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

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Empirical signal decomposition for acoustic noise detection 基于经验信号分解的噪声检测
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569740
L. Zão, R. Coelho
{"title":"Empirical signal decomposition for acoustic noise detection","authors":"L. Zão, R. Coelho","doi":"10.1109/SAM.2016.7569740","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569740","url":null,"abstract":"This paper introduces an adaptive noise detection method for non-stationary acoustic noisy signals. The proposed approach is based on the empirical mode decomposition (EMD) and a vector of Hurst exponent coefficients. The scheme is investigated considering real acoustic noisy signals with different non-stationarity degree and signal-to-noise ratio (SNR). The results demonstrate that the EMD-based noise detector enables a better separation between the clean and noisy signals when compared to the competing methods. It also leads to an average SNR improvement of 4.4 dB for the resulting enhanced signals.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"188 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":"121276358","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: 3
Efficient linearized Bregman iteration for sparse adaptive filters and Kaczmarz solvers 稀疏自适应滤波器和Kaczmarz解算器的有效线性Bregman迭代
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569759
M. Lunglmayr, M. Huemer
{"title":"Efficient linearized Bregman iteration for sparse adaptive filters and Kaczmarz solvers","authors":"M. Lunglmayr, M. Huemer","doi":"10.1109/SAM.2016.7569759","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569759","url":null,"abstract":"Linearized Bregman iterations are low complexity and high precision approaches for solving the combined l1/l2 minimization problem. In this work we give a derivation of the linearized Bregman iteration and show the links to Kaczmarz's algorithm as well as to sparse least mean squares (LMS) filters. We present a novel extension allowing to perform combined l1/l2 minimization either in an LMS based adaptive filter or in a Kaczmarz based batch solution. By means of simulations we demonstrate that the performance of our extension is comparable to the original linearized Bregman approaches. Furthermore, we show that with this extension l1/l2 minimization can be performed with less complexity than the corresponding l2 minimization.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"74 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":"122622106","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: 8
Position estimation from range measurements using adaptive networks 利用自适应网络从距离测量中估计位置
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569739
G. S. Vicinansa, Yannick Plaino Bergamo, C. G. Lopes
{"title":"Position estimation from range measurements using adaptive networks","authors":"G. S. Vicinansa, Yannick Plaino Bergamo, C. G. Lopes","doi":"10.1109/SAM.2016.7569739","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569739","url":null,"abstract":"In this paper we introduce a new multilateration-based method for source localization. Sensors with known positions collect noisy signals whose strength depends on the relative position between the sensors and the source. A nonlinear system of equations is obtained which is then recast into a linearized least squares (LS) problem, which resembles a multilateration scenario. From the LS problem a low-complexity Adaptive Network (AN) solution is proposed via a distributed Diffusion Least Mean Square (LMS) implementation. Two well known signal strenght models are reformulated to fit the AN framework. The resulting distributed solution inherits the robustness, low complexity and intrinsic tracking ability of ANs. Simulations show the effectiveness of the new method in noisy scenarios when localizing stationary sources and when tracking moving targets, even for a small number of sensors.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"26 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":"132628655","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: 4
An ℓ1-norm Linearly Constrained Affine Projection Algorithm 一种1范数线性约束仿射投影算法
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569729
J. F. D. Andrade, M. Campos, J. A. Apolinário
{"title":"An ℓ1-norm Linearly Constrained Affine Projection Algorithm","authors":"J. F. D. Andrade, M. Campos, J. A. Apolinário","doi":"10.1109/SAM.2016.7569729","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569729","url":null,"abstract":"In this work, we detail an ℓ1-norm Linearly Constrained Affine Projection Algorithm (ℓ1-CAPA) to be employed in solving problems whose solutions have some degree of sparsity, such as in beamforming or when dealing with sparse communication channels. The effectiveness of this algorithm, in terms of MSE and convergence rate, is demonstrated via computer simulations.","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":"131365923","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: 2
On JEVD of semi-definite positive matrices and CPD of nonnegative tensors 半定正矩阵的JEVD和非负张量的CPD
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569738
Rémi André, L. Albera, Xavier Luciani, E. Moreau
{"title":"On JEVD of semi-definite positive matrices and CPD of nonnegative tensors","authors":"Rémi André, L. Albera, Xavier Luciani, E. Moreau","doi":"10.1109/SAM.2016.7569738","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569738","url":null,"abstract":"In this paper, we mainly address the problem of Joint EigenValue Decomposition (JEVD) subject to nonnegative constraints on the eigenvalues of the matrices to be diagonalized. An efficient method based on the Alternating Direction Method of Multipliers (ADMM) is designed. ADMM provides an elegant approach for handling nonnegativity constraints, while taking advantage of the structure of the objective function. Numerical tests on simulated matrices show the interest of the proposed method for low Signal-to-Noise Ratio (SNR) values when the similarity transformation matrix is ill-conditioned. The ADMM was recently used for the Canonical Polyadic Decomposition (CPD) of nonnegative tensors leading to the ADMoM algorithm. We show through computer results that DIAG+, a semi-algebraic CPD method using our ADMM-based JEVD+ algorithm, will give a better estimation of factors than ADMoM in the presence of swamps. DIAG+ also appears to be less time-consuming than ADMoM when low-rank tensors of high dimensions are considered.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"32 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":"125688268","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
Mixed source prior for the fast independent vector analysis algorithm 混合源先验为快速独立矢量分析算法
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569631
Waqas Rafique, S. M. Naqvi, J. Chambers
{"title":"Mixed source prior for the fast independent vector analysis algorithm","authors":"Waqas Rafique, S. M. Naqvi, J. Chambers","doi":"10.1109/SAM.2016.7569631","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569631","url":null,"abstract":"A novel method to improve the separation performance and the convergence speed of fast independent vector analysis (FastIVA) for frequency domain operation is proposed. In this paper, a mixture of super Gaussian distributions is adopted as a source prior for the FastIVA algorithm. In this mixed source prior, a Student's t distribution is adopted to model the high amplitude information in the spectrum of a speech signal and another super Gaussian distribution with less heavy tails is used to model the rest of the information. Moreover, in the proposed mixed source prior the weight of both distributions can be varied according to the frequency dependent amplitude of the speech signals. The performance of the proposed algorithm is demonstrated by using the imaging method and binaural room impulse responses and comparison is made with the FastIVA using the original super Gaussian source prior.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"28 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":"121473467","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: 6
Long-term antenna selection for large-scale MIMO links 大规模MIMO链路的长期天线选择
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569647
Hans-Georg Engler, Eduard Axel Jorswieck
{"title":"Long-term antenna selection for large-scale MIMO links","authors":"Hans-Georg Engler, Eduard Axel Jorswieck","doi":"10.1109/SAM.2016.7569647","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569647","url":null,"abstract":"In this paper antenna selection algorithms for large scale MIMO links are developed under the assumption of incomplete channel state information (CSI) at both receiver and transmitter. More specific, we assume that only long-term CSI is available for the antenna selection, but perfect CSI can be obtained for the significantly smaller effective channel that results from the antenna selection. Aim of the development is the maximization of the capacity of the resulting channels namely for the cases of high and low SNR. Additionally, the proposed selection algorithms avoid exhaustive search over all possible antenna sets, as this approach is infeasible for large antenna arrays. Finally, all results are illustrated by numeric simulations. When selecting 8 × 8 antennas out of 128 × 128 with the proposed algorithms, for Ricean K factor K = 1, a medium signal-to-noise-ratio of 10dB and strong spatial correlation the maximum spectral efficiency can be increased by more than 50 percent compared to a conventional 8 × 8 MIMO system with the same parameters.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"24 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":"121923084","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: 2
On spectral noncircularity of natural signals 自然信号的谱非圆性
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569672
Scott Wisdom, L. Atlas, J. Pitton
{"title":"On spectral noncircularity of natural signals","authors":"Scott Wisdom, L. Atlas, J. Pitton","doi":"10.1109/SAM.2016.7569672","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569672","url":null,"abstract":"Natural signals are typically nonstationary. The complex-valued frequency spectra of nonstationary signals do not have zero spectral correlation, as is assumed for wide-sense stationary processes. Instead, these spectra have non-zero second-order noncircular statistics-that is, they are not rotationally invariant-that are potentially useful for detection, classification, and enhancement. These noncircular statistics are especially significant for transient events, which are common in many natural signals. In this paper we provide practical and effective estimators for spectral noncircularity and spectral correlation. We illustrate the behavior of our spectral noncircularity estimators for synthetic signals. Then, we derive a generalized likelihood ratio test using both circular and noncircular models and show how estimates of spectral noncircularity provide performance improvements for detection of natural acoustic events.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"213 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":"115556090","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: 4
Coordinated waveform design and receiver filter optimization for cognitive radar networks 认知雷达网络协调波形设计与接收机滤波器优化
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569623
Gaia Rossetti, S. Lambotharan
{"title":"Coordinated waveform design and receiver filter optimization for cognitive radar networks","authors":"Gaia Rossetti, S. Lambotharan","doi":"10.1109/SAM.2016.7569623","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569623","url":null,"abstract":"We propose convex optimization based waveform designs for cognitive radar networks. The Multiple Input Multiple Output (MIMO) nature of the considered model as well as the cognitive capabilities provided by the joint adaptation of receiver and transmitter and some initial knowledge on the environment, make these algorithms suitable for cognitive radar networks. In particular, we propose two complementary optimization techniques. The first one aims at optimizing the Signal to Interference and Noise Ratio (SINR) of a specific radar while keeping the SINR of the other radars at desired levels. The second approach optimizes the SINR of all radars using a Max-Min optimization criterion. The optimization framework includes many constraints on the waveforms such as mutual orthogonality and transmission power. The simulation results confirm the efficiency of the algorithms.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"60 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":"123492286","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
Deterministic fourier-based dictionary design for sparse reconstruction 基于确定性傅立叶的稀疏重建字典设计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569630
D. D. Ariananda, H. J. Rad, Zijian Tang, G. Leus, X. Campman
{"title":"Deterministic fourier-based dictionary design for sparse reconstruction","authors":"D. D. Ariananda, H. J. Rad, Zijian Tang, G. Leus, X. Campman","doi":"10.1109/SAM.2016.7569630","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569630","url":null,"abstract":"This paper focuses on the design of a Fourier dictionary matrix formed by selecting specific rows of the inverse discrete Fourier transform matrix based on coherence-related metrics. While maximum coherence is a popular metric in compressive sampling, we also consider rms LN-coherence, which focuses on the largest LN (instead of one) inner products between different columns of the dictionary matrix. Finding a dictionary matrix optimizing either the maximum or the rms LN-coherence lead to a complicated optimization problem. Hence, we introduce a new metric called coherence deviation (CD), which gives a measure on the variation of all the inner products between different columns of the dictionary matrix, and motivate its use as an amenable alternative for both the maximum and rms LN-coherence. While finding a dictionary matrix optimizing the CD leads to a simplified optimization problem, the resulting cost function is a quartic function of a binary vector variable. Hence, we propose Greedy-β algorithm to provide sub-optimal solutions.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"52 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":"115709468","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
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