{"title":"Line of sight rate estimation of strapdown imaging seeker based on Particle Filter","authors":"Zhang Ying-chun, Lian Jing-jing, Li Hua-yi","doi":"10.1109/ISSCAA.2010.5633193","DOIUrl":null,"url":null,"abstract":"A method to estimate the line of sight(LOS) rate of strapdown imaging seeker based on Particle Filter(PF) is presented. PF algorithm is firstly put forward and then the model of strapdown imaging seeker is introduced. Because of the high nonlinearity in both process and measurement equations and more seriously nonGaussian noise in the measurements, the normally used Extended Kalman Filter(EKF) can not completely meet the requirements of filtering. Comparatively, PF is a congruent method for tracking in the conditions of nonlinearity and nonGaussian noise. At the last, PF and EKF are applied to estimate the LOS rate of strapdown imaging seeker respectively. Simulation results show that PF is more precise than EKF in LOS rate estimation.","PeriodicalId":324652,"journal":{"name":"2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCAA.2010.5633193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
A method to estimate the line of sight(LOS) rate of strapdown imaging seeker based on Particle Filter(PF) is presented. PF algorithm is firstly put forward and then the model of strapdown imaging seeker is introduced. Because of the high nonlinearity in both process and measurement equations and more seriously nonGaussian noise in the measurements, the normally used Extended Kalman Filter(EKF) can not completely meet the requirements of filtering. Comparatively, PF is a congruent method for tracking in the conditions of nonlinearity and nonGaussian noise. At the last, PF and EKF are applied to estimate the LOS rate of strapdown imaging seeker respectively. Simulation results show that PF is more precise than EKF in LOS rate estimation.
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