The estimation of lower refractivity uncertainty from radar sea clutter using the Bayesian-MCMC method  

作　　者：盛峥 

机构地区：[1]College of Meteorology and Oceangraphy,PLA University of Science and Technology

出　　处：《Chinese Physics B》2013年第2期580-585,共6页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (Grant No. 41105013);the National Natural Science Foundation of Jiangsu Province,China (Grant No. BK2011122);the Open Issue Foundation of Key Laboratory of Meteorological Disaster of Ministry of Education,China (Grant No. KLME1109);the City Meteorological Scientific Research Fund,China (Grant No. IUMKY&UMRF201111)

摘　　要：The estimation of lower atmospheric refractivity from radar sea clutter（RFC） is a complicated nonlinear optimization problem.This paper deals with the RFC problem in a Bayesian framework.It uses the unbiased Markov Chain Monte Carlo（MCMC） sampling technique,which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework.In contrast to the global optimization algorithm,the Bayesian-MCMC can obtain not only the approximate solutions,but also the probability distributions of the solutions,that is,uncertainty analyses of solutions.The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar seaclutter data.Reference data are assumed to be simulation data and refractivity profiles are obtained using a helicopter.The inversion algorithm is assessed（i） by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data;（ii） the one-dimensional（1D） and two-dimensional（2D） posterior probability distribution of solutions.The estimation of lower atmospheric refractivity from radar sea clutter（RFC） is a complicated nonlinear optimization problem.This paper deals with the RFC problem in a Bayesian framework.It uses the unbiased Markov Chain Monte Carlo（MCMC） sampling technique,which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework.In contrast to the global optimization algorithm,the Bayesian-MCMC can obtain not only the approximate solutions,but also the probability distributions of the solutions,that is,uncertainty analyses of solutions.The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar seaclutter data.Reference data are assumed to be simulation data and refractivity profiles are obtained using a helicopter.The inversion algorithm is assessed（i） by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data;（ii） the one-dimensional（1D） and two-dimensional（2D） posterior probability distribution of solutions.

关 键 词：refractivity from clutter terrain parabolic equation propagation model Bayesian-Markov chain Monte Carlo uncertainty analysis 

分 类 号：TN957.51[电子电信—信号与信息处理]