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2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)最新文献

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Serial-inspired diffusion based on message passing for distributed estimation in adaptive networks 自适应网络中基于消息传递的串行启发扩散分布式估计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569677
Cornelius T. Healy, R. D. Lamare
{"title":"Serial-inspired diffusion based on message passing for distributed estimation in adaptive networks","authors":"Cornelius T. Healy, R. D. Lamare","doi":"10.1109/SAM.2016.7569677","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569677","url":null,"abstract":"Distributed estimation and processing in networks modeled by graphs have received a great deal of interest recently, due to the benefits of decentralised processing in terms of performance and robustness to communications link failure between nodes of the network. Diffusion-based algorithms have been demonstrated to be among the most effective for distributed signal processing problems, through the combination of local node estimate updates and sharing of information with neighbour nodes through diffusion. In this work, we develop a serial-inspired approach based on message-passing strategies that provides a significant improvement in performance over prior art. The concept of serial processing in the graph has been successfully applied in sum-product based algorithms and here provides inspiration for an algorithm which makes use of the most up-to-date information in the graph in combination with the diffusion approach to offer improved performance.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124786166","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}
引用次数: 1
Compressive sensing in time reversal radars: Incoherency analysis 时间反转雷达中的压缩感知:非相干分析
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569607
M. Sajjadieh, A. Asif
{"title":"Compressive sensing in time reversal radars: Incoherency analysis","authors":"M. Sajjadieh, A. Asif","doi":"10.1109/SAM.2016.7569607","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569607","url":null,"abstract":"In array processing, compressive sensing (CS) has recently emerged as a new sampling paradigm for estimating the direction-of-arrival (DOA) from a relatively small number of observations. The paper derives an analytical expression for the mutual coherence of the CS dictionary for a single-input, multiple output (SIMO) radar to advocate the consideration of incoherency issues by researchers exploring potential gains with CS. We couple time reversal (TR) with the Cs/SIMO radar to illustrate a possible increase in the incoherency of the CS dictionary with time reversal.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129290033","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}
引用次数: 0
Array interpolation based on multivariate adaptive regression splines 基于多元自适应回归样条的阵列插值
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569704
M. A. M. Marinho, J. Costa, F. Antreich, A. D. Almeida, G. D. Galdo, E. P. Freitas, A. Vinel
{"title":"Array interpolation based on multivariate adaptive regression splines","authors":"M. A. M. Marinho, J. Costa, F. Antreich, A. D. Almeida, G. D. Galdo, E. P. Freitas, A. Vinel","doi":"10.1109/SAM.2016.7569704","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569704","url":null,"abstract":"Many important signal processing techniques such as Spatial Smoothing, Forward Backward Averaging and Root-MUSIC, rely on antenna arrays with specific and precise structures. Arrays with such ideal structures, such as a centro-hermitian structure, are often hard to build in practice. Array interpolation is used to enable the usage of these techniques with imperfect (not having a centro-hermitian structure) arrays. Most interpolation methods rely on methods based on least squares (LS) to map the output of a perfect virtual array based on the real array. In this work, the usage of Multivariate Adaptive Regression Splines (MARS) is proposed instead of the traditional LS to interpolate arrays with responses largely different from the ideal.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116326002","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}
引用次数: 7
Effect of data representations on deep learning in fall detection 数据表示对跌倒检测中深度学习的影响
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569734
B. Jokanović, M. Amin, F. Ahmad
{"title":"Effect of data representations on deep learning in fall detection","authors":"B. Jokanović, M. Amin, F. Ahmad","doi":"10.1109/SAM.2016.7569734","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569734","url":null,"abstract":"Fall-related injuries can have a significant impact on the quality of life of the elderly population. Because of the upward trend in the elderly for continued independent living, there is a growing need for reliable fall detectors that can enable prompt assistance in case of falls. Doppler radar technology offers a number of desirable attributes for realization of fall detection and health monitoring systems that can facilitate self-dependent living. Human motions generate changes in Doppler frequencies that can be accurately captured using time-frequency representations. A variety of time-frequency distributions have been proposed in the literature. In this paper, we investigate the impact of different time-frequency representations on the performance of a deep neural network based fall detector. Using real data, we demonstrate that the choice of data representation in the time-frequency domain is important for enhancing the accuracy of the fall detector.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124535487","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}
引用次数: 27
A coupled joint eigenvalue decomposition algorithm for canonical polyadic decomposition of tensors 张量正则多进分解的耦合联合特征值分解算法
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569697
Rémi André, Xavier Luciani, E. Moreau
{"title":"A coupled joint eigenvalue decomposition algorithm for canonical polyadic decomposition of tensors","authors":"Rémi André, Xavier Luciani, E. Moreau","doi":"10.1109/SAM.2016.7569697","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569697","url":null,"abstract":"In this paper we propose a novel algorithm to compute the joint eigenvalue decomposition of a set of squares matrices. This problem is at the heart of recent direct canonical polyadic decomposition algorithms. Contrary to the existing approaches the proposed algorithm can deal equally with real or complex-valued matrices without any modifications. The algorithm is based on the algebraic polar decomposition which allows to make the optimization step directly with complex parameters. Furthermore, both factorization matrices are estimated jointly. This “coupled” approach allows us to limit the numerical complexity of the algorithm. We then show with the help of numerical simulations that this approach is suitable for tensors canonical polyadic decomposition.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126448282","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}
引用次数: 7
Downlink path-based precoding in FDD massive MIMO systems without CSI feedback 无CSI反馈FDD大规模MIMO系统中基于下行路径的预编码
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569644
Ming-Fu Tang, Chih-Chi Chen, B. Su
{"title":"Downlink path-based precoding in FDD massive MIMO systems without CSI feedback","authors":"Ming-Fu Tang, Chih-Chi Chen, B. Su","doi":"10.1109/SAM.2016.7569644","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569644","url":null,"abstract":"For a massive multiple-input-multiple-output (MIMO) system operated under frequency-division duplex (FDD), downlink training was usually considered impractical due to huge amount of pilot signals and feedback overhead. Although some efforts have been spent to reduce such an overhead in recent works, the reduction is still limited. More recently, some precoding methods that do not require downlink training and channel state information (CSI) feedback have been proposed by recognizing some similarity between uplink and downlink channels. The base station may take advantage of such similarity and acquire partial knowledge of the downlink channels using previous uplink channel estimates. In this paper, we propose to further exploit the diversity of a multipath channel in a downlink precoding method without CSI feedback. Specifically, space-time block code (STBC) is applied to enhance the robustness of downlink transmission based on the partial CSI at the transmitter (CSI-T). Simulation results suggest that the proposed method achieves a competitive performance to other methods with only the partial CSI-T.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133384864","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}
引用次数: 5
Widely-linear Gaussian sum filter 宽线性高斯和滤波器
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569616
Arash Mohammadi, Argin Margoosian, K. Plataniotis
{"title":"Widely-linear Gaussian sum filter","authors":"Arash Mohammadi, Argin Margoosian, K. Plataniotis","doi":"10.1109/SAM.2016.7569616","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569616","url":null,"abstract":"Motivated by application of widely-linear signal processing techniques in recursive Bayesian estimation, the paper proposes a novel widely-linear Gaussian sum filter (WL/GSF) for non-linear state estimation problems. Although the literature on non-linear state estimation using Gaussian sum filters is rich, its widely-linear counterpart which incorporates the full second-order statistics of the system and can potentially cope with non-Gaussian/non-circular measurements, have not yet been investigated in the literature. The paper addresses this gap. The WL/GSF resolves the computational burden of the Gaussian sum approach by incorporating a collapsing step. The number of components in the WL/GSF is controlled adaptively at each step utilizing a Bayesian learning technique to collapse, in an intelligent way, the resulting non-Gaussian sum mixture to an equivalent Gaussian term. Simulation results provided as proof of concepts and show that the proposed WL/GSF algorithm outperforms its counterparts in non-linear filtering problems with non-circular and non-Gaussian observations.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114305206","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}
引用次数: 0
Relaying with finite blocklength: Challenge vs. opportunity 有限块长度的中继:挑战vs.机会
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569613
Yulin Hu, A. Schmeink, J. Gross
{"title":"Relaying with finite blocklength: Challenge vs. opportunity","authors":"Yulin Hu, A. Schmeink, J. Gross","doi":"10.1109/SAM.2016.7569613","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569613","url":null,"abstract":"In this paper, we study the performance of a system with multiple decode-and-forward (DF) relays under the finite blocklength (FBL) regime. We derive the FBL-Throughput under both perfect CSI and average CSI scenarios while the corresponding throughputs under an infinite blocklength assumption (IBL-throughput) are discussed as performance references. Through numerical analysis, we evaluate the system performance. We show a higher throughput under the FBL assumption than under the IBL assumption under the perfect CSI scenario.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116920843","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}
引用次数: 5
Stationary graph processes: Nonparametric spectral estimation 平稳图处理:非参数谱估计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569746
Santiago Segarra, A. Marques, G. Leus, Alejandro Ribeiro
{"title":"Stationary graph processes: Nonparametric spectral estimation","authors":"Santiago Segarra, A. Marques, G. Leus, Alejandro Ribeiro","doi":"10.1109/SAM.2016.7569746","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569746","url":null,"abstract":"Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios the information of interest resides in more irregular graph domains. The contribution in this paper is twofold. First, we propose several equivalent notions of weak stationarity for random graph signals, all taking into account the structure of the graph where the random process takes place. Second, we analyze the properties of the induced power spectral density along with nonparametric approaches to estimate it, including average and window-based periodograms.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127539146","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}
引用次数: 12
Wideband Sparse Bayesian Learning for DOA estimation from multiple snapshots 基于宽带稀疏贝叶斯学习的多快照DOA估计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569745
P. Gerstoft, C. Mecklenbräuker
{"title":"Wideband Sparse Bayesian Learning for DOA estimation from multiple snapshots","authors":"P. Gerstoft, C. Mecklenbräuker","doi":"10.1109/SAM.2016.7569745","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569745","url":null,"abstract":"The directions of arrival (DOA) of plane waves are estimated from multi-frequency multi-snapshot sensor array data using Sparse Bayesian Learning (SBL). The prior for the source amplitudes is assumed to be independently zero-mean complex Gaussian distributed with hyperparameters being the unknown variances (i.e. the source powers). For a complex Gaussian likelihood with unknown noise variance hyperparameter, the corresponding Gaussian posterior distribution is derived. For a given number of DOAs, the hyperparameters are automatically selected by maximizing the evidence and promote sparse DOA estimates. The SBL scheme for DOA estimation is discussed and evaluated competitively against MUSIC.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130078681","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}
引用次数: 9
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