scholarly journals DEVELOPMENT OF A REAL-TIME RUNOFF PREDICTION SYSTEMS USING PARTICLE FILTER AND ACCURACY COMPARISON OF PREDICTION SYSTEMS WITH DIFFERENT STATE VARIABLES

2018 ◽  
Vol 74 (2) ◽  
pp. 32-43
Author(s):  
Yasuto TACHIKAWA ◽  
Shoko FUJITA ◽  
Yuji TANAKA ◽  
Kazuaki YOROZU ◽  
Yutaka ICHIKAWA
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
State Variables ◽  
Accuracy Comparison ◽  
Runoff Prediction ◽  
Prediction Systems

EFFECTIVE APPLICATION OF A REAL-TIME RUNOFF PREDICTION SYSTEM USING A PARTICLE FILTER

2019 ◽  
Vol 75 (2) ◽  
pp. I_241-I_246
Author(s):  
Yuji TANAKA ◽  
Yasuto TACHIKAWA ◽  
Kazuaki YOROZU ◽  
Yutaka ICHIKAWA ◽  
Sunmin KIM
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Prediction System ◽  
Effective Application ◽  
Runoff Prediction

scholarly journals Real Time Color Object Tracking on Cell Broadband Engine Using Particle Filters

2010 ◽  
Vol 14 (3) ◽  
pp. 272-280 ◽  
Author(s):  
Norikazu Ikoma ◽  
◽  
Akihiro Asahara ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Real Time ◽  
Visual Tracking ◽  
Single Object ◽  
Cell Broadband Engine ◽  
Time Performance ◽  
Large Size ◽  
Particle Filter Algorithm ◽  
Local Store

Real time visual tracking by particle filter has been implemented on Cell Broadband Engine in parallel. Major problem for the implementation is small size of Local Store (LS) in SPEs (Synergistic PEs), which are computational cores, to deal with image of large size. As a first step for the implementation, we focus on color single object tracking, which is one of the most simple case of visual tracking. By elaborating to compress the color extracted image into bit-wise representation of binary image, all information of the color extracted image can be stored in LS for 640×480 size of original image. By applying our previous implementation of general particle filter algorithm on Cell/B.E. to this specific case, we have achieved real time performance of visual tracking on PlayStation®3 about 7 fps with a camera of maximum 15 fps.

scholarly journals Implementation of Hybrid Model of Particle Filter and Kalman Filter based Real-Time Tracking for handling Occlusion on Beagleboard-xM

2014 ◽  
Vol 95 (7) ◽  
pp. 31-37 ◽  
Author(s):  
Jharna Majumdar ◽  
Parashar Dhakal ◽  
Nabin Sharma Rijal ◽  
Amar Mani Aryal ◽  
Nilesh Kumar Mishra
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Real Time ◽  
Hybrid Model ◽  
Real Time Tracking

PARTICLE FILTERS IN A MULTISCALE ENVIRONMENT: WITH APPLICATION TO THE LORENZ-96 ATMOSPHERIC MODEL

2011 ◽  
Vol 11 (02n03) ◽  
pp. 569-591 ◽  
Author(s):  
HOONG CHIEH YEONG ◽  
JUN HYUN PARK ◽  
N. SRI NAMACHCHIVAYA
Keyword(s):  
Dynamical Systems ◽  
Particle Filter ◽  
Observational Data ◽  
Particle Filtering ◽  
Atmospheric Model ◽  
Random Dynamical Systems ◽  
Coarse Grained ◽  
Conditional Density ◽  
State Variables ◽  
Lorenz 96

The study of random dynamical systems involves understanding the evolution of state variables that contain uncertainties and that are usually hidden, or not directly observable. Therefore, state variables have to be estimated and updated based on system models using information from observational data, which themselves are noisy, in the sense that they contain uncertainties and disturbances due to imperfections in observational devices and disturbances in the environment within which data are being collected. The development of efficient data assimilation methods for integrating observational data in predicting the evolution of random state variables is thus an important aspect in the study of random dynamical systems. In this paper, we consider a particle filtering approach to nonlinear filtering in multiscale dynamical systems. Particle filtering methods [1–3] utilizes ensembles of particles to represent the conditional density of state variables using particle positions, distributed over a sample space. The distribution of an ensemble of particles is updated using observational data to obtain the best representation of the conditional density of the state variables of interest. On the other hand, homogenization theory [4, 5], allows us to estimate the coarse-grained (slow) dynamics of a multiscale system on a larger timescale without having to explicitly study the fast variable evolution on a small timescale. The results of filter convergence presented in [6] shows the convergence of the filter of the actual state variable to a homogenized solution to the original multiscale system, and thus we develop a particle filtering scheme for multiscale random dynamical systems that utilizes this convergence result. This particle filtering method is called the Homogenized Hybird Particle Filter, and it incorporates a multiscale computation scheme, the Heterogeneous Multiscale Method developed in [7], with the novel branching particle filter described in [8–10]. By incorporating a multiscale scheme based on homogenization of the original system, estimation of the coarse-grained dynamics using observational data is performed over a larger timescale, thus resulting in computational time and cost reduction in terms of the evolution of the state variables as well as functional evaluations for the filtering aspect. We describe the theory behind this combined scheme and its general algorithm, concluded with an application to the Lorenz-96 [11] atmospheric model that mimics midlatitude geophysical dynamics with microscopic convective processes.

Real-time lane tracking using Rao-Blackwellized particle filter

2012 ◽  
Vol 11 (1) ◽  
pp. 179-191 ◽  
Author(s):  
Marcos Nieto ◽  
Andoni Cortés ◽  
Oihana Otaegui ◽  
Jon Arróspide ◽  
Luis Salgado
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Lane Tracking
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