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

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Simplified matrix polynomial-aided block diagonalization precoding for massive MIMO systems 大规模MIMO系统的简化矩阵多项式辅助块对角化预编码
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569711
Wence Zhang, R. D. Lamare, Cunhua Pan, Ming Chen, J. Dai, Bingyang Wu
{"title":"Simplified matrix polynomial-aided block diagonalization precoding for massive MIMO systems","authors":"Wence Zhang, R. D. Lamare, Cunhua Pan, Ming Chen, J. Dai, Bingyang Wu","doi":"10.1109/SAM.2016.7569711","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569711","url":null,"abstract":"In the downlink of Massive Multiple-Input-Multiple-Output (MIMO) systems, the high computational cost of precoding is a major challenge for real-time data transmission. In this paper, we propose a simplified matrix polynomial-aided block diagonalization (SMP-BD) precoding scheme to simplify the conventional block diagonalization (BD) type precoding schemes for users with multiple antennas. By replacing the singular value decomposition (SVD) operation with a matrix inversion, which is then approximated by matrix polynomials with optimized coefficients, SMP-BD is shown to be hardware-efficient, reduce the transmission overhead and obtain high performance. The optimized coefficients can be calculated offline and the convergence of the matrix polynomials is guaranteed. Simulations show that SMP-BD with polynomial order L = 3 performs close to previously reported algorithms based on matrix inversions, while being much simpler.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"10 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":"127877818","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
Parameters estimate of Riemannian Gaussian distribution in the manifold of covariance matrices 协方差矩阵流形中黎曼高斯分布的参数估计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569687
P. Zanini, M. Congedo, C. Jutten, S. Said, Y. Berthoumieu
{"title":"Parameters estimate of Riemannian Gaussian distribution in the manifold of covariance matrices","authors":"P. Zanini, M. Congedo, C. Jutten, S. Said, Y. Berthoumieu","doi":"10.1109/SAM.2016.7569687","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569687","url":null,"abstract":"The study of Pm, the manifold of m × m symmetric positive definite matrices, has recently become widely popular in many engineering applications, like radar signal processing, mechanics, computer vision, image processing, and medical imaging. A large body of literature is devoted to the barycentre of a set of points in Pm and the concept of barycentre has become essential to many applications and procedures, for instance classification of SPD matrices. However this concept is often used alone in order to define and characterize a set of points. Less attention is paid to the characterization of the shape of samples in the manifold, or to the definition of a probabilistic model, to represent the statistical variability of data in Pm. Here we consider Gaussian distributions and mixtures of Gaussian distributions on Pm. In particular we deal with parameter estimation of such distributions. This problem, while it is simple in the manifold P2, becomes harder for higher dimensions, since there are some quantities involved whose analytic expression is difficult to derive. In this paper we introduce a smooth estimate of these quantities using convex cubic splines, and we show that in this case the parameters estimate is coherent with theoretical results. We also present some simulations and a real EEG data analysis.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"19 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":"126480252","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: 14
Joint frequency and direction of arrival estimation with space-time array 空时阵列联合到达频率和方向估计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569645
Achanna Anil Kumar, S. G. Razul, M. Chandra, C. See, P. Balamuralidhar
{"title":"Joint frequency and direction of arrival estimation with space-time array","authors":"Achanna Anil Kumar, S. G. Razul, M. Chandra, C. See, P. Balamuralidhar","doi":"10.1109/SAM.2016.7569645","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569645","url":null,"abstract":"Joint frequency and DOA estimation of more sources than the number of sensors is considered in this paper. We assume a simple uniform linear array (ULA) and propose to employ a multiple delay channel network at every sensor, which can easily be realized by sampling at a slightly higher rate. By combining the outputs of the ULA and the delay network, we show that the manifold matrix is analogous to that of a uniform rectangular array, and hence we appropriately refer to this array as the space-time array. Further, estimation of parameters via the rotational invariance technique (ESPRIT) based algorithm referred to as space-time (ST)-Euler-ESPRIT is proposed. ST-Euler-ESPRIT, similar to well known unitary-ESPRIT provides automatically paired frequencies and their DOAs. We further show that with the proposed approach for a M element ULA and with N-1 delay channel, O(MN) frequencies and their DOAs can be estimated. The performance of ST-Euler-ESPRIT is verified by simulations, where it shows consistently better performance than the unitary-ESPRIT algorithm under noisy conditions.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"13 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":"122301900","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: 10
Multi-sensor signal processing on a PSD matrix manifold PSD矩阵歧管上的多传感器信号处理
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569757
K. M. Wong, Jian-Kang Zhang, Huiying Jiang
{"title":"Multi-sensor signal processing on a PSD matrix manifold","authors":"K. M. Wong, Jian-Kang Zhang, Huiying Jiang","doi":"10.1109/SAM.2016.7569757","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569757","url":null,"abstract":"We propose to use the power spectral density (PSD) matrices of the received signals of a multi-sensor system as the feature of processing. PSD matrices have structural constraints and they form a manifold in signal space. We introduce two new Riemannian distance (RD) measures on the PSD matrix manifold and developed algorithms to locate the means and medians of PSD matrices in terms of these RD. These concepts are then applied to the detection of narrow-band sonar signals in noise and the results are encouraging.","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":"121673560","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
Modeling time warping in tensor decomposition 张量分解中的时间翘曲建模
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569733
B. Rivet, Jeremy E. Cohen
{"title":"Modeling time warping in tensor decomposition","authors":"B. Rivet, Jeremy E. Cohen","doi":"10.1109/SAM.2016.7569733","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569733","url":null,"abstract":"Taking into account subject variability in data mining is one of the great challenges of modern biomedical engineering. In EEG recordings, the assumption that time sources are exactly shared by multiple subjects, multiple recordings of the same subject, or even multiples instances of the sources in one recording is especially wrong. In this paper, we propose to deal with shared underlying sources expressed through time warping in multiple EEG recordings, in the context of ocular artifact removal. Diffeomorphisms are used to model the time warping operators. We derive an algorithm that extracts all sources and diffeomorphism in the model and show successful simulations, giving a proof of concept that subject variability can be tackled with tensor modeling.","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":"131215420","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
Effects of range spread and aspect angle on radar fall detection 距离扩展和俯角对雷达坠落探测的影响
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569741
B. Erol, M. Amin
{"title":"Effects of range spread and aspect angle on radar fall detection","authors":"B. Erol, M. Amin","doi":"10.1109/SAM.2016.7569741","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569741","url":null,"abstract":"Single-sensor Doppler radar faces many challenges in elderly fall detection. The similarities of the Doppler signatures between falls and other motions of fast time transitions render CW-based EM sensing insufficient for proper motion discriminator. Further, motion articulations in the directions of a small or zero projections along the line-of-sight generate noisy Doppler signatures and greatly contribute to the confusion matrix. In this paper, the benefit from the range information is used to distinguish between fall and non-fall motions with similar Doppler features. We also examine the effects of the aspect angle on fall detection. Simulation results using Kinect-based radar simulator and 24 GHz UWB radar sensing system, demonstrate the merits of the proposed platform for indoor motion monitoring serving assisted living applications.","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":"133519696","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: 15
Using wavelet packets to analyze FM LPI radar signals 利用小波包分析调频LPI雷达信号
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569703
Sergio R. Neves, Aline de Oliveira, Rafael Serra, Luiz Eugenio Segadilha, Fatima Monteiro, Jean-Marc Lopez
{"title":"Using wavelet packets to analyze FM LPI radar signals","authors":"Sergio R. Neves, Aline de Oliveira, Rafael Serra, Luiz Eugenio Segadilha, Fatima Monteiro, Jean-Marc Lopez","doi":"10.1109/SAM.2016.7569703","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569703","url":null,"abstract":"The FM (Frequency Modulated) LPI (Low Probability of Intercept) radars, unlike conventional radars, transmit a long or a continuous signal, with low power, using causal modulation. These LPI radar characteristics make it virtually invisible to conventional RWRs (Radar Warning Receivers) and ESM (Electronic Support Measures) receivers. Many studies in the EW (Electronic Warfare) field are being carried out to deal with this LPI radar advantage. One promising approach is the employment by ESM equipments of time-frequency transform methods to find causality in the spectrum's noise. This paper applies the WPT (Wavelet Packets Transform) to the matter of generating a portrait of the electromagnetic spectrum, aiming the milestone for an automatic classification method. We compare the time-frequency portrait of FM LPI radar signals obtained through the WPT and the well-known Choi-Williams and Fourier transforms. Results obtained from real data show some advantages to the WPT.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"50 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114042540","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
Array of sensors: A spatiotemporal-state-space model for target trajectory tracking 传感器阵列:用于目标轨迹跟踪的时空-状态-空间模型
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569756
A. Manikas, V. Sridhar, Y. Kamil
{"title":"Array of sensors: A spatiotemporal-state-space model for target trajectory tracking","authors":"A. Manikas, V. Sridhar, Y. Kamil","doi":"10.1109/SAM.2016.7569756","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569756","url":null,"abstract":"In this paper, with the objective of tracking the trajectory of multiple mobile targets, a novel spatiotemporal-state-space model is introduced for an array of sensors distributed in space. Under the wideband assumption, the proposed model incorporates the array geometry in conjunction with crucial target parameters namely (i) ranges, (ii) directions, (iii) velocities and (iv) associated Doppler effects. Computer simulation studies show some representative examples where the proposed model is utilised to track the locations of sources in space with a very high accuracy.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"7 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":"126126612","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
Median burg robust spectral estimation for inhomogeneous and stationary segments 非均匀和平稳段的中值burg鲁棒谱估计
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569662
F. Barbaresco, Alexis Decurninge
{"title":"Median burg robust spectral estimation for inhomogeneous and stationary segments","authors":"F. Barbaresco, Alexis Decurninge","doi":"10.1109/SAM.2016.7569662","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569662","url":null,"abstract":"In order to estimate parameters of Gaussian autoregressive processes, Burg method is often used in case of stationarity for its efficiency when few samples are available. We are interested in the case when multiple inhomogeneous (not necessarily Gaussian) segments of time series are available. We then study robust modification of Burg algorithms, especially based on Frechet medians defined for the Euclidean or the Poincare metric, to estimate the parameters of autoregressive processes in presence of outliers and/or contaminating distributions. Moreover, we will show that the introduced estimators are robust with respect to the power distribution of the time series. The considered modelization is motivated by radar applications, the performances of our methods will then be compared to the very popular Fixed Point and OS-CFAR estimators through radar simulated scenarios.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"79 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":"126320509","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
Avoiding divergence in the constant modulus algorithm for blind equalization of MIMO systems MIMO系统盲均衡常模算法中的避免发散
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) Pub Date : 2016-07-10 DOI: 10.1109/SAM.2016.7569728
Flávio R. M. Pavan, Magno T. M. Silva, M. D. Miranda
{"title":"Avoiding divergence in the constant modulus algorithm for blind equalization of MIMO systems","authors":"Flávio R. M. Pavan, Magno T. M. Silva, M. D. Miranda","doi":"10.1109/SAM.2016.7569728","DOIUrl":"https://doi.org/10.1109/SAM.2016.7569728","url":null,"abstract":"The multiuser constant modulus algorithm (MU-CMA) can be employed for blind equalization of multiple-input multiple-output (MIMO) communication systems. Due to the multi-modality of the constant modulus cost function, some adverse situations can cause inconsistency in the nonlinear estimates, which in turn can lead the algorithm to diverge. In order to avoid divergence, we propose a dual-mode multiuser algorithm, which works as a normalized multimodulus version of MU-CMA in the first mode, and rejects nonlinear estimates in the second mode. We present a deterministic stability analysis of the proposed algorithm and confirm its good performance by means of numerical simulations.","PeriodicalId":159236,"journal":{"name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"21 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":"126464603","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
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