发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Global D.C. Optimization for Multi-User Interference Systems 多用户干扰系统的全局直流优化
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498002
Yang Xu, T. Le-Ngoc
{"title":"Global D.C. Optimization for Multi-User Interference Systems","authors":"Yang Xu, T. Le-Ngoc","doi":"10.1109/CAMSAP.2007.4498002","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498002","url":null,"abstract":"The weighted sum capacity of a Gaussian interference system is a nonconvex function of transmit power allocation vector of all users in the system. This paper shows that, by representing its objective function as a difference of two convex functions (d.c.), the non-convex optimization problem can be converted into an equivalent d.c. global optimization problem, which can be solved efficiently by various developed d.c. algorithms. In particular, a modified prismatic branch -and-bound algorithm that only requires solving a sequence of linear programming sub-problems, is introduced to find the global optimum. Simulation results in wireless flat-fading channel show that the proposed global d.c. optimization formulation outperforms considerably the local optimization methods in terms of achievable ergodic sum-rate capacity.","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":"127772509","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
A Lower Bound for Sequential Estimators 序列估计量的下界
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498019
G. Bouleux, R. Boyer
{"title":"A Lower Bound for Sequential Estimators","authors":"G. Bouleux, R. Boyer","doi":"10.1109/CAMSAP.2007.4498019","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498019","url":null,"abstract":"A popular class of parameter estimation method is based on a sequential/iterative scheme. In this framework, each component is estimated one by one and at each iteration the underlying model is based on the estimation of a single component corrupted by a structured interference (the other components) and by an unstructured Gaussian noise. So, in the context of the bearing estimation problem, we derive the deterministic Cramer-Rao Bound, called Interfering CRB (I-CRB), associated with this model. In particular, we show that for low Interference to Noise Ratio (INR), the I-CRB reaches the CRB for a single component (without structured interference). Inversely, for high INR, the I-CRB is equal to the Prior-CRB where we assume the exact knowledge of the structured interference. In addition, we show that in the closely-spaced bearings, the I-CRB has two typical regimes depending of the INR.","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":"127011650","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
Implementation of Batch-Based Particle Filters for Multi-Sensor Tracking 基于批处理的多传感器跟踪粒子滤波的实现
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498014
R. Velmurugan, V. Cevher, J. McClellan
{"title":"Implementation of Batch-Based Particle Filters for Multi-Sensor Tracking","authors":"R. Velmurugan, V. Cevher, J. McClellan","doi":"10.1109/CAMSAP.2007.4498014","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498014","url":null,"abstract":"In this paper, we demonstrate fixed-point FPGA implementations of state space systems using Particle Filters, especially multi-target bearing and range tracking systems. These trackers operate either as independent organic trackers or as a joint tracker to estimate a moving target's state in the x-y plane. For the efficiency of the particle filter, we consider factorized posterior approximations based on the Laplacian approximation, which uses a Newton-Raphson search. We delineate the computation and memory resources needed for real-time performance of the range and bearing particle filter trackers. Our implementations are demonstrated using the Xilinx System Generator. As part of the FPGA implementation, a floating-point, soft- and hard-core implementation of the Newton search algorithm is also developed.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"48 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":"117224136","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
Adaptive Orthogonal Beamforming for the Mimo Broadcast Channel Mimo广播信道的自适应正交波束形成
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497969
J. Duplicy, D. Palomar, L. Vandendorpe
{"title":"Adaptive Orthogonal Beamforming for the Mimo Broadcast Channel","authors":"J. Duplicy, D. Palomar, L. Vandendorpe","doi":"10.1109/CAMSAP.2007.4497969","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497969","url":null,"abstract":"This paper addresses both the user selection and the linear precoding design of a MIMO broadcast channel where the number of users is assumed to be greater than the number of transmit antennas. The optimization criterion considered is the sum-rate. A greedy algorithm tackling jointly the two issues is developed. It sequentially adds new users so as their beamformers build up an orthonormal basis which is optimized with the help of the channel knowledge assumed to be exhaustive. This solution benefits asymptotically (in the limit of a large number of users) from the full multiplexing and multiuser diversity gains alike the capacity achieving dirty paper coding. Moreover, it exhibits better results than existing schemes and offers some advantages resulting from the choice of an orthogonal structure.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"11 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":"131812933","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
Adaptive Sensing of Dynamic Target State in Heavy Sea Clutter 海杂波环境下动态目标状态的自适应感知
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497952
Y. Li, S. P. Sira, B. Moran, S. Suvorova, D. Cochran, D. Morrell, A. Papandreou-Suppappola
{"title":"Adaptive Sensing of Dynamic Target State in Heavy Sea Clutter","authors":"Y. Li, S. P. Sira, B. Moran, S. Suvorova, D. Cochran, D. Morrell, A. Papandreou-Suppappola","doi":"10.1109/CAMSAP.2007.4497952","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497952","url":null,"abstract":"We propose an adaptive estimation method for the spatio- temporal covariance matrix of sea clutter. The motivation is to enable adaptive detection approaches that rely on accurate estimation of this matrix. The method involves vectorization of the equations for the dynamical system model governing the temporal evolution of the clutter matrix followed by a multiple particle filtering approach to deal with the high dimensionality of the formulation. The estimated sea clutter covariance matrix is applied to the problem of detection of a small target in heavy clutter; effectiveness is demonstrated via simulations.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"45 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":"127403396","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
Approximation Bound for Semidefinite Relaxation Based Multicast Transmit Beamforming 基于半定松弛的组播发射波束形成的逼近界
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497998
Tsung-Hui Chang, Z. Luo, Chong-Yung Chi
{"title":"Approximation Bound for Semidefinite Relaxation Based Multicast Transmit Beamforming","authors":"Tsung-Hui Chang, Z. Luo, Chong-Yung Chi","doi":"10.1109/CAMSAP.2007.4497998","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497998","url":null,"abstract":"The max-min-fair transmit beamforming problem in multigroup broadcasting has been shown to be NP-hard in general. Recently, a polynomial time approximation approach based on semidefinite relaxation (SDR) has been proposed [1]. It was found [1], through computer simulations, that this method is capable of giving a good approximate solution in polynomial time. This paper shows that the SDR based approach can guarantee as least an 0(1/M) approximation quality, where M is the total number of receivers. The existence of such a data independent bound certifies the worst case approximation quality of the SDR algorithm for any problem instance and any number of transmit antennas.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"29 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":"121893483","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
Integrated Imaging and Inversion of Multi-Physics Data for Exploration Geopysics Applications 多物理场数据综合成像反演在勘探地球物理中的应用
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497992
W. Hu, A. Abubakar, T. Habashy
{"title":"Integrated Imaging and Inversion of Multi-Physics Data for Exploration Geopysics Applications","authors":"W. Hu, A. Abubakar, T. Habashy","doi":"10.1109/CAMSAP.2007.4497992","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497992","url":null,"abstract":"We present a multi-physics frequency-domain data inversion method for large-scale problems in reservoir evaluation applications, hi this work, the seismic data and the marine controlled-source electromagnetic (CSEM) data inversion algorithms were combined through a constraint that enforces the structural similarity between the conductivity image and the P-wave velocity image. In this work, the inverse algorithm that we develop is based on a regularized Gauss-Newton approach. We employ the multiplicative regularization to automatically choose the regularization parameters. The weighted L2-norm is used to reconstruct structures with sharp boundaries. According to the simulation results, the joint inversion algorithm based on the cross-gradient constraint shows significant improvement over the regular separate CSEM or seismic inversion. This joint inversion algorithm can be used not only in the integration of the marine CSEM and seismic survey data for the sub-sea exploration applications, but also for the joint inversion of the cross-well electromagnetic and the cross-well seismic to obtain the structural information.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"24 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":"132050466","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
Expected likelihood estimation: Asymptotic properties for "stochastic" complex Gaussian models 期望似然估计:“随机”复高斯模型的渐近性质
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4497958
Y. Abramovich, B.A. Johnson
{"title":"Expected likelihood estimation: Asymptotic properties for \"stochastic\" complex Gaussian models","authors":"Y. Abramovich, B.A. Johnson","doi":"10.1109/CAMSAP.2007.4497958","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4497958","url":null,"abstract":"Expected likelihood estimation allows for the \"quality assessment\" of potential parameter estimates based on the likelihood ratio (LR) of the covariance matrix model constructed with parameter estimates. A solution is considered acceptable and further iterative refinement of the estimation process is terminated when the observed LR is statistically as good as the LR of the unknown true solution. We derive the asymptotic performance of expected likelihood and show it has a larger average error than the Cramer-Rao bound and is therefore not technically efficient. However, the degradation in the error is fixed, relatively small, and a function of the dimension of the data vector M, so expected likelihood can be used to impose useful statistical bounds on the likelihood function (LF) value.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"60 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":"132572372","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
Robust Autonomous Detection of the Faulty Sensors of a Sensor Array 传感器阵列故障传感器的鲁棒自主检测
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498008
Siddhartha Ghosh, A. Freitas, I. Marshall
{"title":"Robust Autonomous Detection of the Faulty Sensors of a Sensor Array","authors":"Siddhartha Ghosh, A. Freitas, I. Marshall","doi":"10.1109/CAMSAP.2007.4498008","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498008","url":null,"abstract":"We propose a technique for the autonomous detection of the faulty sensors of a sensor array that are aberrant relative to the rest. Our approach is based on probabilistically modeling the distribution of the differences between the sensor measurements as a mixture of Gaussians and then classifying further instances of the sensor differences using a naive Bayes classifier. We demonstrate the applicability of this technique to the diagnosis of the sensors/photosites of a CCD array, using sensor array data comprising of randomly selected images. Our technique performs well for different combinations of parameter settings at the detection of the faulty photosites of a CCD array.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"52 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":"116090423","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
A Collaborative Training Algorithm for Multi-Sensor Adaptive Processing 多传感器自适应处理的协同训练算法
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Pub Date : 2007-12-01 DOI: 10.1109/CAMSAP.2007.4498024
Joel B. Predd, Sanjeev R. Kulkarni, H. V. Poor
{"title":"A Collaborative Training Algorithm for Multi-Sensor Adaptive Processing","authors":"Joel B. Predd, Sanjeev R. Kulkarni, H. V. Poor","doi":"10.1109/CAMSAP.2007.4498024","DOIUrl":"https://doi.org/10.1109/CAMSAP.2007.4498024","url":null,"abstract":"In this paper, we discuss a local message passing algorithm for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is constructed to solve a relaxation of the classical centralized kernel- linear least-squares regression problem. A statistical analysis shows that the generalization error afforded agents by the collaborative training algorithm can be bounded in terms of the relationship between the network topology and the representational capacity of the relevant reproducing kernel Hilbert space; this is in contrast to related approaches which relate the similarity structure encoded in the kernel and the network topology. The algorithm is relevant to the problem of distributed learning in wireless sensor networks by virtue of its exploitation of local communication.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"12 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":"122302579","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信