2021 IEEE 24th International Conference on Information Fusion (FUSION)最新文献

Toward Measuring Information Value in a Multi-Intelligence Context 多智能环境下的信息价值测量研究

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627063

A. Jousselme, T. Wickramarathne, P. Kowalski

{"title":"Toward Measuring Information Value in a Multi-Intelligence Context","authors":"A. Jousselme, T. Wickramarathne, P. Kowalski","doi":"10.23919/fusion49465.2021.9627063","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627063","url":null,"abstract":"In decision-making under uncertainty, the objective of measuring information value is to assist a decision-maker toward making good decisions by generating necessary metadata about the information that is being considered for the decision-making task. Epistemic decisions about which sources to trust or query are critical for a decision-maker when the end-goal (or final) decisions are to be made using limited number of information sources. This is even more critical in a multi-intelligence context, where information sources are highly heterogeneous, prone to errors, partially informed, deceptive, or even malicious. In these contexts, the ability to distinguish between aleatory and epistemic uncertainty (ignorance) becomes a fundamental requirement. In this paper, we propose some extensions to classical measures of value of information for imprecise belief states represented by belief functions relying on a general observation model. The proposed measures allow a decision-maker to highlight critical parameters, such as the probability of source reliability and the degree of confidence expressed by the source. We compare several decision models and illustrate the use of proposed measures in a maritime surveillance scenario, where the decision-maker has to make a rational selection of information sources that consists of both physical sensors and human sources. We conclude by providing some insights on future research directions to expand this preliminary exploration.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123255906","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}

引用次数: 3

Method of Basic Belief Assignment Determination Based on Density Estimation of Ambiguous Samples 基于模糊样本密度估计的基本信念赋值确定方法

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626848

Wei Li, Deqiang Han, Xiaojing Fan, Bo Dong

引用次数: 0

Particle filters with auxiliary Markov transition. Application to crossover and to multitarget tracking 辅助马尔可夫跃迁的粒子滤波。应用于交叉和多目标跟踪

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627056

Audrey Cuillery, F. Gland

引用次数: 0

Modeling of the tire-road friction using neural networks including quantification of the prediction uncertainty 用神经网络对轮胎-路面摩擦进行建模，包括对预测不确定性的量化

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626974

Magnus Malmström, I. Skog, Daniel Axehill, F. Gustafsson

{"title":"Modeling of the tire-road friction using neural networks including quantification of the prediction uncertainty","authors":"Magnus Malmström, I. Skog, Daniel Axehill, F. Gustafsson","doi":"10.23919/fusion49465.2021.9626974","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626974","url":null,"abstract":"Despite the great success of neural networks (NN) in many application areas, it is still not obvious how to integrate an NN in a sensor fusion framework. The reason is that the computation of the for fusion required variance of NN is still a rather immature area. Here, we apply a methodology from system identification where uncertainty of the parameters in the NN are first estimated in the training phase, and then this uncertainty is propagated to the output in the prediction phase. This local approach is based on linearization, and it implicitly assumes a good signal-to-noise ratio and persistency of excitation. We illustrate the proposed method on a fundamental problem in advanced driver assistance systems (ADAS), namely to estimate the tire-road friction. This is a single input single output static nonlinear relation that is simple enough to provide insight and it enables comparisons with other parametric approaches. We compare both to existing methods for how to assess uncertainty in NN and standard methods for this problem, and evaluate on real data. The goal is not to improve on simpler methods for this particular application, but rather to validate that our method is on par with simpler model structures, where output variance is immediately provided.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124750265","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}

引用次数: 1

Multi-Frame Joint Tracking and Shape Estimation Method for Weak Extended Targets 弱扩展目标多帧联合跟踪与形状估计方法

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627019

Desheng Zhang, Wujun Li, Wei Yi

引用次数: 1

Uncertainty Evaluation of Temporal Trust in a Fusion System Using the URREF Ontology 基于URREF本体的融合系统时间信任的不确定性评估

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627007

J. D. Villiers, G. Pavlin, J. Ziegler, A. Jousselme, P. Costa, Eric Blasch, Kathryn B. Laskey, C. Laudy, A. D. Waal, J.-H. Cho

{"title":"Uncertainty Evaluation of Temporal Trust in a Fusion System Using the URREF Ontology","authors":"J. D. Villiers, G. Pavlin, J. Ziegler, A. Jousselme, P. Costa, Eric Blasch, Kathryn B. Laskey, C. Laudy, A. D. Waal, J.-H. Cho","doi":"10.23919/fusion49465.2021.9627007","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627007","url":null,"abstract":"To be employed effectively, an information fusion system must be trusted. As information fusion systems grow more complex, the question of trust grows more pressing. This paper addresses the question of evaluating trust in the uncertainty representation and reasoning aspects of an information fusion system. The four main aspects of trust in information fusion systems considered in this paper are: (1) how trust manifests itself in an information fusion system; (2) the temporal aspects of trust and its effect on the decision process; (3) the uncertainty associated with trust; and (4) exploring the evaluation of the uncertainty associated with trust using the Uncertainty Representation and Reasoning Framework (URREF) ontology and other trust related ontologies. The focus of the paper is on measuring trust related uncertainty to engage users towards adopting information fusion systems in mission systems. Mapping trust constructs into the Uncertainty Representation and Reasoning Framework provides measurable criteria for trust uncertainty analysis and evaluation. The ideas put forward in this paper serve as a foundation for further discussions within the Evaluation Techniques for Uncertainty Representation Working Group (ETURWG) on how to evaluate the uncertainty aspects of trust in information fusion systems, and to further cement application of the URREF ontology for the evaluation of trust uncertainty.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131364006","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}

引用次数: 3

Observability Analysis of Multipath Assisted Target Tracking with Unknown Reflection Surface 未知反射面下多径辅助目标跟踪的可观测性分析

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626858

Aranee Balachandran, R. Tharmarasa

引用次数: 1

A Mutli-feature Correlation Filter Tracker with Different Hash Algorithm 不同哈希算法的多特征相关滤波跟踪器

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626963

Sixian Zhang, Yi Yang, Meng Zhang, Pengbo Mi

引用次数: 0

Composite Transportation Dissimilarity in Consistent Gaussian Mixture Reduction 一致高斯混合还原中的复合输运差异

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627011

A. D'Ortenzio, C. Manes

{"title":"Composite Transportation Dissimilarity in Consistent Gaussian Mixture Reduction","authors":"A. D'Ortenzio, C. Manes","doi":"10.23919/fusion49465.2021.9627011","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627011","url":null,"abstract":"Gaussian Mixtures (GMs) are a powerful tool for approximating probability distributions across a variety of fields. In some applications the number of GM components rapidly grows with time, so that reduction algorithms are necessary. Given a GM with a large number of components, the problem of Gaussian Mixture Reduction (GMR) consists in finding a GM with considerably less components that is not too dissimilar from the original one. There are many issues that make non trivial this problem. First of all, many dissimilarity measures exist for GMs, although most of them lack closed forms, and their numerical computation is a demanding task, especially for distributions in high dimensions. Moreover, some basic reduction actions can be simple or complex tasks depending on which dissimilarity measure is chosen. It follows that most reduction procedures proposed in the literature are made of steps that are aimed at maintaining low dissimilarity according to different measures, thus leading to a pipeline of actions that are not mutually consistent. In this paper Composite Transportation Dissimilarities are discussed and exploited to formulate a GMR framework that preserves consistency with a unique dissimilarity measure, and provides a generalization of the celebrated Runnalls’ GMR approach.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133671392","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}

引用次数: 6

Likeness-Based dissimilarity measures for Gaussian Mixture Reduction and Data Fusion 基于相似度的高斯混合约简与数据融合的不相似度量

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626978

A. D'Ortenzio, C. Manes

引用次数: 5