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Chinese Control Conference

作者： Yaohua Liu Yimin Zhou Chaofang Hu Qingtian Wu Guilin University of Electronic Technology Guilin China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China Huawei Technologies Co. Ltd Shenzhen China

In recent years, multisensor information fusion has been widely used in military field, automatic aircraft navigation, robotics, image processing and other related fields. This paper mainly introduces the concept and architecture model of multi-sensor information fusion. The most frequently used algorithms are also illustrated such as weighted average method, Kalman filter, particle filter, Bayes estimation and neural network. Furthermore, the main applications of multisensor information fusion are demonstrated in the military, industrial field, navigation and robotics. The prospects of multisensor information fusion is discussed in the paper as well.

关键词： Kalman filters Mathematical model Estimation Robot sensing systems Neural networks Nonlinear systems Task analysis

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In-network Stochastic Consensus for WSN Surveillance applications 9

In-network Stochastic Consensus for WSN Surveillance Applica...

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9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Dragana, Cristian Mihai, viorel Stamatescu, Grigore Popescu, Dan Univ Politehn Bucuresti Dept Automat Control & Ind Informat Bucharest 060042 Romania

ISBN: (纸本)9781509064588

Surveillance applications require reliable monitoring system architectures and cost-efficient in-network data processing mechanisms able to provide effective information extraction for event tracking. Wireless sensor Networks (WSNs) appear to be the most suitable technology due to some well known benefits and the rapid development of embedded devices. Current research is mostly focused on improving and developing new decentralized sensor fusion schemes able to overcome the limitations introduced by the battery operated sensor nodes, but most of the time evaluating the proposed algorithms is performed with a serious lack of precise channel modeling. To close this gap, we provide a stochastic consensus implementation for lossy wireless networks comprising fixed and mobile sensor nodes, as a heterogeneous surveillance system based on ground sensor nodes and UAvs (Unmanned Aerial vehicles). We developed a simulation framework tailored for fixed and mobile sensor nodes and we are able to evaluate the performance of consensus algorithms from a comparative standpoint, considering deterministic and probabilistic packet propagation models described by common models such as Free Space and Nakagami models. In this paper, we consider a complex in-network adaptive estimation using the distributed least-mean square algorithm tailored for WSN. We perform a mean-square error (MSE) performance analysis for both deterministic and probabilistic propagation models.

关键词： consensus WSN stochastic monitoring systems probabilistic models

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量测不一致性条件下多传感器融合算法研究

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现代制造工程 2018年第8期 123-127,47页

作者：徐建亮顾怡红魏小华衢州职业技术学院机电工程学院衢州324000 浙江大学机械工程学院杭州310027

由于传感器提供的抽象数据总是受到测量过程中不确定性因素的影响,多传感器利用多源数据融合算法,消除传感器间可能存在的虚假或错误数据,以降低其不确定性。提出一种基于粒子滤波算法(PFA)的改进贝叶斯方法(IBA),其中基于粒子滤波有3... 详细信息

由于传感器提供的抽象数据总是受到测量过程中不确定性因素的影响,多传感器利用多源数据融合算法,消除传感器间可能存在的虚假或错误数据,以降低其不确定性。提出一种基于粒子滤波算法(PFA)的改进贝叶斯方法(IBA),其中基于粒子滤波有3种算法结构,即:前置粒子滤波算法(P-IBA)、后置粒子滤波算法(IBA-P)和前后置粒子滤波算法(P-IBA-P)。通过三维点云数据分析实验表明,P-IBA具有最小的CPU运行时间t,P-IBA-P比IBA-P拥有最小的评价函数值EF,可见,在P-IBA-P和IBA-P两种算法中,由于粒子滤波器的存在且起到决定性的作用,使得基于粒子滤波的改进贝叶斯算法(P-IBA-P)误差最小,精度最高,融合效果最好。改进的贝叶斯方法与粒子滤波相结合的技术,有助于处理集中式和分散式融合架构的数据不一致问题,在对测量时间要求不高的情况下,使用P-IBA-P算法的测量效果最好。

关键词：多传感器融合改进贝叶斯方法粒子滤波算法

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Range-Doppler Radar sensor fusion for Fall Detection

Range-Doppler Radar Sensor Fusion for Fall Detection

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IEEE Radar Conference (RadarConf)

作者： Erol, Baris Amin, Moeness G. Boashash, Boualem Villanova Univ Ctr Adv Commun Villanova PA 19085 USA Qatar Univ Coll Engn Dept Elect Engn Doha Qatar

ISBN: (纸本)9781467388238

Falls are the major cause of accidents in the elderly population. Propelled by their non-intrusive sensing capabilities and robustness to heat and lighting conditions, radar-based automated fall detection systems have emerged as a candidate technology for reliable fall detection in assisted living. The use of a multiple radar system, in lieu of a single radar unit, for indoor monitoring combats occlusion and supported by the fact that motion articulations in the directions away from the line of sight generate weak Doppler signatures that are difficult to detect and classify. fusion of the data from two radars is deemed to improve performance and reduce false alarms. Utilizing two 24 GHz ultra-wide band (UWB) radar sensing systems, we present different fusion architectures and sensor selection methods, demonstrating the merits of two-sensor platform for indoor motion monitoring and elderly care applications.

关键词： Transportation Supply and demand Programming Linear programming Approximation algorithms Resource management

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Prototyping and evaluation of interactive and customized interface and control algorithms for robotic assistive devices using Kinect and infrared sensor

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INTERNATIONAL JOURNAL OF ADvANCED ROBOTIC SYSTEMS 2018年第2期15卷

作者： McFassel, Grace Hsieh, Sheng-Jen Peng, Bo George Washington Univ Dept Elect & Comp Engn Washington DC USA Texas A&M Univ Dept Engn Technol College Stn TX 77843 USA Texas A&M Univ Dept Mech Engn College Stn TX 77843 USA

Robotic assistive devices are popular in research and medical fields for their potential to automate tasks or to improve the quality of life of disabled users. They may be used for physical therapy, as exoskeletons, teleoperation devices, or to assist users in tasks in their home. Common methods for controlling these devices use both broad and difficult-to-maintain gesturing or peripherals such as mouse pointers or joysticks which are not designed for the task required of them. In addition, these devices are often not adaptive to the user and can only be minimally customized. This article proposes a fusion of infrared camera data for stress detection with Kinect body tracking to develop a customizable control method for a robotic limb. Devices such as the Microsoft Kinect have seen use in physical therapy applications but only some use in teleoperation. In addition, studies have shown potential in using infrared imaging to detect human stress. The objectives of this study are to design and build an interactive interface and adaptive control system and to evaluate its performance. Stress detection using infrared was tested using a Compix 222 and neural networks to categorize emotional states. Kinect v2 accuracy and reliability was tested by comparing joint positions to detected angles and perceived output angles. Our work suggests that infrared imaging and the Kinect v2 show potential to make a real-time adaptive system in which a control program can adapt its output when it detects stress from its user.

关键词： Interface design control algorithms human stress infrared imaging robotic assistive device Microsoft Kinect

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A multi-camera dataset for depth estimation in an indoor scenario

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DATA IN BRIEF 2019年 27卷 104619-104619页

作者： Marin, Giulio Agresti, Gianluca Minto, Ludovico Zanuttigh, Pietro Univ Padua Padua Italy

Time-of-Flight (ToF) sensors and stereo vision systems are two of the most diffused depth acquisition devices for commercial and industrial applications. They share complementary strengths and weaknesses. For this reason, the combination of data acquired from these devices can improve the final depth estimation accuracy. This paper introduces a dataset acquired with a multi-camera system composed by a Microsoft Kinect v2 ToF sensor, an Intel RealSense R200 active stereo sensor and a Stereolabs ZED passive stereo camera system. The acquired scenes include indoor settings with different external lighting conditions. The depth ground truth has been acquired for each scene of the dataset using a line laser. The data can be used for developing fusion and denoising algorithms for depth estimation and test with different lighting conditions. A subset of the data has already been used for the experimental evaluation of the work "Stereo and ToF Data fusion by Learning from Synthetic Data". (c) 2019 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Time-of-Flight Stereo vision Active stereo Data fusion Depth estimation

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Integrity and Collaboration in Dynamic sensor Networks

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sensorS 2018年第7期18卷 2400-2400页

作者： Schoen, Steffen Brenner, Claus Alkhatib, Hamza Coenen, Max Dbouk, Hani Garcia-Fernandez, Nicolas Fischer, Colin Heipke, Christian Lohmann, Katja Neumann, Ingo Uyen Nguyen Paffenholz, Jens-Andre Peters, Torben Rottensteiner, Franz Schachtschneider, Julia Sester, Monika Sun, Ligang vogel, Soeren voges, Raphael Wagner, Bernardo Leibniz Univ Hannover Inst Erdmessung D-30167 Hannover Germany Leibniz Univ Hannover Inst Cartog & Geoinformat D-30167 Hannover Germany Leibniz Univ Hannover Geodet Inst D-30167 Hannover Germany Leibniz Univ Hannover Inst Photogrammetry & GeoInformat D-30167 Hannover Germany Leibniz Univ Hannover Inst Syst Engn Real Time Syst Grp D-30167 Hannover Germany

Global Navigation Satellite Systems (GNSS) deliver absolute position and velocity, as well as time information (P, v, T). However, in urban areas, the GNSS navigation performance is restricted due to signal obstructions and multipath. This is especially true for applications dealing with highly automatic or even autonomous driving. Subsequently, multi-sensor platforms including laser scanners and cameras, as well as map data are used to enhance the navigation performance, namely in accuracy, integrity, continuity and availability. Although well-established procedures for integrity monitoring exist for aircraft navigation, for sensors and fusion algorithms used in automotive navigation, these concepts are still lacking. The research training group ***, integrity and collaboration in dynamic sensor networks, aims to fill this gap and to contribute to relevant topics. This includes the definition of alternative integrity concepts for space and time based on set theory and interval mathematics, establishing new types of maps that report on the trustworthiness of the represented information, as well as taking advantage of collaboration by improved filters incorporating person and object tracking. In this paper, we describe our approach and summarize the preliminary results.

关键词： integrity collaboration GNSS laser scanner camera interval mathematics set theory

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Sampling Jitter Mitigation in Latency-Critical State-Estimation applications using Particle Filters 3

Sampling Jitter Mitigation in Latency-Critical State-Estimat...

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3rd SICE International Symposium on Control Systems (SICE ISCS)

作者： El Hakim, viktorio S. Bekooij, Marco J. G. Univ Twente Dept Comp Architectures Embedded Syst Enschede Netherlands NXP Semicond Dept Embedded Software & Signal Proc Eindhoven Netherlands

ISBN: (纸本)9784907764548

State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.

关键词： Sampling jitter Particle filtering

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Discretization of SO(3) Using Recursive Tesseract Subdivision 13

Discretization of SO(3) Using Recursive Tesseract Subdivisio...

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13th IEEE International Conference on Multisensor fusion and Integration for Intelligent Systems (MFI)

作者： Kurz, Gerhard Pfaff, Florian Hanebeck, Uwe D. Karlsruhe Inst Technol Inst Anthropomat & Robot Intelligent Sensor Actuator Syst Lab ISAS Karlsruhe Germany

ISBN: (纸本)9781509060641

The group of rotations in three dimensions SO(3) plays a crucial role in applications ranging from robotics and aeronautics to computer graphics. Rotations have three degrees of freedom, but representing rotations is a nontrivial matter and different methods, such as Euler angles, quaternions, rotation matrices, and Rodrigues vectors are commonly used. Unfortunately, none of these representations allows easy discretization of orientations on evenly spaced grids. We present a novel discretization method that is based on a quaternion representation in conjunction with a recursive subdivision scheme of the four-dimensional hypercube, also known as the tesseract.

关键词： Quaternions Three-dimensional displays Robot sensing systems Computer graphics Hypercubes Approximation algorithms

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Basic Human Activity Recognition based on sensor fusion in smartphones 15

Basic Human Activity Recognition based on sensor fusion in s...

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15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017

作者： San Buenaventura, Charlene v. Tiglao, Nestor Michael C. Electrical and Electronics Engineering Institute University of the Philippines Velasquez St. Diliman Quezon City1101 Philippines

ISBN: (纸本)9783901882890

Smartphones are ubiquitous devices that offer endless possibilities for health-related applications such as Ambient Assisted Living (AAL). They are rich in sensors that can be used for Human Activity Recognition (HAR) and monitoring. The emerging problem now is the selection of optimal combinations of these sensors and existing methods to accurately and efficiently perform activity recognition in a resource and computationally constrained environment. To accomplish efficient activity recognition on mobile devices, the most discriminative features and classification algorithms must be chosen carefully. In this study, sensor fusion is employed to improve the classification results of a lightweight classifier. Furthermore, the recognition performance of accelerometer, gyroscope and magnetometer when used separately and simultaneously on a feature-level sensor fusion is examined to gain valuable knowledge that can be used in dynamic sensing and data collection. Six ambulatory activities, namely, walking, running, sitting, standing, walking upstairs and walking downstairs, are inferred from low-sensor data collected from the right trousers pocket of the subjects and feature selection is performed to further optimize resource use. © 2017 IFIP.

关键词： Smartphones

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