Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021218
S. Das, R. Grey, P. Gonsalves
We present here an approach to battlefield situation assessment based on a level 2 fusion processing of incoming information via probabilistic Bayesian Belief Network technology. A belief network (BN) can be thought of as a graphical program script representing causal relationships among various battlefield concepts represented as nodes to which observed significant events are posted as evidence. In our approach, each BN can be constructed in real-time from a library of smaller component-like BNs to assess a specific high-level situation or address mission-specific high-level intelligence requirements. Furthermore, by distributing components of a BN across a set of networked computers, we enhance inferencing efficiency and allow computation at various levels of abstraction suitable for military hierarchical organizations. We demonstrate them effectiveness of our approach by modeling the situation assessment tasks in the context of a battlefield scenario and implementing on our in-house software engine BNet 2000.
{"title":"Situation assessment via Bayesian belief networks","authors":"S. Das, R. Grey, P. Gonsalves","doi":"10.1109/ICIF.2002.1021218","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021218","url":null,"abstract":"We present here an approach to battlefield situation assessment based on a level 2 fusion processing of incoming information via probabilistic Bayesian Belief Network technology. A belief network (BN) can be thought of as a graphical program script representing causal relationships among various battlefield concepts represented as nodes to which observed significant events are posted as evidence. In our approach, each BN can be constructed in real-time from a library of smaller component-like BNs to assess a specific high-level situation or address mission-specific high-level intelligence requirements. Furthermore, by distributing components of a BN across a set of networked computers, we enhance inferencing efficiency and allow computation at various levels of abstraction suitable for military hierarchical organizations. We demonstrate them effectiveness of our approach by modeling the situation assessment tasks in the context of a battlefield scenario and implementing on our in-house software engine BNet 2000.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128367527","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020926
W. Koch
Tracking of ground moving vehicles with GMTI radar is a challenging task, which calls for efficient exploitation of all information sources available. For well-separated vehicles as well as for convoy targets we focus on information fusion aspects comprising both, fusion of data from multiple dislocated sensors as well as incorporation of background information (refined models of the sensor performance, road maps, terrain screening, and simple tactical rules). Under suitably formulated modeling assumptions algorithmic solutions within the context of Gaussian sum approximations are discussed. Methods originally proposed for well-separated vehicles can be embedded into an expectation-maximization approach for dealing with collectively moving convoy targets. By this in particular, early detection of a stopping event is alleviated.
{"title":"Information fusion aspects related to GMTI convoy tracking","authors":"W. Koch","doi":"10.1109/ICIF.2002.1020926","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020926","url":null,"abstract":"Tracking of ground moving vehicles with GMTI radar is a challenging task, which calls for efficient exploitation of all information sources available. For well-separated vehicles as well as for convoy targets we focus on information fusion aspects comprising both, fusion of data from multiple dislocated sensors as well as incorporation of background information (refined models of the sensor performance, road maps, terrain screening, and simple tactical rules). Under suitably formulated modeling assumptions algorithmic solutions within the context of Gaussian sum approximations are discussed. Methods originally proposed for well-separated vehicles can be embedded into an expectation-maximization approach for dealing with collectively moving convoy targets. By this in particular, early detection of a stopping event is alleviated.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130214247","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020888
A. Berk, S. Adler-Golden, A. Ratkowski, G. Felde, G. Anderson, M. Hoke, T. Cooley, J. Chetwynd, J. Gardner, M. Matthew, L. Bernstein, P. Acharya, D. Miller, P. Lewis
Terrain categorization and target detection algorithms applied to hyperspectral imagery (HSI) typically operate on the measured reflectance (of sun and sky illumination) by an object or scene. Since the reflectance is a non-dimensional ratio, the reflectance by an object is nominally not affected by variations in lighting conditions. Atmospheric correction (referred to as atmospheric compensation, characterization, etc.) algorithms (ACAs) are used in applications of remotely sensed HSI data to correct for the effects of atmospheric propagation on measurements acquired by air and space-borne systems. The fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) algorithm is an ACA created for HSI applications in the visible through shortwave infrared (Vis-SWIR) spectral regime. FLAASH derives its 'physics-based' mathematics from MODTRAN4.
{"title":"Exploiting MODTRAN radiation transport for atmospheric correction: The FLAASH algorithm","authors":"A. Berk, S. Adler-Golden, A. Ratkowski, G. Felde, G. Anderson, M. Hoke, T. Cooley, J. Chetwynd, J. Gardner, M. Matthew, L. Bernstein, P. Acharya, D. Miller, P. Lewis","doi":"10.1109/ICIF.2002.1020888","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020888","url":null,"abstract":"Terrain categorization and target detection algorithms applied to hyperspectral imagery (HSI) typically operate on the measured reflectance (of sun and sky illumination) by an object or scene. Since the reflectance is a non-dimensional ratio, the reflectance by an object is nominally not affected by variations in lighting conditions. Atmospheric correction (referred to as atmospheric compensation, characterization, etc.) algorithms (ACAs) are used in applications of remotely sensed HSI data to correct for the effects of atmospheric propagation on measurements acquired by air and space-borne systems. The fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) algorithm is an ACA created for HSI applications in the visible through shortwave infrared (Vis-SWIR) spectral regime. FLAASH derives its 'physics-based' mathematics from MODTRAN4.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131070348","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020963
X. Nguyen
The paper reports the results of a research program on target threat assessment. A threat assessment process will be discussed. There are basically two components in this process: intent assessment and capability assessment. Threat assessment was analysed using Cognitive Work Domain Analysis technique. A model of intent assessment based upon Bayesian Networks will be discussed with its test results.
{"title":"Threat assessment in tactical airborne environments","authors":"X. Nguyen","doi":"10.1109/ICIF.2002.1020963","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020963","url":null,"abstract":"The paper reports the results of a research program on target threat assessment. A threat assessment process will be discussed. There are basically two components in this process: intent assessment and capability assessment. Threat assessment was analysed using Cognitive Work Domain Analysis technique. A model of intent assessment based upon Bayesian Networks will be discussed with its test results.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"31 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134096575","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1021188
A. Mohammad-Djafari
In many image reconstruction applications, more and more, we need techniques to combine different kind of data. This is the case, for example, in computed tomography (CT) medical imaging where one may use anatomic atlas data with X ray radiographic data or in non destructive testing (NDT) techniques where one wants to use both gamma rays and ultrasound echo-graphic data. In this paper, First we present the basics of Bayesian estimation approach and will see how the compound or hierarchical Markov modeling will give us the necessary tools for data fusion. Then, we present two examples: one in medical imaging CT application and the second in industrial NDT. In both cases, we consider an X ray CT image reconstruction problem using two different kind of data: classical X-rays radiographic data and some geometrical informations and propose new methods for these data fusion problems. The geometrical information we use are of two kind: partial knowledge of values in some regions and partial knowledge of the edges of some other regions. We show the advantages of using such informations on increasing the quality of reconstructions. We also show some results to analyze the effects of some errors in these data on the reconstruction results.
{"title":"Bayesian approach with hierarchical Markov modeling for data fusion in image reconstruction applications","authors":"A. Mohammad-Djafari","doi":"10.1109/ICIF.2002.1021188","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021188","url":null,"abstract":"In many image reconstruction applications, more and more, we need techniques to combine different kind of data. This is the case, for example, in computed tomography (CT) medical imaging where one may use anatomic atlas data with X ray radiographic data or in non destructive testing (NDT) techniques where one wants to use both gamma rays and ultrasound echo-graphic data. In this paper, First we present the basics of Bayesian estimation approach and will see how the compound or hierarchical Markov modeling will give us the necessary tools for data fusion. Then, we present two examples: one in medical imaging CT application and the second in industrial NDT. In both cases, we consider an X ray CT image reconstruction problem using two different kind of data: classical X-rays radiographic data and some geometrical informations and propose new methods for these data fusion problems. The geometrical information we use are of two kind: partial knowledge of values in some regions and partial knowledge of the edges of some other regions. We show the advantages of using such informations on increasing the quality of reconstructions. We also show some results to analyze the effects of some errors in these data on the reconstruction results.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134507237","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020942
M. Contat, V. Nimier, R. Reynaud
In a multitarget and multisensor environment a faithful and precise operational situation is needed as much as a fast data acquisition and processing in order to make reliable and reactive decisions. From this perspective, we introduced in our last paper the separation degree, which is a measure of discrimination between two fuzzy sets. It is used as a criterion to obtain an order among the target's attributes or among the sensor's modes. Thus it helps the choice of the attribute that is the most characteristic for the targeted object, by selecting the most discriminating fuzzy sets, which would give the less ambiguous result. From this perspective we propose a method to select the sensor's mode, which takes contextual information about the targeted object and sensor's cost into account.
{"title":"Request management using contextual information for classification","authors":"M. Contat, V. Nimier, R. Reynaud","doi":"10.1109/ICIF.2002.1020942","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020942","url":null,"abstract":"In a multitarget and multisensor environment a faithful and precise operational situation is needed as much as a fast data acquisition and processing in order to make reliable and reactive decisions. From this perspective, we introduced in our last paper the separation degree, which is a measure of discrimination between two fuzzy sets. It is used as a criterion to obtain an order among the target's attributes or among the sensor's modes. Thus it helps the choice of the attribute that is the most characteristic for the targeted object, by selecting the most discriminating fuzzy sets, which would give the less ambiguous result. From this perspective we propose a method to select the sensor's mode, which takes contextual information about the targeted object and sensor's cost into account.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"10 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132512504","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020924
M.L. Hernandez, A. Marrs, S. Maskell, M. Orton
Recent interest in the development of wireless sensor networks for surveillance introduces new problems that will need to be addressed when developing target tracking algorithms for use in such networks. Specifically the power and stealth requirements when combined with the wireless communications architecture will lead to potentially significant delays in the measurement collection process. The recent development of out-of-sequence tracking algorithms and posterior Cramer-Rao lower bounds for tracking with measurement origin uncertainty makes it possible to investigate how robust these new tracking algorithms are to a wide range of communications delays and a range of false alarm densities. This paper brings together these various components and presents the performance analysis for a simulated wireless network. Results show that position estimate accuracy close to the lower bound should be possible for communications intervals up to 4 s for challenging false alarm densities.
{"title":"Tracking and fusion for wireless sensor networks","authors":"M.L. Hernandez, A. Marrs, S. Maskell, M. Orton","doi":"10.1109/ICIF.2002.1020924","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020924","url":null,"abstract":"Recent interest in the development of wireless sensor networks for surveillance introduces new problems that will need to be addressed when developing target tracking algorithms for use in such networks. Specifically the power and stealth requirements when combined with the wireless communications architecture will lead to potentially significant delays in the measurement collection process. The recent development of out-of-sequence tracking algorithms and posterior Cramer-Rao lower bounds for tracking with measurement origin uncertainty makes it possible to investigate how robust these new tracking algorithms are to a wide range of communications delays and a range of false alarm densities. This paper brings together these various components and presents the performance analysis for a simulated wireless network. Results show that position estimate accuracy close to the lower bound should be possible for communications intervals up to 4 s for challenging false alarm densities.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"80 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114050366","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1020991
C. Musso, P. Dodin
We analyse theoretically a maximisation quadratic program which can arise in multi-target/multi-sensor area. The goal is to find the point x which minimizes the quadratic distance between x and a given point y. This optimum must lie in a convex constrained region defined by linear inequalities. We present a characterisation of this optimum in a compact dual form. This optimisation framework can be helpful, for example, in muti-objective programming like decentralized resource allocation.
{"title":"Characterization of the optimum of a quadratic program with convex constraints. Application to sensor data fusion","authors":"C. Musso, P. Dodin","doi":"10.1109/ICIF.2002.1020991","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020991","url":null,"abstract":"We analyse theoretically a maximisation quadratic program which can arise in multi-target/multi-sensor area. The goal is to find the point x which minimizes the quadratic distance between x and a given point y. This optimum must lie in a convex constrained region defined by linear inequalities. We present a characterisation of this optimum in a compact dual form. This optimisation framework can be helpful, for example, in muti-objective programming like decentralized resource allocation.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177629","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1021224
H. Blom, E. A. Bloem
The paper combines IMM and JPDA for tracking of multiple possibly maneuvering targets in case of clutter and possibly missed measurements while avoiding sensitivity to track coalescence. The effectiveness of the filter is illustrated through Monte Carlo simulations.
{"title":"Combining IMM and JPDA for tracking multiple maneuvering targets in clutter","authors":"H. Blom, E. A. Bloem","doi":"10.1109/ICIF.2002.1021224","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021224","url":null,"abstract":"The paper combines IMM and JPDA for tracking of multiple possibly maneuvering targets in case of clutter and possibly missed measurements while avoiding sensitivity to track coalescence. The effectiveness of the filter is illustrated through Monte Carlo simulations.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"440 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114585066","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 : 2002-07-08DOI: 10.1109/ICIF.2002.1021207
P. Dodin, V. Nimier
This paper addresses the "distributed tracking" problem, or the problem of local integration of estimation communicated by different sensors through a fixed multicast communication topology. The recursive nature of this shared information which can be delayed by communication links suggests a careful integration because of the cross correlation in the estimation errors. The work of Durrant-Whyte and Grime (1992) has proposed channel filters for a multicast tree topology. Other authors have proposed the utilisation of Covariance Intersection to solve the distributed tracking problem for any topology. In this paper one explore the possibility of extending the channel filtering principle to any topology by constraining the number of shortest path.
{"title":"Distributed tracking systems and their optimal inference topology","authors":"P. Dodin, V. Nimier","doi":"10.1109/ICIF.2002.1021207","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021207","url":null,"abstract":"This paper addresses the \"distributed tracking\" problem, or the problem of local integration of estimation communicated by different sensors through a fixed multicast communication topology. The recursive nature of this shared information which can be delayed by communication links suggests a careful integration because of the cross correlation in the estimation errors. The work of Durrant-Whyte and Grime (1992) has proposed channel filters for a multicast tree topology. Other authors have proposed the utilisation of Covariance Intersection to solve the distributed tracking problem for any topology. In this paper one explore the possibility of extending the channel filtering principle to any topology by constraining the number of shortest path.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115035087","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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