Open AccessTracking refractivity from radar clutter using particle filter
Author(s) -
Sheng Zheng,
Jiaqing Chen,
XU Ru-hai
Publication year - 2012
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.61.069301
Subject(s) - extended kalman filter , ensemble kalman filter , clutter , particle filter , computer science , invariant extended kalman filter , algorithm , kalman filter , nonlinear system , gaussian , radar , inversion (geology) , nonlinear filter , control theory (sociology) , monte carlo method , radar tracker , alpha beta filter , filter (signal processing) , moving horizon estimation , filter design , physics , mathematics , computer vision , artificial intelligence , telecommunications , paleontology , statistics , control (management) , quantum mechanics , structural basin , biology
Particle filter(PF) is an effective algorithm for the state recursive estimation in nonlinear and non-Gaussian dynamic systems by utilizing the Monte Carlo simulation, and it is applicable for solving the nonlinear and non-Gaussian RFC(refractivity from radar clutter) problems. The basic idea and the specific algorithm of PF are introduced; the implementation of the iterative inversion algorithm is derived finally. The experimental result indicates that the particle filter is suited to solve the nonlinear inversion problem and can effectively increase the stability and the accuracy of inversion results compared with the extended Kalman filter (EKF) and the unscented kalman filter (UKF).