones.
{"title":"Tensor-based Nonlocal MRI Reconstruction with Compressed Sensing","authors":"Qidi Wu, Yibing Li, Yun Lin","doi":"10.1109/ICDSP.2018.8631792","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631792","url":null,"abstract":"Compressed sensing(CS) is a significant technology in MRI reconstruction, which can reconstruct the image with few undersampled data and speed up the imaging. The conventional CS-based MRI is implemented on the global image, which not only loss many local structures but also fails in preserving the detail information. To improve the reconstruction quality, we proposed a novel CS-based reconstruction model, which is incorporated with nonlocal technology to gain extra details preservation. The proposed model grouped the similar patches within the nonlocal area, and stacked them to form a 3D array. Then, to process the array in a realistic 3D way, a tensor-based sparsity constraint is developed as the regularization on the reconstructed image. Experimental results show that the proposed method is more effectiveness and efficiency than the conventional ones.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133170801","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631866
Z. Li, Wei Deng, Wenjiang Pei, Yili Xia, D. Mandic
In this article, we explore new opportunities to freshen up the curriculum of a lecture-based course on digital communication. In particular, the radio frequency identity (RFID) technology is introduced to design a hands-on exercise, in which the software-defined radio (SDR) hardware and the Matlab programming environment are respectively used as the experimental platform and the post-processing tool. Binary images generated by different shapes and patterns are customised and encoded into RFID tags as the electronic product code (EPC). After acquiring the digital signal from SDR hardware, the students performed a decoding task to identify the customised data. In this way, they not only acquire deeper understanding of the building blocks of digital communication, but also they are highly motivated by being kept within practical constraints of the course in a self-directed manner. Survey results from the students reveal that this participatory coursework was perceived as intellectually stimulating.
{"title":"Refreshing Digital Communications Curriculum with RFID Technology: A Participatory Approach","authors":"Z. Li, Wei Deng, Wenjiang Pei, Yili Xia, D. Mandic","doi":"10.1109/ICDSP.2018.8631866","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631866","url":null,"abstract":"In this article, we explore new opportunities to freshen up the curriculum of a lecture-based course on digital communication. In particular, the radio frequency identity (RFID) technology is introduced to design a hands-on exercise, in which the software-defined radio (SDR) hardware and the Matlab programming environment are respectively used as the experimental platform and the post-processing tool. Binary images generated by different shapes and patterns are customised and encoded into RFID tags as the electronic product code (EPC). After acquiring the digital signal from SDR hardware, the students performed a decoding task to identify the customised data. In this way, they not only acquire deeper understanding of the building blocks of digital communication, but also they are highly motivated by being kept within practical constraints of the course in a self-directed manner. Survey results from the students reveal that this participatory coursework was perceived as intellectually stimulating.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131271356","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631700
Yanqing Zhao, K. Adjallah, A. Sava
This paper investigates the effects of both intermittent wave amplitude and sampling frequency ratio (between sampling frequency and maximum frequency in the signal) on the mode mixing alleviation performance for improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The root relative squared error (RRSE) and the mean absolute error (MAE) are used to evaluate and study the influence of both intermittent wave amplitude and sampling frequency ratio on the mode mixing alleviation performance. The analysis results show that the intermittent wave amplitude and sampling frequency ratio dramatically affect the mode mixing alleviation performance of ICEEMDAN, and that the suitable sampling frequency ratio for alleviating mode mixing varies with the intermittent wave amplitude. The optimal selection of the sampling frequency ratio according to the amplitude of intermittent wave can improve the mode mixing alleviation performance.
{"title":"Influence study of the intermittent wave amplitude vs. the sampling frequency ratio on ICEEMDAN mode mixing alleviation performance","authors":"Yanqing Zhao, K. Adjallah, A. Sava","doi":"10.1109/ICDSP.2018.8631700","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631700","url":null,"abstract":"This paper investigates the effects of both intermittent wave amplitude and sampling frequency ratio (between sampling frequency and maximum frequency in the signal) on the mode mixing alleviation performance for improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The root relative squared error (RRSE) and the mean absolute error (MAE) are used to evaluate and study the influence of both intermittent wave amplitude and sampling frequency ratio on the mode mixing alleviation performance. The analysis results show that the intermittent wave amplitude and sampling frequency ratio dramatically affect the mode mixing alleviation performance of ICEEMDAN, and that the suitable sampling frequency ratio for alleviating mode mixing varies with the intermittent wave amplitude. The optimal selection of the sampling frequency ratio according to the amplitude of intermittent wave can improve the mode mixing alleviation performance.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131857692","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631797
Sanaa S. A. Al-Samahi, K. C. Ho, N. Islam
Outlier measurements often presence when locating an object from a number of sensors, which could decrease the positioning performance considerably. This paper addresses the problem of outlier detection in locating an object using TOA measurements. The detection is based on the construction of a spectral graph through pairwise intersection between the lines of position from a measurement pair. Crucial to this technique is the determination for intersection, and we have derived such conditions for 2-D and 3-D positionings. The detected outliers are removed and the remaining measurements are used for the Maximum Likelihood estimator to obtain the object position. Simulation shows that the proposed outlier detection method is very effective with the probability of detection and the probability of false alarms examined. The positioning accuracy is able to reach the CRLB performance after removing the detected outliers.
{"title":"Improving TOA Localization Through Outlier Detection Using Intersection of Lines of Position","authors":"Sanaa S. A. Al-Samahi, K. C. Ho, N. Islam","doi":"10.1109/ICDSP.2018.8631797","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631797","url":null,"abstract":"Outlier measurements often presence when locating an object from a number of sensors, which could decrease the positioning performance considerably. This paper addresses the problem of outlier detection in locating an object using TOA measurements. The detection is based on the construction of a spectral graph through pairwise intersection between the lines of position from a measurement pair. Crucial to this technique is the determination for intersection, and we have derived such conditions for 2-D and 3-D positionings. The detected outliers are removed and the remaining measurements are used for the Maximum Likelihood estimator to obtain the object position. Simulation shows that the proposed outlier detection method is very effective with the probability of detection and the probability of false alarms examined. The positioning accuracy is able to reach the CRLB performance after removing the detected outliers.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115516282","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}
Human movement analysis often relies on obtaining and processing digital signals from the lab-based biomechanical equipment such as motion capture system and force plate. This paper introduced a machine-learning based method, known as the Dynamics Time Wrapping (DTW) for human movement analysis. The DTW is used to classify four basketball playing movements including shoot, layup, dribble and pass. The kinematic raw data were obtained during an experiment session. The sample kinematic data were selected and normalized to create the templates. The DTW compared the kinematic data from each movement with the template. A 3-fold cross validation was used to validate the method. The results show that this method can achieve a high activity classification accuracy.
{"title":"Automatic Activity Classification Based on Human Body Kinematics and Dynamic Time Wrapping","authors":"Xinyao Hu, Shaorong Mo, D. Peng, Fei Shen, Chuang Luo, Xingda Qu","doi":"10.1109/ICDSP.2018.8631669","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631669","url":null,"abstract":"Human movement analysis often relies on obtaining and processing digital signals from the lab-based biomechanical equipment such as motion capture system and force plate. This paper introduced a machine-learning based method, known as the Dynamics Time Wrapping (DTW) for human movement analysis. The DTW is used to classify four basketball playing movements including shoot, layup, dribble and pass. The kinematic raw data were obtained during an experiment session. The sample kinematic data were selected and normalized to create the templates. The DTW compared the kinematic data from each movement with the template. A 3-fold cross validation was used to validate the method. The results show that this method can achieve a high activity classification accuracy.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115564998","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631666
Yuan Niu, Jianping Zheng
The generalized frequency division multiplexing with index modulation (GFDM-IM) is a recently developed multi-carrier technique, which has the signal feature that only part of subcarriers are activated. In this paper, the message passing (MP)-based signal detection of GFDM-IM is studied, and two MP detectors are presented. In the first MP detector, MP algorithm is performed directly in the factor graph constructed by the product of GFDM modulation matrix and channel matrix. In the second MP detector, the received signal is first frequency-domain equalized, and then MP algorithm is performed based on a sparse factor graph by utilizing the structured sparsity of the modulation matrix. In both MP detectors, an additional pattern node is introduced to leverage the relation in the variable nodes belonging to the same IM block introduced by activation pattern constraint. Simulation results show that, the proposed MP detectors show some superiority over conventional linear detectors in terms of error performance and/or complexity.
{"title":"Message Passing Algorithm for GFDM-IM Detection","authors":"Yuan Niu, Jianping Zheng","doi":"10.1109/ICDSP.2018.8631666","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631666","url":null,"abstract":"The generalized frequency division multiplexing with index modulation (GFDM-IM) is a recently developed multi-carrier technique, which has the signal feature that only part of subcarriers are activated. In this paper, the message passing (MP)-based signal detection of GFDM-IM is studied, and two MP detectors are presented. In the first MP detector, MP algorithm is performed directly in the factor graph constructed by the product of GFDM modulation matrix and channel matrix. In the second MP detector, the received signal is first frequency-domain equalized, and then MP algorithm is performed based on a sparse factor graph by utilizing the structured sparsity of the modulation matrix. In both MP detectors, an additional pattern node is introduced to leverage the relation in the variable nodes belonging to the same IM block introduced by activation pattern constraint. Simulation results show that, the proposed MP detectors show some superiority over conventional linear detectors in terms of error performance and/or complexity.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114496786","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631665
Lina Liu, Zhiyou Wu
In this paper, we propose a novel nonparallel linear hyperplane classifier called linear $nu $-nonparallel support vector machine ($ L_{1}-nu $-NPSVM) for binary classification. Based on $L_{1}-$ NPSVM (Linear Nonparallel Support Vector Machine), and combining the $nu $-support vector classification and $nu $-support vector regression together, the primal problem of $ L_{1}-nu $-NPSVM is obtained. Compared to $L_{1}$-NPSVM, $ L_{1}-nu $-NPSVM has the following advantages: (1) By introducing a new parameter $nu $ to effectively control the number of support vectors, the model's generalization ability and accuracy can be improved; (2) By introducing a new parameter v, we can eliminate one of the other free parameters of the $L_{1}$-NPSVM to reduce the difficulty of selecting parameters. Moreover, experimental results on data sets show the effectiveness of our method.
本文提出了一种新的非并行线性超平面分类器，称为线性$nu $-非并行支持向量机($ L_{1}-nu $- npsvm)。基于$L_{1}-$ NPSVM (Linear Nonparallel Support Vector Machine)，将$nu $-支持向量分类和$nu $-支持向量回归相结合，得到$L_{1}- nu $-NPSVM的原始问题。与$L_{1}$- npsvm相比，$L_{1} -nu $- npsvm具有以下优点:(1)通过引入新的参数$nu $来有效控制支持向量的数量，提高了模型的泛化能力和精度;(2)通过引入新的参数v，可以消除$L_{1}$-NPSVM的另一个自由参数，降低参数选择的难度。在数据集上的实验结果表明了该方法的有效性。
{"title":"Linear lJ-nonparallel support vector machine for pattern classification","authors":"Lina Liu, Zhiyou Wu","doi":"10.1109/ICDSP.2018.8631665","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631665","url":null,"abstract":"In this paper, we propose a novel nonparallel linear hyperplane classifier called linear $nu $-nonparallel support vector machine ($ L_{1}-nu $-NPSVM) for binary classification. Based on $L_{1}-$ NPSVM (Linear Nonparallel Support Vector Machine), and combining the $nu $-support vector classification and $nu $-support vector regression together, the primal problem of $ L_{1}-nu $-NPSVM is obtained. Compared to $L_{1}$-NPSVM, $ L_{1}-nu $-NPSVM has the following advantages: (1) By introducing a new parameter $nu $ to effectively control the number of support vectors, the model's generalization ability and accuracy can be improved; (2) By introducing a new parameter v, we can eliminate one of the other free parameters of the $L_{1}$-NPSVM to reduce the difficulty of selecting parameters. Moreover, experimental results on data sets show the effectiveness of our method.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114778302","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631817
M. Awais, H. Ghayvat, Wei Chen
A quality sleep at night plays a vibrant role in healthy life. 7-8 hours quality sleep at the right times especially at night help human to maintain a proper physical and mental health. While sleeping, it has been incorporated that facial muscles contraction/extraction especially in eyes regions are the most common absorbed features while sleeping. This paper presents a preprocessing outcome of detecting a person face and facial features while taking nap. Face Detection algorithms known as Ada-boost and Local Binary Pattern (LBP) has been used to detect the facial regions and its features. As these algorithm work for frontal faces, so when person is taking nap in soldier position and a face orientation is in $120^{circ}-60^{circ}$, Ada-boost and LBP is able to detect face and its features. Results shows that LBP face/features detection accuracy is higher than Ada-boost. This pre-processing study/results help us in designing the novel post processing algorithms to classify sleep stages for overnight sleep monitoring using image processing that will be unobtrusive as compared to existing techniques.
{"title":"Face and Its Features Detection during Nap","authors":"M. Awais, H. Ghayvat, Wei Chen","doi":"10.1109/ICDSP.2018.8631817","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631817","url":null,"abstract":"A quality sleep at night plays a vibrant role in healthy life. 7-8 hours quality sleep at the right times especially at night help human to maintain a proper physical and mental health. While sleeping, it has been incorporated that facial muscles contraction/extraction especially in eyes regions are the most common absorbed features while sleeping. This paper presents a preprocessing outcome of detecting a person face and facial features while taking nap. Face Detection algorithms known as Ada-boost and Local Binary Pattern (LBP) has been used to detect the facial regions and its features. As these algorithm work for frontal faces, so when person is taking nap in soldier position and a face orientation is in $120^{circ}-60^{circ}$, Ada-boost and LBP is able to detect face and its features. Results shows that LBP face/features detection accuracy is higher than Ada-boost. This pre-processing study/results help us in designing the novel post processing algorithms to classify sleep stages for overnight sleep monitoring using image processing that will be unobtrusive as compared to existing techniques.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114814595","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 : 2018-11-01DOI: 10.1109/ICDSP.2018.8631705
Zongyu Zhang, Chengwei Zhou, Yujie Gu, Zhiguo Shi
In this paper, we propose an inverse discrete Fourier transform (IDFT)-based direction-of-arrival (DOA) estimation algorithm for coprime array, where both DOAs and power of the sources can be efficiently estimated with an increased number of degrees-of-freedom. Specifically, the IDFT is generalized to realize the transformation between the defined angular-spatial domain and the spatial domain. With such a relationship, the IDFT is directly implemented on the second-order virtual signals characterized by the angular-spatial frequencies, and it is proved that both the DOAs and the sources power can be estimated from the resulting spatial response. Meanwhile, the window method and the zero-padding technique are sequentially incorporated to alleviate the spectral leakage and improve the estimation accuracy, respectively. The direct IDFT solution presents a remarkably reduced computational complexity as compared to the existing algorithms exploiting coprime array, and the simulation results validate the effectiveness of the proposed DOA estimation algorithm.
{"title":"Efficient DOA Estimation for Coprime Array via Inverse Discrete Fourier Transform","authors":"Zongyu Zhang, Chengwei Zhou, Yujie Gu, Zhiguo Shi","doi":"10.1109/ICDSP.2018.8631705","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631705","url":null,"abstract":"In this paper, we propose an inverse discrete Fourier transform (IDFT)-based direction-of-arrival (DOA) estimation algorithm for coprime array, where both DOAs and power of the sources can be efficiently estimated with an increased number of degrees-of-freedom. Specifically, the IDFT is generalized to realize the transformation between the defined angular-spatial domain and the spatial domain. With such a relationship, the IDFT is directly implemented on the second-order virtual signals characterized by the angular-spatial frequencies, and it is proved that both the DOAs and the sources power can be estimated from the resulting spatial response. Meanwhile, the window method and the zero-padding technique are sequentially incorporated to alleviate the spectral leakage and improve the estimation accuracy, respectively. The direct IDFT solution presents a remarkably reduced computational complexity as compared to the existing algorithms exploiting coprime array, and the simulation results validate the effectiveness of the proposed DOA estimation algorithm.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121959194","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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