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2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)最新文献

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SDF 2018 Cover Page SDF 2018封面
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/sdf.2018.8547133
{"title":"SDF 2018 Cover Page","authors":"","doi":"10.1109/sdf.2018.8547133","DOIUrl":"https://doi.org/10.1109/sdf.2018.8547133","url":null,"abstract":"","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115030605","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
Quantum Field Theory and Tracking of Indistinguishable Targets 量子场论与不可分辨目标跟踪
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547122
M. Ulmke
{"title":"Quantum Field Theory and Tracking of Indistinguishable Targets","authors":"M. Ulmke","doi":"10.1109/SDF.2018.8547122","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547122","url":null,"abstract":"In many particle physics, the Fock space approach is used to study systems with unknown and variable particle numbers. Originally introduced for quantum systems, it can be applied to classical statistical systems, too. In this paper we review the main properties of the Fock space approach, including the second quantization formulation, and demonstrate the correspondence to approaches in multi-target tracking dealing with varying numbers of indistinguishable targets. The correspondence include the representation of the multi-object state, the symmetrized probability density for indistinguishable objects, set integrals, the expectation values of linear operators, and the probability generating functional. The challenges of many particle physics and multi-target tracking are contrasted and possible applications of field theoretic techniques to multi-target tracking are discussed.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121578118","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
Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data 基于稀疏雷达数据的行人运动分类与高度估计
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547092
Markus Horn, Ole Schumann, Markus Hahn, J. Dickmann, K. Dietmayer
{"title":"Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data","authors":"Markus Horn, Ole Schumann, Markus Hahn, J. Dickmann, K. Dietmayer","doi":"10.1109/SDF.2018.8547092","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547092","url":null,"abstract":"A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like camera, radar and lidar is crucial for increasing the robustness. The detection and classification of objects like cars, bicycles or pedestrians has been analyzed in the past for many sensor types. Beyond that, it is also helpful to refine these classes and distinguish for example between different pedestrian types or activities. This task is usually performed on camera data, though recent developments are based on radar spectrograms. However, for most automotive radar systems, it is only possible to obtain radar targets instead of the original spectrograms. This work demonstrates that it is possible to estimate the body height of walking pedestrians using 2D radar targets. Furthermore, different pedestrian motion types are classified.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126213069","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
Sequential Quantum Monte-Carlo for Tracking of Indistinguishable Targets 序列量子蒙特卡罗算法用于不可区分目标的跟踪
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547148
M. Ulmke
{"title":"Sequential Quantum Monte-Carlo for Tracking of Indistinguishable Targets","authors":"M. Ulmke","doi":"10.1109/SDF.2018.8547148","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547148","url":null,"abstract":"For indistinguishable targets, the probability density function is symmetric under exchange of the target labels and can be formulated as the square of a symmetric or antisymmetric real-valued wave function. [1] Anti-symmetry implicitly describes objects that cannot share the same physical state at the same time-a property macroscopic real world objects possess. Based on the approach in [1], we develop a sequential Monte Carlo method that propagates and updates the anti-symmetric wave function. Anti-symmetry is maintained using an approximation in the time update step. The algorithm is closely related to Quantum Monte Carlo methods applied in nuclear and condensed matter physics. Preliminary results for a simple two-target scenarios are presented and limitations and possible further developments are discussed.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128038011","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
Classification of Military Aircraft in Real-time Radar Systems based on Supervised Machine Learning with Labelled ADS-B Data 基于标签ADS-B数据的监督式机器学习实时雷达系统军用飞机分类
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547077
Kaeye Dästner, Susie Brunessaux, Elke Schmid, Bastian von Hassler zu Roseneckh-Köhler, F. Opitz
{"title":"Classification of Military Aircraft in Real-time Radar Systems based on Supervised Machine Learning with Labelled ADS-B Data","authors":"Kaeye Dästner, Susie Brunessaux, Elke Schmid, Bastian von Hassler zu Roseneckh-Köhler, F. Opitz","doi":"10.1109/SDF.2018.8547077","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547077","url":null,"abstract":"Air surveillance is usually based on real-time radar tracking systems, which are able to provide object positions, kinematics and a short time history. Due to the density of the air picture, air traffic controllers normally focus on the actual object kinematics and the full identities of each object, which is received from secondary radars and ADS-B. However air surveillance systems in the military domain need additional information on objects classification and identification, since ADS-B of non-cooperative targets are not available. Hence flight characteristics and moving patterns are used as evidence for a military aircraft, which unfortunately are not often recognizable easily in real-time by an operator. This paper describes dedicated machine learning techniques that are trained with ADS-B data to predict military targets. The classifiers can be used within real-time systems.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132053657","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
On a CPD Decomposition of a Multi-Variate Gaussian 多变量高斯分布的CPD分解
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547115
F. Govaers
{"title":"On a CPD Decomposition of a Multi-Variate Gaussian","authors":"F. Govaers","doi":"10.1109/SDF.2018.8547115","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547115","url":null,"abstract":"Tensor decomposition based sensor data fusion is a novel field of numerical solutions to the Bayesian filtering problem. Due to the exponential growth of high dimensional tensors, this approach has not got much attention in the past. This has changed with the rise of efficient decomposition algorithms such as the lCanonical Polyadic Decomposition’ (CPD), which allow a compact representation of the precise, discretized information in the state space. As solutions of the prediction-filtering cycle were developed, it usually is assumed that a decomposition of the likelihood or the initial prior is available. In this paper, we propose a numerical method to compute the CPD form of a multivariate Gaussian, either a likelihood or a prior, in terms of an analytical solution in combination with the Taylor approximation of the pointwise tensor exponential.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130715230","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
Short-Term Traffic Prediction with Vicinity Gaussian Process in the Presence of Missing Data 缺失数据下的邻近高斯过程短期交通预测
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547118
Peng Wang, Young-jin Kim, Lubos Vaci, Haoze Yang, L. Mihaylova
{"title":"Short-Term Traffic Prediction with Vicinity Gaussian Process in the Presence of Missing Data","authors":"Peng Wang, Young-jin Kim, Lubos Vaci, Haoze Yang, L. Mihaylova","doi":"10.1109/SDF.2018.8547118","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547118","url":null,"abstract":"This paper considers the problem of short-term traffic flow prediction in the context of missing data and other measurement errors. These can be caused by many factors due to the complexity of the large scale city road network, such as sensors not being operational and communication failures. The proposed method called vicinity Gaussian Processes provides a flexible framework for dealing with missing data and prediction in vehicular traffic network. First, a weighted directed graph of the network is built up. Next, a dissimilarity matrix is derived that accounts for the selection of training subsets. A suitable cost function to find the best subsets is also defined. Experimental results show that with appropriately selected subsets, the prediction root mean square error of the traffic flow obtained by the vicinity Gaussian Processes method reaches 18.9% average improvement with lower costs, which is with comparison to inappropriately chosen training subsets.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116543156","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: 12
Multiobject Tracking with Track Continuity: An Efficient Random Finite Set Based Algorithm 具有跟踪连续性的多目标跟踪:一种高效的随机有限集跟踪算法
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547059
Thomas Kropfreiter, F. Hlawatsch
{"title":"Multiobject Tracking with Track Continuity: An Efficient Random Finite Set Based Algorithm","authors":"Thomas Kropfreiter, F. Hlawatsch","doi":"10.1109/SDF.2018.8547059","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547059","url":null,"abstract":"We propose a random finite set (RFS) based algorithm for tracking multiple objects while maintaining track continuity. In our approach, the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. Low complexity is achieved through several judiciously chosen approximations in the update step. In particular, the computationally less demanding Poisson part of our algorithm is used to track potential objects whose existence is highly uncertain. A new labeled Bernoulli component is generated only if there is sufficient evidence of object existence, and then the corresponding object state is tracked by the more accurate but more complex LMB part of the algorithm. Simulation results for a challenging scenario demonstrate an attractive accuracy-complexity tradeoff and a significant complexity reduction relative to other RFS-based algorithms with comparable performance.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132075791","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
Task-Oriented Path Planning Algorithm Considering POIs and Dynamic Collaborative Targets Distribution 考虑poi和动态协同目标分布的面向任务的路径规划算法
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547109
R. Liu, K. Greve, Nan Jiang, Ming Xu
{"title":"Task-Oriented Path Planning Algorithm Considering POIs and Dynamic Collaborative Targets Distribution","authors":"R. Liu, K. Greve, Nan Jiang, Ming Xu","doi":"10.1109/SDF.2018.8547109","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547109","url":null,"abstract":"Navigation path planning is the main part of the collaborative navigation system service. And the research on key technologies of collaborative navigation path planning lays the foundation for the multi-mode service of the navigation system and the united application of land, sea, and air navigation information. The key of researching collaborative navigation path planning technology is to construct a collaborative path planning algorithm oriented to the needs of multi-navigation tasks. As an important path evaluation index in the navigation path planning process, the distribution of points of interest(POIs) and dynamic collaborative targets(DCTs) is a key factor to be considered, when design a collaborative navigation path planning algorithm. This paper will study multi-objective collaborative path planning algorithms for multi-navigation tasks based on the analysis and evaluation of POIs and DCTs distribution.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128597180","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
An approximate maximum-likelihood estimator for localisation using bistatic measurements 使用双基地测量定位的近似最大似然估计
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2018-10-01 DOI: 10.1109/SDF.2018.8547074
D. Fränken
{"title":"An approximate maximum-likelihood estimator for localisation using bistatic measurements","authors":"D. Fränken","doi":"10.1109/SDF.2018.8547074","DOIUrl":"https://doi.org/10.1109/SDF.2018.8547074","url":null,"abstract":"This paper discusses algorithms that can be used to estimate the position of an object by means of bistatic measurements. Some methods known from literature are compared with a new algorithm that is an approximation to a maximum-likelihood estimator for this non-linear localisation problem. Simulation results confirm that the proposed estimator yields errors close to Cramer-Rao lower bound for lower levels of measurement noise while still providing the best performance among the investigated algorithms when the statistical errors on the measurements become large.","PeriodicalId":357592,"journal":{"name":"2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"86 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123067584","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
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