Pub Date : 2017-07-01DOI: 10.23919/ICIF.2017.8009736
J. Dezert, A. Tchamova, P. Konstantinova, Erik Blasch
This paper presents a comparative analysis of performances of two types of multi-target tracking algorithms: 1) the Joint Probabilistic Data Association Filter (JPDAF), and 2) classical Kalman Filter based algorithms for multi-target tracking improved with Quality Assessment of Data Association (QADA) method using optimal data association. The evaluation is based on Monte Carlo simulations for difficult maneuvering multiple-target tracking (MTT) problems in clutter.
{"title":"A comparative analysis of QADA-KF with JPDAF for multitarget tracking in clutter","authors":"J. Dezert, A. Tchamova, P. Konstantinova, Erik Blasch","doi":"10.23919/ICIF.2017.8009736","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009736","url":null,"abstract":"This paper presents a comparative analysis of performances of two types of multi-target tracking algorithms: 1) the Joint Probabilistic Data Association Filter (JPDAF), and 2) classical Kalman Filter based algorithms for multi-target tracking improved with Quality Assessment of Data Association (QADA) method using optimal data association. The evaluation is based on Monte Carlo simulations for difficult maneuvering multiple-target tracking (MTT) problems in clutter.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"52 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":"123766108","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.2017.8009683
W. Richter
Big classes of directional distribution laws generalizing the von Mises distribution are provided in [4] following a general geometric offset approach in [20]. Once a distribution law is estimated for modeling a given data set, one of the next steps of statistical analysis is simulating from such distribution. The von Mises distribution was simulated in [1] using an acceptance-rejection simulation scheme. Several types of envelopes needed for such approach to simulate generalized von Mises distributions are exploited in [6] and [17]. Adapting such technique, here we provide an algorithm for simulating certain polyhedrally contoured generalizations of the von Mises distribution. This particular class of geometrically generalized von Mises distributions will be derived from the class of polyhedral star-shaped distributions which for its part was introduced in [22].
{"title":"Simulation of geometrically generalized von Mises distributions","authors":"W. Richter","doi":"10.23919/ICIF.2017.8009683","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009683","url":null,"abstract":"Big classes of directional distribution laws generalizing the von Mises distribution are provided in [4] following a general geometric offset approach in [20]. Once a distribution law is estimated for modeling a given data set, one of the next steps of statistical analysis is simulating from such distribution. The von Mises distribution was simulated in [1] using an acceptance-rejection simulation scheme. Several types of envelopes needed for such approach to simulate generalized von Mises distributions are exploited in [6] and [17]. Adapting such technique, here we provide an algorithm for simulating certain polyhedrally contoured generalizations of the von Mises distribution. This particular class of geometrically generalized von Mises distributions will be derived from the class of polyhedral star-shaped distributions which for its part was introduced in [22].","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":"126760623","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.2017.8009831
G. Kurz, F. Pfaff, U. Hanebeck
Estimation of periodic quantities such as angles or phase values is a common problem. However, standard approaches, for example the Kalman filter and extensions thereof, have difficulties when estimating periodic quantities. To address this problem, circular filtering algorithms have been proposed but they are limited to just a single angle. In order to deal with multiple, possibly correlated angles, toroidal filtering algorithms are necessary. We have previously proposed a bivariate filtering algorithm on the torus [1] that is limited to identity system and measurement models. In this paper, we show how the algorithm can be extended to handle nonlinear system and measurement models. The novel approach relies on the bivariate wrapped normal distribution for representing the uncertainty and it makes use of a deterministic sampling scheme for the torus. We provide a thorough evaluation of the proposed method using simulations.
{"title":"Nonlinear toroidal filtering based on bivariate wrapped normal distributions","authors":"G. Kurz, F. Pfaff, U. Hanebeck","doi":"10.23919/ICIF.2017.8009831","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009831","url":null,"abstract":"Estimation of periodic quantities such as angles or phase values is a common problem. However, standard approaches, for example the Kalman filter and extensions thereof, have difficulties when estimating periodic quantities. To address this problem, circular filtering algorithms have been proposed but they are limited to just a single angle. In order to deal with multiple, possibly correlated angles, toroidal filtering algorithms are necessary. We have previously proposed a bivariate filtering algorithm on the torus [1] that is limited to identity system and measurement models. In this paper, we show how the algorithm can be extended to handle nonlinear system and measurement models. The novel approach relies on the bivariate wrapped normal distribution for representing the uncertainty and it makes use of a deterministic sampling scheme for the torus. We provide a thorough evaluation of the proposed method using simulations.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"3 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":"115201597","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.2017.8009705
Wei Qiu, Wenke Wang, Zhiyun Zhuang, P. Lei, Le Li
In multiple-target tracking problem, data association technique plays an significant role. When targets move closely or crosswise, performances of conventional data association algorithms which use kinematic information only may be degraded. Actually, beside the kinematic information, sensors always can obtain feature information about the target, and incorporating the features into data association problem can improve the performance of data association. In this paper, for underwater target tracking, we propose a ship radiated noise spectrum feature aided probabilistic data association (PDA) algorithm to improve the data association and target tracking performance. Simulations are finally carried out and results show that the proposed method can improve the tracking performance over the conventional PDA algorithm in close-space-targets scenario.
{"title":"Using ship radiated noise spectrum feature for data association in underwater target tracking","authors":"Wei Qiu, Wenke Wang, Zhiyun Zhuang, P. Lei, Le Li","doi":"10.23919/ICIF.2017.8009705","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009705","url":null,"abstract":"In multiple-target tracking problem, data association technique plays an significant role. When targets move closely or crosswise, performances of conventional data association algorithms which use kinematic information only may be degraded. Actually, beside the kinematic information, sensors always can obtain feature information about the target, and incorporating the features into data association problem can improve the performance of data association. In this paper, for underwater target tracking, we propose a ship radiated noise spectrum feature aided probabilistic data association (PDA) algorithm to improve the data association and target tracking performance. Simulations are finally carried out and results show that the proposed method can improve the tracking performance over the conventional PDA algorithm in close-space-targets scenario.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"47 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":"114062458","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.2017.8009817
Ruiqing Xi, Jian Lan
An inertial navigation network (INN) is composed of a high-precision master inertial navigation system (MINS) and multiple low-precision slave inertial navigation systems (SINS). The MINS located at the center of a carrier provides accurate global navigation parameters, while the SINSs at different locations of a carrier provide local navigation parameters. The outputs of the MINS are used to improve the local navigation performance of the SINSs through transfer alignment. However, the SINSs may suffer from abrupt faults which will largely degrade the performance of the transfer alignment and even break down the whole network. To solve this problem, a joint transfer alignment and fault diagnosis (JTAFD) approach is proposed. In JTAFD, we propose a hybrid system with multiple models corresponding to different particular failure modes, respectively. The lever-arm effect with flexure angles is also fully considered in the hybrid system. Then the transfer alignment and fault diagnosis results are jointly obtained. The local navigation parameters of fault SINSs can also be recovered automatically. The effectiveness of the proposed JTAFD approach is demonstrated by simulation results for integrated navigation using an INN on a ship.
{"title":"Joint transfer alignment and fault diagnosis of inertial navigation network","authors":"Ruiqing Xi, Jian Lan","doi":"10.23919/ICIF.2017.8009817","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009817","url":null,"abstract":"An inertial navigation network (INN) is composed of a high-precision master inertial navigation system (MINS) and multiple low-precision slave inertial navigation systems (SINS). The MINS located at the center of a carrier provides accurate global navigation parameters, while the SINSs at different locations of a carrier provide local navigation parameters. The outputs of the MINS are used to improve the local navigation performance of the SINSs through transfer alignment. However, the SINSs may suffer from abrupt faults which will largely degrade the performance of the transfer alignment and even break down the whole network. To solve this problem, a joint transfer alignment and fault diagnosis (JTAFD) approach is proposed. In JTAFD, we propose a hybrid system with multiple models corresponding to different particular failure modes, respectively. The lever-arm effect with flexure angles is also fully considered in the hybrid system. Then the transfer alignment and fault diagnosis results are jointly obtained. The local navigation parameters of fault SINSs can also be recovered automatically. The effectiveness of the proposed JTAFD approach is demonstrated by simulation results for integrated navigation using an INN on a ship.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"25 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":"114422003","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.2017.8009879
J. D. Villiers, Richard W. Focke, G. Pavlin, A. Jousselme, V. Dragos, Kathryn B. Laskey, P. Costa, Erik Blasch
The International Society of Information Fusion (ISIF) Evaluation Techniques for Uncertainty Representation Working Group (ETURWG) investigates the quantification and evaluation of all types of uncertainty regarding the inputs, reasoning and outputs of the information fusion process. The ETURWG is developing an Uncertainty Representation and Reasoning Framework (URREF) ontology for this purpose. This paper outlines a start towards the process of defining metrics for the URREF data criteria, which will align the URREF ontology with practical application. A criterion can be evaluated according to several metrics, and a metric can be applied to several criteria. As such, the ontology would have to reflect the nature of a many-to-many mapping between criteria and metrics. The main findings and suggestions of the paper advancing the use of URREF are: 1) The Weight of Information (WoI) is dependent on data criteria, which in turn depend on source criteria. 2) Criteria and metrics that apply to evidence (typically an input of the fusion system), could equally apply to the fusion system outputs or internal information, which in turn could form the inputs of another system. As such the word “Evidence” in the terms “Piece of Evidence” and “Weight of Evidence” should be replaced by the word “Information”. 3) Accuracy and precision and associated metrics are ubiquitous in the URREF ontology and can evaluate many parts of the fusion system. 4) The weight of information also assumes an important position in the ontology, as it depends on several source and data criteria.
{"title":"Evaluation metrics for the practical application of URREF ontology: An illustration on data criteria","authors":"J. D. Villiers, Richard W. Focke, G. Pavlin, A. Jousselme, V. Dragos, Kathryn B. Laskey, P. Costa, Erik Blasch","doi":"10.23919/ICIF.2017.8009879","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009879","url":null,"abstract":"The International Society of Information Fusion (ISIF) Evaluation Techniques for Uncertainty Representation Working Group (ETURWG) investigates the quantification and evaluation of all types of uncertainty regarding the inputs, reasoning and outputs of the information fusion process. The ETURWG is developing an Uncertainty Representation and Reasoning Framework (URREF) ontology for this purpose. This paper outlines a start towards the process of defining metrics for the URREF data criteria, which will align the URREF ontology with practical application. A criterion can be evaluated according to several metrics, and a metric can be applied to several criteria. As such, the ontology would have to reflect the nature of a many-to-many mapping between criteria and metrics. The main findings and suggestions of the paper advancing the use of URREF are: 1) The Weight of Information (WoI) is dependent on data criteria, which in turn depend on source criteria. 2) Criteria and metrics that apply to evidence (typically an input of the fusion system), could equally apply to the fusion system outputs or internal information, which in turn could form the inputs of another system. As such the word “Evidence” in the terms “Piece of Evidence” and “Weight of Evidence” should be replaced by the word “Information”. 3) Accuracy and precision and associated metrics are ubiquitous in the URREF ontology and can evaluate many parts of the fusion system. 4) The weight of information also assumes an important position in the ontology, as it depends on several source and data criteria.","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":"128769669","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.2017.8009792
Shuhui Li, Xiaoxue Feng, Feng Pan
The wide-sense auto-regressive moving-average (ARMA) model is widely applied into varieties of fields. The unknown bounded parameter estimation of an ARMA model is an extremely vital research subject. Up to recent, most research is conducted with the known disturbing environment noise or the model of the known noise with the unknown variance. Actually the disturbing noise in the modern control system is really complex and unknown. To the best of our knowledge, less attention on the unknown boundary parameter estimation for the wide-sense stationary hidden ARMA process with unknown noise is paid. In this paper, a dual particle filter-based method to estimate the state and unknown bounded parameter jointly for the hidden wide-sense ARMA processes under the unknown noise is presented, which includes two steps. In the first step, the kernel smoothing particle filter algorithm is utilized to estimate the unknown bounded ARMA model parameter. And sufficient statistics based on Beta distribution is utilized to approach the posterior distribution of the parameter. In the second step, the particle filter algorithm is utilized to estimate the state of an ARMA model with the model parameter obtained in the first step. For the noise model is extremely unknown, the Gaussian mixture model is adopted to approach the posterior probability function in the EM algorithm. Simulation results verify the effectiveness of the proposed scheme.
{"title":"Joint state and parameter estimation for hidden wide-sense stationary ARMA processes under unknown noise","authors":"Shuhui Li, Xiaoxue Feng, Feng Pan","doi":"10.23919/ICIF.2017.8009792","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009792","url":null,"abstract":"The wide-sense auto-regressive moving-average (ARMA) model is widely applied into varieties of fields. The unknown bounded parameter estimation of an ARMA model is an extremely vital research subject. Up to recent, most research is conducted with the known disturbing environment noise or the model of the known noise with the unknown variance. Actually the disturbing noise in the modern control system is really complex and unknown. To the best of our knowledge, less attention on the unknown boundary parameter estimation for the wide-sense stationary hidden ARMA process with unknown noise is paid. In this paper, a dual particle filter-based method to estimate the state and unknown bounded parameter jointly for the hidden wide-sense ARMA processes under the unknown noise is presented, which includes two steps. In the first step, the kernel smoothing particle filter algorithm is utilized to estimate the unknown bounded ARMA model parameter. And sufficient statistics based on Beta distribution is utilized to approach the posterior distribution of the parameter. In the second step, the particle filter algorithm is utilized to estimate the state of an ARMA model with the model parameter obtained in the first step. For the noise model is extremely unknown, the Gaussian mixture model is adopted to approach the posterior probability function in the EM algorithm. Simulation results verify the effectiveness of the proposed scheme.","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":"129655781","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.2017.8009757
Zhe Zhang, Deqiang Han, J. Dezert, Yi Yang
Dempster-Shafer evidence theory (DST) is a theoretical framework for uncertainty modeling and reasoning. The determination of basic belief assignment (BBA) is crucial in DST, however, there is no general theoretical method for BBA determination. In this paper, a method of generating BBA using fuzzy numbers is proposed. First, the training data are modeled as fuzzy numbers. Then, the dissimilarities between each test sample and the training data are measured by the distance between fuzzy numbers. In the final, the BBAs are generated from the normalized dissimilarities. The effectiveness of this method is demonstrated by an application of classification problem. Experimental results show that the proposed method is robust to outliers.
{"title":"Determination of basic belief assignment using fuzzy numbers","authors":"Zhe Zhang, Deqiang Han, J. Dezert, Yi Yang","doi":"10.23919/ICIF.2017.8009757","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009757","url":null,"abstract":"Dempster-Shafer evidence theory (DST) is a theoretical framework for uncertainty modeling and reasoning. The determination of basic belief assignment (BBA) is crucial in DST, however, there is no general theoretical method for BBA determination. In this paper, a method of generating BBA using fuzzy numbers is proposed. First, the training data are modeled as fuzzy numbers. Then, the dissimilarities between each test sample and the training data are measured by the distance between fuzzy numbers. In the final, the BBAs are generated from the normalized dissimilarities. The effectiveness of this method is demonstrated by an application of classification problem. Experimental results show that the proposed method is robust to outliers.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"60 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":"126794600","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.2017.8009819
W. Rekik, S. L. Hégarat-Mascle, Emanuel Aldea
Despite many proposed solutions, multi-object tracking remains a challenging problem in complex situations involving partial occlusions and non-uniform and abrupt illumination changes. Considering modular systems, the tracking performance strongly depends on the consistency of the different blocks relatively to error features. In this work, using the Belief Function framework, we take into account the reliability and the imprecision of the object detection and location to characterize objects and to derive a reliable descriptor. Since this latter is then estimated only on safe object subparts, even in case of crosses between objects, we use a distance between descriptor robust to partial occlusion, namely the recently proposed Bin-Ratio-Distance. Results obtained on various actual sequences underline the interest of the proposed algorithm by outperforming the tested alternative approaches.
{"title":"A novel approach for multi-object tracking using evidential representation for objects","authors":"W. Rekik, S. L. Hégarat-Mascle, Emanuel Aldea","doi":"10.23919/ICIF.2017.8009819","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009819","url":null,"abstract":"Despite many proposed solutions, multi-object tracking remains a challenging problem in complex situations involving partial occlusions and non-uniform and abrupt illumination changes. Considering modular systems, the tracking performance strongly depends on the consistency of the different blocks relatively to error features. In this work, using the Belief Function framework, we take into account the reliability and the imprecision of the object detection and location to characterize objects and to derive a reliable descriptor. Since this latter is then estimated only on safe object subparts, even in case of crosses between objects, we use a distance between descriptor robust to partial occlusion, namely the recently proposed Bin-Ratio-Distance. Results obtained on various actual sequences underline the interest of the proposed algorithm by outperforming the tested alternative approaches.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"17 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":"129232664","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.2017.8009820
A. Jøsang, Dongxia Wang, Jie Zhang
Belief fusion consists of taking into account multiple sources of belief about a domain of interest. This paper describes cumulative and averaging multi-source belief fusion in the formalism of subjective logic, which represent generalisations of binary-source belief fusion operators previously described. The advantage of this approach is that we can model and analyse belief fusion situations involving an arbitrary number of sources.
{"title":"Multi-source fusion in subjective logic","authors":"A. Jøsang, Dongxia Wang, Jie Zhang","doi":"10.23919/ICIF.2017.8009820","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009820","url":null,"abstract":"Belief fusion consists of taking into account multiple sources of belief about a domain of interest. This paper describes cumulative and averaging multi-source belief fusion in the formalism of subjective logic, which represent generalisations of binary-source belief fusion operators previously described. The advantage of this approach is that we can model and analyse belief fusion situations involving an arbitrary number of sources.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"22 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":"123908637","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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