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2010 13th International Conference on Information Fusion最新文献

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The Sliced Gaussian Mixture Filter with adaptive state decomposition depending on linearization error 基于线性化误差自适应状态分解的切片高斯混合滤波器
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5711864
Vesa Klumpp, F. Beutler, U. Hanebeck, D. Fränken
{"title":"The Sliced Gaussian Mixture Filter with adaptive state decomposition depending on linearization error","authors":"Vesa Klumpp, F. Beutler, U. Hanebeck, D. Fränken","doi":"10.1109/ICIF.2010.5711864","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5711864","url":null,"abstract":"In this paper, a novel nonlinear/nonlinear model decomposition for the Sliced Gaussian Mixture Filter is presented. Based on the level of nonlin-earity of the model, the overall estimation problem is decomposed into a \"severely\" nonlinear and a \"slightly\" nonlinear part, which are processed by different estimation techniques. To further improve the efficiency of the estimator, an adaptive state decomposition algorithm is introduced that allows decomposition according to the linearization error for nonlinear system and measurement models. Simulations show that this approach has orders of magnitude less complexity compared to other state of the art estimators, while maintaining comparable estimation errors.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582118","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
Wireless Sensor Networks for data acquisition and information fusion: A case study 用于数据采集和信息融合的无线传感器网络:一个案例研究
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5712035
D. I. Tapia, S. Rodríguez, J. Bajo, J. Corchado, Ó. García
{"title":"Wireless Sensor Networks for data acquisition and information fusion: A case study","authors":"D. I. Tapia, S. Rodríguez, J. Bajo, J. Corchado, Ó. García","doi":"10.1109/ICIF.2010.5712035","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5712035","url":null,"abstract":"Data acquisition is one of the most relevant aspects in tele-monitoring systems. Information fusion helps these systems to better unify data collected from different sources. This paper presents a case study that consists of a tele-monitoring system aimed at enhancing remote healthcare for dependent people at their homes. The system deploys a service-oriented architecture over a heterogeneous Wireless Sensor Networks infrastructure to create smart environments. Such architecture can be executed over multiple wireless devices independently of their microcontroller or the programming language they use. Furthermore, the system allows the interconnection of several networks from different wireless technologies, such as ZigBee or Bluetooth. This approach provides the system better flexibility to change its functionalities and components after deployment than other analyzed proposals. The system description, its architecture, and preliminary results of the system prototype implemented in a real environment are presented.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908206","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: 11
Distributed registration of a network of asynchronous sensors 分布式注册的一个异步传感器网络
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5712002
E. H. Aoki, Marcelo G. S. Bruno
{"title":"Distributed registration of a network of asynchronous sensors","authors":"E. H. Aoki, Marcelo G. S. Bruno","doi":"10.1109/ICIF.2010.5712002","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5712002","url":null,"abstract":"Registration of multiple sensors through common targets of opportunity is an extensively studied problem. The majority of proposed methods for computationally efficient estimation of sensor biases considered only the case of synchronous sensors. The relatively recent EXX method, however, allows exact estimation (under certain conditions) of sensor biases of asynchronous sensors. Unfortunately, the EXX method requires all measurements (or pseudomea-surements) originated by the targets of opportunity, which implies in high communication costs for large networks of sensors. In this paper, we formulate an extension of the EXX method that can be used for distributed bias estimation, i.e. obtains exact joint bias estimates for the entire network of sensors from joint bias estimates from subsets of these sensors. The proposed method can also be hierarchized in any manner, and can work with dissimilar sensors and different forms of sensor biases, thus being highly suited for today's demands of distributed data fusion.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131848126","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
α-Discounting Method for Multi-Criteria Decision Making (α-D MCDM) 多准则决策的α-折现法(α-D MCDM)
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5712044
F. Smarandache
{"title":"α-Discounting Method for Multi-Criteria Decision Making (α-D MCDM)","authors":"F. Smarandache","doi":"10.1109/ICIF.2010.5712044","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5712044","url":null,"abstract":"In this paper we introduce a new procedure called α-Discounting Method for Multi-Criteria Decision Making (α-D MCDM), which is as an alternative and extension of Saaty's Analytical Hierarchy Process (AHP). It works for any set of preferences that can be transformed into a system of homogeneous linear equations. A degree of consistency (and implicitly a degree of inconsistency) of a decision-making problem are defined. α-D MCDM is generalized to a set of preferences that can be transformed into a system of linear and/or nonlinear homogeneous and/or non-homogeneous equations and/or inequalities. Consistent, weak consistent, and strong consistent examples are presented in the sequel for linear and non-linear decision making problems.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134173382","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: 26
A novel interacting multiple model method for nonlinear target tracking 一种非线性目标跟踪的多模型相互作用新方法
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5712021
S. Gadsden, S. Habibi, T. Kirubarajan
{"title":"A novel interacting multiple model method for nonlinear target tracking","authors":"S. Gadsden, S. Habibi, T. Kirubarajan","doi":"10.1109/ICIF.2010.5712021","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5712021","url":null,"abstract":"The state estimation of targets is a difficult task, particularly if the target exhibits nonlinear behaviour, which is often the case. Currently, the most popular filters used in target tracking are the Kalman filter (KF) and its various forms, as well as the particle filter (PF). Introduced in 2007, the smooth variable structure filter (SVSF) is a relatively new predictor-corrector method based on sliding mode estimation. In the past, this filter has been used successfully for the state and parameter estimation of mechanical and electrical systems for the purpose of control. This paper introduces a new interacting multiple model (IMM) method that makes use of the SVSF estimation strategy. An air traffic control (ATC) problem is used to compare the common EKF-IMM with the proposed SVSF-IMM in terms of tracking accuracy, robustness, and computational complexity. Furthermore, this paper demonstrates that the SVSF is an effective method for nonlinear target tracking.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134338780","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: 17
System bounds for multisensor fusion with intermittent observations 间歇观测下多传感器融合的系统边界
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5712032
Robert Distel
{"title":"System bounds for multisensor fusion with intermittent observations","authors":"Robert Distel","doi":"10.1109/ICIF.2010.5712032","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5712032","url":null,"abstract":"A closed form solution for an upper bound to the multi sensor Riccati equation with intermittent observations is derived. This solution is then used as a means to analyse system performance when the message loss process is a function of the number of sensors and to determine a simple analytic closed form method to determine when a message loss process causes a measure of the covariance to decrease as more sensors are added.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133806193","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
Cross-entropy method for K-best dependent-target data association hypothesis selection k -最优相关目标数据关联假设选择的交叉熵方法
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5712018
S. Mori, C. Chong
{"title":"Cross-entropy method for K-best dependent-target data association hypothesis selection","authors":"S. Mori, C. Chong","doi":"10.1109/ICIF.2010.5712018","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5712018","url":null,"abstract":"This paper is concerned with probabilistic evaluation of multiple-frame data association hypotheses in multiple-target tracking problems, in particular, when targets are not necessarily independent a priori. Multiple-target tracking problems with dependent targets naturally arise whenever targets interact with each other, as they move in congested traffic, or as they actively coordinate their movements in other situations. This paper develops a Bayesian data association hypothesis evaluation formula for dependent targets. Because the resulting formula does not have a multiplicative or log-linear form, the best hypothesis cannot be selected by integer linear programming or multi-dimensional assignment algorithms commonly used to solve data association problems in multiple target tracking. Instead, we propose to use Reuven Rubinstein's cross-entropy method as a possible solution. A K-best hypothesis selection extension will be discussed as an application of the generalized Murty's algorithm. This paper focuses on the theoretical aspects as the first step of a solution concept development.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130328127","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 multiframe assignment algorithm for single sensor bearings-only tracking 单传感器方位跟踪的多帧分配算法
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5711836
T. Sathyan, A. Sinha, M. Mallick
{"title":"A multiframe assignment algorithm for single sensor bearings-only tracking","authors":"T. Sathyan, A. Sinha, M. Mallick","doi":"10.1109/ICIF.2010.5711836","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5711836","url":null,"abstract":"Bearings-only tracking (BOT) using a single maneuvering platform has been studied extensively in the past. However, only a few studies exist in the open literature that deal with measurement origin uncertainty. Most publications are concerned with finding the best filtering approach, since BOT is inherently nonlinear, or finding the optimal maneuver strategy for the sensor platform to improve observability. We consider measurement origin uncertainty due to the existence of multiple targets in the surveillance region, non-unity detection probability, and false alarms. Our algorithm uses the multiframe assignment (MFA) to solve the data association problem, and filtering is performed using the unscented Kalman filter (UKF). We employ both the modified and log polar coordinate systems. Simulation results show that the proposed algorithm is very effective in terms of tracking accuracy and track maintenance capability, especially when formulated in the log polar coordinate system.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130373416","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
Predicting an epidemic based on syndromic surveillance 根据症状监测预测流行病
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5711847
A. Skvortsov, B. Ristic, C. Woodruff
{"title":"Predicting an epidemic based on syndromic surveillance","authors":"A. Skvortsov, B. Ristic, C. Woodruff","doi":"10.1109/ICIF.2010.5711847","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5711847","url":null,"abstract":"Early detection and prediction of the size and the peak time of an epidemic outbreak (malicious or natural) is of crucial importance for a timely medical response (quarantine, vaccination, etc). A conventional approach to this problem is based on large scale agent-based computer simulations. This paper proposes an alternative framework formulated in the context of stochastic nonlinear filtering. The framework is based on the stochastic SIR epidemiological model of infection dynamics, with syndromic (often non-medical) observations of the number of infected people (e.g. visits to pharmacies, sale of certain products, absenteeism from work/study etc.). The unknown parameters of the SIR epidemic model are estimated via the sequential Monte Carlo method, with the prediction based on the dynamic model. The numerical results indicate that the proposed framework can provide useful early prediction of the epidemic peak if the uncertainty in prior knowledge of model parameters is not excessive.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114222919","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: 13
Experimental verification of algorithms for detection and estimation of radioactive sources 放射源检测和估计算法的实验验证
2010 13th International Conference on Information Fusion Pub Date : 2010-07-26 DOI: 10.1109/ICIF.2010.5711880
A. Gunatilaka, B. Ristic, M. Morelande
{"title":"Experimental verification of algorithms for detection and estimation of radioactive sources","authors":"A. Gunatilaka, B. Ristic, M. Morelande","doi":"10.1109/ICIF.2010.5711880","DOIUrl":"https://doi.org/10.1109/ICIF.2010.5711880","url":null,"abstract":"The paper considers the problem of estimating the number of radioactive point sources that potentially exist in a designated area and estimating the parameters of these sources (their locations and strengths) using measurements collected by a low-cost Geiger-Müller counter. In a recent publication the authors proposed candidate algorithms for this task: the maximum likelihood estimator (MLE) and the importance sampling estimator based on progressive correction (PC) for source parameter estimation, and the minimum description length (MDL) for the estimation of the number of sources. Using real experimental data acquired during a recent radiological field trial in Pucka-punyal Military Area (Victoria, Australia), in the presence of up to three point sources of gamma radiation, this paper presents an experimental verification of the measurement model and algorithms proposed by us earlier. These experimental results show that while the MLE performs well when no more than two sources are present, the PC performs remarkably well for all data sets, which confirms our previous conclusions based on simulation studies alone.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115883633","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
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