Pub Date : 2007-07-09DOI: 10.1109/ICIF.2007.4408113
A. Stotz, M. Sudit
Information fusion engine for real-time decision- making (INFERD) is a perceptual information fusion engine designed and developed for the purpose of cyber attack tracking and network situational awareness. While the original application was cyber orientated, the engine itself is designed to generalize and has been ported to other application environments such as maritime domain awareness and medical syndromic surveillance. Comparisons and contrasts are drawn to the traditional Kalman ground target tracking science, motivating high level architectural modules and presenting the cyber environment complexities and assumptions. Performance results are presented showing success in both detection accuracy and temporal expedience, an important design goal.
{"title":"INformation fusion engine for real-time decision-making (INFERD): A perceptual system for cyber attack tracking","authors":"A. Stotz, M. Sudit","doi":"10.1109/ICIF.2007.4408113","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4408113","url":null,"abstract":"Information fusion engine for real-time decision- making (INFERD) is a perceptual information fusion engine designed and developed for the purpose of cyber attack tracking and network situational awareness. While the original application was cyber orientated, the engine itself is designed to generalize and has been ported to other application environments such as maritime domain awareness and medical syndromic surveillance. Comparisons and contrasts are drawn to the traditional Kalman ground target tracking science, motivating high level architectural modules and presenting the cyber environment complexities and assumptions. Performance results are presented showing success in both detection accuracy and temporal expedience, an important design goal.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"56 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396749","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4408074
Jesús García, J. M. Molina, J. Besada, G. D. Miguel
This work addresses off-line accurate trajectory reconstruction for air traffic control. We propose the use of specific dynamic models after identification of regular motion patterns. Datasets recorded from opportunity traffic are first segmented in motion segments, based on the mode probabilities of an IMM filter. Then, reconstruction is applied with an optimal smoothing filter operating forward and backward. The parameters describing the specific modes are estimated and then used as external input for smoothing filters. The performance of this approach is compared with a method based on interpolation B-splines. Comparative results on simulated and real data are discussed at the end.
{"title":"Model-based trajectory reconstruction using IMM smoothing and motion pattern identification","authors":"Jesús García, J. M. Molina, J. Besada, G. D. Miguel","doi":"10.1109/ICIF.2007.4408074","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4408074","url":null,"abstract":"This work addresses off-line accurate trajectory reconstruction for air traffic control. We propose the use of specific dynamic models after identification of regular motion patterns. Datasets recorded from opportunity traffic are first segmented in motion segments, based on the mode probabilities of an IMM filter. Then, reconstruction is applied with an optimal smoothing filter operating forward and backward. The parameters describing the specific modes are estimated and then used as external input for smoothing filters. The performance of this approach is compared with a method based on interpolation B-splines. Comparative results on simulated and real data are discussed at the end.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132021992","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4408057
S. Boutoille, S. Reboul, M. Benjelloun
This paper presents a Bayesian off-line fusion segmentation method, applied to the code tracking in a multi-carrier GPS receiver. The tracking is realized with discriminator values obtained on the different carrier frequencies. We suppose that the evolution of the pseudo-ranges satellites-receiver is piecewise linear. We propose a Bayesian method for the fusion of change detection models in the discriminators evolution. In this context, we construct a penalized contrast function to estimate the model parameters. The contrast function is deduced from log-likelihood of the parametric distribution that models the discriminators statistic evolution. We deduced the penalty term from the prior law of change instants. It is composed of parameters that guide the number of changes and of parameters that will bring prior information on the ionospheric delays between the GPS signals on the different carrier frequencies. We show on synthetic and real data the feasibility and the contribution of our method.
{"title":"Bayesian off-line segmentation applied to multi-carrier GPS signals fusion","authors":"S. Boutoille, S. Reboul, M. Benjelloun","doi":"10.1109/ICIF.2007.4408057","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4408057","url":null,"abstract":"This paper presents a Bayesian off-line fusion segmentation method, applied to the code tracking in a multi-carrier GPS receiver. The tracking is realized with discriminator values obtained on the different carrier frequencies. We suppose that the evolution of the pseudo-ranges satellites-receiver is piecewise linear. We propose a Bayesian method for the fusion of change detection models in the discriminators evolution. In this context, we construct a penalized contrast function to estimate the model parameters. The contrast function is deduced from log-likelihood of the parametric distribution that models the discriminators statistic evolution. We deduced the penalty term from the prior law of change instants. It is composed of parameters that guide the number of changes and of parameters that will bring prior information on the ionospheric delays between the GPS signals on the different carrier frequencies. We show on synthetic and real data the feasibility and the contribution of our method.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134092403","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4407974
P. H. Foo, G. Ng
The interacting multiple model (IMM) algorithm is a widely accepted state estimation scheme for solving maneuvering target tracking problems, which are generally nonlinear. During the IMM filtering process, serious errors can arise when a Gaussian mixture of posterior probability density functions is approximated by a single Gaussian. Particle filters (PFs) are effective in dealing with nonlinearity and non-Gaussianity. This work considers an IMM algorithm that includes a constant velocity model, a constant acceleration model and a 3D turning rate (3DTR) model for tracking three-dimensional (3D) target motion, using various combinations of nonlinear filters. In existing literature on combining IMM and particle filtering techniques to tackle difficult target maneuvers, a PF is usually used in every model In comparison, simulation results show that by using a computationally economical PF in the 3DTR model and Kalman filters in the remaining models, superior performance can be achieved with significant reduction in computational costs.
{"title":"Combining IMM Method with Particle filters for 3D maneuvering target tracking","authors":"P. H. Foo, G. Ng","doi":"10.1109/ICIF.2007.4407974","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4407974","url":null,"abstract":"The interacting multiple model (IMM) algorithm is a widely accepted state estimation scheme for solving maneuvering target tracking problems, which are generally nonlinear. During the IMM filtering process, serious errors can arise when a Gaussian mixture of posterior probability density functions is approximated by a single Gaussian. Particle filters (PFs) are effective in dealing with nonlinearity and non-Gaussianity. This work considers an IMM algorithm that includes a constant velocity model, a constant acceleration model and a 3D turning rate (3DTR) model for tracking three-dimensional (3D) target motion, using various combinations of nonlinear filters. In existing literature on combining IMM and particle filtering techniques to tackle difficult target maneuvers, a PF is usually used in every model In comparison, simulation results show that by using a computationally economical PF in the 3DTR model and Kalman filters in the remaining models, superior performance can be achieved with significant reduction in computational costs.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133281995","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4408195
Cécile Simonin, J. Cadre, F. Dambreville
This paper introduces a common method, based on the cross-entropy method, in order to solve a variety of search problems when search resources are scarce compared to the size of the space of search. In particular, we solve: detection and information search problems, a detection search game, and a two-targets detection search problem. Our approach is built of two steps: first, decompose a problem in a hierarchical manner (two optimization levels) and then, solve the global level using the cross-entropy method. At local level, different solutions are conceivable, depending of the kind of the problem. Problems of interest are in the field of combinatorial optimization and are considered to be hard to solve: we find optimal solution in most cases with a reasonable computation time.
{"title":"The cross-entropy method for solving a variety of hierarchical search problems","authors":"Cécile Simonin, J. Cadre, F. Dambreville","doi":"10.1109/ICIF.2007.4408195","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4408195","url":null,"abstract":"This paper introduces a common method, based on the cross-entropy method, in order to solve a variety of search problems when search resources are scarce compared to the size of the space of search. In particular, we solve: detection and information search problems, a detection search game, and a two-targets detection search problem. Our approach is built of two steps: first, decompose a problem in a hierarchical manner (two optimization levels) and then, solve the global level using the cross-entropy method. At local level, different solutions are conceivable, depending of the kind of the problem. Problems of interest are in the field of combinatorial optimization and are considered to be hard to solve: we find optimal solution in most cases with a reasonable computation time.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133701611","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4408123
E. Santos, R. Sabourin, P. Maupin
Dynamic classifier selection has traditionally focused on selecting the most accurate classifier to predict the class of a particular test pattern. In this paper we propose a new dynamic selection method to select, from a population of ensembles, the most confident ensemble of classifiers to label the test sample. Such a level of confidence is measured by calculating the ambiguity of the ensemble on each test sample. We show theoretically and experimentally that choosing the ensemble of classifiers, from a population of high accurate ensembles, with lowest ambiguity among its members leads to increase the level of confidence of classification, consequently, increasing the generalization performance. Experimental results conducted to compare the proposed method to static selection and DCS-LA, demonstrate that our method outperforms both DCS-LA and static selection strategies when a population of high accurate ensembles is available.
{"title":"Ambiguity-guided dynamic selection of ensemble of classifiers","authors":"E. Santos, R. Sabourin, P. Maupin","doi":"10.1109/ICIF.2007.4408123","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4408123","url":null,"abstract":"Dynamic classifier selection has traditionally focused on selecting the most accurate classifier to predict the class of a particular test pattern. In this paper we propose a new dynamic selection method to select, from a population of ensembles, the most confident ensemble of classifiers to label the test sample. Such a level of confidence is measured by calculating the ambiguity of the ensemble on each test sample. We show theoretically and experimentally that choosing the ensemble of classifiers, from a population of high accurate ensembles, with lowest ambiguity among its members leads to increase the level of confidence of classification, consequently, increasing the generalization performance. Experimental results conducted to compare the proposed method to static selection and DCS-LA, demonstrate that our method outperforms both DCS-LA and static selection strategies when a population of high accurate ensembles is available.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132585265","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4408040
R. Streit
An alternating directions method is presented for joint maximum a posteriori estimation of target track and sensor field using bistatic range data. The algorithm cycles over two sub-algorithms: one improves the target state estimate conditioned on sensor field state, and the other improves the sensor field state estimate conditioned on target state. Nonlinearities in the sub-algorithms are mitigated by decomposing their likelihood functions using integral representations. The kernels of these integrals are linear-Gaussian densities in the states to be estimated, a fact that facilitates the use of missing data methods. The resulting sub-algorithms are equivalent to linear-Gaussian Kalman smoothers. The alternating directions algorithm is guaranteed to converge to (at least) a local maximum of the joint target-field likelihood function.
{"title":"Likelihood function decomposition for multistatic tracking and field stabilization","authors":"R. Streit","doi":"10.1109/ICIF.2007.4408040","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4408040","url":null,"abstract":"An alternating directions method is presented for joint maximum a posteriori estimation of target track and sensor field using bistatic range data. The algorithm cycles over two sub-algorithms: one improves the target state estimate conditioned on sensor field state, and the other improves the sensor field state estimate conditioned on target state. Nonlinearities in the sub-algorithms are mitigated by decomposing their likelihood functions using integral representations. The kernels of these integrals are linear-Gaussian densities in the states to be estimated, a fact that facilitates the use of missing data methods. The resulting sub-algorithms are equivalent to linear-Gaussian Kalman smoothers. The alternating directions algorithm is guaranteed to converge to (at least) a local maximum of the joint target-field likelihood function.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132934475","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4407991
F. Leduc, Daniel A. Lavigne
In this paper we present a comparison of three AFE tools used in the context of ship and vehicle detection based on high resolution data. The three tools (Genie Pro - Los Alamos National Laboratory, Feature Analyst - Visual Learning Systems and eCognition - Definiens AG) were chosen because they were defined as promising and were to be analyzed by NGA in the framework of the STAR program. The comparison is presented here in terms of detection and false alarm rates and also in terms of pros and cons of each tool.
在本文中，我们提出了基于高分辨率数据的船舶和车辆检测中使用的三种AFE工具的比较。选择这三个工具(Genie Pro -洛斯阿拉莫斯国家实验室，特征分析-视觉学习系统和识别- Definiens AG)是因为它们被定义为有前途的，并且将由NGA在STAR计划的框架内进行分析。本文在检测率和虚警率以及每种工具的优缺点方面进行了比较。
{"title":"Comparing several AFE tools in the context of ships and vehicles detection based on RGB and EO data","authors":"F. Leduc, Daniel A. Lavigne","doi":"10.1109/ICIF.2007.4407991","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4407991","url":null,"abstract":"In this paper we present a comparison of three AFE tools used in the context of ship and vehicle detection based on high resolution data. The three tools (Genie Pro - Los Alamos National Laboratory, Feature Analyst - Visual Learning Systems and eCognition - Definiens AG) were chosen because they were defined as promising and were to be analyzed by NGA in the framework of the STAR program. The comparison is presented here in terms of detection and false alarm rates and also in terms of pros and cons of each tool.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122139722","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4408089
H. Wehn, R. Yates, P. Valin, A. Guitouni, É. Bossé, A. Dlugan, H. Zwick
MacDonald Dettwiler is leading a PRECARN partnership project to develop an advanced simulation testbed for the evaluation of the effectiveness of Network Enabled Operations in a coastal large volume surveillance situation. The main focus of this testbed is to study concepts like distributed information fusion, dynamic resources and networks configuration management, and self synchronising units and agents. This article presents the system architecture with an emphasis on our approach for distributed information fusion.
{"title":"A distributed information fusion testbed for coastal surveillance","authors":"H. Wehn, R. Yates, P. Valin, A. Guitouni, É. Bossé, A. Dlugan, H. Zwick","doi":"10.1109/ICIF.2007.4408089","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4408089","url":null,"abstract":"MacDonald Dettwiler is leading a PRECARN partnership project to develop an advanced simulation testbed for the evaluation of the effectiveness of Network Enabled Operations in a coastal large volume surveillance situation. The main focus of this testbed is to study concepts like distributed information fusion, dynamic resources and networks configuration management, and self synchronising units and agents. This article presents the system architecture with an emphasis on our approach for distributed information fusion.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115278948","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 : 2007-07-09DOI: 10.1109/ICIF.2007.4407992
Wang Chen, M. Zhenjiang, Meng Xiao
This paper studies the feasibility of information analysis processing technology, which fuses speech and image together in the real-time monitoring system. It emphasizes particularly on speech analysis and fuses these two technologies in terms of scoring strategy. It also makes some improvement on MFCC feature extraction and proposes a quick MFCC algorithm. The proposed algorithm can reach the requirement of real-time system in case of the high precision. To prove it, this paper compares its algorithm with LPC and FFT. The experiment indicates that the EER of LPC is 13.9% and the EER of FFT is 11.1%, but by using the Quick MFCC the EER is only 4.2%. And compared with the traditional MFCC algorithm, the quick MFCC algorithm reduces the run time greatly while maintaining recognition accuracy of the system. Finally the rate of fusion recognition is about 97.8%, which is a good result for the real-time monitoring system.
{"title":"The application of information fusion in the real-time monitoring system","authors":"Wang Chen, M. Zhenjiang, Meng Xiao","doi":"10.1109/ICIF.2007.4407992","DOIUrl":"https://doi.org/10.1109/ICIF.2007.4407992","url":null,"abstract":"This paper studies the feasibility of information analysis processing technology, which fuses speech and image together in the real-time monitoring system. It emphasizes particularly on speech analysis and fuses these two technologies in terms of scoring strategy. It also makes some improvement on MFCC feature extraction and proposes a quick MFCC algorithm. The proposed algorithm can reach the requirement of real-time system in case of the high precision. To prove it, this paper compares its algorithm with LPC and FFT. The experiment indicates that the EER of LPC is 13.9% and the EER of FFT is 11.1%, but by using the Quick MFCC the EER is only 4.2%. And compared with the traditional MFCC algorithm, the quick MFCC algorithm reduces the run time greatly while maintaining recognition accuracy of the system. Finally the rate of fusion recognition is about 97.8%, which is a good result for the real-time monitoring system.","PeriodicalId":298941,"journal":{"name":"2007 10th International Conference on Information Fusion","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116207375","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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