Pub Date : 2005-12-01DOI: 10.1109/CAMAP.2005.1574180
Alejandro Ribeiro, Renqiu Wang, G. Giannakis
Commonly used protocols involving J cooperating communicators are based on repetition encoding and achieve diversity of order J with bandwidth efficiency 1/J. We introduce a protocol capable of achieving the same diversity with bandwidth efficiency essentially equal to 1/2. The protocol is based on linear complex-field coded (LCFC) relay transmissions over orthogonal frequency division multiplexed (OFDM) subcarriers. Cooperators provide diversity by repeating delayed versions of the original packet, thereby generating a frequency-selective multipath channel. The so enabled diversity is collected by standard LCFC-OFDM decoders. Analysis and corroborating simulations establish that the novel protocol achieves diversity order equal to the number of users.
{"title":"Linear complex-field coding for cooperative networking","authors":"Alejandro Ribeiro, Renqiu Wang, G. Giannakis","doi":"10.1109/CAMAP.2005.1574180","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574180","url":null,"abstract":"Commonly used protocols involving J cooperating communicators are based on repetition encoding and achieve diversity of order J with bandwidth efficiency 1/J. We introduce a protocol capable of achieving the same diversity with bandwidth efficiency essentially equal to 1/2. The protocol is based on linear complex-field coded (LCFC) relay transmissions over orthogonal frequency division multiplexed (OFDM) subcarriers. Cooperators provide diversity by repeating delayed versions of the original packet, thereby generating a frequency-selective multipath channel. The so enabled diversity is collected by standard LCFC-OFDM decoders. Analysis and corroborating simulations establish that the novel protocol achieves diversity order equal to the number of users.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"29 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":"124910799","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}
Pub Date : 2005-12-01DOI: 10.1109/CAMAP.2005.1574184
Georgios Latsoudas, N. Sidiropoulos
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.
{"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}
Pub Date : 2005-12-01DOI: 10.1109/CAMAP.2005.1574183
A. Scaglione, Y. Hong
What is the difference between classical remote sensing and sensor networks? What kind of data models that one can assume in the context of sensor networks? Can the sensors in the network concurrently contribute to the sensing objective, without creating network conflicts? It is becoming apparent that methodologies designed to resolve network resource allocation conflicts in the communications among open systems have several bottlenecks when applied to sustain networking among concurrent sensing nodes. Can we structure the network activities so that they are always directly beneficial to the sensing task? The goal of this paper is to articulate these questions and indicate how some resource allocation conflicts can be removed embracing collaborative networking approaches among the sensors.
{"title":"Cooperative models for synchronization, scheduling and transmission in large scale sensor networks: an overview","authors":"A. Scaglione, Y. Hong","doi":"10.1109/CAMAP.2005.1574183","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574183","url":null,"abstract":"What is the difference between classical remote sensing and sensor networks? What kind of data models that one can assume in the context of sensor networks? Can the sensors in the network concurrently contribute to the sensing objective, without creating network conflicts? It is becoming apparent that methodologies designed to resolve network resource allocation conflicts in the communications among open systems have several bottlenecks when applied to sustain networking among concurrent sensing nodes. Can we structure the network activities so that they are always directly beneficial to the sensing task? The goal of this paper is to articulate these questions and indicate how some resource allocation conflicts can be removed embracing collaborative networking approaches among the sensors.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"57 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":"133150116","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}
Pub Date : 2005-09-16DOI: 10.1109/CAMAP.2005.1574192
S. Godsill, J. Li, W. Ng
In this paper we propose methods for tracking multiple maneuvering objects using variable rate particle filters with multiple sensors. Unlike more standard approaches the proposed method assumes that the states change at different and unknown rates compared with the observation process, and hence is able to model parsimoniously the maneuvering behaviour of an object. Furthermore, a Poisson model is used to model both target and clutter measurements, avoiding the data association difficulties associated with traditional tracking approaches. Computer simulations demonstrate the potential of the proposed method for tracking highly maneuverable targets in a hostile environment with high clutter density.
{"title":"Multiple and extended object tracking with Poisson spatial processes and variable rate filters","authors":"S. Godsill, J. Li, W. Ng","doi":"10.1109/CAMAP.2005.1574192","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574192","url":null,"abstract":"In this paper we propose methods for tracking multiple maneuvering objects using variable rate particle filters with multiple sensors. Unlike more standard approaches the proposed method assumes that the states change at different and unknown rates compared with the observation process, and hence is able to model parsimoniously the maneuvering behaviour of an object. Furthermore, a Poisson model is used to model both target and clutter measurements, avoiding the data association difficulties associated with traditional tracking approaches. Computer simulations demonstrate the potential of the proposed method for tracking highly maneuverable targets in a hostile environment with high clutter density.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125266906","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}
Pub Date : 1900-01-01DOI: 10.1109/CAMAP.2005.1574168
S. Haykin
In my previous publication, I described, for the first time, the novel idea of cognitive radar, its attributes and potential applications. This article expands on one of the applications described therein - namely, cognitive radar networks. After briefly describing the constitution of this new radar system, we focus on the specific application of homeland security, for which a cognitive radar network is rather well suited.
{"title":"Cognitive radar networks","authors":"S. Haykin","doi":"10.1109/CAMAP.2005.1574168","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574168","url":null,"abstract":"In my previous publication, I described, for the first time, the novel idea of cognitive radar, its attributes and potential applications. This article expands on one of the applications described therein - namely, cognitive radar networks. After briefly describing the constitution of this new radar system, we focus on the specific application of homeland security, for which a cognitive radar network is rather well suited.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"6 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":"122478447","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}
Pub Date : 1900-01-01DOI: 10.1109/CAMAP.2005.1574189
R. Brcich, A. Zoubir
We consider the problem of global bandwidth optimisation and confidence interval estimation for spectral density estimates obtained by applying a nonparametric curve estimator to the periodogram. The use of a local quadratic regression smoother is examined as a possible way to reduce the bias inherent in classical kernel spectral density estimators, which are simply local mean regression smoothers. It is found that while quadratic smoothers are much less sensitive to a poor choice of bandwidth, they do not always outperform mean smoothers.
{"title":"Bootstrap based nonparametric curve and confidence band estimates for spectral densities","authors":"R. Brcich, A. Zoubir","doi":"10.1109/CAMAP.2005.1574189","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574189","url":null,"abstract":"We consider the problem of global bandwidth optimisation and confidence interval estimation for spectral density estimates obtained by applying a nonparametric curve estimator to the periodogram. The use of a local quadratic regression smoother is examined as a possible way to reduce the bias inherent in classical kernel spectral density estimators, which are simply local mean regression smoothers. It is found that while quadratic smoothers are much less sensitive to a poor choice of bandwidth, they do not always outperform mean smoothers.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"77 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":"127594018","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}
Pub Date : 1900-01-01DOI: 10.1109/CAMAP.2005.1574205
A. El-Keyi, T. Kirubarajan, A. Gershman
In this paper, we develop a state-space approach to the blind multiuser detection problem with robustness against arbitrary mismatches in the desired user signature. A state-space approach to the decision directed detection problem and an algorithm for switching between the two detection techniques are also presented. The proposed switching algorithm can achieve an output signal-to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training even in the presence of desired user signature mismatches
{"title":"A state-space approach to robust multiuser detection","authors":"A. El-Keyi, T. Kirubarajan, A. Gershman","doi":"10.1109/CAMAP.2005.1574205","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574205","url":null,"abstract":"In this paper, we develop a state-space approach to the blind multiuser detection problem with robustness against arbitrary mismatches in the desired user signature. A state-space approach to the decision directed detection problem and an algorithm for switching between the two detection techniques are also presented. The proposed switching algorithm can achieve an output signal-to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training even in the presence of desired user signature mismatches","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"36 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":"123456416","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}
Pub Date : 1900-01-01DOI: 10.1109/CAMAP.2005.1574177
M. Wicks, B. Himed, J. Bracken, H. Bascom, J. Clancy
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.
{"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}
Pub Date : 1900-01-01DOI: 10.1109/CAMAP.2005.1574187
I. Chudinovich, C. Constanda
A dual approximation method is developed for computing the displacements in bending of a plate with transverse shear deformation, in which the domain of the dual functional does not require restrictions on its elements even in the absence of an elastic foundation. This is important in the study of materials and structures used in the manufacturing and nondestructive testing of mechanical sensors for industrial containers.
{"title":"Dual methods for sensor testing of industrial containers. II. A nonclassical approach","authors":"I. Chudinovich, C. Constanda","doi":"10.1109/CAMAP.2005.1574187","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574187","url":null,"abstract":"A dual approximation method is developed for computing the displacements in bending of a plate with transverse shear deformation, in which the domain of the dual functional does not require restrictions on its elements even in the absence of an elastic foundation. This is important in the study of materials and structures used in the manufacturing and nondestructive testing of mechanical sensors for industrial containers.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"516 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":"123089193","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}
Pub Date : 1900-01-01DOI: 10.1109/CAMAP.2005.1574194
S. Vorobyov, Yonina C. Eldar, A. Nemirovski, A. Gershman
The problem of estimating a random signal vector x observed through a linear transformation H and corrupted by an additive noise is considered. A linear estimator that minimizes the mean squared error (MSE) with a certain selected probability is derived under the assumption that both the additive noise and random signal vectors are zero mean Gaussian with known covariance matrices. Our approach can be viewed as a robust generalization of the Wiener filter. It simplifies to the recently proposed robust minimax estimator in some special cases.
{"title":"Probability-constrained approach to estimation of random Gaussian parameters","authors":"S. Vorobyov, Yonina C. Eldar, A. Nemirovski, A. Gershman","doi":"10.1109/CAMAP.2005.1574194","DOIUrl":"https://doi.org/10.1109/CAMAP.2005.1574194","url":null,"abstract":"The problem of estimating a random signal vector x observed through a linear transformation H and corrupted by an additive noise is considered. A linear estimator that minimizes the mean squared error (MSE) with a certain selected probability is derived under the assumption that both the additive noise and random signal vectors are zero mean Gaussian with known covariance matrices. Our approach can be viewed as a robust generalization of the Wiener filter. It simplifies to the recently proposed robust minimax estimator in some special cases.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"23 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":"123657080","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}
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