Pub Date : 2006-07-10DOI: 10.1109/ICIF.2006.301769
N. Rao
We consider a class of simple, idealized plumes which are specified by a product of injection and distance decay terms. The plume propagates with a constant velocity, and its distance term decays exponentially with respect to distance in a planar region. If the intensity sensors are error-free, the difference triangulation method can identify the origin of plume both in time and space within a specified precision. In our case, the sensors are subject to random, correlated errors with unknown distributions in measuring the plume intensity. The sensors are available or in place to conduct controlled experiments and collect measurements. We present a training method that utilizes the plume equation together with controlled sensor measurements to identify the plume's origin with distribution-free probabilistic performance guarantees. The training consists of utilizing the measurements to compute a suitable precision value for the difference triangulation method to account for sensor distributions. We present a distribution-free relationship between the training sample size and the precision and probability with which plume's origin is identified
{"title":"Identification of Simple Product-Form Plumes Using Networks of Sensors With Random Errors","authors":"N. Rao","doi":"10.1109/ICIF.2006.301769","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301769","url":null,"abstract":"We consider a class of simple, idealized plumes which are specified by a product of injection and distance decay terms. The plume propagates with a constant velocity, and its distance term decays exponentially with respect to distance in a planar region. If the intensity sensors are error-free, the difference triangulation method can identify the origin of plume both in time and space within a specified precision. In our case, the sensors are subject to random, correlated errors with unknown distributions in measuring the plume intensity. The sensors are available or in place to conduct controlled experiments and collect measurements. We present a training method that utilizes the plume equation together with controlled sensor measurements to identify the plume's origin with distribution-free probabilistic performance guarantees. The training consists of utilizing the measurements to compute a suitable precision value for the difference triangulation method to account for sensor distributions. We present a distribution-free relationship between the training sample size and the precision and probability with which plume's origin is identified","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130120748","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301644
Tuwe Löfström, Rikard König, U. Johansson, L. Niklasson, Mattias Strand, T. Ziemke
In this paper a mapping between retail concepts and the JDL model is proposed. More specifically, the benefits of using solutions to military problems as inspiration to retail specific problems are discussed. The somewhat surprising conclusion is that there are several similarities between the military and retail domains, and that these similarities potentially could be exploited. A few examples of retail problems that could benefit from theories and techniques commonly used in the information fusion community are given. All examples are taken from recently started or planned projects within the information fusion research program at the University of Skovde, Sweden
{"title":"Benefits of relating the Retail Domain and Information Fusion","authors":"Tuwe Löfström, Rikard König, U. Johansson, L. Niklasson, Mattias Strand, T. Ziemke","doi":"10.1109/ICIF.2006.301644","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301644","url":null,"abstract":"In this paper a mapping between retail concepts and the JDL model is proposed. More specifically, the benefits of using solutions to military problems as inspiration to retail specific problems are discussed. The somewhat surprising conclusion is that there are several similarities between the military and retail domains, and that these similarities potentially could be exploited. A few examples of retail problems that could benefit from theories and techniques commonly used in the information fusion community are given. All examples are taken from recently started or planned projects within the information fusion research program at the University of Skovde, Sweden","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"14 2 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131134426","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301820
P. Arambel, Jeffrey Silver, Matthew E. Antone, T. Strat
Tracking ground moving objects using aerial video sensors is very challenging when the objects go through periods of occlusion caused by trees or buildings. If the occlusion interval is relatively large, there are confusing objects in the vicinity, or the object performs abrupt maneuvers while occluded, maintaining continuous tracks after the occlusion requires advanced exploitation of the imagery. This paper presents a signature-aided multiple hypothesis tracking system where signatures are extracted during periods of certainty and used after the occlusion to resolve association ambiguity. The discussion focuses on the interaction between the tracker and the signature extraction/exploitation module, as well as other tracking aspects within the signature-aided tracking paradigm
{"title":"Signature-Aided Air-to-Ground Video Tracking","authors":"P. Arambel, Jeffrey Silver, Matthew E. Antone, T. Strat","doi":"10.1109/ICIF.2006.301820","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301820","url":null,"abstract":"Tracking ground moving objects using aerial video sensors is very challenging when the objects go through periods of occlusion caused by trees or buildings. If the occlusion interval is relatively large, there are confusing objects in the vicinity, or the object performs abrupt maneuvers while occluded, maintaining continuous tracks after the occlusion requires advanced exploitation of the imagery. This paper presents a signature-aided multiple hypothesis tracking system where signatures are extracted during periods of certainty and used after the occlusion to resolve association ambiguity. The discussion focuses on the interaction between the tracker and the signature extraction/exploitation module, as well as other tracking aspects within the signature-aided tracking paradigm","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127817777","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301664
Md. Mahmudur Rahman, B. Desai, P. Bhattacharya
This paper presents a fusion-based similarity matching framework for content-based image retrieval on a combination of global, semi-global and local region specific features at different levels of abstraction. In this framework, an image is represented by global color and edge histogram descriptors, semi-global color and texture descriptors from grid based overlapping sub-images and local color features from a clustering-based segmented regions. As a result, image similarities are obtained through a weighted combination of overall similarity fusing global, semi-global and local region-based image level similarities. This fusing approach decreases the impact of inaccurate segmentation and increases retrieval effectiveness as constituent features are of a complementary nature. The experimental results on a general-purpose image database indicate that the aggregation or fusion-based technique provides an effective and flexible tool for similarity calculation based on a combination of descriptors from different levels of image representation
{"title":"A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval","authors":"Md. Mahmudur Rahman, B. Desai, P. Bhattacharya","doi":"10.1109/ICIF.2006.301664","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301664","url":null,"abstract":"This paper presents a fusion-based similarity matching framework for content-based image retrieval on a combination of global, semi-global and local region specific features at different levels of abstraction. In this framework, an image is represented by global color and edge histogram descriptors, semi-global color and texture descriptors from grid based overlapping sub-images and local color features from a clustering-based segmented regions. As a result, image similarities are obtained through a weighted combination of overall similarity fusing global, semi-global and local region-based image level similarities. This fusing approach decreases the impact of inaccurate segmentation and increases retrieval effectiveness as constituent features are of a complementary nature. The experimental results on a general-purpose image database indicate that the aggregation or fusion-based technique provides an effective and flexible tool for similarity calculation based on a combination of descriptors from different levels of image representation","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133219143","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301739
P. Willett, S. Coraluppi
The MLPDA is based on maximizing statistical likelihood according to a precise model in which there is no process noise. The PMHT (probabilistic multi-hypothesis tracker) provides an alternative perspective: each contact may be taken as independent and a-priori equally-equipped to be target-generated. Our results indicate that the MLPMHT is the better tracker in multi-static data. A further advantage of the MLPMHT is that optimal data association with multiple targets is easily incorporated, whereas in the MLPDA it is approximated by excision of measurements that are "taken" by previously-discovered targets. In this paper we apply the MLPMHT and MLPDAF to several data-sets from the MSTWG (multi-static tracking working group) library: two synthetic and two real ones from NURC, plus one from ARL/UT. We also compare the ML trackers to the IMMPDAFAI, a tracker with no "depth" to its assignments: it is found that the IMMPDAFAI is not able to track effectively in such noisy data. Finally, we report on a new genetic implementation of the MLPMHT
{"title":"MLPDA and MLPMHT Applied to Some MSTWG Data","authors":"P. Willett, S. Coraluppi","doi":"10.1109/ICIF.2006.301739","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301739","url":null,"abstract":"The MLPDA is based on maximizing statistical likelihood according to a precise model in which there is no process noise. The PMHT (probabilistic multi-hypothesis tracker) provides an alternative perspective: each contact may be taken as independent and a-priori equally-equipped to be target-generated. Our results indicate that the MLPMHT is the better tracker in multi-static data. A further advantage of the MLPMHT is that optimal data association with multiple targets is easily incorporated, whereas in the MLPDA it is approximated by excision of measurements that are \"taken\" by previously-discovered targets. In this paper we apply the MLPMHT and MLPDAF to several data-sets from the MSTWG (multi-static tracking working group) library: two synthetic and two real ones from NURC, plus one from ARL/UT. We also compare the ML trackers to the IMMPDAFAI, a tracker with no \"depth\" to its assignments: it is found that the IMMPDAFAI is not able to track effectively in such noisy data. Finally, we report on a new genetic implementation of the MLPMHT","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131894962","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301690
M. Xiang
In this paper, the problem of distributed detection under Neyman-Pearson criterion is considered. We assume that the observations of different sensors are conditionally dependent. First, an important property of the overall ROCs is investigated. Based on this property, necessary conditions for optimal fusion rule and sensor decision rules are then obtained. In the derivation of our optimality conditions, no assumption regarding the convexity of the overall ROC is assumed. Instead, we assume the differentiability of the overall ROCs. The method used here is straightforward, and the result obtained is clear and simple. Some relations between our results and the Lagrange method exist, and the implication of our results to the validity of Lagrange method is also investigated
{"title":"Optimization of Distributed Detection Systems under Neyman-Pearson Criterion","authors":"M. Xiang","doi":"10.1109/ICIF.2006.301690","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301690","url":null,"abstract":"In this paper, the problem of distributed detection under Neyman-Pearson criterion is considered. We assume that the observations of different sensors are conditionally dependent. First, an important property of the overall ROCs is investigated. Based on this property, necessary conditions for optimal fusion rule and sensor decision rules are then obtained. In the derivation of our optimality conditions, no assumption regarding the convexity of the overall ROC is assumed. Instead, we assume the differentiability of the overall ROCs. The method used here is straightforward, and the result obtained is clear and simple. Some relations between our results and the Lagrange method exist, and the implication of our results to the validity of Lagrange method is also investigated","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134317182","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301713
Francis Faux, F. Luthon
This paper describes a real time face detection and tracking system. The method consists in modelling the skin face by a pixel fusion process of three colour sources within the framework of the Demster-Shafer theory. The algorithm is composed of two phases. In a simple and fast initialising stage, the user selects successively in an image, a shadowy, an overexposed and a zone of mean intensity of the face. Then the fusion process models the face skin colour. Next, on the video sequence, a tracking phase uses the key idea that the face exterior edges are well approximated as an ellipse including the skin colour blob resulting from the fusion process. As ellipse detection gets easily disturbed in cluttered environments by edges caused by non-face objects, a simple and fast efficient least squares method for ellipse fitting is used. The ellipse parameters are taken into account by a stochastic algorithm using a particle filter in order to realise a robust face tracking in position, size and pose. The originality of the method consists in modelling the face skin by a pixel fusion process of three independant cognitive colour sources. Moreover, mass sets are determined from a priori models taking into account contextual variables specific to the face under study. Hence, the face specificity which is to present shadowy (neck) and overexposed zones (nose, front) is considered, so that sensitivity to lighting conditions decreases. Results of face skin modelling, fusion, ellipse fitting and tracking are illustrated and discussed in this paper. The limits of the method and future work are also commented in conclusion
{"title":"Robust face tracking using colour Dempster-Shafer fusion and particle filter","authors":"Francis Faux, F. Luthon","doi":"10.1109/ICIF.2006.301713","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301713","url":null,"abstract":"This paper describes a real time face detection and tracking system. The method consists in modelling the skin face by a pixel fusion process of three colour sources within the framework of the Demster-Shafer theory. The algorithm is composed of two phases. In a simple and fast initialising stage, the user selects successively in an image, a shadowy, an overexposed and a zone of mean intensity of the face. Then the fusion process models the face skin colour. Next, on the video sequence, a tracking phase uses the key idea that the face exterior edges are well approximated as an ellipse including the skin colour blob resulting from the fusion process. As ellipse detection gets easily disturbed in cluttered environments by edges caused by non-face objects, a simple and fast efficient least squares method for ellipse fitting is used. The ellipse parameters are taken into account by a stochastic algorithm using a particle filter in order to realise a robust face tracking in position, size and pose. The originality of the method consists in modelling the face skin by a pixel fusion process of three independant cognitive colour sources. Moreover, mass sets are determined from a priori models taking into account contextual variables specific to the face under study. Hence, the face specificity which is to present shadowy (neck) and overexposed zones (nose, front) is considered, so that sensitivity to lighting conditions decreases. Results of face skin modelling, fusion, ellipse fitting and tracking are illustrated and discussed in this paper. The limits of the method and future work are also commented in conclusion","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"70 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141204","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301722
D. Akselrod, A. Sinha, C. V. Goldman, T. Kirubarajan
Network-centric multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present a solution to one of the main problems of network-centric tracking, namely, decentralized information sharing among the platforms participating in the distributed data fusion. This paper presents a decision mechanism that provides each platform with the required data for the distributed data fusion process while reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogeneous, and potentially unreliable. The proposed approach, which is based on Markov decision processes and decentralized lookup substrate, will control the information exchange process based, among the other parameters, on tracking performance metrics of individual platforms, thereby enhancing the whole distributed system's reliability as well as that of each participating platform. Simulation examples demonstrate the operation and the performance results of the system
{"title":"Efficient Control of Information Flow for Distributed Multisensor Fusion Using Markov Decision Processes","authors":"D. Akselrod, A. Sinha, C. V. Goldman, T. Kirubarajan","doi":"10.1109/ICIF.2006.301722","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301722","url":null,"abstract":"Network-centric multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present a solution to one of the main problems of network-centric tracking, namely, decentralized information sharing among the platforms participating in the distributed data fusion. This paper presents a decision mechanism that provides each platform with the required data for the distributed data fusion process while reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogeneous, and potentially unreliable. The proposed approach, which is based on Markov decision processes and decentralized lookup substrate, will control the information exchange process based, among the other parameters, on tracking performance metrics of individual platforms, thereby enhancing the whole distributed system's reliability as well as that of each participating platform. Simulation examples demonstrate the operation and the performance results of the system","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786213","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301667
F. Flitti, C. Collet
This paper is concerned with multi-component image segmentation which plays an important role in many imagery applications. Unfortunately, we are faced with the Hughes phenomenon when the number of components increases, and a space dimensionality reduction is often carried out as a preprocessing step before segmentation. An interesting solution is the mixtures of latent variable models which recover clusters in the observation structure and establish a local linear mapping on a reduced dimension space for each cluster. Thus, a globally nonlinear model is obtained to reduce dimensionality. Furthermore, a likelihood to each local model is often available which allows a well formulation of the mixture model and a maximum likelihood based decision for the clustering task. However for D-component images classification, such clustering, based only on the distance between observations in the D-dimensional space is not adapted since it neglects the observation spatial locations in the image. We propose to use a Markov a priori associated with such models to regularize D-dimensional pixel classification. Thus segmentation and reduction are performed simultaneously. In this paper, we focus on the probabilistic principal component analysis (PPCA) as latent model, and the hidden Markov quad-tree (HMT) as a Markov a priori
{"title":"Markov Regularization of Mixture of Latent variable Models for Multi-component Image Unsupervised Joint Reduction/Segmentatin","authors":"F. Flitti, C. Collet","doi":"10.1109/ICIF.2006.301667","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301667","url":null,"abstract":"This paper is concerned with multi-component image segmentation which plays an important role in many imagery applications. Unfortunately, we are faced with the Hughes phenomenon when the number of components increases, and a space dimensionality reduction is often carried out as a preprocessing step before segmentation. An interesting solution is the mixtures of latent variable models which recover clusters in the observation structure and establish a local linear mapping on a reduced dimension space for each cluster. Thus, a globally nonlinear model is obtained to reduce dimensionality. Furthermore, a likelihood to each local model is often available which allows a well formulation of the mixture model and a maximum likelihood based decision for the clustering task. However for D-component images classification, such clustering, based only on the distance between observations in the D-dimensional space is not adapted since it neglects the observation spatial locations in the image. We propose to use a Markov a priori associated with such models to regularize D-dimensional pixel classification. Thus segmentation and reduction are performed simultaneously. In this paper, we focus on the probabilistic principal component analysis (PPCA) as latent model, and the hidden Markov quad-tree (HMT) as a Markov a priori","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123060396","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 : 2006-07-10DOI: 10.1109/ICIF.2006.301709
M. Vemula, M. Bugallo, P. Djurić
We propose a distributed algorithm for sensor localization using beacon nodes. In this algorithm, beacon nodes broadcast distributions which contain information about their location. Nearby sensor nodes with unknown location information use this transmitted information and received beacon signal characteristics to estimate their positions. Sensors that estimate their positions become new beacons. A Monte Carlo method known as importance sampling is used for fusing these distributions and for obtaining approximations of the posterior distributions of the sensor locations. We also compute the Bayesian Cramer-Rao bounds for self-localization of sensors and study the impact of the beacons' prior location information and other system parameters. We analyze the performance of the proposed algorithm through computer simulations and compare it with numerically obtained bounds
{"title":"Fusion of Information for Sensor Self-Localization by a Monte Carlo Method","authors":"M. Vemula, M. Bugallo, P. Djurić","doi":"10.1109/ICIF.2006.301709","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301709","url":null,"abstract":"We propose a distributed algorithm for sensor localization using beacon nodes. In this algorithm, beacon nodes broadcast distributions which contain information about their location. Nearby sensor nodes with unknown location information use this transmitted information and received beacon signal characteristics to estimate their positions. Sensors that estimate their positions become new beacons. A Monte Carlo method known as importance sampling is used for fusing these distributions and for obtaining approximations of the posterior distributions of the sensor locations. We also compute the Bayesian Cramer-Rao bounds for self-localization of sensors and study the impact of the beacons' prior location information and other system parameters. We analyze the performance of the proposed algorithm through computer simulations and compare it with numerically obtained bounds","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125472327","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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