1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.最新文献_第6页

Linear complex-field coding for cooperative networking 合作网络的线性复域编码

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 2005-12-01 DOI: 10.1109/CAMAP.2005.1574180

Alejandro Ribeiro, Renqiu Wang, G. Giannakis

引用次数: 0

A two-stage fastmap-MDS approach for node localization in sensor networks 传感器网络节点定位的两阶段快速地图- mds方法

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 2005-12-01 DOI: 10.1109/CAMAP.2005.1574184

Georgios Latsoudas, N. Sidiropoulos

{"title":"A two-stage fastmap-MDS approach for node localization in sensor networks","authors":"Georgios Latsoudas, N. Sidiropoulos","doi":"10.1109/CAMAP.2005.1574184","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574184","url":null,"abstract":"Given a set of pairwise distance estimates between nodes, it is often of interest to generate a map of node locations. This is an old problem that has attracted renewed interest in the signal processing community, due to the recent emergence of wireless sensor networks and ad-hoc networks. Sensor maps are useful for estimating the spatial distribution of measured phenomena, as well as for routing purposes. Both centralized and decentralized solutions have been developed, along with ways to cope with missing data, accounting for the reliability of individual measurements, etc. We revisit the basic version of the problem, and propose a two-stage algorithm that combines algebraic initialization and gradient descent. In particular, we borrow an algebraic solution from the database literature and adapt it to the sensor network context, using a specific choice of anchor/pivot nodes. The resulting estimates are fed to gradient descent iteration. The overall algorithm offers better performance at lower complexity than existing centralized full-connectivity solutions. Also, its performance is relatively close to the corresponding Cramer-Rao bound, especially for small values of range error variance.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133084732","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

Cooperative models for synchronization, scheduling and transmission in large scale sensor networks: an overview 大规模传感器网络中同步、调度和传输的协作模型综述

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 2005-12-01 DOI: 10.1109/CAMAP.2005.1574183

A. Scaglione, Y. Hong

引用次数: 13

Multiple and extended object tracking with Poisson spatial processes and variable rate filters 基于泊松空间过程和可变速率滤波器的多目标和扩展目标跟踪

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 2005-09-16 DOI: 10.1109/CAMAP.2005.1574192

S. Godsill, J. Li, W. Ng

引用次数: 12

Cognitive radar networks 认知雷达网络

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 1900-01-01 DOI: 10.1109/CAMAP.2005.1574168

S. Haykin

引用次数: 14

Bootstrap based nonparametric curve and confidence band estimates for spectral densities 基于自举法的非参数曲线和谱密度置信带估计

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 1900-01-01 DOI: 10.1109/CAMAP.2005.1574189

R. Brcich, A. Zoubir

引用次数: 1

A state-space approach to robust multiuser detection 鲁棒多用户检测的状态空间方法

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 1900-01-01 DOI: 10.1109/CAMAP.2005.1574205

A. El-Keyi, T. Kirubarajan, A. Gershman

引用次数: 0

Ultra narrow band adaptive tomographic radar 超窄带自适应层析雷达

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 1900-01-01 DOI: 10.1109/CAMAP.2005.1574177

M. Wicks, B. Himed, J. Bracken, H. Bascom, J. Clancy

{"title":"Ultra narrow band adaptive tomographic radar","authors":"M. Wicks, B. Himed, J. Bracken, H. Bascom, J. Clancy","doi":"10.1109/CAMAP.2005.1574177","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574177","url":null,"abstract":"This paper addresses the issue of spatial diversity in radar applications. Typically, information concerning ground and air targets is obtained via monostatic radar. Increased information is often equated with increased bandwidth in these radar systems. However, geometric diversity obtained through multistatic radar operations also affords the user the opportunity to obtain additional information concerning threat targets. With the appropriate signal processing, this translates directly into increased probability of detection and reduced probability of false alarm. In the extreme case, only discrete ultra narrow band (UNB) frequencies of operation may be available for both commercial and military applications. With limited spectrum, UNB in the limiting case, the need for geometric diversity becomes imperative. This occurs because the electromagnetic spectrum available for commercial and military radar applications is continuously being eroded while the need for increased information via radio frequency (RF) detection of threat targets is increasing. In addition, geometric diversity improves target position accuracy and image resolution, which would otherwise remain unavailable with monostatic radar.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"146 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":"125868509","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: 59

Dual methods for sensor testing of industrial containers. II. A nonclassical approach 工业容器传感器试验的双重方法。2非经典方法

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 1900-01-01 DOI: 10.1109/CAMAP.2005.1574187

I. Chudinovich, C. Constanda

引用次数: 0

Probability-constrained approach to estimation of random Gaussian parameters 随机高斯参数估计的概率约束方法

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 1900-01-01 DOI: 10.1109/CAMAP.2005.1574194

S. Vorobyov, Yonina C. Eldar, A. Nemirovski, A. Gershman

引用次数: 9