Statistical maritime radar duct estimation using hybrid genetic algorithm–Markov chain Monte Carlo method

Abstract

[1] This paper addresses the problem of estimating the lower atmospheric refractivity (M profile) under nonstandard propagation conditions frequently encountered in lowaltitude 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.Citation: Yardim, C., P. Gerstoft, and W. S. Hodgkiss (2007), Statistical maritime radar duct estimation using hybrid genetic algorithm -Markov chain Monte Carlo method, Radio Sci., 42, RS3014,

Refractivity From Clutter by Variational Adjoint Approach

“…However, the measured radar sea clutter power is only one value at each range. Hence, it is typically approximate the signal that is scattered at a given range from the PE field at a designated height z 0 near the surface [9][10][11]. A drawing of the RFC measurement geometry is shown in Fig.…”

“…Vasudevan et al exploited the inherent Markovian structure of the fast Fourier transform (FFT) to parabolic equation (PE) approximation and used a particle filtering approach to retrieve range-dependent atmospheric refractivity profiles [8]. Yardim et al adopted Markov chain Monte Carlo (MCMC) sampling approach, a hybrid GA-MCMC method and Kalman Filters, respectively, to investigate RFC problem [9][10][11]. Through establishing many pre-computed, modeled radar clutter returns for different refractive environments in a database, Douvenot et al inverted refractivity profiles based on finding the optimal environment from the database [12,13].…”

Estimation of the Atmospheric Duct From Radar Sea Clutter Using Artificial Bee Colony Optimization Algorithm

“…In recent years, refractivity from clutter (RFC) technique has been a novel and promising method to estimate the atmospheric duct instead of using the traditional ways mentioned above. Atmospheric duct is usually associated with increased sea clutter due to the heavy interaction between the sea surface and the electromagnetic wave trapped within the electromagnetic duct, and this unwanted sea clutter is a rich source of information about the low-altitude maritime environment and can be used to estimate the atmospheric duct [3]. Estimation of atmosphere duct using RFC technique is an inverse problem, and the relationship between the forward propagation model and atmosphere duct parameters is a complex nonlinear model.…”

Using particle filter to track horizontal variations of atmospheric duct structure from radar sea clutter

“…Although GA does well in estimating the maximum a posteriori (MAP) solution, it gives poor results in calculating the multi-dimensional integrals required to obtain means, variances and underlying probability distribution functions of the estimated parameters. Accurate distributions can be obtained using MCMC (Markov chain Monte Carlo) samplers, such as the Metropolis-Hastings and Gibbs sampling algorithms (Yardim et al, 2007). The drawback of MCMC is that it requires a large number of samples and becomes impractical with increasing number of unknowns.…”

“…In the last decade, many advances have been made in remotely sensing refractivity parameters from radar sea clutter. In order to simplify the computation, most of these works treat the refractive environment horizontally homogeneous (Rogers et al, 2000;Barrios, 2004;Kraut et al, 2004;Yardim et al, 2006Yardim et al, , 2009Douvenot et al, 2008;Huang et al, 2009;Wang et al, 2009;Huang, 2011, 2012). Although the spatial change of tropospheric refractivity is larger with height than with range and generally the horizontal homogeneity assumptions of the refractive environments are demonstrated to be reasonable (Hitney et al, 1985;Goldhirsh and Dockery, 1998), the environment can change drastically at air/mass boundaries associated with wave clones and land/ocean interfaces (Barrios, 1992).…”