Pub Date : 2009-07-08DOI: 10.1109/CISDA.2009.5356564
D. Calitoiu
In this paper we address the general question of what is the best strategy to search efficiently for randomly located objects (target sites). We propose a new agent based algorithm for searching in an unpredictable environment. The originality of our work consists in applying a non-cooperative strategy, namely the distributed Goore Game model, as opposed to applying the classical collaborative and competitive strategies, or individual strategies. This paper covers only the non-destructive search that occurs when the agent visits the same target many times. The nondestructive search can be performed in either of the two cases: if the target becomes temporarily inactive or if it leaves the area. The proposed algorithm has two versions: one when the agent can move with a step equal to unity and the other when the step of the agent follows a Levy flight distribution. The latter version is inspired by the work of A.M. Reynolds, motivated by biological examples.
{"title":"New search algorithm for randomly located objects: A non-cooperative agent based approach","authors":"D. Calitoiu","doi":"10.1109/CISDA.2009.5356564","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356564","url":null,"abstract":"In this paper we address the general question of what is the best strategy to search efficiently for randomly located objects (target sites). We propose a new agent based algorithm for searching in an unpredictable environment. The originality of our work consists in applying a non-cooperative strategy, namely the distributed Goore Game model, as opposed to applying the classical collaborative and competitive strategies, or individual strategies. This paper covers only the non-destructive search that occurs when the agent visits the same target many times. The nondestructive search can be performed in either of the two cases: if the target becomes temporarily inactive or if it leaves the area. The proposed algorithm has two versions: one when the agent can move with a step equal to unity and the other when the step of the agent follows a Levy flight distribution. The latter version is inspired by the work of A.M. Reynolds, motivated by biological examples.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"389 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76449655","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356535
P. Sujit, J. Sousa, F. Pereira
In a search and prosecute mission, multiple heterogeneous unmanned aerial vehicles UAVs that carry different resources need to perform the classify, prosecute and battle damage assessment (BDA) tasks on targets sequentially. Depending on the target resource requirement, it may be necessary to deploy a coalition of UAVs to perform the action. In this paper, we propose coalition formation algorithms that have low computational overhead to determine coalitions for the prosecute and the BDA tasks. We also develop a simultaneous strike mechanism based on Dubins curves for the UAVs to prosecute the target simultaneously. Monte-Carlo simulation results are presented to show how the algorithms work and the effect of increasing the number of BDA tasks on the mission performance.
{"title":"Multiple UAV teams for multiple tasks","authors":"P. Sujit, J. Sousa, F. Pereira","doi":"10.1109/CISDA.2009.5356535","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356535","url":null,"abstract":"In a search and prosecute mission, multiple heterogeneous unmanned aerial vehicles UAVs that carry different resources need to perform the classify, prosecute and battle damage assessment (BDA) tasks on targets sequentially. Depending on the target resource requirement, it may be necessary to deploy a coalition of UAVs to perform the action. In this paper, we propose coalition formation algorithms that have low computational overhead to determine coalitions for the prosecute and the BDA tasks. We also develop a simultaneous strike mechanism based on Dubins curves for the UAVs to prosecute the target simultaneously. Monte-Carlo simulation results are presented to show how the algorithms work and the effect of increasing the number of BDA tasks on the mission performance.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"6 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83539669","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356532
Terrill L. Frantz, Kathleen M. Carley
We present a brief report on a controlled experiment that provides valuable statistics to network-oriented defence analysts involved in threat identification. These statistics estimate the accuracy of the top-central actor findings that have been derived from relational data classically found in real-world datasets, such as those collected on distributed, covert organizations. Our experiment involved cellular social-networks with four types of data error: missing links, missing actors, extra links, and extra actors. We provide statistical results for top threat identification from the perspective of four traditional measures of network centrality: degree, betweenness, closeness and eigenvector. The results from our experiment provide a statistical estimate of the accuracy of the top-1 and top-3 actors as indicated by the observed data. Using these statistics a quantitative indication of reliability can be provided along with defence intelligence estimates of covert-organization leadership derived from relational network data. We provide lookup tables for the specific situations created for this experiment, from which other conditions may be loosely estimated. This work has highly practical implications for operational analysts and consumers of such analyses, particularly in the terrorist network and drug-trafficking domains. This work also lays the groundwork for developing more intricate estimates of reliability for other network-related, analytic tasks of analysts — from more extensive key-actor identification tasks to assessing the statistical reliability of the centrality measures in and of themselves.
{"title":"Information assurances and threat identification in networked organizations","authors":"Terrill L. Frantz, Kathleen M. Carley","doi":"10.1109/CISDA.2009.5356532","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356532","url":null,"abstract":"We present a brief report on a controlled experiment that provides valuable statistics to network-oriented defence analysts involved in threat identification. These statistics estimate the accuracy of the top-central actor findings that have been derived from relational data classically found in real-world datasets, such as those collected on distributed, covert organizations. Our experiment involved cellular social-networks with four types of data error: missing links, missing actors, extra links, and extra actors. We provide statistical results for top threat identification from the perspective of four traditional measures of network centrality: degree, betweenness, closeness and eigenvector. The results from our experiment provide a statistical estimate of the accuracy of the top-1 and top-3 actors as indicated by the observed data. Using these statistics a quantitative indication of reliability can be provided along with defence intelligence estimates of covert-organization leadership derived from relational network data. We provide lookup tables for the specific situations created for this experiment, from which other conditions may be loosely estimated. This work has highly practical implications for operational analysts and consumers of such analyses, particularly in the terrorist network and drug-trafficking domains. This work also lays the groundwork for developing more intricate estimates of reliability for other network-related, analytic tasks of analysts — from more extensive key-actor identification tasks to assessing the statistical reliability of the centrality measures in and of themselves.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"24 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77827768","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356563
T. Martin, Yun Shen
Recent initiatives in defence related information systems have emphasised the need to bring together information from multiple sources and fuse it into a form suitable for decision makers. This paper outlines a four stage system for fusing unstructured and semi-structured text and numerical data by extraction of entities and relations, identification of duplicate entities, organisation into the most appropriate hierarchical categories and determination of relations between fuzzy categories. The novel contribution of this paper is in the final stage of the process, where we determine associations between fuzzy categories and identify strong and/or unusual levels of association as well as changes over time. A demonstrator application shows how information on terrorist incidents from multiple sources can be integrated and monitored.
{"title":"mTRACK - Monitoring time-varying relations in approximately categorised knowledge","authors":"T. Martin, Yun Shen","doi":"10.1109/CISDA.2009.5356563","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356563","url":null,"abstract":"Recent initiatives in defence related information systems have emphasised the need to bring together information from multiple sources and fuse it into a form suitable for decision makers. This paper outlines a four stage system for fusing unstructured and semi-structured text and numerical data by extraction of entities and relations, identification of duplicate entities, organisation into the most appropriate hierarchical categories and determination of relations between fuzzy categories. The novel contribution of this paper is in the final stage of the process, where we determine associations between fuzzy categories and identify strong and/or unusual levels of association as well as changes over time. A demonstrator application shows how information on terrorist incidents from multiple sources can be integrated and monitored.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"3 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89045655","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356526
D. Akselrod, R. Tharmarasa, T. Kirubarajan, Z. Ding, T. Ponsford
In this paper we present a multisensor-multitarget tracking testbed for large-scale distributed scenarios. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the current stage of the project, the IMM/Assignment tracker, and the Particle Filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the Multiple Hypothesis Tracker (MHT) are also planned for the future.
{"title":"Multisensor-multitarget tracking testbed","authors":"D. Akselrod, R. Tharmarasa, T. Kirubarajan, Z. Ding, T. Ponsford","doi":"10.1109/CISDA.2009.5356526","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356526","url":null,"abstract":"In this paper we present a multisensor-multitarget tracking testbed for large-scale distributed scenarios. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the current stage of the project, the IMM/Assignment tracker, and the Particle Filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the Multiple Hypothesis Tracker (MHT) are also planned for the future.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86589504","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356536
R. Tharmarasa, T. Kirubarajan, M. McDonald
Passive Coherent Location (PCL), which uses commercial signals (e.g., FM broadcast, digital TV) as illuminators of opportunity, is an emerging technology in air defense systems. The advantages of PCL are low cost, low vulnerability to electronic counter measures, early detection of stealthy targets and low-altitude detection. However, limitations of PCL include lack of control over illuminators, limited observability and poor detection due to low Signal-to-Noise Ratio (SNR). This leads to high clutter with low probability of detection of target of interest. In this paper, multiple target tracking algorithms for PCL systems are analyzed to handle low probability of detection and high nonlinearity in the measurement model due to high measurement error. The converted measurement Kalman filter, unscented Kalman filter and particle filter based PHD filter are implemented and compared for PCL radar systems. The feasibility of using transmitters of opportunity for tracking airborne targets is shown on simulated and real data sets.
{"title":"Passive multitarget tracking using transmitters of opportunity","authors":"R. Tharmarasa, T. Kirubarajan, M. McDonald","doi":"10.1109/CISDA.2009.5356536","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356536","url":null,"abstract":"Passive Coherent Location (PCL), which uses commercial signals (e.g., FM broadcast, digital TV) as illuminators of opportunity, is an emerging technology in air defense systems. The advantages of PCL are low cost, low vulnerability to electronic counter measures, early detection of stealthy targets and low-altitude detection. However, limitations of PCL include lack of control over illuminators, limited observability and poor detection due to low Signal-to-Noise Ratio (SNR). This leads to high clutter with low probability of detection of target of interest. In this paper, multiple target tracking algorithms for PCL systems are analyzed to handle low probability of detection and high nonlinearity in the measurement model due to high measurement error. The converted measurement Kalman filter, unscented Kalman filter and particle filter based PHD filter are implemented and compared for PCL radar systems. The feasibility of using transmitters of opportunity for tracking airborne targets is shown on simulated and real data sets.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"91 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74207011","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356548
S. Sundaram, Hai-Jun Rong, N. Sundararajan
This paper presents an optimization tool for launch vehicle design and trajectory optimization using bio-inspired computing algorithms and nonlinear programming. The objective is to size a launch vehicle such that the payload to lift-of-weight ratio is maximized (i.e the lift off weight is a minimum). Here, the staging problem is solved using Particle Swarm Optimization (PSO) method. With the above vehicle, an optimal trajectory is arrived at using a Real-Coded Genetic Algorithm (RCGA) and solving a nonlinear programming (NLP) by the direct shooting method. The solutions from PSO and RCGA are used for initialization of NLP variables. A case study is carried out that establishes the advantage of the proposed approach.
{"title":"Bio-inspired computing for launch vehicle design and trajectory optimization","authors":"S. Sundaram, Hai-Jun Rong, N. Sundararajan","doi":"10.1109/CISDA.2009.5356548","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356548","url":null,"abstract":"This paper presents an optimization tool for launch vehicle design and trajectory optimization using bio-inspired computing algorithms and nonlinear programming. The objective is to size a launch vehicle such that the payload to lift-of-weight ratio is maximized (i.e the lift off weight is a minimum). Here, the staging problem is solved using Particle Swarm Optimization (PSO) method. With the above vehicle, an optimal trajectory is arrived at using a Real-Coded Genetic Algorithm (RCGA) and solving a nonlinear programming (NLP) by the direct shooting method. The solutions from PSO and RCGA are used for initialization of NLP variables. A case study is carried out that establishes the advantage of the proposed approach.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"30 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85645536","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 : 2009-07-01DOI: 10.1109/CISDA.2009.5356542
Wael Khreich, Eric Granger, A. Miri, R. Sabourin
Hidden Markov Models (HMMs) have been shown to provide a high level performance for detecting anomalies in intrusion detection systems. Since incomplete training data is always employed in practice, and environments being monitored are susceptible to changes, a system for anomaly detection should update its HMM parameters in response to new training data from the environment. Several techniques have been proposed in literature for on-line learning of HMM parameters. However, the theoretical convergence of these algorithms is based on an infinite stream of data for optimal performances. When learning sequences with a finite length, on-line incremental versions of these algorithms can improve discrimination by allowing for convergence over several training iterations. In this paper, the performance of these techniques is compared for learning new sequences of training data in host-based intrusion detection. The discrimination of HMMs trained with different techniques is assessed from data corresponding to sequences of system calls to the operating system kernel. In addition, the resource requirements are assessed through an analysis of time and memory complexity. Results suggest that the techniques for online incremental learning of HMM parameters can provide a higher level of discrimination than those for on-line learning, yet require significantly fewer resources than with batch training. On-line incremental learning techniques may provide a promising solution for adaptive intrusion detection systems.
{"title":"A comparison of techniques for on-line incremental learning of HMM parameters in anomaly detection","authors":"Wael Khreich, Eric Granger, A. Miri, R. Sabourin","doi":"10.1109/CISDA.2009.5356542","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356542","url":null,"abstract":"Hidden Markov Models (HMMs) have been shown to provide a high level performance for detecting anomalies in intrusion detection systems. Since incomplete training data is always employed in practice, and environments being monitored are susceptible to changes, a system for anomaly detection should update its HMM parameters in response to new training data from the environment. Several techniques have been proposed in literature for on-line learning of HMM parameters. However, the theoretical convergence of these algorithms is based on an infinite stream of data for optimal performances. When learning sequences with a finite length, on-line incremental versions of these algorithms can improve discrimination by allowing for convergence over several training iterations. In this paper, the performance of these techniques is compared for learning new sequences of training data in host-based intrusion detection. The discrimination of HMMs trained with different techniques is assessed from data corresponding to sequences of system calls to the operating system kernel. In addition, the resource requirements are assessed through an analysis of time and memory complexity. Results suggest that the techniques for online incremental learning of HMM parameters can provide a higher level of discrimination than those for on-line learning, yet require significantly fewer resources than with batch training. On-line incremental learning techniques may provide a promising solution for adaptive intrusion detection systems.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"4 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79547493","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}
Since inexpensive passive sensors have become available, it is possible to deploy a large number of them for tracking purposes in Anti-Submarine Warfare (ASW). However, modern submarines are quiet and difficult to track with passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition to passive receivers (e.g., sonobouys), have the potential to improve the tracking performance. The performance can be improved further by moving the transmitters according to existing target states and any possible new target states. Even though a large number of passive sensors are available, due to frequency, processing power and other physical limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the transmitters and select a subset from the available passive sensors in order to optimize the tracking performance. In this paper, the Posterior Crame´r-Rao Lower Bound (PCRLB), which gives a lower bound on estimation uncertainty, is used as the performance measure. An algorithm is presented to decide jointly the optimal path of the movable transmitters, by considering transmitters' operational constraints, and the optimal subset of passive sensors that should be used at each time steps for tracking multiple, possibly time-varying, number of targets. The effect of sensor location uncertainties, due to deployment error and possible sensor drifting, on the tracking performance is addressed in the sensor management algorithm. Simulation results illustrating the performance of the proposed algorithm are presented.
{"title":"Joint path planning and sensor subset selection for multistatic sensor networks","authors":"R. Tharmarasa, T. Kirubarajan, T. Lang","doi":"10.1117/12.779217","DOIUrl":"https://doi.org/10.1117/12.779217","url":null,"abstract":"Since inexpensive passive sensors have become available, it is possible to deploy a large number of them for tracking purposes in Anti-Submarine Warfare (ASW). However, modern submarines are quiet and difficult to track with passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition to passive receivers (e.g., sonobouys), have the potential to improve the tracking performance. The performance can be improved further by moving the transmitters according to existing target states and any possible new target states. Even though a large number of passive sensors are available, due to frequency, processing power and other physical limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the transmitters and select a subset from the available passive sensors in order to optimize the tracking performance. In this paper, the Posterior Crame´r-Rao Lower Bound (PCRLB), which gives a lower bound on estimation uncertainty, is used as the performance measure. An algorithm is presented to decide jointly the optimal path of the movable transmitters, by considering transmitters' operational constraints, and the optimal subset of passive sensors that should be used at each time steps for tracking multiple, possibly time-varying, number of targets. The effect of sensor location uncertainties, due to deployment error and possible sensor drifting, on the tracking performance is addressed in the sensor management algorithm. Simulation results illustrating the performance of the proposed algorithm are presented.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"3 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2008-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80859280","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 : 2007-01-01DOI: 10.1109/CISDA.2007.368139
Huimin Chai, Baoshu Wang
{"title":"A Template-based Method for Force Group Classification in Situation Assessment","authors":"Huimin Chai, Baoshu Wang","doi":"10.1109/CISDA.2007.368139","DOIUrl":"https://doi.org/10.1109/CISDA.2007.368139","url":null,"abstract":"","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"61 1","pages":"85-91"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81123198","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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