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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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Multi-Channel Parametric Estimator Fast Block Matrix Inverses 多通道参数估计快速块矩阵逆
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-04-15 DOI: 10.1109/ICASSP.2007.366441
S. Marple, P. Corbell, M. Rangaswamy
{"title":"Multi-Channel Parametric Estimator Fast Block Matrix Inverses","authors":"S. Marple, P. Corbell, M. Rangaswamy","doi":"10.1109/ICASSP.2007.366441","DOIUrl":"https://doi.org/10.1109/ICASSP.2007.366441","url":null,"abstract":"The optimal (adaptive) linear combiner (beamformer) weights for a sensor array are expressed in terms of the inverse of the multi-channel (MC) covariance matrix. Also, minimum variance (Capon) spectral estimators of the sensor array also depend on the same inverse. Rather than form an estimate of the covariance matrix directly from the available data and inverting it, an alternative direct estimate of the inverse may be obtained by forming parametric MC linear prediction estimates and then expressing the inverse in terms of these parametric MC estimates. The resulting parametric estimate of the inverse is typically more accurate than inverting the estimate of the covariance matrix. This paper reveals the structure of the the inverse of the covariance matrix for the MC version of the covariance least squares linear prediction algorithm. The inverse structure involves products of triangular block MC Toeplitz matrices, which leads to fast computational solutions. An example of a fast MC minimum variance spectral estimator illustrates this exploitation.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129576105","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
Robust Multi-Antenna Broadcasting with Imperfect Channel State Information 信道状态信息不完全的鲁棒多天线广播
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 1900-01-01 DOI: 10.1109/CAMSAP.2007.4498003
I. Wajid, A. Gershman, S.A. Vowbyov, Y.A. Karanouh
{"title":"Robust Multi-Antenna Broadcasting with Imperfect Channel State Information","authors":"I. Wajid, A. Gershman, S.A. Vowbyov, Y.A. Karanouh","doi":"10.1109/CAMSAP.2007.4498003","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498003","url":null,"abstract":"The problem of robust multi-antenna broadcasting is considered in the case of erroneous channel state information (CSI) at the transmitter. The criterion of minimum transmission power is used subject to the constraints guaranteeing that the signal-to-noise ratio (SNR) of each intended receiver satisfies a prescribed quality of service (QoS) requirement for the worst-case norm-bounded CSI mismatch. The resulting robust broadcasting problem is non-convex and, therefore, is difficult to solve. However, a suitable semi-definite relaxation (SDR) of this problem is proposed that enables to transform it to a convex form and to compute the optimal transmitter weight vector.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125634674","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
Basis Expansion Adaptive Filters for Time-Varying System Identification 时变系统辨识的基扩展自适应滤波器
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 1900-01-01 DOI: 10.1109/CAMSAP.2007.4497988
L. Rugini, G. Leus
{"title":"Basis Expansion Adaptive Filters for Time-Varying System Identification","authors":"L. Rugini, G. Leus","doi":"10.1109/CAMSAP.2007.4497988","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497988","url":null,"abstract":"In this paper, we extend the concept of block adaptive filters to what we call basis expansion adaptive filters. While in block adaptive filters the system is assumed to be constant within a block, our basis expansion adaptive filters model the time variation of the system within a block by a set of basis functions. This allows us to improve the tracking performance of block adaptive filters considerably. We focus on stochastic gradient type of adaptive filters, although extensions to other types of adaptive filters can be envisioned.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123473000","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
Determining the Number of Propagation Paths from Broadband Mimo Measurements via Bootstrapped Likelihoods and the False Discovery Rate Criterion - Part I: Methodology 通过自举似然和错误发现率标准确定宽带Mimo测量的传播路径数-第一部分:方法
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 1900-01-01 DOI: 10.1109/camsap.2007.4497964
Pei-Jung Chung, D. Maiwald, N. Czink, C. Mecklenbrauker, B. Fleury
{"title":"Determining the Number of Propagation Paths from Broadband Mimo Measurements via Bootstrapped Likelihoods and the False Discovery Rate Criterion - Part I: Methodology","authors":"Pei-Jung Chung, D. Maiwald, N. Czink, C. Mecklenbrauker, B. Fleury","doi":"10.1109/camsap.2007.4497964","DOIUrl":"https://doi.org/10.1109/camsap.2007.4497964","url":null,"abstract":"In this paper, we propose a multiple hypotheses test for determining the number of propagation paths from broadband MIMO channel measurements. For this test, maximum-likelihood (ML) estimates for propagation delay, direction of arrival, direction of departure, and Doppler shifts are required for each potential number of propagation paths. The ML-estimator is implemented via a variant of the space alternating generalized expectation-maximization (SAGE) algorithm. The proposed test is based on the Benjamini- Hochberg procedure for guaranteeing a false discovery rate and employs the simple bootstrap approach for approximating the required p-values for the multiple test. In a companion paper, we apply the proposed test to real broadband MIMO antenna array measurements and discuss its performance.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"505 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116557644","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
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