Pub Date : 2017-07-01DOI: 10.23919/ICIF.2017.8009882
J. Ru, Cuichun Xu
Conventional point object model based automotive radar tracking system assumes at most one detection received from the target object at a time. However, in real applications for an extended object, such as a passenger car, located within a close range to a high-resolution radar or LIDAR, the system usually receives multiple reflections from different parts of the object. This can introduce large bias into a velocity estimation performed by a point object model based tracking system. Doppler-azimuth profile based approach accounts for the cluster of detections and could give a very accurate velocity vector of the extended object. However, depending on the position and orientation of the object, the linear equation set could be ill-conditioned, in which case the estimated velocity will suffer from substantial error. In this paper, we first propose a new approach to estimate the heading of a moving object using principle component analysis based on the detection cluster trajectory. We then propose an approach to fuse the three above-mentioned velocity estimators as each estimator faces challenges in different situations. Road data from a 77GHz radar is used for performance illustration.
{"title":"Improvement on velocity estimation of an extended object","authors":"J. Ru, Cuichun Xu","doi":"10.23919/ICIF.2017.8009882","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009882","url":null,"abstract":"Conventional point object model based automotive radar tracking system assumes at most one detection received from the target object at a time. However, in real applications for an extended object, such as a passenger car, located within a close range to a high-resolution radar or LIDAR, the system usually receives multiple reflections from different parts of the object. This can introduce large bias into a velocity estimation performed by a point object model based tracking system. Doppler-azimuth profile based approach accounts for the cluster of detections and could give a very accurate velocity vector of the extended object. However, depending on the position and orientation of the object, the linear equation set could be ill-conditioned, in which case the estimated velocity will suffer from substantial error. In this paper, we first propose a new approach to estimate the heading of a moving object using principle component analysis based on the detection cluster trajectory. We then propose an approach to fuse the three above-mentioned velocity estimators as each estimator faces challenges in different situations. Road data from a 77GHz radar is used for performance illustration.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131445109","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009678
Qiyng. Hu, H. Ji, Yongquan Zhang
The Gaussian inverse Wishart (GIW) filter is a promising filter for extended target tracking and draws tremendous attention in recent years. The Gaussian and the inverse Wishart distributions are used to describe the target's kinematical and extended states, respectively. However, the filter for estimating the extended state contains predicting position error and causes large error of the extended state estimation, especially for the scenarios with high-maneuvering. In this paper, we eliminate the influence of the predicting position error via reconstructing the updated equation for estimating extended state. Based on GIW probability hypotheses density (GIW-PHD) framework, the improved filter is tested in a maneuvering scenario and the comparative results verify the superior performance of the filter in terms of the extended state estimation.
{"title":"An improved extended state estimation approach for maneuvering target tracking using random matrix","authors":"Qiyng. Hu, H. Ji, Yongquan Zhang","doi":"10.23919/ICIF.2017.8009678","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009678","url":null,"abstract":"The Gaussian inverse Wishart (GIW) filter is a promising filter for extended target tracking and draws tremendous attention in recent years. The Gaussian and the inverse Wishart distributions are used to describe the target's kinematical and extended states, respectively. However, the filter for estimating the extended state contains predicting position error and causes large error of the extended state estimation, especially for the scenarios with high-maneuvering. In this paper, we eliminate the influence of the predicting position error via reconstructing the updated equation for estimating extended state. Based on GIW probability hypotheses density (GIW-PHD) framework, the improved filter is tested in a maneuvering scenario and the comparative results verify the superior performance of the filter in terms of the extended state estimation.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131647732","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}
In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of fully to improve classification accuracy. Experimental results show that Multi-Task Linear Dependency Modeling outperforms existing decision level and feature level combination methods and achieves the best performance.
{"title":"Multi-Task Linear Dependency Modeling for drug-related webpages classification","authors":"Ruiguang Hu, Mengxi Hao, Songzhi Jin, Hao Wang, Shibo Gao, Liping Xiao","doi":"10.23919/ICIF.2017.8009781","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009781","url":null,"abstract":"In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of fully to improve classification accuracy. Experimental results show that Multi-Task Linear Dependency Modeling outperforms existing decision level and feature level combination methods and achieves the best performance.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123906382","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009834
Yuan Huang, Xueying Wang, Yulan Guo, W. An
A problem of state estimation with destination constraint is considered in this paper. An anti-radiation missile (ARM) often moves towards the target along a trajectory which is almost linear in the X-Y plane. The linear constraint for trajectory and target position are known as priori and can be used to enhance the performance of a tracking filter. In this paper, a destination constrained Kalman filter (DCKF) is first revised for our problem. Then, two methods are proposed to incorporate the prior knowledge by estimating the slope of the trajectory. In the first method, the slope is estimated directly at each time using the point estimated by a unconstrained Kalman filter and the destination point. In the second method, a least square method is used to estimate the slope from all measurements. Several effective linear equality constrained state estimation methods can be used to exploit the estimated slop and the destination point. A typical ARM tracking scenario is established to test the proposed Kalman filter. A comprehensive comparison to recent work is also presented, including unconstrained nonlinear filtering methods and the Posterior Cramer-Rao Lower Bound (PCRLB). Monte-Carlo simulation results are presented to illustrate the effectiveness of the proposed methods for state estimation with destination constraint.
{"title":"State estimation with incomplete linear constraint","authors":"Yuan Huang, Xueying Wang, Yulan Guo, W. An","doi":"10.23919/ICIF.2017.8009834","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009834","url":null,"abstract":"A problem of state estimation with destination constraint is considered in this paper. An anti-radiation missile (ARM) often moves towards the target along a trajectory which is almost linear in the X-Y plane. The linear constraint for trajectory and target position are known as priori and can be used to enhance the performance of a tracking filter. In this paper, a destination constrained Kalman filter (DCKF) is first revised for our problem. Then, two methods are proposed to incorporate the prior knowledge by estimating the slope of the trajectory. In the first method, the slope is estimated directly at each time using the point estimated by a unconstrained Kalman filter and the destination point. In the second method, a least square method is used to estimate the slope from all measurements. Several effective linear equality constrained state estimation methods can be used to exploit the estimated slop and the destination point. A typical ARM tracking scenario is established to test the proposed Kalman filter. A comprehensive comparison to recent work is also presented, including unconstrained nonlinear filtering methods and the Posterior Cramer-Rao Lower Bound (PCRLB). Monte-Carlo simulation results are presented to illustrate the effectiveness of the proposed methods for state estimation with destination constraint.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375526","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009877
Mark Locher, P. Costa
Aiding decision-makers is a key function of a fusion system. In designing decision-aiding modules for fusion systems, it is necessary to understand the elements of the decision model and the dependencies that connect them. An ontology is a disciplined means to codify that understanding. Many fusion systems have a Bayesian Network (BN) component to support probabilistic reasoning under uncertainty. Decision graphs (DG) are an extension that adds decision aiding to BNs. Both BNs and DGs have limited logical expressivity, able to model propositions, but cannot directly model variable numbers of entities or variations in their attributes and relationships. This important capability is called first-order expressivity. Multi-Entity Bayesian Network (MEBN) was developed to provide first-order logic expressivity to BNs. We are developing Multi-Entity Decision Graph (MEDG) to do the same for decision graphs. We found that a decision ontology is useful to our efforts. The literature has a limited discussion of decision ontologies. Almost all focus on the entities and the entity hierarchy. But BNs and DGs emphasize relationships and the dependencies between relationships. The key for probabilistic first-order expressivity is to identify the relationships that enable dependencies between entity instances. We developed a MEDG Decision Ontology that highlights both the entities and key relationships that any decision model needs to address. It is designed to support decision model developers, including fusion model developers, in building comprehensive decision aiding capabilities.
{"title":"The multi-entity decision graph decision ontology: A decision ontology for fusion support","authors":"Mark Locher, P. Costa","doi":"10.23919/ICIF.2017.8009877","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009877","url":null,"abstract":"Aiding decision-makers is a key function of a fusion system. In designing decision-aiding modules for fusion systems, it is necessary to understand the elements of the decision model and the dependencies that connect them. An ontology is a disciplined means to codify that understanding. Many fusion systems have a Bayesian Network (BN) component to support probabilistic reasoning under uncertainty. Decision graphs (DG) are an extension that adds decision aiding to BNs. Both BNs and DGs have limited logical expressivity, able to model propositions, but cannot directly model variable numbers of entities or variations in their attributes and relationships. This important capability is called first-order expressivity. Multi-Entity Bayesian Network (MEBN) was developed to provide first-order logic expressivity to BNs. We are developing Multi-Entity Decision Graph (MEDG) to do the same for decision graphs. We found that a decision ontology is useful to our efforts. The literature has a limited discussion of decision ontologies. Almost all focus on the entities and the entity hierarchy. But BNs and DGs emphasize relationships and the dependencies between relationships. The key for probabilistic first-order expressivity is to identify the relationships that enable dependencies between entity instances. We developed a MEDG Decision Ontology that highlights both the entities and key relationships that any decision model needs to address. It is designed to support decision model developers, including fusion model developers, in building comprehensive decision aiding capabilities.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128162997","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009826
Jiadong Liang, Tianxian Zhang, Yichuan Yang, G. Cui, L. Kong, Xiaobo Yang, Jianyu Yang
In this paper, under the situation of multiple interference regions, an optimal antenna placement problem for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple interference regions, we solve the antenna placement problem by utilizing antenna placement method based on Multi-Objective Particle Swarm Optimization (MOPSO). However, it is not clear when to stop the iteration for which no knowledge about the optimum result is available. Hence, computational resource may be wasted over iterations. Nevertheless, time and computational resource is limited in real application. Therefore, to obtain the optimal placement result with limited time and computational resource, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the optimization process efficiently when the optimal antenna placement algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.
{"title":"An efficient antenna placement method for MIMO radar under the situation of multiple interference regions","authors":"Jiadong Liang, Tianxian Zhang, Yichuan Yang, G. Cui, L. Kong, Xiaobo Yang, Jianyu Yang","doi":"10.23919/ICIF.2017.8009826","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009826","url":null,"abstract":"In this paper, under the situation of multiple interference regions, an optimal antenna placement problem for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple interference regions, we solve the antenna placement problem by utilizing antenna placement method based on Multi-Objective Particle Swarm Optimization (MOPSO). However, it is not clear when to stop the iteration for which no knowledge about the optimum result is available. Hence, computational resource may be wasted over iterations. Nevertheless, time and computational resource is limited in real application. Therefore, to obtain the optimal placement result with limited time and computational resource, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the optimization process efficiently when the optimal antenna placement algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131045906","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009801
Yingjie Zhang, Jian Lan
For nonlinear estimation, the Gaussian sum filter (GSF) provides a flexible and effective framework. It approximates the posterior probability density function (pdf) by a Gaussian mixture in which each Gaussian component is obtained using a linear minimum mean squared error (LMMSE) estimator. However, for a highly nonlinear problem with large measurement noise, the estimation performance of the LMMSE estimator is largely limited, since it is the best only within the class of linear estimators. This may further degrade the performance of the GSF, especially if a small number of these components are used. To improve the estimation performance, this paper proposes a Gaussian sum uncorrelated conversion (UC) based filter (GS-UCF), where the recently proposed uncorrelated conversion based filter (UCF) is applied to obtain the Gaussian components for Gaussian sum filtering. The UCF which is the LMMSE estimator using the measurement augmented by its uncorrelated conversions can outperform the original LMMSE estimator. Thus, the first two moments of the Gaussian component obtained by UCF can be more accurate than those obtained by the LMMSE estimator, which further improves the performance of the GSF. As an integration of the UCF and the GSF framework, the obtained filter is named as the Gaussian sum uncorrelated conversion based filter (GS-UCF). Simulation results show the effectiveness of the proposed estimator.
{"title":"Gaussian sum filtering using uncorrelatec conversion for nonlinear estimation","authors":"Yingjie Zhang, Jian Lan","doi":"10.23919/ICIF.2017.8009801","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009801","url":null,"abstract":"For nonlinear estimation, the Gaussian sum filter (GSF) provides a flexible and effective framework. It approximates the posterior probability density function (pdf) by a Gaussian mixture in which each Gaussian component is obtained using a linear minimum mean squared error (LMMSE) estimator. However, for a highly nonlinear problem with large measurement noise, the estimation performance of the LMMSE estimator is largely limited, since it is the best only within the class of linear estimators. This may further degrade the performance of the GSF, especially if a small number of these components are used. To improve the estimation performance, this paper proposes a Gaussian sum uncorrelated conversion (UC) based filter (GS-UCF), where the recently proposed uncorrelated conversion based filter (UCF) is applied to obtain the Gaussian components for Gaussian sum filtering. The UCF which is the LMMSE estimator using the measurement augmented by its uncorrelated conversions can outperform the original LMMSE estimator. Thus, the first two moments of the Gaussian component obtained by UCF can be more accurate than those obtained by the LMMSE estimator, which further improves the performance of the GSF. As an integration of the UCF and the GSF framework, the obtained filter is named as the Gaussian sum uncorrelated conversion based filter (GS-UCF). Simulation results show the effectiveness of the proposed estimator.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126777242","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009813
A. Boscaro, S. Jacquir, K. Sanchez, P. Perdu, S. Binczak
Defect localization in Very Large Integration Circuits (VLSI) requires to use multi-sensor information such as electrical waveforms, emission microscopy images and frequency mapping in order to detect, localize and identify the failure. Each sensor provides a specific kind of feature modeling the evidence. Thus, the defect localization in VLSI can be summarized as a problem of data fusion with heterogeneous and imprecise information. This study illustrates how to reproduce the human decision for modeling and fusing the different multi-sensor features by using the Demspter-Shafer theory. We propose not only an automatic decision rule for mass functions computing but also confidence intervals to quantify the final decision and to bring a decision help for the analysts expertise. Finally, a case of study is reported to attest the expert decision reproducibility.
{"title":"Automatic defect localization in VLSI circuits: A fusion approach based on the Dempster-Shafer theory","authors":"A. Boscaro, S. Jacquir, K. Sanchez, P. Perdu, S. Binczak","doi":"10.23919/ICIF.2017.8009813","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009813","url":null,"abstract":"Defect localization in Very Large Integration Circuits (VLSI) requires to use multi-sensor information such as electrical waveforms, emission microscopy images and frequency mapping in order to detect, localize and identify the failure. Each sensor provides a specific kind of feature modeling the evidence. Thus, the defect localization in VLSI can be summarized as a problem of data fusion with heterogeneous and imprecise information. This study illustrates how to reproduce the human decision for modeling and fusing the different multi-sensor features by using the Demspter-Shafer theory. We propose not only an automatic decision rule for mass functions computing but also confidence intervals to quantify the final decision and to bring a decision help for the analysts expertise. Finally, a case of study is reported to attest the expert decision reproducibility.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123293797","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009816
Junjun Guo, Chongzhao Han
This paper considers the sensor selection problem for target tracking in large-scale sensor networks. We propose a new sensor selection strategy based on dual-criterion optimization. Both the bias change detection and information gain maximization are considered as criteria in our proposed sensor selection strategy. This new approach extends the sensor selection problem from single criterion optimization to dual-criterion optimization. Therefore, our proposed approach can be used widely in many target tracking applications. Simulation results show the effectiveness of our proposed sensor selection approach.
{"title":"A new sensor selection approach based on dual-criterion optimization for sensor networks","authors":"Junjun Guo, Chongzhao Han","doi":"10.23919/ICIF.2017.8009816","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009816","url":null,"abstract":"This paper considers the sensor selection problem for target tracking in large-scale sensor networks. We propose a new sensor selection strategy based on dual-criterion optimization. Both the bias change detection and information gain maximization are considered as criteria in our proposed sensor selection strategy. This new approach extends the sensor selection problem from single criterion optimization to dual-criterion optimization. Therefore, our proposed approach can be used widely in many target tracking applications. Simulation results show the effectiveness of our proposed sensor selection approach.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122325940","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009876
Fei He, N. Rao, Chris Y. T. Ma
Large-scale infrastructures are critical to economic and social development, and hence their continued performance and security are of high national importance. Such an infrastructure often is a system of systems, and its functionality critically depends on the inherent robustness of its constituent systems and its defense strategy for countering attacks. Additionally, interdependencies between the systems play another critical role in determining the infrastructure robustness specified by its survival probability. In this paper, we develop game-theoretic models between a defender and an attacker for a generic system of systems using inherent parameters and conditional survival probabilities that characterize the interdependencies. We derive Nash Equilibrium conditions for the cases of interdependent and independent systems of systems under sum-form utility functions. We derive expressions for the infrastructure survival probability that capture its dependence on cost and system parameters, and also on dependencies that are specified by conditional probabilities. We apply the results to cyber-physical systems which show the effects on system survival probability due to defense and attack intensities, inherent robustness, unit cost, target valuation, and interdependencies.
{"title":"Game-theoretic analysis of system of systems with inherent robustness parameters","authors":"Fei He, N. Rao, Chris Y. T. Ma","doi":"10.23919/ICIF.2017.8009876","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009876","url":null,"abstract":"Large-scale infrastructures are critical to economic and social development, and hence their continued performance and security are of high national importance. Such an infrastructure often is a system of systems, and its functionality critically depends on the inherent robustness of its constituent systems and its defense strategy for countering attacks. Additionally, interdependencies between the systems play another critical role in determining the infrastructure robustness specified by its survival probability. In this paper, we develop game-theoretic models between a defender and an attacker for a generic system of systems using inherent parameters and conditional survival probabilities that characterize the interdependencies. We derive Nash Equilibrium conditions for the cases of interdependent and independent systems of systems under sum-form utility functions. We derive expressions for the infrastructure survival probability that capture its dependence on cost and system parameters, and also on dependencies that are specified by conditional probabilities. We apply the results to cyber-physical systems which show the effects on system survival probability due to defense and attack intensities, inherent robustness, unit cost, target valuation, and interdependencies.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121147586","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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