2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第5页

Robust Positioning Based on Opportunistic Radio Sources and Doppler 基于机会射电源和多普勒的鲁棒定位

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235228

D. Lindgren, Andreas Nordzell

引用次数: 0

Detecting Low-level Radiation Sources Using Border Monitoring Gamma Sensors 利用边界监测伽马传感器探测低水平辐射源

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235252

S. Sen, N. Rao, C. Wu, R. Brooks, Christopher Temples

{"title":"Detecting Low-level Radiation Sources Using Border Monitoring Gamma Sensors","authors":"S. Sen, N. Rao, C. Wu, R. Brooks, Christopher Temples","doi":"10.1109/MFI49285.2020.9235252","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235252","url":null,"abstract":"We consider a problem of detecting a low-level radiation source using a network of Gamma spectral sensors placed on the periphery of a monitored region. We propose a computationally light-weight, correlation-based method which is primarily intended for systems with limited computing capacity. Sensor measurements are combined at the fusion by first generating decisions at each time step and then taking their majority vote within a time widow. At each time step, decisions are generated using two strategies: (i) SUM method based on a threshold decision on a correlation statistic derived from measurements from all sensors, and (ii) OR method based on logical-OR of threshold decisions based on correlations statistics of individual sensor measurements. We derive analytical performance bounds for false alarm rates of SUM and OR methods, and show that their performance is enhanced by the temporal smoothing of majority vote within a time window. Using measurements from a test campaign, we generate a border monitoring scenario with twelve 2\" ×2\" NaI Gamma sensors deployed on the periphery of 42 × 42 m2 outdoor region. A Cs-137 source is moved in a straight-line across this region, starting several meters outside and finally moving away from it. We illustrate the performance of both correlation-based detection methods, and compare their performances with each other and with a particle filter method. Overall, under small false-alarm conditions, the OR fusion is found to produce better detection performance.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128103109","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

Heterogeneous Decentralized Fusion Using Conditionally Factorized Channel Filters 基于条件分解信道滤波器的异构分散融合

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235266

O. Dagan, N. Ahmed

{"title":"Heterogeneous Decentralized Fusion Using Conditionally Factorized Channel Filters","authors":"O. Dagan, N. Ahmed","doi":"10.1109/MFI49285.2020.9235266","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235266","url":null,"abstract":"This paper studies a family of heterogeneous Bayesian decentralized data fusion problems. Heterogeneous fusion considers the set of problems in which either the communicated or the estimated distributions describe different, but overlapping, states of interest which are subsets of a larger full global joint state. On the other hand, in homogeneous decentralized fusion, each agent is required to process and communicate the full global joint distribution. This might lead to high computation and communication costs irrespective of relevancy to an agent's particular mission, for example, in autonomous multi-platform multi-target tracking problems, since the number of states scales with the number of targets and agent platforms, not with each agent’s specific local mission. In this paper, we exploit the conditional independence structure of such problems and provide a rigorous derivation for a family of exact and approximate, heterogeneous, conditionally factorized channel filter methods. Numerical examples show more than 95% potential communication reduction for heterogeneous channel filter fusion, and a multi-target tracking simulation shows that these methods provide consistent estimates.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130905263","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

Temporal Smoothing for Joint Probabilistic People Detection in a Depth Sensor Network 深度传感器网络联合概率人检测的时间平滑

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235267

J. Wetzel, Astrid Laubenheimer, M. Heizmann

引用次数: 0

LMB Filter Based Tracking Allowing for Multiple Hypotheses in Object Reference Point Association 基于LMB滤波的目标参考点关联多假设跟踪

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235251

M. Herrmann, Aldi Piroli, Jan Strohbeck, Johannes Müller, M. Buchholz

{"title":"LMB Filter Based Tracking Allowing for Multiple Hypotheses in Object Reference Point Association","authors":"M. Herrmann, Aldi Piroli, Jan Strohbeck, Johannes Müller, M. Buchholz","doi":"10.1109/MFI49285.2020.9235251","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235251","url":null,"abstract":"Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system based on a multi-sensor setup can provide the required environment model for the vehicles. Previously, we have published a concept of object reference points (e.g. the corners of an object), which allows for generic sensor \"plug and play\" interfaces and relatively cheap sensors. This paper describes a novel method to additionally incorporate multiple hypotheses for fusing the measurements of the object reference points using an extension to the previously presented Labeled Multi-Bernoulli (LMB) filter. In contrast to the previous work, this approach improves the tracking quality in the cases where the correct association of the measurement and the object reference point is unknown. Furthermore, this paper identifies options based on physical models to sort out inconsistent and unfeasible associations at an early stage in order to keep the method computationally tractable for real-time applications. The method is evaluated on simulations as well as on real scenarios. In comparison to comparable methods, the proposed approach shows a considerable performance increase, especially the number of non-continuous tracks is decreased significantly.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123183556","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

Mathematical Modeling and Optimal Inference of Guided Markov-Like Trajectory 制导类马尔可夫轨迹的数学建模与最优推理

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235241

R. Rezaie, X. Rong Li

引用次数: 1

Dynamic Adaption of Noise Covariance for Accurate Indoor Localization of Mobile Robots in Non-Line-of-Sight Environments 非视距环境下移动机器人室内精确定位的噪声协方差动态自适应

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235245

Dibyendu Ghosh, V. Honkote, Karthik Narayanan

{"title":"Dynamic Adaption of Noise Covariance for Accurate Indoor Localization of Mobile Robots in Non-Line-of-Sight Environments","authors":"Dibyendu Ghosh, V. Honkote, Karthik Narayanan","doi":"10.1109/MFI49285.2020.9235245","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235245","url":null,"abstract":"The estimation of robot pose in an indoor and unknown environment is a challenging problem. Traditional methods using wheel odometry and inertial measurement unit (IMU) are inaccurate due to wheel slippage and drift related issues. Ultra-wide-band (UWB) technology fused with extended Kalman filter (EKF) approach provides relatively accurate ranging and localization in a line-of-sight (LOS) scenario. However, the presence of physical obstacles {such as, walls, doors etc. called as non-line-of-sight (NLOS)} in an indoor environment pose additional challenges which are difficult to address using UWB alone. Identification of LOS/NLOS information can greatly benefit many location-related applications. To this end, an algorithm based on variance measurement technique of distance estimates along with power envelope of the received signal is proposed for NLOS identification. Further, adaptive adjustment of sensor noise covariance approach is devised to mitigate the NLOS effect. The proposed method-ology is computationally light and is thoroughly tested. The results demonstrate that the proposed method achieves 2X improvement in accuracy compared to existing approach.∼","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124791683","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 Application of IMM Based Sensor Fusion Algorithm in Train Positioning System 基于IMM的传感器融合算法在列车定位系统中的应用

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235250

Süleyman Fatih Kara, Burak Basaran

引用次数: 0

Unsupervised optimization approach to in situ calibration of collaborative human-robot interaction tools 协作式人机交互工具现场标定的无监督优化方法

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235229

Bruno Maric, Marsela Polic, Tomislav Tabak, M. Orsag

引用次数: 8

Extended Object Framework based on Weighted Exponential Products 基于加权指数积的扩展对象框架

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2020-09-14 DOI: 10.1109/MFI49285.2020.9235247

Dennis Bruggner, Daniel Clarke, Dhiraj Gulati

引用次数: 0