Premium
Statistical maritime radar duct estimation using hybrid genetic algorithm–Markov chain Monte Carlo method
Author(s) -
Yardim Caglar,
Gerstoft Peter,
Hodgkiss William S.
Publication year - 2007
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2006rs003561
Subject(s) - markov chain monte carlo , radar , algorithm , clutter , computer science , maximum a posteriori estimation , gibbs sampling , monte carlo method , data assimilation , remote sensing , mathematics , statistics , bayesian probability , meteorology , geology , maximum likelihood , physics , telecommunications
This paper addresses the problem of estimating the lower atmospheric refractivity (M profile) under nonstandard propagation conditions frequently encountered in low‐altitude maritime radar applications. This is done by statistically estimating the duct strength (range‐ and height‐dependent atmospheric index of refraction) from the sea surface reflected radar clutter. These environmental statistics can then be used to predict the radar performance. In previous work, genetic algorithms (GA) and Markov chain Monte Carlo (MCMC) samplers were used to calculate the atmospheric refractivity from returned radar clutter. Although GA is fast and estimates the maximum a posteriori (MAP) solution well, it poorly calculates the multidimensional integrals required to obtain the means, variances, and underlying posterior probability distribution functions of the estimated parameters. More accurate distributions and integral calculations can be obtained using MCMC samplers, such as the Metropolis‐Hastings and Gibbs sampling (GS) algorithms. Their drawback is that they require a large number of samples relative to the global optimization techniques such as GA and become impractical with an increasing number of unknowns. A hybrid GA‐MCMC method based on the nearest neighborhood algorithm is implemented in this paper. It is an improved GA method which improves integral calculation accuracy through hybridization with a MCMC sampler. Since the number of forward models is determined by GA, it requires fewer forward model samples than a MCMC, enabling inversion of atmospheric models with a larger number of unknowns.