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Refractivity from clutter (RFC) estimation using a hybrid genetic algorithm-Markov chain Monte Carlo method

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4 Author(s)
C. Yardim ; Marine Phys. Lab., Univ. of California, La Jolla, CA, USA ; P. Gerstoft ; W. S. Hodgkiss ; C. F. Huang

A hybrid genetic algorithm - Markov chain Monte Carlo sampler (GA-MCMC) is introduced for estimation of low altitude atmospheric radio refractivity. This is done by inverting for the environmental parameters using the returned radar clutter data. A classical Bayesian framework is used so that the solution can be described in terms of a posterior probability distribution (PPD). An electromagnetic split-step fast Fourier transform (FFT) parabolic equation is used as the forward propagation model. The problem is solved with five different optimizers/samplers including the exhaustive search, genetic algorithms, Metropolis-Hastings and Gibbs samplers, some of which were used in previous literature, as well as the new GA-MCMC hybrid based on the nearest neighborhood algorithm (NN). The results show that the new hybrid method improves the speed of a conventional MCMC sampler by a factor of 10 or more while conserving the accuracy in estimating the probability distributions of the inverted parameters

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2005 IEEE Antennas and Propagation Society International Symposium  (Volume:1B )

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