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2018 IEEE Statistical Signal Processing Workshop (SSP)最新文献

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Regularized Lattice Reduction-Aided Ordered Successive Interference Cancellation for MIMO Detection 正则格约简辅助有序逐次干扰消除MIMO检测
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450791
Jun Tong, Qinghua Guo, J. Xi, Yanguang Yu, P. Schreier
{"title":"Regularized Lattice Reduction-Aided Ordered Successive Interference Cancellation for MIMO Detection","authors":"Jun Tong, Qinghua Guo, J. Xi, Yanguang Yu, P. Schreier","doi":"10.1109/SSP.2018.8450791","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450791","url":null,"abstract":"Lattice reduction-aided ordered successive interference cancellation (LRA-OSIC) detection is capable of achieving optimal diversity orders for multiple-input multiple-output (MIMO) communications. When the number of antennas is large, however, there can still be a significant gap between the performance achievable with the LRA-OSIC detector and the maximum likelihood detector (MLD). This paper introduces a regularization approach to enhance the performance of LRA-OSIC detectors. Multiple approximate models for the same MIMO channel are generated and a standard LRA-OSIC detector is then constructed for each model. The best detector is determined for each instantaneous received symbol, using a residual-based method. The search can be terminated using a stopping criterion. Simulation results show that significant performance enhancements can be achieved by the proposed design at only a moderate increase of complexity1.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125625291","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
Convmd: Convolutive Matrix Decomposition For Classification Of Matrix Data 卷积矩阵分解用于矩阵数据的分类
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450731
Phung Lai, R. Raich, M. Megraw
{"title":"Convmd: Convolutive Matrix Decomposition For Classification Of Matrix Data","authors":"Phung Lai, R. Raich, M. Megraw","doi":"10.1109/SSP.2018.8450731","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450731","url":null,"abstract":"In this paper, we consider the use of convolutive matrix decomposition for matrix data classification. Matrix decomposition has been broadly used as means of dimensionality reduction in a variety of learning tasks. In this approach, columns of a matrix are represented as a linear combination over a basis. For applications in which relevant information is encoded in a sequence of columns instead of a single column, the use of a single column basis is insufficient. In this paper, we present a matrix classification framework that relies on a convolutive-based matrix decomposition approach that captures structure among neighboring columns. In particular, we present a latent variable graphical model for classification of matrices that is based on the proposed matrix decomposition. We present experimental results with promising performance on a DNA dataset associated with protein production.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132321131","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
A Probabilistic Approach for Adaptive State-Space Partitioning 一种自适应状态空间划分的概率方法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450821
J. Vilà‐Valls, P. Closas, M. Bugallo, J. Míguez
{"title":"A Probabilistic Approach for Adaptive State-Space Partitioning","authors":"J. Vilà‐Valls, P. Closas, M. Bugallo, J. Míguez","doi":"10.1109/SSP.2018.8450821","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450821","url":null,"abstract":"The multiple Bayesian filtering approach is based on the partitioning of the state-space in several lower dimensional subspaces, combined with a set of parallel filters that characterize the marginal subspace posteriors. This solution has been shown to perform well and solve some of the problems typically suffered by standard Bayesian filters, such as the curse-of-dimensionality, in some scenarios. An inherent problem in the application of multiple Gaussian filters (MGF) and multiple particle filters (MPF) proposed in the literature is how to partition the state-space. A closed answer does not exist because this is an application-dependent problem. In this contribution we further elaborate on the multiple filtering approach, and propose a probabilistic adaptive state-partitioning strategy based on the crosscorrelation computed at each filter.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132878113","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
Adaptive Reconstruction Along Mobile Sensing Paths 移动传感路径的自适应重构
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450757
Ariel Shallom, H. Kirshner, M. Porat
{"title":"Adaptive Reconstruction Along Mobile Sensing Paths","authors":"Ariel Shallom, H. Kirshner, M. Porat","doi":"10.1109/SSP.2018.8450757","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450757","url":null,"abstract":"We address the problem of reconstructing missing parts of mobile sensing signals. Such a case occurs when sensor information is occluded or not transmitted over finite periods of time. As the sampling rate along each path is essentially unlimited, we consider the asymptotic case of having continuous-time information from the sensor. We embed the partially available signal in a functional space of smooth and finite-energy functions, while adapting the parameters of the space to the signal at hand. We then analytically solve a specifically designed error measure and obtain a minimum-norm reconstruction for the missing parts. We demonstrate the proposed algorithm for both simulated- and real data.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132091276","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
Low Resolution Sampling for Joint Millimeter-Wave MIMO Communication-Radar 联合毫米波MIMO通信雷达的低分辨率采样
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450774
P. Kumari, K. Mazher, A. Mezghani, R. Heath
{"title":"Low Resolution Sampling for Joint Millimeter-Wave MIMO Communication-Radar","authors":"P. Kumari, K. Mazher, A. Mezghani, R. Heath","doi":"10.1109/SSP.2018.8450774","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450774","url":null,"abstract":"We propose the deployment of millimeter-wave MIMO for joint vehicular high speed communication and high resolution radar sensing. To cope with the significant hardware complexity, we consider the use of low resolution analog-to-digital converters (ADC) while maintaining a separate radio-frequency chain per antenna. The system performance is analyzed in terms of Cramér Rao lower bound and achievable data rate, and compared to the ideal case with infinite resolution ADCs. Additionally, we study the impact of quantization on the trade-off between these performance metrics. Numerical results demonstrate the potential of the concept to meet the challenging requirements of next-generation vehicles.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127644995","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: 17
Geometry and Radiometry Invariant Matched Manifold Detection and Robust Homography Estimation 几何与辐射不变性匹配流形检测与鲁棒单应性估计
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450683
Ziv Yavo, J. Francos
{"title":"Geometry and Radiometry Invariant Matched Manifold Detection and Robust Homography Estimation","authors":"Ziv Yavo, J. Francos","doi":"10.1109/SSP.2018.8450683","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450683","url":null,"abstract":"We elaborate on the problem of robust homography estimation based on a novel framework of geometry and radiometry invariant matched manifold detection: Any two observations on the same planar surface are related through a geometric transformation described by a homography, and some radiometric transformation. Using the proposed approach the surface image is tessellated into tiles, such that locally on each tile, the geometric transformation is approximately affine, and the radiometric transformation is monotone. Applying to each of the observations on a surface tile, the radiometry invariant universal manifold embedding (RIUME) operator, the set of all possible observations on that tile is mapped to a single linear subspace of some high dimensional Euclidean space - invariant to monotonic amplitude transformations, and to affine geometric transformations. Thus, by tessellating the observed surface into a set of tiles and matching each tile using the RIUME matched manifold detector to the hypothesized corresponding tile in the other observation on that surface, an efficient method for robust and dense matching of large patches on different observations of the surface is established. Due to the high accuracy of the obtained tile matches, the outliers problem is eliminated. Hence a linear algorithm like the DLT yields accurate estimates of the homography parameters.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115831166","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
A Riemannian Approach for Graph-Based Clustering by Doubly Stochastic Matrices 基于双随机矩阵的图聚类的黎曼方法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450685
Ahmed Douik, B. Hassibi
{"title":"A Riemannian Approach for Graph-Based Clustering by Doubly Stochastic Matrices","authors":"Ahmed Douik, B. Hassibi","doi":"10.1109/SSP.2018.8450685","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450685","url":null,"abstract":"Convex optimization is a well-established area with applications in almost all fields. However, these convex methods can be rather slow and computationally intensive for high dimensional problems. For a particular class of problems, this paper considers a different approach, namely Riemannian optimization. The main idea is to view the constrained optimization problem as an unconstrained one over a restricted search space (the manifold). Riemannian optimization explicitly exploits the geometry of the problem and often reduces its dimension, thereby potentially allowing significant speedup as compared to conventional approaches. The paper introduces the doubly stochastic, the symmetric, and the definite multinomial manifolds which generalize the simplex. The method is applied to a convex and a non-convex graph-based clustering problem. Theoretical analysis and simulation results demonstrate the efficiency of the proposed method over the state of the art as it outperforms conventional generic and specialized solvers, especially in high dimensions.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114353065","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
Shape Parameter Estimation for K-Distribution Using Variational Bayesian Approach 基于变分贝叶斯方法的k分布形状参数估计
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450847
A. Turlapaty
{"title":"Shape Parameter Estimation for K-Distribution Using Variational Bayesian Approach","authors":"A. Turlapaty","doi":"10.1109/SSP.2018.8450847","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450847","url":null,"abstract":"The sea clutter component in some of the radar and sonar signal models can be statistically characterized as following a K-distribution. This distribution has a shape parameter that is directly related to the number of scatterers. Hence, the estimation of this shape parameter is an important problem and is traditionally addressed using the maximum likelihood (ML), the method of moments (MoM) and their variants. A shortcoming of these methods is lesser accuracy in comparison to the theoretical CRB. In this paper, a variational Bayesian algorithm is proposed that provides both improved convergence and superior accuracy in comparison to the existing algorithms.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114908372","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
Parameter Estimation in a Gibbs-Markov Field Texture Model Based on a Coding Approach 基于编码方法的Gibbs-Markov场纹理模型参数估计
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450826
Jorge Martinez, Silvina Pistonesi, M. C. Maciel, A. G. Flesia
{"title":"Parameter Estimation in a Gibbs-Markov Field Texture Model Based on a Coding Approach","authors":"Jorge Martinez, Silvina Pistonesi, M. C. Maciel, A. G. Flesia","doi":"10.1109/SSP.2018.8450826","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450826","url":null,"abstract":"In this paper, we present a novel approach of the Conditional Least Square (CLS) estimator based on a coding scheme, for estimating the parameter vector associated with an Auto-Binomial model. This method provides a parallel solver for the estimation process. In order to illustrate the performance of the proposed approach, we carried out a Monte Carlo study and a real application for landscape classification using a high-resolution Pléiades-1A satellite image. Experimental results demonstrated the effectiveness of our estimation approach as well as CLS method, but in a lower runtime.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1486 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114908726","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
Quadratic–Inverse Estimates Of Autocorrelation 自相关的二次-逆估计
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450755
D. Thomson
{"title":"Quadratic–Inverse Estimates Of Autocorrelation","authors":"D. Thomson","doi":"10.1109/SSP.2018.8450755","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450755","url":null,"abstract":"We reconsider the classical problem of estimating the auto-correlation sequence of a stationary time series using quadratic-inverse spectrum estimates. This paper collapses the free-parameter expansion ambiguity of quadratic-inverse spectrum estimates and results in estimates of autocorrelations that have simultaneously low bias and variance.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117299975","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
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