2021 IEEE 24th International Conference on Information Fusion (FUSION)最新文献_第10页

Learning Motion Patterns in AIS Data and Detecting Anomalous Vessel Behavior 学习AIS数据中的运动模式和检测异常船只行为

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627027

Anton Kullberg, I. Skog, Gustaf Hendeby

引用次数: 2

On Tracking Closely-Spaced Targets in a PARAFAC-Representation of the Fermionic Wave Function Formulation 费米子波函数公式的parafac中近间隔目标跟踪

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627033

Joshua Gehlen, F. Govaers, W. Koch

引用次数: 0

Pedestrian Detection by Fusion of RGB and Infrared Images in Low-Light Environment 基于RGB和红外图像融合的低光环境下行人检测

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626853

Qing Deng, Wei Tian, Yuyao Huang, Lu Xiong, Xin Bi

引用次数: 3

Improved Virtual Landmark Approximation for Belief-Space Planning 改进的虚拟地标近似置信空间规划

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626933

J. Nordlöf, Gustaf Hendeby, Daniel Axehill

引用次数: 0

Estimation of copulas between solar wind parameters and a geomagnetic index during intense geomagnetic storms 强地磁风暴期间太阳风参数与地磁指数间的关联估计

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626857

S. Lotz, A. D. Waal, C. Roux

{"title":"Estimation of copulas between solar wind parameters and a geomagnetic index during intense geomagnetic storms","authors":"S. Lotz, A. D. Waal, C. Roux","doi":"10.23919/fusion49465.2021.9626857","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626857","url":null,"abstract":"Solar activity, through geomagnetic storms, has the ability to cause a number of negative effects on critical technologies such as power grids and various communication systems. Geomagnetic storms are intervals of disturbed geomagnetic field lasting ∼ 10 hours. The most intense storms are caused by energetic plasma from coronal mass ejections impacting the geomagnetic field after propagating the 1.5 × 108km (= 1AU) via the solar wind to Earth. The relationship between the shocked solar wind and the geomagnetic field can be viewed as a highly non-linear, non-stationary transfer function. Fully understanding the coupling between the solar wind and the magnetosphere is an important task for space physicists striving to provide accurate predictions of geomagnetic storms. With this in mind we investigate the use of copulas as a way to quantify the coupling efficiency between the solar wind and magnetosphere for the three known phases of storms: onset, main and recovery. Seven intense storms are identified and the dynamic and static copulas between two solar wind parameters (BZ and Vsw) and a geomagnetic disturbance index (SYM-H) are calculated. We find that copula functions can be used to reliably identify storm phase changes, and to quantify the changes in coupling efficiency for different storm phases.","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":"128290453","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

Multi-sensor Distributed Estimation Fusion Based on Minimizing the Bhattacharyya Distance Sum 基于最小Bhattacharyya距离和的多传感器分布式估计融合

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626992

Qichao Tang, Z. Duan

{"title":"Multi-sensor Distributed Estimation Fusion Based on Minimizing the Bhattacharyya Distance Sum","authors":"Qichao Tang, Z. Duan","doi":"10.23919/fusion49465.2021.9626992","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626992","url":null,"abstract":"In multi-sensor distributed estimation fusion, local estimation errors are generally correlated among local estimates. Usually, correlation is known to exist but unavailable or unclear to be how large it is, and needed to be considered. For this situation, a sensible way is to set up an optimality criterion and optimize it over all possible such correlations. Based on the framework of minimizing the statistical distance sum between the fused density and local posterior densities, a new method is proposed by utilizing Bhattacharyya distance, which is commonly used in measuring the closeness or similarity between two densities. First, the objective function is introduced. Then, we investigate the convexity form of the objective function, and separate the solving procedure into two steps during settling the original optimization problem, which benefit us to acquire the solution of that problem. At last, the acquired solution (fused estimate) is given in an implicit form, however, it can be obtained through iterative algorithm. And, it is pessimistic definite in mean square error (MSE). Numerical examples illustrate this and show the effectiveness of the proposed distributed method by comparing with several other fusion methods under the same framework but using other kinds of statistical distance.","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":"128922823","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

An Algebra of Machine Learners with Applications 机器学习代数及其应用

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626918

N. Rao

引用次数: 1

Maritime Anomaly Detection of Malicious Data Spoofing and Stealth Deviations from Nominal Route Exploiting Heterogeneous Sources of Information 利用异构信息源的恶意数据欺骗和名义路由隐身偏差的海上异常检测

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627049

Enrica d’Afflisio, P. Braca, L. Chisci, G. Battistelli, P. Willett

引用次数: 0

Bridging Heuristic and Deep Learning Approaches to Sensor Tasking 传感器任务处理的桥接启发式和深度学习方法

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627020

Ashton Harvey, Kathryn B. Laskey, Kuo-Chu Chang

{"title":"Bridging Heuristic and Deep Learning Approaches to Sensor Tasking","authors":"Ashton Harvey, Kathryn B. Laskey, Kuo-Chu Chang","doi":"10.23919/fusion49465.2021.9627020","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627020","url":null,"abstract":"Space is becoming a more crowded and contested domain, but the techniques used to task the sensors monitoring this environment have not significantly changed since the implementation of James Miller’s marginal analysis technique used in the Special Perturbations (SP) Tasker in 2007. Centralized tasker / scheduler approaches have used a Markov Decision Process (MDP) formulation, but myopic solutions fail to account for future states and non-myopic solutions tend to be computationally infeasible at scale. Linares and Furfaro proposed solving an MDP formulation of the Sensor Allocation Problem (SAP) using Deep Reinforcement Learning (DRL). DRL has been instrumental in solving many high-dimensional control problems previously considered too complex to solve at an expert level, including Go, Atari 2600, Dota 2, Starcraft 2 and autonomous driving. Linares and Furfaro showed DRL could converge on effective policies for sets of up to 300 objects in the same orbital plane. Jones expanded on that work to a full three-dimensional case with objects in diverse orbits. DRL methods can require significant training time to learn from an a priori state. This paper builds on past work by applying imitation learning to bootstrap DRL methods with existing heuristic solutions. We show that a Demonstration Guided DRL (DG-DRL) approach can effectively replicate a near-optimal tasker’s performance using trajectories from a sub-optimal heuristic. Further, we show that our approach avoids the poor initial performance typical of online DRL approaches. Code is available as an open source library at: https://github.com/AshHarvey/ssa-gym","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":"124465413","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

Angle-Only, Range-Only and Multistatic Tracking Based on GM-PHD Filter 基于GM-PHD滤波器的单角度、单距离和多静态跟踪

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626841

Dimitri Hamidi, Elad Kevelevitch, P. Arora, Rick Gentile, Vincent Pellissier

{"title":"Angle-Only, Range-Only and Multistatic Tracking Based on GM-PHD Filter","authors":"Dimitri Hamidi, Elad Kevelevitch, P. Arora, Rick Gentile, Vincent Pellissier","doi":"10.23919/fusion49465.2021.9626841","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626841","url":null,"abstract":"Multi-object detection and tracking with spatially distributed sensor networks are used in many applications across the domains of autonomous and surveillance systems. The sensors typically used in these systems often provide incomplete observations such as bistatic and angle- or range-only measurements, thus posing a challenge to the task of retrieving the targets and estimating their state. In this paper, we first present a new variant of a multi-sensor tracking algorithm based on the Gaussian-mixture probability hypothesis density (GM-PHD) filter. Next, we show how it can be applied on fusing incomplete observations. For tracking asynchronous range- and angle-only measurements, we leverage the well-known concepts of angle and range parametrization, respectively, to describe the adaptive target birth density based on the parameters of received observations. In the case of multistatic tracking, we propose parametrizing the birth density from target hypotheses, generated by statically fusing bistatic range measurements, using the M-best S-D assignment algorithm. We investigate the performance using challenging simulation scenarios and evaluate it with established tracking metrics. Our preliminary results demonstrate the effectiveness of the proposed algorithms. Furthermore, for range- and angle-only fusion, the more common use case of unsynchronized sensor measurements is supported. While many algorithms in the literature are tailored for a specific problem, we show that the proposed GM-PHD tracker is generic and can be potentially leveraged in a wide range of sensor fusion and tracking applications.","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":"126408863","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