2019 22th International Conference on Information Fusion (FUSION)最新文献_第7页

On GNSS Jamming Threat from the Maritime Navigation Perspective 从海上导航角度分析GNSS干扰威胁

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011348

D. Medina, Christoph Lass, E. P. Marcos, R. Ziebold, P. Closas, Jesús García

引用次数: 18

Exploiting the Momentary Dependence of Radar Observations for Non-Cooperative Target Recognition 利用雷达观测的瞬时依赖性进行非合作目标识别

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011215

Minna Väilä, Juha Jylhä, M. Ruotsalainen, Henna Perälä

引用次数: 0

Improved Covariance Matrix Estimators by Multi-Penalty Regularization 基于多罚正则化的改进协方差矩阵估计

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011165

Bin Zhang, Jie Zhou, Jianbo Li

{"title":"Improved Covariance Matrix Estimators by Multi-Penalty Regularization","authors":"Bin Zhang, Jie Zhou, Jianbo Li","doi":"10.23919/fusion43075.2019.9011165","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011165","url":null,"abstract":"In this paper, we deal with the problem of estimating a covariance matrix in limited observation scenarios. We revisit the regularization of Gaussian likelihood function and investigate the multi-penalty regularization strategies to improve the flexibility of covariance matrix estimators. Firstly, for an arbitrary target matrix, we jointly consider two penalty terms based on ridge type and Frobenius norm, and obtain a covariance matrix estimator in closed form through maximizing the corresponding multiply penalized log-likelihood function. Secondly, we generalize the existing regularized estimators by simultaneously employing multiple target matrices. The proposed regularized estimators enjoy various desirable statistical properties including positive definiteness (even when the dimensionality exceeds the number of observations), asymptotical unbiasedness and consistency in large sample scenarios. Moreover, we choose the involved tuning parameters in the sense of minimizing an approximate mean squared error based on cross-validation method. Some numerical simulations and an example application to direction-of-arrival estimation are provided for illustrating the performance of proposed estimators.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122157000","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

Type II approximate Bayes perspective to multiple hypothesis tracking 第二类近似贝叶斯视角的多假设跟踪

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011264

Murat Uney

{"title":"Type II approximate Bayes perspective to multiple hypothesis tracking","authors":"Murat Uney","doi":"10.23919/fusion43075.2019.9011264","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011264","url":null,"abstract":"Multiple hypothesis tracking (MHT) is a computational procedure for recursively estimating multi-object configurations and states from measurements with association uncertainties, noise, false alarms and less than one probability of detection. From a probabilistic modelling perspective, the complete multi-object tracking (MT) model is intractable to perform statistical inference as the multi-object and measurement association configurations constitute infinite sets. In this article, we provide explicit formulae to demonstrate that MHT is a type II maximum a posteriori (MAP) approximate Bayes inference procedure over the complete MT model. In particular, we introduce a MT model that captures all typical uncertainties and show that the joint density of the global model hypotheses and the other variables involved is well defined. This model allows us to define the MT problem mathematically and contrast MHT and sequential Bayesian filtering. We argue that the computational procedures constituting an MHT algorithm such as model hypothesis pruning can be treated as a second stage of approximation for finding near-optimal solutions to the MAP problem given a computational budget.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"22 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120889056","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

Multiple Hypothesis Tracking for Processing Tracklets 多假设跟踪处理轨迹

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011202

C. Chong, S. Mori

引用次数: 0

Flexible Expected Shortfall Estimation Using Parametric & Non-Parametric Methods with Applications in Finance, Insurance & Climatology 基于参数和非参数方法的灵活预期缺口估计及其在金融、保险和气候学中的应用

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011398

Sabyasachi Guharay, KC Chang, Jie Xu

{"title":"Flexible Expected Shortfall Estimation Using Parametric & Non-Parametric Methods with Applications in Finance, Insurance & Climatology","authors":"Sabyasachi Guharay, KC Chang, Jie Xu","doi":"10.23919/fusion43075.2019.9011398","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011398","url":null,"abstract":"Techniques employing Data fusion concepts are regularly being used in Quantitative Risk Management (QRM) for robust analysis. In our previous work, we studied the most commonly used risk metric of interest, Value-at-Risk $(mathbf{VaR})$. While VaR is a commonly used risk metric, an alternative risk metric, Expected Shortfall (ES) is well known to have better theoretical properties than VaR. We extend our previous work on studying VaR to include estimating the ES also known as Conditional Value-at-Risk (CVaR). The standard approach of estimating CVaR involves using Monte Carlo simulation (MCS) approach (denoted henceforth as classical approach). This approach involves breaking down the losses into loss severity and loss frequency assuming independence among them. In practice, this assumption may not always hold. To overcome this limitation and handle cases with both light & heavy-tail data, we propose using both a parametric & non-parametric approach. We implement Data-driven Partitioning of Frequency and Severity (DPFS) using K-means Clustering, and Copula-based Parametric modeling of Frequency and Severity (CPFS). These two approaches are verified using simulation experiments on synthetic data and validated on five publicly available datasets from diverse domains. The classical approach estimates CVaR inaccurately for 80% of the simulated data sets and for 60% of the real-world data sets studied in this work. Both the DPFS and the CPFS methodologies attain CVaR estimates within 99% historical bootstrap confidence interval bounds for both simulated and realworld data. Overall, we find that the CPFS method performs better in CVaR estimation for real-world datasets than our previous studies for VaR estimation.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126622133","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

On Decentralized Self-localization and Tracking Under Measurement Origin Uncertainty 测量原点不确定下的分散自定位与跟踪

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011400

Pranay Sharma, A. Saucan, Donald J. Bucci, P. Varshney

引用次数: 0

Quality measure and optimization for grid flow approaches 网格流方法的质量测量与优化

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011160

Carolyn Kalender

引用次数: 2

Extremely Tiny Siamese Networks with Multi-level Fusions for Visual Object Tracking 极其微小的Siamese网络与多层次融合视觉对象跟踪

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011338

Yi Cao, H. Ji, Wenbo Zhang, S. Shirani

{"title":"Extremely Tiny Siamese Networks with Multi-level Fusions for Visual Object Tracking","authors":"Yi Cao, H. Ji, Wenbo Zhang, S. Shirani","doi":"10.23919/fusion43075.2019.9011338","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011338","url":null,"abstract":"Siamese architectures have enhanced the performance of visual object tracking a lot these years. Though their great influence, less work focuses on designing tiny networks for tracking. In this paper, we propose a novel tiny Siamese (TinySiam) architecture with extremely tiny parameters and computations. Due to the limited computation requirement, the tracker could run in an extremely fast speed and has the potential to be exploited directly in embedded devices. For efficient designs in the tiny network, we first utilize the layer-level fusion between different layers by concatenating their features in the building block, which ensures the information reusing. Second, we use channel shuffle and channel split operations to ensure the channel-level feature fusion in different convolution groups, which increases the information interaction between groups. Third, we utilize the depth-wise convolution to effectively decrease convolution parameters, which benefits fast tracking a lot. The final constructed network (24K parameters and 59M FLOPs) drastically lowers model complexity. Experimental results on GOT-10k and DTB70 benchmarks for both ordinary and aerial tracking illustrate the excellently real-time attribute (129 FPS on GOT-10k and 166 FPS on DTB70) and the robust tracking performance of our TinySiam Tracker.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123771982","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

Domain Specific Fusion of Unstructured Text for Situation Understanding (Poster) 面向情境理解的非结构化文本领域特定融合(海报)

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011243

E. Bosse, Julian Falardeau, Isabelle Prévost, E. Shahbazian, Olivier Labonté

引用次数: 1