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2020 IEEE 23rd International Conference on Information Fusion (FUSION)最新文献

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State Estimation from Range-Only Measurements 仅距离测量的状态估计
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190550
Zhengkun Guo, Gongjian Zhou
{"title":"State Estimation from Range-Only Measurements","authors":"Zhengkun Guo, Gongjian Zhou","doi":"10.23919/FUSION45008.2020.9190550","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190550","url":null,"abstract":"In this paper, a method of state estimation from range-only measurements is presented. This makes it possible to use low-cost sensor networks which cannot provide accurate azimuth information to replace large antennas for target tracking and other applications. Firstly, the time evolving equation of target range is formulated. The sub-state vector is constructed by range, Doppler and the derivative of the product of range and Doppler with respect to time. The sub-state equation corresponding to the nearly constant velocity (NCV) motion is derived by explicit substitutions according to the relationship between sub-state and Cartesian states. Then the unscented Kalman filter (UKF) is employed to extract the sub-state from range-only measurements. The filter initialization is derived by two-point differencing method. At last, the performance of the proposed method is compared against the posterior Cramer-Rao Lower Bound (PCRLB) for filtering in sub-state space and the existing method with approximate model in the numerical simulations. The results demonstrate the effectiveness of the proposed method.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124783781","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
Risk-based Autonomous Maritime Collision Avoidance Considering Obstacle Intentions 考虑障碍物意图的基于风险的自主海上避碰
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190212
Trym Tengesdal, T. Johansen, E. Brekke
{"title":"Risk-based Autonomous Maritime Collision Avoidance Considering Obstacle Intentions","authors":"Trym Tengesdal, T. Johansen, E. Brekke","doi":"10.23919/FUSION45008.2020.9190212","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190212","url":null,"abstract":"A robust and efficient Collision Avoidance (COLAV) system for autonomous ships is dependent on a high degree of situational awareness. This includes inference of the intent of nearby obstacles, including compliance with traffic rules such as COLREGS, in order to enable more intelligent decision making for the autonomous agent. Here, a generalized framework for obstacle intent inference is introduced. Different obstacle intentions are then considered in the Probabilistic Scenario-Based Model Predictive Control (PSB-MPC) COLAV algorithm using an examplatory intent model, when statistics about traffic rules compliance and the next waypoint for an obstacle are assumed known. Simulation results show that the resulting COLAV system is able to make safer decisions when utilizing the extra intent information.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121980364","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: 8
Time-Dependent State Prediction for the Kalman Filter Based on Recurrent Neural Networks 基于递归神经网络的卡尔曼滤波时变状态预测
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190484
Steffen Jung, Isabel Schlangen, A. Charlish
{"title":"Time-Dependent State Prediction for the Kalman Filter Based on Recurrent Neural Networks","authors":"Steffen Jung, Isabel Schlangen, A. Charlish","doi":"10.23919/FUSION45008.2020.9190484","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190484","url":null,"abstract":"Traditional formulations of the well-established Kalman filter build upon prediction models which are linear and Gaussian, moreover they usually adopt the Markov property which excludes any form of long-term temporal dependencies. However, targets might follow specific behavioural patterns based on, e.g., their origin or destination, therefore time dependencies become highly relevant. In this article, the recently developed Mnemonic Kalman Filter is analysed which predicts the full Gaussian density of a target based on its previous position using a recurrent neural network with Long Short-Term Memory. For comparison, a simpler Long Short-Term Memory Kalman Filter is introduced which only provides a prediction of the target state vector. The presented experiments suggest that the learning-based approaches are highly relevant for time-dependent scenarios with low detection rates or possible occlusions. Furthermore, uncertainty estimation plays an important role in the filtering process.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"23 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120864188","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
A Cramer-Rao Lower Bound for the Estimation of Bias with a Single Bearing-Only Sensor 单方位传感器偏差估计的Cramer-Rao下界
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190625
Sean R. Martin, M. Abernathy, N. Moshtagh
{"title":"A Cramer-Rao Lower Bound for the Estimation of Bias with a Single Bearing-Only Sensor","authors":"Sean R. Martin, M. Abernathy, N. Moshtagh","doi":"10.23919/FUSION45008.2020.9190625","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190625","url":null,"abstract":"This paper presents a metric for finding optimal sensor and target geometries that provide accurate estimates of bias during target tracking with a single sensor taking measurements of bearing. Since the bias cannot be measured directly, it is shown how to manipulate the equations of a Kalman filter to produce a pseudo measurement of bias and its associated measurement error covariance. These measurement error covariances are used to form a Cramer-Rao lower bound (CRLB) on the bias estimation variance as a function of sensor and target geometries. It is shown that highly accurate estimates of bias can be produced using a single sensor, even if the kinematic state estimate of the target is poor.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131511161","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
GNSS-Free Maritime Navigation using Radar and Digital Elevation Models 使用雷达和数字高程模型的无gnss海上导航
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190450
Jonatan Olofsson, Gustaf Hendeby, F. Gustafsson, Deran Maas, Stefano Maranò
{"title":"GNSS-Free Maritime Navigation using Radar and Digital Elevation Models","authors":"Jonatan Olofsson, Gustaf Hendeby, F. Gustafsson, Deran Maas, Stefano Maranò","doi":"10.23919/FUSION45008.2020.9190450","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190450","url":null,"abstract":"Modern maritime navigation is heavily dependent on satellite systems. Availability of an accurate position is critical for safe operations, but satellite-based navigation systems are vulnerable to interference, jamming, and spoofing. In this work, we propose a method for maritime navigation independent of GNSS, able to provide absolute positioning of the vessel based on marine radar scans. A measurement model is presented where a Digital Elevation Model is used to predict the output of a marine radar, given a hypothetical position. The model, as used by an on-line particle filter, is used to track the movements of a ship from real recorded data. This demonstrates the feasibility of this method for robust positioning, without the need of external positioning signals, in a maritime environment. The tracking only uses sensors commonly available on maritime vessels, and demonstrates its application using freely available elevation data.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000725","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
Observability in Target Motion Analysis from the Sum or the Difference of Ranges with Two Stationary Sensors 从两个静止传感器距离和或差分析目标运动的可观察性
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190568
Annie-Claude Pérez, C. Jauffret
{"title":"Observability in Target Motion Analysis from the Sum or the Difference of Ranges with Two Stationary Sensors","authors":"Annie-Claude Pérez, C. Jauffret","doi":"10.23919/FUSION45008.2020.9190568","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190568","url":null,"abstract":"We address in this paper the problem of observability in target motion analyses (TMA), when the measurements are a sum or a difference of ranges between a target and two motionless observers. Necessary and sufficient conditions of observability are given in time difference of arrival (TDOA) situation and in a bistatic situation.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124049591","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 Multi-layer Fusion for an Accurate Iris Presentation Attack Detection 基于深度学习多层融合的虹膜呈现攻击准确检测
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190424
Meiling Fang, N. Damer, F. Boutros, Florian Kirchbuchner, Arjan Kuijper
{"title":"Deep Learning Multi-layer Fusion for an Accurate Iris Presentation Attack Detection","authors":"Meiling Fang, N. Damer, F. Boutros, Florian Kirchbuchner, Arjan Kuijper","doi":"10.23919/FUSION45008.2020.9190424","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190424","url":null,"abstract":"Iris presentation attack detection (PAD) algorithms are developed to address the vulnerability of iris recognition systems to presentation attacks. Taking into account that the deep features successfully improved computer vision performance in various fields including iris recognition, it is natural to use features extracted from deep neural networks for iris PAD. Each layer in a deep learning network carries features of different level of abstraction. The features extracted from the first layer to the higher layers become more complex and more abstract. This might point our complementary information in these features that can collaborate towards an accurate PAD decision. Therefore, we propose an iris PAD solution based on multi-layer fusion. The information extracted from the last several convolutional layers are fused on two levels, feature-level and score-level. We demonstrated experiments on both, off-the-shelf pre-trained network and network trained from scratch. An extensive experiment also explores the complementary between different layer combinations of deep features. Our experimental results show that feature-level based multi-layer fusion method performs better than the best single layer feature extractor in most cases. In addition, our fusion results achieve similar or better results than the state-of-the-art algorithms on the Notre Dame and IIITD-WVU databases of the Iris Liveness Detection Competition 2017 (LivDet-Iris 2017).","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124591731","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: 15
Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps 基于支持向量机分类的大尺度降水图区域降水预测
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190285
Eslam A. Hussein, Mehrdad Ghaziasgar, C. Thron
{"title":"Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps","authors":"Eslam A. Hussein, Mehrdad Ghaziasgar, C. Thron","doi":"10.23919/FUSION45008.2020.9190285","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190285","url":null,"abstract":"Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1–30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continental US, with rainfall quantized at 3 levels: light or no rain; moderate; and heavy rain. Three regions were selected, corresponding to three squares from a $5times 5$ grid covering the map area. Rainfall predictions up to 30 days ahead for these three regions were based on a support vector machine (SVM) applied to consecutive sequences of prior daily rainfall map images. The results show that predictions for corner squares in the grid were less accurate than predictions obtained by a simple untrained classifier. However, SVM predictions for a central region outperformed the other two regions, as well as the untrained classifier. We conclude that there is some evidence that SVMs applied to large-scale precipitation maps can under some conditions give useful information for predicting regional rainfall, but care must be taken to avoid pitfalls.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129147655","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: 5
Special Sessions 特别会议
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/fusion45008.2020.9190208
{"title":"Special Sessions","authors":"","doi":"10.23919/fusion45008.2020.9190208","DOIUrl":"https://doi.org/10.23919/fusion45008.2020.9190208","url":null,"abstract":"","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128687873","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
Track-Before-Detect Labeled Multi-Bernoulli Smoothing for Multiple Extended Objects 多扩展目标的检测前跟踪标记多伯努利平滑
2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190360
Boqian Yu, Egon Ye
{"title":"Track-Before-Detect Labeled Multi-Bernoulli Smoothing for Multiple Extended Objects","authors":"Boqian Yu, Egon Ye","doi":"10.23919/FUSION45008.2020.9190360","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190360","url":null,"abstract":"For the evaluation of autonomous driving systems, this paper provides a new approach of generating reference data for multiple extended object tracking. In our approach, we apply a forward-backward smoother for objects with star-convex shapes based on the Labeled Multi-Bernoulli (LMB) Random Finite Set (RFS) and recursive Gaussian processes. We further propose to combine a robust birth policy with a backward filter to solve the conflict between robustness and completeness of tracking. Thereby, cluster candidates are evaluated based on a quality measure to only initialize objects from more reliable clusters in the forward pass. Missing states will then be recovered by the backward filter through post-processing the unassociated data after the smoothing process. Simulations and real-world experiments demonstrate superior performance of the proposed method in both cardinality and individual state estimation compared to naive LMB filter and smoother for extended objects.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116919444","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: 5
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