Posterior distributions of a statistic of propagation loss inferred from radar sea clutter

Abstract-This paper describes a Markov chain Monte Carlo (MCMC) sampling approach for the estimation of not only the radio refractivity profiles from radar clutter but also the uncertainties in these estimates. This is done by treating the refractivity from clutter (RFC) problem in a Bayesian framework. It uses unbiased MCMC sampling techniques, such as Metropolis and Gibbs sampling algorithms, to gather more accurate information about the uncertainties. Application of these sampling techniques using an electromagnetic split-step fast Fourier transform parabolic equation propagation model within a Bayesian inversion framework can provide accurate posterior probability distributions of the estimated refractivity parameters. Then these distributions can be used to estimate the uncertainties in the parameters of interest. Two different MCMC samplers (Metropolis and Gibbs) are analyzed and the results compared not only with the exhaustive search results but also with the genetic algorithm results and helicopter refractivity profile measurements. Although it is slower than global optimizers, the probability densities obtained by this method are closer to the true distributions.

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“…An alternative approach, which does not make use of likelihood, is given in [6]. The likelihood formulations are based on those used in [7].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Radio Refractivity From Radar Clutter Using Bayesian Monte Carlo Analysis

Yardim

Gerstoft

Hodgkiss³

2006

IEEE Trans. Antennas Propagat.

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“…The results of this reseach are documented in five refered jornal papers [1][2][3][4][5][6] and one conference proceeding [7].…”

Section: Research Summarymentioning

confidence: 99%

“…This entails developing likelihood formulations and importance sampling algorithms. This inversion approach shows the information content in the data, the importance of each parameter, and the quality of the inversions [1][2].…”

Section: Research Summarymentioning

confidence: 99%

“…We have concentrated on demonstrating the feasibility of RFC using an efficient 11-parameter description of the environment. The quality of the inversion was addressed by comparing the field using the estimated parameters to a measured field [1][2][3][4][5][6]. Little has been done to indicate the quality of the solution for each parameter, either with the variance of parameter-estimate or preferably the complete a posteriori distribution.…”

Section: Research Summarymentioning

confidence: 99%

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Bayesian Inversion of Radar Clutter

Gerstoft¹,

Hodgkiss²,

Kuperman³

2005

Self Cite

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations end Reports f0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)2 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)Office of This report describes the research performed on behalf of the Office of Naval Research (grant #NOOO 14-03-1-0393) to estimation of refractivity profiles from radar clutter returned. Through simulation and experimental results it is shown that the radar clutter return can be used for extracting refractivity profiles. Of particular interest is the uncertainty in these estimates and it is demonstrated how these parameters can be used for radar performance prediction.

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“…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. Recently, many researchers dedicated to the study of estimating the atmosphere duct [4][5][6][7][8][9][10][11][12][13][14] with efficient optimization methods, estimation model and analyze the performance of the optimization algorithm, and the detailed estimation steps and the latest research progress about the RFC technique can be founded in [4,5]. Due to the nonlinear relationship between the forward propagation model and atmospheric duct parameters, the intelligent optimization algorithms, such as genetic algorithm (GA) [15], particle swarm optimization (PSO) [16], differential evolution (DE) [17] and ant colony optimization (ACO) [18], are good candidates to estimate the atmospheric duct from radar sea clutter.…”

Section: Introductionmentioning

confidence: 99%

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

Yang¹

2013

PIER

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Abstract-In this study, the Artificial Bee Colony Optimization (ABCO) algorithm has been proposed to estimate the atmospheric duct in maritime environment. The radar sea clutter power is calculated by the parabolic equation method. In order to validate the accuracy and robustness of ABCO algorithm, the experimental and simulation study are respectively carried out in the current research. In the simulation study, the statistical analysis of the estimation results in term of the mean squared error (MSE), mean absolute deviation (MAD) and mean relative error (MRE) are presented to analyze the optimization performance with different noise standard deviation, and the comparative study of the performance of ABCO and particle swarm optimization (PSO) algorithm are also shown. The investigation presented indicate that the ABCO algorithm can be accurately and effectively utilized to estimate the evaporation duct and surface-based duct using refractivity from clutter (RFC) technique in maritime environment. In addition, the performance of ABCO algorithm is clearly superior to that of the PSO algorithm according to the statistical analysis results, especially for the four-parameter surface-based duct estimation.

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Posterior distributions of a statistic of propagation loss inferred from radar sea clutter

Cited by 20 publications

References 12 publications

Estimation of Radio Refractivity From Radar Clutter Using Bayesian Monte Carlo Analysis

Estimation of Radio Refractivity From Radar Clutter Using Bayesian Monte Carlo Analysis

Bayesian Inversion of Radar Clutter

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

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