2021 IEEE 24th International Conference on Information Fusion (FUSION)最新文献_第5页

Comparison of Confidence Sets Designs for Various Degrees of Knowledge 不同知识程度的置信集设计比较

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626991

Jiří Ajgl, O. Straka

引用次数: 1

Policy Rollout Action Selection with Knowledge Gradient for Sensor Path Planning 基于知识梯度的传感器路径规划策略展开动作选择

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626874

Thore Gerlach, Folker Hoffmann, A. Charlish

引用次数: 3

Recursive LMMSE Sequential Fusion with Multi-Radar Measurements for Target Tracking 基于多雷达测量的递推LMMSE序列融合目标跟踪

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626993

Donglin Zhang, Z. Duan

引用次数: 1

Animal Tracking within a Formation of Drones 无人机编队中的动物追踪

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626844

J. T. Marcos, S. Utete

{"title":"Animal Tracking within a Formation of Drones","authors":"J. T. Marcos, S. Utete","doi":"10.23919/fusion49465.2021.9626844","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626844","url":null,"abstract":"In this study, we develop a distributed system that can be used by unmanned aerial vehicles (UAVs) or drones for single-animal tracking in terrestrial settings. The system involves a video object tracking (VOT) solution and a drone formation. The proposed VOT solution is based on the particle filter (PF) with two measurement providers: a colour image segmentation (CIS) approach and a machine learning (ML) technique. They are switched based on the structural similarity (SSIM) index between the initial and the current target appearances to mitigate the limitation of computational resources of civilian drones, and to ensure good tracking performance. At first, the deep learning object detector You Only Look Once version three (YOLOv3) is used as the second measurement provider. The proposed VOT solution has been tested on wildlife footage recorded by drones (and obtained from an animal behaviour group). The tests demonstrate amongst other results that the proposed VOT solution is more efficient when YOLOv3 is replaced by other methods such as boosting and channel and spatial reliability tracking (CSRT). The results suggest the utility of the proposed VOT solution in single-animal tracking with cooperative drones for wildlife preservation.","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":"133906344","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

Deep Learning for Financial Time Series Forecast Fusion and Optimal Portfolio Rebalancing 基于深度学习的金融时间序列预测融合与最优投资组合再平衡

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626945

Siddeeq Laher, A. Paskaramoorthy, Terence L van Zyl

引用次数: 10

Making Sense of It All: Measurement Cluster Sequencing for Enhanced Situational Awareness with Ubiquitous Sensing 这一切的意义:测量集群排序增强态势感知与无处不在的传感

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626851

Varun K. Garg, Brooks P. Saunders, T. Wickramarathne

{"title":"Making Sense of It All: Measurement Cluster Sequencing for Enhanced Situational Awareness with Ubiquitous Sensing","authors":"Varun K. Garg, Brooks P. Saunders, T. Wickramarathne","doi":"10.23919/fusion49465.2021.9626851","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626851","url":null,"abstract":"Situational awareness methods aim to identify and map what is happening in an operational environment in terms of operational terms that define certain decision-making contexts. The underlying assumption here is that an appropriate decision-making context is either known or can be identified a priori for accurately mapping incoming evidence. However, in many complex and unstructured operational environments where situational awareness systems are most useful (e.g., asymmetric battlegrounds, urban reconnaissance), the decision-making context is neither known a priori nor it is easy to determine by, say subject matter experts. This paper presents a data-driven approach for gaining insights on the decision-making context via judicious processing of ubiquitous soft (i.e., human-based) and hard (e.g., physics-based) data streams generated by voluntarily participating mobile sensors that are traversing the operational environment. In particular, by using spectral clustering in tandem with variable length sequence decoding methods, ubiquitous data stream are clustered and then processed for early identification of specific scenarios of interest (that may have generated the sensor measurements). This will enable a decision-maker to understand emerging situations in the operational environment to set the correct decision-making context and proactively identify what information will be most relevant to reducing uncertainty associated with them. Our approach is illustrated via a simulated example that provides insights into its behavior, performance and sensitivity to parameters.","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":"133644857","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 Diffusion-Based Distributed Time Difference Of Arrival Source Positioning 一种基于扩散的到达源分布时差定位方法

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627001

Asaf Gendler, S. Peleg, A. Amar

引用次数: 1

Supporting Agile User Fusion Analytics through Human-Agent Knowledge Fusion 通过人-代理知识融合支持敏捷用户融合分析

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627072

Dave Braines, A. Preece, Colin Roberts, E. Blasch

{"title":"Supporting Agile User Fusion Analytics through Human-Agent Knowledge Fusion","authors":"Dave Braines, A. Preece, Colin Roberts, E. Blasch","doi":"10.23919/fusion49465.2021.9627072","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627072","url":null,"abstract":"For many types of data and information fusion, input from human users is essential, both in terms of defining or adjusting the processing steps, as well as in interacting with, understanding, and communicating the results. In many cases, information fusion should increase understanding for the human user(s) working as part of a team of interacting agents, taking into account the needs of each user type, and the factors that might affect individual and team performance. This paper focuses on the decision support that could be provided to users, by presenting a candidate environment to support comprehensive information fusion and exchange in support of human-agent knowledge fusion (HAKF). The paper outlines two distinct HAKF use cases of (1) foraging data for open source intelligence analysis, and (2) sensemaking fusion from sensors and machine agents, using Cogni-sketch. In the first use case, a traditional open source intelligence gathering exercise demonstrates information gathered from multiple sources and maps it to a common model of sensemaking. The second use case shows machine-led activities including fusion of machine vision and object identification, and the utilization of human-led semantic definitions of events and situations in support of sensemaking.","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":"130145686","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

Resilient Collaborative All-source Navigation 弹性协作全源导航

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626892

Jonathon S. Gipson, R. Leishman

{"title":"Resilient Collaborative All-source Navigation","authors":"Jonathon S. Gipson, R. Leishman","doi":"10.23919/fusion49465.2021.9626892","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626892","url":null,"abstract":"The Autonomous and Resilient Management of All-source Sensors with Stable Observability Monitoring (ARMAS-SOM) framework fuses collaborative all-source sensor information in a resilient manner with fault detection, exclusion, and integrity solutions recognizable to a Global Navigation Satellite System (GNSS) user. This framework uses a multi-filter residual monitoring approach for fault detection and exclusion which is augmented with an additional \"observability\" Extended Kalman Filter (EKF) sub-layer for resilience. We monitor the a posteriori state covariances in this sub-layer to provide intrinsic awareness when navigation state observability assumptions required for integrity are in danger. The framework leverages this to selectively augment with offboard information and preserve resilience. By maintaining split parallel collaborative and proprioceptive frameworks and employing a novel \"stingy collaboration\" technique, we are able maximize efficient use of network resources, limit the propagation of unknown corruption to a single donor, prioritize high fidelity donors, and maintain consistent collaborative navigation without fear of double-counting in a scalable processing footprint. Lastly, we preserve the ability to return to autonomy and are able to use the same intrinsic awareness to notify the user when it is safe to do so.","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":"114048401","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

Deep Likelihood Learning for 2-D Orientation Estimation Using a Fourier Filter 基于傅里叶滤波器的二维方向估计的深度似然学习

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627032

F. Pfaff, Kailai Li, U. Hanebeck

引用次数: 1