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IEEE Intelligent Vehicles Symposium (IV)

作者： Danescu, Radu Oniga, Florin Nedevschi, Sergiu Tech Univ Cluj Napoca Dept Comp Sci Str C Daicoviciu 15 Cluj Napoca Romania

ISBN: (纸本)9781424478668

This paper presents an occupancy grid tracking solution based on particles. The particles will have a dual nature - they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell depending on their motion model and motion parameters, but they will also be created and destroyed using a weighting-resampling mechanism specific to particle filter algorithms. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The resulted system is a flexible, real-time tracking solution for dynamic unstructured driving environments.

关键词： environmental perception TRACKING SYSTEM particle filters Cells Building blocks particle filtering algorithm topographic maps

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Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks

Bayesian Tracking in Cooperative Localization for Cognitive ...

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IEEE Vehicular Technology Conference

作者： Sithamparanathan Kandeepan Sam Reisenfeld Tuncer Can Aysal David Lowe Radoslaw Piesiewicz Broadband and Wireless Lab Create-Net International Research Centre Centre for Real-Time Information Networks (CRiN) Faculty of Engineering University Technology

ISBN: (纸本)9781424425167

In this paper we consider cooperative localization and tracking of primary users (PU) in a cognitive radio network using Bayesian techniques. We use particle filtering methods to track the location of a PU in the network using cooperative localization techniques and present some results for noisy measurements. The cognitive radio (CR) nodes estimate the information related to the geographical position of the PU based on existing location identification and localization techniques and forward the noisy information to a cognitive radio base station (CRB), which then fuses the information to estimate the position of the PU in the network in order to perform a radio scene analysis. We propose a particle filtering approach that is suitable for tracking Gaussian and non-Gaussian noisy signals at the CRB to estimate the position of a PU, two importance-functions relative to the particle filtering algorithm are also presented. Simulations are performed on the proposed tracking algorithm and the results are presented in terms of the mean squared error of the positional estimates.

关键词： Bayesian phase tracking particle filter Cooperative localization and tracking Cognitive radios Cognitive radio particle filters Plutonium complementary return to bias particle filtering algorithm Radio networks tracking Noise

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An Improved particle Filter for WSN-aided Robot Localization

An Improved Particle Filter for WSN-aided Robot Localization

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2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

作者： Huaping Zhou Hui Zhang Yong Li Ying Qu Dan Hai Dachuan Wang School of Mechatronics and Automation National University of Defense TechnologyChangshaHunan 41007 National University of Defense Technology ChangshaHunan 410073 China mailbox of NO.5117 Beijing100094China

ISBN: (纸本)9781424447749

An improved particle Filter algorithm is proposed for WSN-aided robot localization. This algorithm introduces node reliability and proposes its expression to realize information fusion among nodes. After having computed node reliability, sensor nodes with high reliability are selected to aid robot localization, and take effect on updating particles according to their reliability. Furthermore, a novel simulation system for WSN-Robot based on USARSim is developed, which is used to test our algorithm in this paper. The simulation result shows the good performance of the proposed algorithm in precision of robot localization and convergence speed of particle filter, and the conformity of the simulation system to the requirement of WSN-robot system research.

关键词： particle filters Simulation Systems Robot Information fusion Simulation results Ganglia Social Conformity particle filtering algorithm

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New method of spatial extrapolation of meteorological fields on the mesoscale level using a Kalman filter algorithm for a four-dimensional dynamic-stochastic model

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JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 2007年第2期24卷 182-193页

作者： Komarov, V. S. Lavrinenko, A. V. Kreminskii, A. V. Lomakina, N. Ya. Popov, Yu. B. Popova, A. I. RAS SB Inst Atmospher Opt Tomsk 634055 Russia

A new method and an algorithm of spatial extrapolation of mesometeorological fields to a territory uncovered with observations are suggested. The algorithm uses a linear Kalman filter for a four-dimensional dynamic-stochastic model of space-time variations of the atmospheric parameters. The results of statistical estimation of the quality of the algorithm used for spatial extrapolation of mesoscale temperature and wind velocity fields are discussed.

关键词： linear Kalman filter filtering algorithms Statistical Estimation mesoscale home range time variations Fields image filtering algorithm Kalman filters extrapolation method particle filtering algorithm algorithms new methods four-dimensional

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A particle filter based fusion framework for video-radio tracking in smart spaces

A particle filter based fusion framework for video-radio tra...

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IEEE Conference on Advanced Video and Signal Based Surveillance

作者： Dore, Alessio Cattoni, Andrea F. Regazzoni, Carlo S. Univ Genoa Dept Biophys & Elect Engn Genoa Italy

ISBN: (纸本)9781424416950

One of the main issues for Ambient Intelligence (AmI) systems is to continuously localize the user and to detect his/her identity in order to provide dedicated services. A video-radio fusion methodology, relying on the particle Filter algorithm, is here proposed to track objects in a complex extensive environment, exploiting the complementary benefits provided by both systems. Visual tracking commonly outperforms radio localization in terms of precision but it is inefficient because of occlusions and illumination changes. Instead, radio measurements, gathered by a user's radio device, are unambiguously associated to the respective target through the "virtual" identity (i.e. MAC/IP addresses). The joint usage of the two data typologies allows a more robust tracking and a major flexibility in the architectural setting up of the AmI system. The method has been extensively tested in a simulated and off-line framework and on real world data proving its effectiveness.

关键词： particle filters Ambient intelligence RADIO TRACKING FUSION Radio equipment particle filtering algorithm Radio Frameworks tracking

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Mixed-mode implementation of particle filters

Mixed-mode implementation of particle filters

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Velmurugan, Rajbabu Subramanian, Shyam Cevher, Volkan McClellan, James H. Anderson, David V. Georgia Inst Technol Atlanta GA 30332 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781424411894

In this paper, we develop new mixed-mode implementations for particle filters and compare them to a digital implementation. The motivation for the mixed-mode implementation is to achieve low-power implementation of particle filters. The specific application considered is a bearings-only, single-target tracking algorithm. Specifically, we develop mixed-mode implementations that use analog components to realize nonlinear functions in the particle filter algorithm. The analog implementation of nonlinear functions uses low-power multiple-input translinear element (MITE) networks. Simulation results for one mixed-mode implementation of the bearings-only tracker show that the analog errors are low enough to support accurate tracking. Redesign of the mixed-mode implementation in a second form with more analog components will result in nearly twenty times less power dissipation.

关键词： Hybrid modes particle filters nonlinear functions particle filtering algorithm LOW POWER

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Statistical comparison of a hybrid approach with approximate and exact inference models for fusion 2+

Statistical comparison of a hybrid approach with approximate...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XVI

作者： Lee, K. David Wiesenfeld, Eric Gelfand, Andrew Decis Analyt Corp 1235 S Clark St Suite 400 Arlington VA 22202 USA

ISBN: (纸本)9780819466891

One of the greatest challenges in modem combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of realtime, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using particle filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

关键词： fusion 2+ situation awareness Bayesian networks fuzzy logic junction tree algorithm min/max algorithm particle filtering algorithm

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Experimental Evaluation of a Distributed Kalman Filter algorithm

Experimental Evaluation of a Distributed Kalman Filter Algor...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 3220-3677

作者： Peter Alriksson Anders Rantzer Department of Automatic Control Faculty of Engineering Lund University Lund Sweden

ISBN: (纸本)9781424414970;1424414970

This paper evaluates the performance of a distributed Kalman filter applied to an ultrasound based positioning application with seven sensor nodes. By distributed we mean that all nodes in the network desires an estimate of the full state of the observed system and there is no centralized computation center after deployment. Communication only takes place between neighbors and only once each sampling interval. The problem is solved by communicating estimates between neighbors and then forming a weighted average as the new estimate. The weights are optimized to yield a small estimation error covariance in stationarity. The minimization can be done off line thus allowing only estimates to be communicated. In the experimental setup the distributed solution performs almost as good as a centralized solution. The proposed algorithm also proved very robust against packet loss.

关键词： Kalman filters Distribute Weighted Average Packet loss Ganglia filtering algorithms image filtering algorithm particle filtering algorithm network node Neighbor algorithms observation system sampling interval Ultrasonic Waves

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Automotive-grade MEMS sensors used for general aviation

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2004年第10期19卷 13-16页

作者： Sherry, L Brown, C Motazed, B Vos, D Athena Technol Inc Warrenton VA USA

The Attitude Heading Reference System (AHRS) provides data for primary flight instruments, head-up displays, autopilots, and moving map navigation systems. Advances in solid-state MEMS rate sensors, coupled with Kalman Filter algorithms designed to mitigate high drift rates, provide the basis for low-cost, high-performance AHRS for general aviation. This paper describes the performance of a low cost miniaturized AHRS using automotive-grade MEMS sensors. The performance of the system is detailed. The implications for certification of this class of system and fault tolerance are discussed.

关键词： aircraft displays aircraft navigation avionics head-up displays microsensors Kalman filter algorithms attitude heading reference system automotive-grade MEMS sensors autopilots fault tolerance flight instruments general aviation map navi Fault tolerance avionics aircraft displays Automatic pilots FLIGHT INSTRUMENTS image filtering algorithm Microsensors filtering algorithms particle filtering algorithm head-up displays aircraft navigation General aviation

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Tracking inside the ALICE Inner Tracking System

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2002年第1-2期485卷 15-22页

作者： Badalà, A Barbera, R Lo Re, G Palmeri, A Pulvirenti, A Pappalardo, GS Riggi, F Catania Univ Ist Nazl Fis Nucl Sez Catania Catania Italy Catania Univ Dipartimento Fis & Astron Catania Italy

One of the main purposes of the ALICE Inner Tracking System (ITS) is to improve the resolution of the track parameters found in the main ALICE tracker detector, the Time Projection Chamber (TPC). Some results about tracking efficiency and resolution of track parameters obtained with a tracking code, based on the Kalman filter algorithm, are presented. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： ALICE Projection rooms tracking particle filtering algorithm filtering algorithms TRACKING SYSTEM Resolving power image filtering algorithm

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