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

Source Localization with AOA-Only and Hybrid RSS/AOA Measurements via Semidefinite Programming 基于半定规划的纯AOA和混合RSS/AOA测量的源定位

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190594

Qi Wang, Z. Duan, X. Li

{"title":"Source Localization with AOA-Only and Hybrid RSS/AOA Measurements via Semidefinite Programming","authors":"Qi Wang, Z. Duan, X. Li","doi":"10.23919/FUSION45008.2020.9190594","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190594","url":null,"abstract":"Angle of arrival (AOA) and received signal strength (RSS) measurements have been commonly used in wireless localization due to easy access and simple implementation. In this paper, we investigate source localization using the AOA-only and hybrid RSS/AOA measurements, respectively. In AOA localization, we approximate the angle error using a range-related quantity. Then the optimization problem based on maximum likelihood (ML) is converted to a convex semidefinite programming (SDP) problem. In hybrid AOA/RSS localization, the ML estimator is decomposed into an RSS part and an AOA part. The AOA part follows a similar procedure as in the AOA localization. Taylor series expansion and relaxation are applied in optimizing the RSS part. These two parts are closely related through the range. The proposed methods avoid the nonconvexity in the original ML estimators for both AOA-only and hybrid AOA/RSS localization problems. Numerical examples show good performance of the proposed methods in both AOA and hybrid AOA/RSS localizations. They are close to or better than the LS methods in the literature.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"36 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":"115155388","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: 4

Kalman Filter with Moving Reference for Jump-Free, Multi-Sensor Odometry with Application in Autonomous Driving 无跳多传感器里程计的运动参考卡尔曼滤波及其在自动驾驶中的应用

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190464

J. Clemens, Constantin Wellhausen, Tom L. Koller, U. Frese, K. Schill

{"title":"Kalman Filter with Moving Reference for Jump-Free, Multi-Sensor Odometry with Application in Autonomous Driving","authors":"J. Clemens, Constantin Wellhausen, Tom L. Koller, U. Frese, K. Schill","doi":"10.23919/FUSION45008.2020.9190464","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190464","url":null,"abstract":"Control, tracking, and obstacle detection algorithms for mobile robots, including autonomous cars, rely on a jump-free estimate of the vehicle's pose. While one cannot completely avoid jumps in global solutions like INS/GNSS and SLAM, relative localization (i.e., odometry) does not suffer from this problem. Methods based on graph optimization are popular in that field, but they do not scale very well with high-frequency measurements. Kalman filters (KFs) are able to cope with those measurements, but they face the issue of a continuously growing covariance. This results in instabilities and eventually jumps in the state estimate. We present an approach to handle this problem by periodically moving the reference state forward in time, which is realized using two filters. The equations for implementing this in both the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are derived. The algorithm is evaluated using real-world datasets covering different scenarios of autonomous driving. We show that our method provides a smooth and stable estimate even over long time periods and that it achieves a better localization performance than the standard approach.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"110 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":"117204015","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: 4

Analysis of MHT and GBT Approaches to Disparate-Sensor Fusion 差分传感器融合的MHT和GBT方法分析

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190256

C. Carthel, J. LeNoach, S. Coraluppi, A. Willsky, Brandon Bale

引用次数: 2

Dual-frequency Collaborative Positioning for Minimization of GNSS Errors in Urban Canyons 城市峡谷中GNSS误差最小化的双频协同定位

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190612

Simon Ollander, F. Schiegg, Friedrich-Wilhelm Bode, M. Baum

{"title":"Dual-frequency Collaborative Positioning for Minimization of GNSS Errors in Urban Canyons","authors":"Simon Ollander, F. Schiegg, Friedrich-Wilhelm Bode, M. Baum","doi":"10.23919/FUSION45008.2020.9190612","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190612","url":null,"abstract":"Global Navigation Satellite Systems (GNSS) provide precise positioning under open-sky conditions, such as highways. However, in urban canyons, buildings block and reflect the signals, causing multipath positioning errors. Multi-frequency transmission and collaborative positioning are two technologies that have been proposed to reduce multipath errors. Still, the magnitude of their individual and combined advantage in reducing multipath errors is unknown. To fill this gap, we simulated dual-frequency collaborative positioning with four vehicles in an open-sky environment and in an urban environment. We compared two solution algorithms for position estimation: the Gauss-Newton solver (GN) and the extended Kalman filter (EKF). This paper presents the performance of these two algorithms under the previously mentioned assumptions. Furthermore, we show how the information from dual-frequency reception can be used to select the most relevant satellites. In the urban environment, the GN and the EKF using dual-frequency reception and collaborative positioning are the solutions with the smallest RMS positioning error (under 2.5 m), Additionally, in the simulated urban environment, dual-frequency reception contributes more to reducing multipath errors than collaborative positioning. As a consequence, when developing automotive positioning systems, multi-frequency reception and collaborative positioning should ideally be combined, but with higher priority on multi-frequency reception.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"30 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":"124843350","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 Two-stage Particle Filter for Equality Constrained Systems 等式约束系统的两级粒子滤波

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190445

Chongyang Hu, Yan Liang, Linfeng Xu

引用次数: 0

Performance-Agnostic Fusion of Probabilistic Classifier Outputs 概率分类器输出的性能不可知融合

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190171

Jordan F. Masakuna, S. Utete, Steve Kroon

{"title":"Performance-Agnostic Fusion of Probabilistic Classifier Outputs","authors":"Jordan F. Masakuna, S. Utete, Steve Kroon","doi":"10.23919/FUSION45008.2020.9190171","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190171","url":null,"abstract":"We propose a method for combining probabilistic outputs of classifiers to make a single consensus class prediction when no further information about the individual classifiers is available, beyond that they have been trained for the same task. The lack of relevant prior information rules out typical applications of Bayesian or Dempster-Shafer methods, and the default approach here would be methods based on the principle of indifference, such as the sum or product rule, which essentially weight all classifiers equally. In contrast, our approach considers the diversity between the outputs of the various classifiers, iteratively updating predictions based on their correspondence with other predictions until the predictions converge to a consensus decision. The intuition behind this approach is that classifiers trained for the same task should typically exhibit regularities in their outputs on a new task; the predictions of classifiers which differ significantly from those of others are thus given less credence using our approach. The approach implicitly assumes a symmetric loss function, in that the relative cost of various prediction errors are not taken into account. Performance of the model is demonstrated on different benchmark datasets. Our proposed method works well in situations where accuracy is the performance metric; however, it does not output calibrated probabilities, so it is not suitable in situations where such probabilities are required for further processing.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"31 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":"123522508","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

Line-of-Sight Rate Estimation for Barrel-Roll Maneuvering Target Tracking 桶滚机动目标跟踪的视距估计

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190423

Xiaoxiao Guo, Jian Lan

引用次数: 3

Selective Information Transmission using Convolutional Neural Networks for Cooperative Underwater Surveillance 基于卷积神经网络的水下协同监视选择性信息传输

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190461

Giovanni De Magistris, Murat Üney, P. Stinco, G. Ferri, A. Tesei, K. L. Page

{"title":"Selective Information Transmission using Convolutional Neural Networks for Cooperative Underwater Surveillance","authors":"Giovanni De Magistris, Murat Üney, P. Stinco, G. Ferri, A. Tesei, K. L. Page","doi":"10.23919/FUSION45008.2020.9190461","DOIUrl":"https://doi.org/10.23919/FUSION45008.2020.9190461","url":null,"abstract":"Cooperation among multiple autonomous surface and underwater vehicles is an important capability for detection and tracking of underwater objects. Cooperative autonomy in the underwater environment, however, is challenged by the communication bandwidth. In this work, we propose a selective communication scheme that underpins collaborative surveillance under communication constraints. This scheme classifies signal reflections of sonar pulses that are detected by on-board sensor processing as contacts with the object of interest or background using a convolutional neural network. This network is trained using previously labelled contact spectrograms obtained during three sea trials carried out between 2016–2018. The classification scores at the CNN output are ordered to select the few contacts that the underwater modem bandwidth allows for transmission to the network. First, we evaluate the accuracy of the data-driven information selection scheme using recall scores and similar performance measures. Then, we find the accuracy in Bayesian recursive filtering (tracking) of these contacts for different communication rates using established error metrics. The results suggest that the selective scheme yields a favourable surveillance performance communication cost trade-off.","PeriodicalId":419881,"journal":{"name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","volume":"21 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":"128234089","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

The Spline Multi-Target Multi-Bernoulli Filter 样条多目标多伯努利滤波器

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190412

Yiqi Chen, P. Wei, Gaiyou Li, Lin Gao, Yuansheng Li

引用次数: 0

Outlier-Robust Schmidt-Kalman Filter Using Variational Inference 基于变分推理的离群鲁棒施密特-卡尔曼滤波

2020 IEEE 23rd International Conference on Information Fusion (FUSION) Pub Date : 2020-07-01 DOI: 10.23919/FUSION45008.2020.9190507

Yi Liu, Xi Li, Yanbo Xue, S. Weddell, Le Yang, L. Mihaylova

引用次数: 0