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A new multi-sensor fusion algorithm based on the Information Filter framework

A new multi-sensor fusion algorithm based on the Information...

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Seminar on Detection Systems architectures and Technologies (DAT)

作者： Ammar Cherchar Messaoud Thameri Adel Belouchrani Ecole Supérieure Ali CHABATI Réghaia Algeria Ecole Nationale Polytechnique Algiers Algeria

ISBN: (纸本)9781509045099

his paper presents a new efficient track-to-track fusion (T2TF) algorithm based on the Information Filter (IF) framework which takes into account phenomena encountered in practical applications. In fact, it combines a modified version of the IF framework and a self-tuning fusion procedure based on likelihood functions to address issues such as the correlation of the estimates, the transmission shortcomings and the measurement origin uncertainty. The proposed method is evaluated through simulations and the obtained results show that the proposed algorithm performs as well the optimal centralized fusion schema in an idealized environment while it exhibits better robustness capabilities than existing decentralized algorithms when observation origin uncertainty is considered. Moreover, its reduced complexity cost is suitable for real time applications.

关键词： Microwave measurement Antenna measurements Microstrip antenna arrays Antenna radiation patterns Microwave antenna arrays

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In-Drilling Alignment Scheme for Borehole Assembly Trajectory Tracking over a Wireless Ad hoc Network

In-Drilling Alignment Scheme for Borehole Assembly Trajector...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Odei-Lartey, E. Hartmann, K. Univ Siegen ZESS Zentrum Sensorsyst Paul Bonatz Str 9-11 D-57076 Siegen Germany

ISBN: (纸本)9781510601130

This paper describes the feasibility and implementation of an In-drilling alignment method for an inertia navigation system based measurement while drilling system as applied to the vertical drilling process, where there is seldom direct access to a reference measurement due the communication network architecture and telemetry framework constraints. It involves the sequential measurement update of multiple nodes by the propagation of the reference measurement sequentially over nodes, each of which has an integrated inertia measurement unit sensor and runs the extended Kalman filter based signal processing unit, until the final measurement update of the main sensor node embedded in the borehole assembly is done. This is done particularly to keep track of the yaw angle position during the drilling process.

关键词： In-drilling alignment extended Kalman filter inertial navigation system vertical drilling measurement update sensor node low-cost IMU gyroscope

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Infrastructure-free indoor pedestrian tracking based on foot mounted UWB/IMU sensor fusion 11

Infrastructure-free indoor pedestrian tracking based on foot...

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11th International conference on Signal Processing and Communication Systems (ICSPCS)

作者： Zeng, Zhuoqi Liu, Steven Wang, Wei Wang, Lei Bosch China Investment Ltd CR RTC5 AP Shanghai Peoples R China Univ Kaiserslautern Inst Control Syst Kaiserslautern Germany Tongji Univ Sino German Sch Postgrad Studies Shanghai Peoples R China

ISBN: (纸本)9781538628874

Accurate indoor human localization without requiring any pre-installed infrastructure is essential for many applications, such as search and rescue in fire disaster areas or human social interaction. Ultra-wideband (UWB) is a very promising technology for accurate indoor positioning with pre-installed receivers. An infrastructure-free methodology, called Pedestrian Dead-Reckoning (PDR), which uses an inertial measurement unit (IMU), can also be used for position estimation. In this approach, the drift errors of IMU in each step length estimation are compensated based on zero-velocity update (ZUPT), zero angular rate update (ZARU) and heuristic heading drift reduction (HDR) algorithms. An accurate step detection can be achieved by relying on the data provided by accelerometers and gyroscopes. In order to further improve the accuracy, a novel approach, which combines IMU PDR and UWB ranging measurements by Extended Kalman filter (EKF) without any pre-installed infrastructure, is proposed. All the components in this approach, the IMU, the mobile station (MS) and the receiver of the UWB are mounted on the feet. The biases in the IMU measurements, which cause inaccurate step length estimation, can be compensated by range measurements provided by UWB. The performance of the normal PDR with EKF is evaluated as comparison to the proposed approach. The real test results show that the proposed approach with EKF is the most effective way to reduce the error.

关键词： Accurate human localization infrastructure-free PDR IMU ZUPT ZARU HDR UWB EKF

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Heterogeneous sensor Information fusion based on Kernel Adaptive Filtering for UAVs' Localization

Heterogeneous Sensor Information Fusion based on Kernel Adap...

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IEEE International conference on Information and Automation (ICIA)

作者： Chen, Zhiheng Wang, Can Wang, Huiguo Ma, Yue Liang, Guoyuan Wu, Xinyu Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synerg Syst Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Beijing Peoples R China

ISBN: (纸本)9781538631546

Due to the low weight of monocular camera, monocular Simultaneous Localization and Mapping (SLAM) is an area of popular research and promotes countless applications of micro Unmanned Aerial Vehicles (UAVs), especially in some GPS-denied indoor environments. Nevertheless, the motion of UAVs is often faster and more complex than that of ground based robots. It would also lead to error accumulation if we calculate the trajectory only through the ego-motion. For purpose of higher accuracy and lower power cost, the fusion of visual and inertial measurement sensors is presented in UAV's indoor navigation. In this paper, we propose a novel loosely-coupled system to integrate monocular visual odometry (VO) with reading from Inertial navigation system (INS) for UAVs' indoor localization. We acquire the data from Inertial Measurement Unit (IMU) and VO results individually and map them into a same feature space. The space is defined by the Tensor Product of the individual Kernels for each source. Based on the method of Kernel Adaptive Filtering method-kernel space least mean squares (KLMS), these data are fused in the high dimensional space. Then, experiments are made to verify this method. Compared with the vision-only algorithms, it can be confirmed that the Kernel Adaptive Filtering method makes some improvements in localization accuracy of UAVs.

关键词： Localization VO INS Tensor Product KLMS

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ACM International conference Proceeding Series

ACM International Conference Proceeding Series

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2017 Mediterranean Symposium on Smart City Application, SCAMS 2017

ISBN: (纸本)9781450352116

The proceedings contain 30 papers. The topics discussed include: towards a system for integrating heterogeneous health records;applying text mining to predict learners' cognitive engagement;an adult learner's knowledge model based on ontologies and rule reasoning;big data architecture for decision making in protocols and medications assignment;3D GIS for smart cities;cloud-based integrated information system for medical offices;ICT assessment axes for the smart city approach;XACML policies into Mongodb for privacy access control;implementation of the algorithms of ECG signal processing on embedded architectures: a survey;perception of a new framework for detecting phishing web pages;TV home-box based IoT for smart home;smart adaptive learning based on Moodle platform;SVG image comparison using commands of element path;wireless sensors networks challenges;energy dynamic MANET on-demand with error rate QOS (e-DYMO-Er) routing protocol for wireless sensor networks;towards a big data analytics framework for smart cities;remote controlled human navigational assistance for the blind using intelligent computing;study of the impact of load and density on the behavior of routing protocols AODV and DSDV in the VANET networks;modeling of piezoelectric sensors for feat heart rate signal;first Africa and morocco NB-IoT experimental results and deployment scenario: new approach to improve main 5G KPIs for smart water management;survey on nature inspired algorithm for smart city applications;and detection of driver drowsiness based on the Viola & Jones method and logistic regression analysis.

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Optimal Rotation Sequences for Active Perception

Optimal Rotation Sequences for Active Perception

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Nakath, David Rachuy, Carsten Clemens, Joachim Schill, Kerstin Univ Bremen Cognit Neuroinformat D-28359 Bremen Germany

ISBN: (纸本)9781510601130

One major objective of autonomous systems navigating in dynamic environments is gathering information needed for self localization, decision making, and path planning. To account for this, such systems are usually equipped with multiple types of sensors. As these sensors often have a limited field of view and a fixed orientation, the task of active perception breaks down to the problem of calculating alignment sequences which maximize the information gain regarding expected measurements. Action sequences that rotate the system according to the calculated optimal patterns then have to be generated. In this paper we present an approach for calculating these sequences for an autonomous system equipped with multiple sensors. We use a particle filter for multi sensor fusion and state estimation. The planning task is modeled as a Markov decision process (MDP), where the system decides in each step, what actions to perform next. The optimal control policy, which provides the best action depending on the current estimated state, maximizes the expected cumulative reward. The latter is computed from the expected information gain of all sensors over time using value iteration. The algorithm is applied to a manifold representation of the joint space of rotation and time. We show the performance of the approach in a spacecraft navigation scenario where the information gain is changing over time, caused by the dynamic environment and the continuous movement of the spacecraft.

关键词： Markov decision process manifold representation particle filter value iteration multi-sensor fusion optimal decision making active perception information gain

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Resilient Communication, Object Classification and Data fusion in Unmanned Aerial Systems

Resilient Communication, Object Classification and Data Fusi...

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International conference on Unmanned Aircraft Systems

作者： Jacob A. Allison Raymond Ptucha Sergey E. Lyshevski Department of Electrical and Microelectronic Engineering Rochester Institute of Technology Rochester NY USA Department of Computer Engineering Rochester Institute of Technology Rochester NY USA

This paper researches robust data fusion, descriptive reality and control solutions. Cognitive autonomy, vehicle-to-vehicle and vehicle-to-ground communication, as well as wireless hybrid mobile mesh networks are considered. In dynamic and contested environments, the nodes and autonomous swarms must transmit and assess states, evaluate shared data, adapt to dynamic countermeasures, reconfigure, and, ensure secure communication. Robust algorithms and protocols must accomplish data management, perform vulnerability analysis, support data trustworthiness assessment, support physical-virtual reality, and evaluate functionality. Reliability of information sources and communication should be guaranteed despite adversary electronic warfare and disruptive actions. We examine science and technology of communication and data management in distributed processing, control and autonomy applications typifying adversarial environments. Our results ensure sufficient throughput, low latency, robustness, connectivity and secure communication. Topics on target recognition, object classification and dynamic tracking are researched with experimental substantiations.

关键词： Silicon Intelligent sensors Task analysis sensor fusion Kernel Electronic warfare

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sensor Data fusion with MPSoCSim in the Context of Electric Vehicle Charging Stations 2

Sensor Data Fusion with MPSoCSim in the Context of Electric ...

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2nd IEEE Nordic Circuits and Systems conference (NORCAS)

作者： Stoychev, Ivan Wehner, Philipp Rettkowski, Jens Kalb, Tobias Goehringer, Diana Oehm, Juergen Ruhr Univ Bochum Analogue Integrated Circuits Res Grp Univ Str 150 D-44780 Bochum Germany Ruhr Univ Bochum Applicat Specif Multicore Architectures Res Grp Univ Str 150 D-44780 Bochum Germany

ISBN: (纸本)9781509010950

This paper presents a platform for evaluating sensor data fusion algorithms based on a Network-on-Chip (NoC). The NoC is simulated by MPSoCSim that contains an ARM and several MicroBlaze processors. The approach is evaluated in an automotive context. Charging stations for electric vehicles consist of expensive sensors, as the energy measurement system is an important part used for billing purposes, for safety of the charging process and for security. The sensors, that are usually used, are special made solutions, which are thermally stable and highly accurate. The NoC infrastructure presented in this paper results in a new method for sensor data fusion, which decreases costs by using inexpensive sensor units. The target of the sensor data fusion approach is to replace expensive sensors with many inexpensive one without loosing accuracy. The presented simulation platform allows an easy exchange of sensor models and sensor data fusion algorithms. Complex algorithms can furthermore be transferred to programmable FPGA logic, which allows the simultaneous processing of sensor data.

关键词： sensor data fusion charging stations electric cars FPGA signal processing Network-on-Chip simulation MPSoC

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Information Form Distributed Kalman Filtering (IDKF) with Explicit Inputs 20

Information Form Distributed Kalman Filtering (IDKF) with Ex...

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20th International conference on Information fusion (fusion)

作者： Pfaff, Florian Noack, Benjamin Hanebeck, Uwe D. Govaers, Felix Koch, Wolfgang KIT Intelligent Sensor Actuator Syst Lab ISAS Inst Anthropomat & Robot Karlsruhe Germany Sensor Data & Informat Fus Fraunhofer FKIE Wachtberg Germany

ISBN: (纸本)9780996452700

With the ubiquity of information distributed in networks, performing recursive Bayesian estimation using distributed calculations is becoming more and more important. There are a wide variety of algorithms catering to different applications and requiring different degrees of knowledge about the other nodes involved. One recently developed algorithm is the distributed Kalman filter (DKF), which assumes that all knowledge about the measurements, except the measurements themselves, are known to all nodes. If this condition is met, the DKF allows deriving the optimal estimate if all information is combined in one node at an arbitrary time step. In this paper, we present an information form of the distributed Kalman filter (IDKF) that allows the use of explicit system inputs at the individual nodes while still yielding the same results as a centralized Kalman filter.

关键词： Distributed Kalman filter recursive Bayesian estimation information filter track-to-track fusion

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Towards the Exploitation of Formal Methods for Information fusion

Towards the Exploitation of Formal Methods for Information F...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Clemens, Joachim Wille, Robert Schill, Kerstin Univ Bremen Cognit Neuroinformat D-28359 Bremen Germany Johannes Kepler Univ Linz Inst Integrated Circuits Linz Austria DFKI GmbH Cyber Phys Syst Bremen Germany

ISBN: (纸本)9781510601130

When an autonomous system has to act in or interact with an environment, a suitable representation of it is required. In the past decades, many different representation forms - especially spacial ones - have been proposed and even more information fusion techniques were developed in order to build these representations from multiple information sources. However, most of these algorithms do not exploit the full potential of the available information. This is caused by the fact that they are not able to handle the full complexity of all possible solutions compatible with the information and that they rely on restrictive assumptions (i.e. independencies) in order to make the computation feasible. In this work, a new methodology is envisioned that utilizes formal methods, in particular solvers for Pseudo-Boolean Optimization, to drop some of these assumptions. In order to illustrate the ideas, information fusion based on belief functions and occupancy grid maps are considered. It is shown that this approach allows for considering dependencies among multiple cells and thus significantly reduces the uncertainty in the resulting representation.

关键词： information fusion formal methods belief function theory occupancy grid maps incomplete information Boolean satisfiability

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