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2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing最新文献

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Consensus-Based Distributed MIMO Decoding Using Semidefinite Relaxation 基于共识的半定松弛分布式MIMO解码
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498000
Hao Zhu, A. Cano, G. Giannakis
{"title":"Consensus-Based Distributed MIMO Decoding Using Semidefinite Relaxation","authors":"Hao Zhu, A. Cano, G. Giannakis","doi":"10.1109/CAMSAP.2007.4498000","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498000","url":null,"abstract":"A distributed algorithm is developed for decoding a message broadcasted from a wireless multi-antenna access point to a network of single-antenna (sensor) nodes. Sensors exchange local messages to reach consensus on the transmitted message. Different from recent distributed detectors where the amount of information exchanges increases exponentially with the alphabet size (i.e., the number of hypotheses tested), the novel consensus-based approach introduced here relies on semi-definite relaxation techniques and can afford inter-sensor exchanges of polynomial order. The resultant near-optimum convexified problem is solved in a distributed fashion using the alternating direction method of multipliers. No constraint is imposed on the network topology so long as it remains fully connected. Preliminary simulations demonstrate the merits of the novel distributed detection algorithm.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128898024","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
Experimental Studies on Direction Finding Via Blind Beamforming 盲波束形成测向的实验研究
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498020
Jie Zhuo, Chao Sun, Yixin Yang
{"title":"Experimental Studies on Direction Finding Via Blind Beamforming","authors":"Jie Zhuo, Chao Sun, Yixin Yang","doi":"10.1109/CAMSAP.2007.4498020","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498020","url":null,"abstract":"Blind beamforming algorithms have the ability to recover the desired signals from array outputs without any prior knowledge of the array geometry. After blind separation of the signals, the DOAs can be estimated for each source individually. In this paper, the performance of the multistage constant modulus (CM) array, one of the most striking blind beamforming algorithms, for the source DOA estimation was analyzed via water tank experiments, and was compared to that of other DOA estimation algorithms including the 'non-blind' and the 'blind' algorithms. Results of water tank experiments showed that the multistage CM array can not only blindly recover the independent signal, but also correctly estimate the DOAs. The angle separating ability of the CM array was beyond the Rayleigh resolution limit.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126954731","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
Mimo Relaying for Multi-Point to Multi-Point Communication in Wireless Networks 无线网络中多点到多点通信的Mimo中继
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498004
B. K. Chalise, L. Vandendorpe, J. Louveaux
{"title":"Mimo Relaying for Multi-Point to Multi-Point Communication in Wireless Networks","authors":"B. K. Chalise, L. Vandendorpe, J. Louveaux","doi":"10.1109/CAMSAP.2007.4498004","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498004","url":null,"abstract":"In this paper, we propose a relaying scheme that uses a fixed multiple-input-multiple-output (MIMO) relay to improve the performance of the multi-point to multi-point communication in wireless networks. Under the assumption that the perfect channel state information (CSI) is known, we propose the MIMO relay which minimizes its total transmit power by satisfying the signal-to-interference-and-noise-ratio (SINR) requirement for all destinations. This paper shows that the aforementioned problem is non-convex but it can be relaxed to a convex problem using the semidefinite relaxation technique. Computer simulations show that the proposed MIMO relay outperforms the conventional all-pass MIMO relay.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126100142","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: 25
Bounded Error Estimation: A Chebyshev Center Approach 有界误差估计:一种Chebyshev中心方法
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498001
Y. Eldar, A. Beck, M. Teboulle
{"title":"Bounded Error Estimation: A Chebyshev Center Approach","authors":"Y. Eldar, A. Beck, M. Teboulle","doi":"10.1109/CAMSAP.2007.4498001","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498001","url":null,"abstract":"We develop a nonlinear minimax estimator for the classical linear regression model assuming that the true parameter vector lies in an intersection of ellipsoids. We seek an estimate that minimizes the worst-case estimation error over the given parameter set. Since this problem is intractable, we approximate it using semidefinite relaxation, and refer to the resulting estimate as the relaxed Chebyshev center (RCC). We then demonstrate through simulations that the RCC can significantly improve the estimation error over the conventional constrained least-squares method.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115649685","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
Bayesian Filtering on the Stiefel Manifold Stiefel流形上的贝叶斯滤波
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498015
Frank Tompkins, P. Wolfe
{"title":"Bayesian Filtering on the Stiefel Manifold","authors":"Frank Tompkins, P. Wolfe","doi":"10.1109/CAMSAP.2007.4498015","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498015","url":null,"abstract":"The Stiefel manifold comprises sets of orthonormal vectors in Euclidean space, and as such arises in a variety of contemporary statistical signal processing contexts. Here we consider the problem of estimating the state of a hidden Markov process evolving on this manifold, given noisy observations in the embedding Euclidean space. We describe an approach using sequential Monte Carlo methods, and provide simulation examples for several cases of interest. We also compare our framework to a recently proposed deterministic algorithm for mode tracking in a related context, and demonstrate superior tracking performance over a range of synthetic examples, albeit at a potentially higher computational cost.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127656267","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: 36
Cross-Channel Interference in Surveillance Radar Networks 监视雷达网络中的跨信道干扰
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497973
M. Greco, F. Gini, A. Farina
{"title":"Cross-Channel Interference in Surveillance Radar Networks","authors":"M. Greco, F. Gini, A. Farina","doi":"10.1109/CAMSAP.2007.4497973","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497973","url":null,"abstract":"In this paper we evaluate the impact of the presence of an interfering radar on the target direction of arrival (DOA) pseudo-monopulse and ML estimation performed by the reference radar. The importance of the use of codes in a radar network is highlighted in a simple scenario of two surveillance radars.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127705870","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 Particle Filtering Blind Equalization Algorithm for Frequency-Selective Mimo Channels with Unknown Noise Variance 噪声方差未知的选频Mimo信道粒子滤波盲均衡算法
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497967
C. Bordin, Marcelo G. S. Bruno
{"title":"A Particle Filtering Blind Equalization Algorithm for Frequency-Selective Mimo Channels with Unknown Noise Variance","authors":"C. Bordin, Marcelo G. S. Bruno","doi":"10.1109/CAMSAP.2007.4497967","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497967","url":null,"abstract":"This paper introduces a new fully Bayesian, particle-filter-based blind equalization algorithm for frequency-selective MIMO channels. By treating the noise variances observed by each receiver as unknown independent random variables, the proposed algorithm offers increased robustness in comparison to previous particle-filter-based methods that relied on the exact knowledge or on suboptimal estimates of those quantities. We also innovate by considering the use of convolutional codes for user separation in MIMO channels. Via numerical simulations, we verify that the proposed approach performs closely to the optimal (MAP) receiver based on the BCJR algorithm, outperforming a linear trained method for medium to low noise levels.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133289897","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
Fast Low-Rank Approximation for Covariance Matrices 协方差矩阵的快速低秩逼近
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498023
M. Belabbas, P. Wolfe
{"title":"Fast Low-Rank Approximation for Covariance Matrices","authors":"M. Belabbas, P. Wolfe","doi":"10.1109/CAMSAP.2007.4498023","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498023","url":null,"abstract":"Computing an efficient low-rank approximation of a given positive definite matrix is a ubiquitous task in statistical signal processing and numerical linear algebra. The optimal solution is well known and is given by the singular value decomposition; however, its complexity scales as the cube of the matrix dimension. Here we introduce a low-complexity alternative which approximates this optimal low-rank solution, together with a bound on its worst-case error. Our methodology also reveals a connection between the approximation of matrix products and Schur complements. We present simulation results that verify performance improvements relative to contemporary randomized algorithms for low-rank approximation.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122672386","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: 19
Incremental Robbins-Monro Gradient Algorithm for Regression in Sensor Networks 传感器网络中的增量罗宾斯-门罗梯度回归算法
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498027
S. Ram, V. Veeravalli, A. Nedić
{"title":"Incremental Robbins-Monro Gradient Algorithm for Regression in Sensor Networks","authors":"S. Ram, V. Veeravalli, A. Nedić","doi":"10.1109/CAMSAP.2007.4498027","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498027","url":null,"abstract":"We consider a network of sensors deployed to sense a spatial field for the purposes of parameter estimation. Each sensor makes a sequence of measurements that is corrupted by noise. The estimation problem is to determine the value of a parameter that minimizes a cost that is a function of the measurements and the unknown parameter. The cost function is such that it can be written as the sum of functions (one corresponding to each sensor), each of which is associated with one sensor's measurements. Such a cost function is of interest in regression. We are interested in solving the resulting optimization problem in a distributed and recursive manner. Towards this end, we combine the incremental gradient approach with the Robbins-Monro approximation algorithm to develop the incremental Robbins-Monro gradient (IRMG) algorithm. We investigate the convergence of the algorithm under a convexity assumption on the cost function and a stochastic model for the sensor measurements. In particular, we show that if the observations at each are independent and identically distributed, then the IRMG algorithm converges to the optimum solution almost surely as the number of observations goes to infinity. We emphasize that the IRMG algorithm itself requires no information about the stochastic model.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125934540","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
Antenna Selection Training in MIMO-OFDM/OFDMA Cellular Systems MIMO-OFDM/OFDMA蜂窝系统天线选择训练
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497978
N. Mehta, A. Molisch, Jinyun Zhang, E. Bala
{"title":"Antenna Selection Training in MIMO-OFDM/OFDMA Cellular Systems","authors":"N. Mehta, A. Molisch, Jinyun Zhang, E. Bala","doi":"10.1109/CAMSAP.2007.4497978","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497978","url":null,"abstract":"Antenna selection allows multiple-antenna systems to achieve most of their promised diversity gain, while keeping the number of RF chains and, thus, cost/complexity low. In this paper we investigate antenna selection for fourth-generation OFDMA- based cellular communications systems, in particular, 3GPP LTE (long-term evolution) systems. We propose a training method for antenna selection that is especially suitable for OFDMA. By means of simulation, we evaluate the SNR-gain that can be achieved with our design. We find that the performance depends on the bandwidth assigned to each user, the scheduling method (round-robin or frequency-domain scheduling), and the Doppler spread. Furthermore, the signal-to-noise ratio of the training sequence plays a critical role. Typical SNR gains are around 2 dB, with larger values obtainable in certain circumstances.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127125397","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: 11
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