Conference Paper

# Evolutionary particle filter: re-sampling from the genetic algorithm perspective

ARC Centre of Excellence for Autonomous Syst., Univ. of Technol., Sydney, NSW, Australia

DOI: 10.1109/IROS.2005.1545119 Conference: Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on Source: IEEE Xplore

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**ABSTRACT:**In this study, a novel method is presented for non-linear, non-Gaussian online state and parameter identification, developed for use in structural health monitoring (SHM) problems. The algorithm consists of a particle filter (PF) that combines the use of the standard PF with mutation operators. The algorithm aims at alleviating the sample impoverishment problem, which is a well-known limitation of the standard PF, yielding it inefficient for demanding non-linear identification problems. To overcome this hurdle, we introduce here an alternative approach, influenced by the principles of evolutionary computation. After the standard PF steps are performed to a point where the sample diversity drops below some threshold, the unfit particles are replaced by either the fittest particles or the current weighted estimate of the state. Next, the time-invariant components of the particles are mutated under some mutation probability, and the new sample is then propagated to the next time step. This process is well suited for joint state and parameter estimation problems, as is usually the case in SHM techniques. As a result, the loss of diversity associated with the standard PF is overcome, and the new PF with mutation is shown to outperform the standard PF and the unscented Kalman filter for the case of high process noise. The method is validated through an established benchmark problem found in the literature, lying outside of the structural identification concept, and a previously referenced 3DOF structural system with hysteresis elaborating the SHM aspect. Copyright © 2012 John Wiley & Sons, Ltd.Structural Control and Health Monitoring 01/2013; 20(7):1081-1095. · 1.54 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.Journal of Geophysical Research Atmospheres 03/2014; · 3.44 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**In this paper a novel filtering procedure that uses a variant of the variable neighborhood search (VNS) algorithm for solving nonlinear global optimization problems is presented. The base of the new estimator is a particle filter enhanced by the VNS algorithm in resampling step. The VNS is used to mitigate degeneracy by iteratively moving weighted samples from starting positions into the parts of the state space where peaks and ridges of a posterior distribution are situated. For testing purposes, bearings-only tracking problem is used, with two static observers and two types of targets: non-maneuvering and maneuvering. Through numerous Monte Carlo simulations, we compared performance of the proposed filtering procedure with the performance of several standard estimation algorithms. The simulation results show that the algorithm mostly performed better than the other estimators used for comparison; it is robust and has fast initial convergence rate. Robustness to modeling errors of this filtering procedure is demonstrated through tracking of the maneuvering target. Moreover, in the paper it is shown that it is possible to combine the proposed algorithm with an interacted multiple model framework.Computers & Operations Research 12/2014; 52:192–202. · 1.72 Impact Factor

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