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IEEE International Symposium on Electromagnetic Compatibility, EMC 2007

作者： Drozd, Andrew L. Kasperovich, Irina Ruixin, Niu Varshney, Pramod K. ANDRO Computational Solutions LLC Beeches Technical Campus 7902 Turin Road Ste. 2-1 Rome NY United States Dept. of Electrical Engineering and Computer Science Syracuse University Link Hall Syracuse NY United States

ISBN: (纸本)1424413508

Real-time fusion of data collected from a variety of colocated radars that acquire information in a cooperative manner from multiple perspectives and/or different frequencies, is being shown to provide a more accurate and effective way of tracking complex targets in a multi-target scenario. This is more advantageous than employing a single radar or a group of radars operating independently. This paper describes a cooperative multi-sensor approach in which multiple radars operate together in a non-interference limited manner. A three-fold approach is presented: (i) applying multiobjective joint optimization algorithms to set limits on the operational parameters of the radars to preclude electromagnetic interference;(ii) measuring and processing radar returns in a shared manner for target feature extraction based on electromagnetic diversity principles in conjunction with target scattering cross sections;and (iii) employing feature-aided track/fusion algorithms to detect, discriminate, and follow real targets from clutter noise. The results of computer simulations are provided that demonstrate the advantages of this approach. © 2007 IEEE.

关键词： Extraction

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Fault detection and isolation in multisensor train navigation systems

Fault detection and isolation in multisensor train navigatio...

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UKACC International Conference on Control 98

作者： Mirabadi, A Mort, N Schmid, F Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)085296708X

In new Automatic Train Control (ATC) and Automatic Train Protection (ATP) systems, the onboard navigation system plays a vital role. Applying multisensor fusion algorithms in the vehicle navigation field has made it possible to make inexpensive and accurate positioning systems which will satisfy the requirements of railway systems. Kalman filtering, as one of the well known state estimation methods, has proved to be the most effective technique in multisensor data fusion for navigation applications. On the other hand reliability of the navigation system is of great concern which necessitates the design of a fault tolerant navigation system in which the detection and isolation of the faults is possible. This paper will first address different multisensor integration methods and later will discuss some of the fault detection methods for navigation systems using Kalman filtering and some simulation results will be presented. Fault isolation using the proposed sensor architecture is another topic which will be discussed in more detail.

关键词： multisensor fusion Kalman filter navigation railways fault detection

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Nodes deployment optimization algorithm based on improved evidence theory 18th

Nodes deployment optimization algorithm based on improved ev...

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18th International Conference on algorithms and architectures for Parallel Processing - ICA3PP 2018 Collocated Workshops: ICA3PP 2018 Workshop on Intelligent algorithms for Large-scale Complex Optimization Problems, IALCOP 2018, ICA3PP 2018 Workshop on Security and Privacy in Data Processing, SPDP 2018

作者： Song, Xiaoli Gong, Yunzhan Jin, Dahai Li, Qiangyi Jing, Hengchang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Henan University of Science and Technology HenanLuoyang471023 China

ISBN: (纸本)9783030052331

Underwater wireless sensor networks (UWSNs) applications for ocean monitoring, deep sea surveillance, and locating natural resources are gaining popularity. To monitor the underwater environment or any object of interest, these applications are required to deploy underwater connected node sensors for obtaining useful data. For thriving UWSNs, it is essential that an efficient and secure node deployment mechanism is in place. In this article, we are presenting a novel nodes deployment scheme which is based on evidence theory approach and cater-for 3D-UWSNs. This scheme implements sonar probability perception and an enhanced data fusion model to improve prior probability deployment algorithm of D-S evidence theory. The viability of our algorithm is verified by performing multiple simulation experiments. The simulation results reveal that as compared to other schemes, our algorithm deploys fewer nodes with enhanced network judgment criteria and expanded detection capabilities for a relatively large area. © Springer Nature Switzerland AG 2018.

关键词： sensor nodes

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons’ Gait

Fusion of Data from Radar and Depth Sensors Applied for Heal...

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Signal Processing algorithms, architectures, Arrangements and applications (SPA)

作者： Paweł Mazurek Jakub Wagner Roman Z. Morawski Faculty of Electronics and Information Technology Institute of Radioelectronics and Multimedia Technology Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781728139906

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation - performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： Monitoring Legged locomotion sensor fusion Estimation Radar Biomedical monitoring

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A Constraint Sufficient Statistics based Distributed Particle Filter for Bearing Only Tracking

A Constraint Sufficient Statistics based Distributed Particl...

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IEEE International Conference on Communications (ICC)

作者： Mohammadi, Arash Asif, Amir York Univ Toronto ON M3J 1P3 Canada

ISBN: (纸本)9781457720536

A constrained sufficient statistic based distributed implementation of the particle filter (CSS/DPF) is proposed for angle/bearing-only tracking (BOT) applications. The CSS/DPF runs localized particle filters at each sensor node and computes the global sufficient statistics (GSS) of the overall system as a function (summation) of the local sufficient statistics (LSS). The CSS/DPF is, therefore, a two stage procedure: (i) First, the means of LSS at local nodes are computed by running average consensus algorithms to derive the GSS, and;(ii) Each node then updates its localized particle filter using the modified GSS. Simulation results show that the CSS/DPF is near-optimal with its performance almost identical to that of the centralized particle filter. The number of average consensus runs in the CSS/DPF are reduced by an order of magnitude of the dimension of the state vector, thereby, reducing the communication complexity and bandwidth requirement of the distributed implementation.

关键词： Consensus Algorithm Data fusion Distributed Estimation Bearing-only Tracking Particle Filters

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Multi-sensor aided deep pose tracking 2nd

Multi-sensor aided deep pose tracking

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2nd Modeling, Estimation and Control Conference (MECC)

作者： Lee, Hojun Toner, Tyler Tilbury, Dawn Barton, Kira Univ Michigan Dept Robot Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

Accurate pose estimation of nearby objects is critical for robots to dynamically interact with their surroundings. The complexity of this task has led researchers to explore deep learning methods. Nowadays, many works have solely focused on developing complicated neural network architectures to estimate pose from a simple monocular camera. However, most of these methods struggle with inherent limitations of a single sensor system, like occlusion, which are commonly encountered in mobile robotics applications. Online, occlusion-robust pose estimation is extremely important in such cases, as mobility of a robot introduces major uncertainty that complicates manipulation. Hence, we present Multi-sensor aided Deep Pose Tracking (MS-DPT), a framework for online object pose estimation to enable robust mobile manipulation. A Convolutional Neural Network (CNN) identifies key objects in an RGB-D image, from which object pose estimates are generated using a variant of the Iterative Closest Point (ICP) algorithm. An extended Kalman filter is used to fuse this pose estimate with onboard motion sensors to compensate for occlusion and robot motion during manipulation. This three stage method focuses on cohering different modalities and algorithms to improve the pose tracking stability and continuity in cases where the target object becomes heavily occluded by an obstacle or a mobile robot itself. The proposed approach accurately tracks textureless objects with high symmetries while operating at 10 FPS during experiments. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Object pose estimation Neural-network model Robot vision Data fusion Tracking

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Exact and Metaheuristic Approaches to Extend Lifetime and Maintain Connectivity in Wireless sensors Networks

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5th International Network Optimization Conference (INOC)

作者： Raiconi, Andrea Gentili, Monica Univ Salerno I-84084 Fisciano SA Italy

ISBN: (纸本)9783642215278

Wireless sensor networks involve a large area of real-world contexts, such as national security, military and environmental control applications, traffic monitoring, among others. These applications generally consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. One of the most important issue that is considered in this context is maximizing network lifetime, that is the amount of time in which this monitoring activity can be performed by opportunely switching the sensors from active to sleep mode. Indeed, the lifetime of the network can be maximized by individuating subset of sensors (i.e., covers) and switching among them. Two important aspects need to be taken into account among others: (i) coverage: each determined cover has to cover the entire set of targets, and (ii) connectivity: each cover should provide satisfactory network connectivity so that sensors can communicate for data gathering or data fusion (connected covers). In this paper we consider the problem of determining the maximum network lifetime to monitor all the targets by means of connected covers. We analyze the problem and propose an exact approach based on column generation and two heuristic approaches, namely a greedy algorithm and a GRASP algorithm, to solve it. We analyze the performance of the heuristic approaches by comparing the obtained solutions with those provided by the exact approach when available. Our preliminary experimental results show the proposed solution algorithms to be promising in terms of tradeoff between quality of solutions and computational effort.

关键词： Data fusion

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Practical considerations in Bayesian fusion of point sensors

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Johnson, Kevin Minor, Christian Naval Research Laboratory Washington DC 20375 United States Nova Research Inc. Alexandria VA 22308 United States

ISBN: (纸本)9780819490858

sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented. © 2012 SPIE.

关键词： sensor data fusion

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Neural Architecture Search for Automotive Grid fusion Networks Under Embedded Hardware Constraints

Neural Architecture Search for Automotive Grid Fusion Networ...

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International Conference on Machine Learning and applications (ICMLA)

作者： Gabor Balazs Walter Stechele Technische Universität München Munich Germany

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

The goal of automotive sensor fusion is to generate an environmental model with low-cost, mass-produced sensors-typically camera and radar. The central fusion controller needs sophisticated post-processing algorithms to achieve this goal despite extensive pre-processing on the sensor side. Recent advances show the capabilities of deep convolutional auto-encoders for sensor fusion but are still limited to high-performance hardware. Limited memory and processing capabilities of embedded devices lead to the multi-objective optimization problem addressed in this work. The proposed evolutionary neural architecture search is configured to find novel sensor fusion network architectures optimized for embedded hardware. The discovered encoding and decoding cells improve fusion performance compared to that of corresponding cells from literature. These lightweight network architectures allow the deployment on constrained embedded devices, thus leverage environmental perception of level 2/2+ automated cars.

关键词： Training Computer architecture sensor fusion Hardware Encoding Decoding Automotive engineering

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Streamlining River Flood Prevention with an Integrated AIoT Framework 20

Streamlining River Flood Prevention with an Integrated AIoT ...

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20th ACS/IEEE International Conference on Computer Systems and applications (AICCSA)

作者： Boulouard, Zakaria Ouaissa, Mariya Ouaissa, Mariyam Krichen, Moez Almutiq, Mutiq Algarni, Mohammad Hassan II Univ LIM Casablanca Morocco Cadi Ayyad Univ Comp Syst Engn Lab Marrakech Morocco Chouaib Doukkali Univ Lab Informat Technol El Jadida Morocco Al Baha Univ Dept Informat Technol Al Baha Saudi Arabia Qassim Univ Dept Management Informat Syst & Prod Management Coll Business & EconPOB 6640 Buraydah 51452 Saudi Arabia Al Baha Univ Comp Sci Dept Al Baha 65431 Saudi Arabia

ISBN: (纸本)9798350319439

River floods stand among the natural disasters with far-reaching impacts, affecting human lives, the economy, infrastructure, agriculture, and more. Substantial investments by organizations are directed toward innovative strategies for flood prevention. The concept of Artificial Intelligence of Things (AIoT), a fusion of Artificial Intelligence and Internet of Things technologies, has showcased its prowess across various domains. In this paper, we introduce an AIoT framework where river flood sensors, located in every region, transmit their data using LoRaWAN technology to local broadcast centers in proximity. These broadcast centers subsequently forward the data via 4G/5G networks to a centralized cloud server. The server employs efficient AI algorithms to analyze the data and predict river conditions nationwide, contributing to proactive flood prevention. This approach has demonstrated effectiveness on multiple fronts. LoRaWAN-based communication between sensor nodes and broadcast centers offers reduced energy consumption and expanded coverage, while AI-driven data analysis enhances the accuracy of river flood predictions.

关键词： Predictive River Flooding sensor Devices LoRaWAN Technology AI (Artificial Intelligence) IoT (Internet of Things) AIoT (Artificial Intelligence of Things)

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