2021 IEEE 24th International Conference on Information Fusion (FUSION)最新文献_第8页

Toward Uncertainty Aware Quickest Change Detection 面向不确定性感知的快速变化检测

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626915

J. Z. Hare, L.M. Kaplan, V. Veeravalli

引用次数: 2

An α-Rényi Divergence Sigmoïd Parametrization For a Multi-Objectives and Context-Adaptive Fault Tolerant Localization 一种多目标、环境自适应容错定位的α- rsamnyi散度Sigmoïd参数化

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626925

Nesrine Harbaoui, Khoder Makkawi, Nourdine Ait Tmazirte, Maan El Badaoui El Najjar

{"title":"An α-Rényi Divergence Sigmoïd Parametrization For a Multi-Objectives and Context-Adaptive Fault Tolerant Localization","authors":"Nesrine Harbaoui, Khoder Makkawi, Nourdine Ait Tmazirte, Maan El Badaoui El Najjar","doi":"10.23919/fusion49465.2021.9626925","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626925","url":null,"abstract":"For a localization function, meeting together safety, accuracy and availability is a challenging task. Targeting one of these Key Performance Indicators (KPIs) remains feasible but when one or more other requirements are expected at the same time, the objectives become antagonistic. To achieve accuracy, a multi-sensor data fusion is recommended. However, it remains insufficient when it comes to safety critical applications as autonomous vehicle. Indeed, a diagnostic layer has to be considered to treat the presence of faults in dynamic environment, which can affect the sensors measurements. The detection algorithm must ensure high fault sensitivity while keeping false alarm rate as low as possible and taking into account both the change of navigation context and the change of targeted KPIs. This paper proposes a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation system) data fusion approach based on an unscented information filter for state estimation boosted by an adaptive diagnostic layer consisting of a Fault Detection and Isolation (FDI) method based on a powerful parametric information divergence: the α-Rényi divergence. The concept of diagnosis adaptability is developed by applying a sigmoïd strategy in order to increase the sensitivity of the selected residual to detect maximum of faults according to the crossed environment. The suitable selection, at each instant, of α, is ensured through the implementation of a generalized logistic function according to the current constraint of the navigation context. Following the detection step, a decision-cost optimized threshold is reevaluated at each instant. Applied to field data, the first experiments show promising results of the developed framework compared to a diagnostic layer based on the well-known Kullback-Leibler divergence.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116338190","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: 0

A Random Finite Set Sensor Control Approach for Vision-based Multi-object Search-While-Tracking 基于视觉的多目标搜索跟踪随机有限集传感器控制方法

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626898

Keith A. LeGrand, Pingping Zhu, S. Ferrari

引用次数: 4

Relation-Aware Neighborhood Aggregation for Cross-lingual Entity Alignment 跨语言实体对齐的关系感知邻域聚合

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626917

Yuanna Liu, Jie Geng, Xinyang Deng, Wen Jiang

引用次数: 0

Radar Resource Management for Multi-Target Tracking Using Model Predictive Control 基于模型预测控制的多目标跟踪雷达资源管理

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626897

Thies de Boer, M. Schöpe, H. Driessen

引用次数: 1

Machine Learning for Soil Moisture Prediction Using Hyperspectral and Multispectral Data 利用高光谱和多光谱数据进行土壤水分预测的机器学习

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627067

Michaela Lobato, W. Norris, R. Nagi, A. Soylemezoglu, Dustin Nottage

{"title":"Machine Learning for Soil Moisture Prediction Using Hyperspectral and Multispectral Data","authors":"Michaela Lobato, W. Norris, R. Nagi, A. Soylemezoglu, Dustin Nottage","doi":"10.23919/fusion49465.2021.9627067","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627067","url":null,"abstract":"Soil moisture content is a key component in terrain characterization for site selection and trafficability assessment. It is laborious and time-consuming to determine soil moisture content using traditional in situ soil moisture sensing methods and may be infeasible for large or dangerous sites. By employing remote sensing techniques, soil moisture content can be determined in a safe and efficient manner. In this work, the results of Keller et al. [1] are expanded upon by reducing the dimensionality of a hyperspectral dataset, resulting in an increase in soil moisture content prediction accuracy. Ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were trained on 5 input variables. The results indicated that soil moisture content could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The validity of this method is confirmed by creating a multispectral dataset and concatenating it to the reduced dimensionality (RD) set for an accuracy increase. The ET model’s estimates of soil moisture content outperformed the baseline hyperspectral dataset: obtaining an increase of 1.3% and 5.4% in R-squared values (with a corresponding decrease of .13 and .22 in mean absolute error MAE) when trained on RD and concatenated multispectral (CM) datasets, respectively.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133850542","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

Use of the URREF towards Information Fusion Accountability Evaluation URREF在信息融合问责评估中的应用

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626847

E. Blasch, J. Pieter de Villiers, Gregor Pavin, A. Jousselme, P. Costa, Kathryn B. Laskey, J. Ziegler

{"title":"Use of the URREF towards Information Fusion Accountability Evaluation","authors":"E. Blasch, J. Pieter de Villiers, Gregor Pavin, A. Jousselme, P. Costa, Kathryn B. Laskey, J. Ziegler","doi":"10.23919/fusion49465.2021.9626847","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626847","url":null,"abstract":"The EXCITE (EXplainability Capability Information Testing Evaluation) approach assesses information fusion interpretability, explainability, and accountability for uncertainty analysis. Amongst many data and information fusion techniques is the need to understand the information fusion system capability for the intended application. While many approaches for data fusion support uncertainty reduction from measured data, there are other contributing factors such as data source credibility, knowledge completeness, multiresolution, and problem alignment. To facilitate the alignment of the data fusion approach to the user’s intended action, there is a need towards a representation of the uncertainty. The paper highlights the approach to leverage recent research efforts in interpretability as methods of data handing in the Uncertainty Representation and Reasoning Evaluation Framework (URREF) while also proposing explainability and accountability as a representation criterion. Accountability is closely associated with the selected decision and the outcome which has these four attributes: amount of data towards the result, distance score of decision selection, accuracy/credibility/timeliness of results, and risk analysis. The risk analysis includes: verifiability, observability, liability, transparency, attributability, and remediabilty. Results are demonstrated on notional example.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115546048","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: 2

TrafficEKF: a Learning Based Traffic Aware Extended Kalman Filter 基于学习的交通感知扩展卡尔曼滤波

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627021

Liang Xu, R. Niu

{"title":"TrafficEKF: a Learning Based Traffic Aware Extended Kalman Filter","authors":"Liang Xu, R. Niu","doi":"10.23919/fusion49465.2021.9627021","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627021","url":null,"abstract":"Most vehicle tracking algorithms only consider the vehicle’s kinematic state but ignore the information about the surrounding environment, which also plays an important role affecting how the driver controls the vehicle. In addition, how to represent the traffic information and its effect on the vesicle’s state is a challenging problem. In this paper, we propose a tracking method called traffic aware extended Kalman filter (TrafficEKF), which not only incorporates the vehicle’s kinematic dynamics, but also the information from the surrounding environment. The traffic information has been represented by a bird-eye-view rasterized image, with the road shape, traffic light conditions, and other objects inside the field of view. The effect of the traffic information on vehicle driving is learned by TrafficEKF from the ground truth data. Through training, the algorithm learns to predict the control input to the vehicle and to optimize the process and measurement noise covariance matrices used by the EKF. Based on experiments with real data, we show that the TrafficEKF significantly outperforms both a manually tuned EKF, and a data trained EKF, which ignore the environment information.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066894","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

Labeled Multi-Bernoulli Filter based Group Target Tracking Using SDE and Graph Theory 基于SDE和图论的标记多伯努利滤波器群目标跟踪

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626976

Li Li, Qinchen Wu, Bin Yan, Shaoming Wei, Jun Wang

引用次数: 2

Investigating suspicious vessel behaviour in light of context 根据情况调查可疑船只的行为

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626985

P. Kowalski, A. Jousselme

{"title":"Investigating suspicious vessel behaviour in light of context","authors":"P. Kowalski, A. Jousselme","doi":"10.23919/fusion49465.2021.9626985","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626985","url":null,"abstract":"Hybrid threat events are rare and cannot be modelled solely based on data. Instead they require a focus on discovery of emergent knowledge through information sharing across agencies and systems. However, multi-intelligence can bring about reasoning challenges with multiple sources such as confirmation biases. In this paper, we present how context can be used to combat these reasoning biases. Firstly, we show how it can reduce the impact of the overly confident sources and secondly, how it can be used to provide counter-evidence. It is shown that when context is used in such a manner the reasoning results display less false confidence while still supporting the original hypothesis. We apply the reasoning scheme to the post-analysis of a real case event. The story of Andromeda was widely reported upon when the vessel loaded with 410 tonnes of explosives supposedly sailing to Libya was arrested near Crete in early 2018. Using media headlines, AIS signals and analyst reports, we show how realistic, uncertain, heterogeneous reports and contextual information can be put together to reason about its intent. We propose a reasoning model framed within the theory of evidence to combine the information from these sources. The modularity of our method allows us to easily compare different approaches to context-aware reasoning. We finally conclude on future steps for this work.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116314568","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