Atmospheric duct estimation from multi-source radar sea clutter returns: theoretical framework and preliminary numerical results

2014 ◽  
Vol 59 (34) ◽  
pp. 4899-4906
Author(s):  
Xiaofeng Zhao ◽  
Dongxiao Wang ◽  
Sixun Huang
Keyword(s):  
Theoretical Framework ◽  
Numerical Results ◽  
Sea Clutter ◽  
Atmospheric Duct

scholarly journals Atmospheric Duct Estimation Using Radar Sea Clutter Returns by the Adjoint Method with Regularization Technique

2014 ◽  
Vol 31 (6) ◽  
pp. 1250-1262 ◽  
Author(s):  
Xiaofeng Zhao ◽  
Sixun Huang
Keyword(s):  
Genetic Algorithm ◽  
Parabolic Equation ◽  
Adjoint Method ◽  
Regularization Parameter ◽  
Background Field ◽  
Sea Clutter ◽  
Regularization Technique ◽  
Atmospheric Duct ◽  
Adjoint Approach ◽  
Space Range

Abstract This paper focuses on retrieving the atmospheric duct structure from radar sea clutter returns by the adjoint approach with the regularization technique. The adjoint is derived from the split-step Fourier parabolic equation method, and the regularization term is constructed by the background refractivity field. To ensure successful implementations of the regularization, the L-curve criterion is used to find the optimal regularization parameter. The feasibility of the proposed method is validated by the numerical simulations of different noise-level clutter returns, as well as a real clutter profile measured by the S-Band Space Range Radar located in Wallops Island. In the process of inversions, the refractivity profile is first obtained by genetic algorithm, and then it is used as the background field for the adjoint method. The retrieved results indicate that, with an appropriate regularization parameter, the structure of the background refractivity profile can be improved by the proposed method.

scholarly journals Analysis and design of InAs nanowire array based ultra broadband perfect absorber

RSC Advances ◽  
2021 ◽  
Vol 11 (59) ◽  
pp. 37595-37603
Author(s):  
Mohammad Muntasir Hassan ◽  
Fariba Islam ◽  
Md Zunaid Baten ◽  
Samia Subrina
Keyword(s):  
Nanowire Array ◽  
Mie Theory ◽  
Theoretical Framework ◽  
Numerical Results ◽  
Resonant Wavelength ◽  
Perfect Absorber ◽  
Analysis And Design ◽  
Different Diameters

Mie theory and GMR based theoretical framework support the numerical results that resonant wavelength increases with increasing InAs NW diameter. By employing NWs of different diameters in a single array, an ultra-broadband perfect absorber has been achieved.

Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

2018 ◽  
Vol 10 (4) ◽  
pp. 437-445 ◽  
Author(s):  
Chao Yang ◽  
Lixin Guo
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Orthogonal Experimental Design ◽  
Sea Clutter ◽  
Particle Swarm Optimizer ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct ◽  
Comparison Results ◽  
Better Than

AbstractIn this paper, an orthogonal crossover artificial bee colony (OCABC) algorithm based on orthogonal experimental design is presented and applied to infer the marine atmospheric duct using the refractivity from clutter technique, and the radar sea clutter power is simulated by the commonly used parabolic equation method. In order to test the accuracy of the OCABC algorithm, the measured data and the simulated clutter power with different noise levels are, respectively, utilized to estimate the evaporation duct and surface duct. The estimation results obtained by the proposed algorithm are also compared with those of the comprehensive learning particle swarm optimizer and the artificial bee colony algorithm combined with opposition-based learning and global best search equation. The comparison results demonstrate that the performance of proposed algorithm is better than those of the compared algorithms for the marine atmospheric duct estimation.

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

2012 ◽  
Vol 5 (4) ◽  
pp. 6059-6082
Author(s):  
X. F. Zhao ◽  
S. X. Huang
Keyword(s):  
Particle Filter ◽  
Real Data ◽  
Forward Simulation ◽  
Sea Clutter ◽  
Radar Clutter ◽  
Atmospheric Duct ◽  
Basic Ideas ◽  
Recursive Bayesian Filter ◽  
Horizontal Variations ◽  
Recursive Bayesian

Abstract. This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF) that is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to check up the feasibility of PF-RFC performances.

scholarly journals Radar sea clutter power modeling under the atmospheric duct propagation conditions

2013 ◽  
Vol 62 (9) ◽  
pp. 099204
Author(s):  
Zhao Xiao-Feng ◽  
Huang Si-Xun
Keyword(s):  
Power Modeling ◽  
Sea Clutter ◽  
Atmospheric Duct

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

2012 ◽  
Vol 5 (11) ◽  
pp. 2859-2866 ◽  
Author(s):  
X. F. Zhao ◽  
S. X. Huang ◽  
D. X. Wang
Keyword(s):  
Particle Filter ◽  
Real Data ◽  
Forward Simulation ◽  
Sea Clutter ◽  
Radar Clutter ◽  
Atmospheric Duct ◽  
Basic Ideas ◽  
Recursive Bayesian Filter ◽  
Horizontal Variations ◽  
Recursive Bayesian

Abstract. This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF), which is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations, is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to confirm the feasibility of PF-RFC performances.

scholarly journals Estimation of Atmospheric Duct Structure Using Radar Sea Clutter

2012 ◽  
Vol 69 (9) ◽  
pp. 2808-2818 ◽  
Author(s):  
Xiaofeng Zhao ◽  
Sixun Huang
Keyword(s):  
Approximate Solutions ◽  
Propagation Loss ◽  
Sea Surface ◽  
Model Parameters ◽  
Sea Clutter ◽  
Radar Clutter ◽  
Atmospheric Duct ◽  
Analytical Transformation ◽  
Ill Posed ◽  
Adjoint Approach

Abstract Retrieving atmospheric refractivity profiles from the sea surface backscattered radar clutter is known as the refractivity-from-clutter (RFC) technique. Because the relationship between refractivity and radar sea clutter is clearly nonlinear and ill posed, it is difficult to get analytical solutions according to current theories. Previous works treat this problem as a model parameter estimation issue and some optimization algorithms are selected to get approximate solutions. Two main factors that limit the accuracy of the estimation are that 1) the refractive environments are described by using some idealized refractivity parameter models that cannot describe the exact information of the refractivity profile, and 2) accurate modeling of the sea surface radar cross section (RCS) is very difficult. Rather than estimating a few model parameters, this paper puts forward possibilities of using the variational adjoint approach to jointly retrieve the every-height refractivity values and sea surface RCS using radar clutter data. The derivation of the adjoint model is accomplished by an analytical transformation of the parabolic equation (PE) in the continuous domain. Numerical simulations including range-independent and range-dependent RCS cases are presented to demonstrate the ability of this method for RFC estimations. Making use of the refractivity retrievals, propagation loss predictions are also presented.

STATISTICAL MECHANICS OF STATIC GRANULAR PACKINGS UNDER GRAVITY

2009 ◽  
Vol 23 (28n29) ◽  
pp. 5345-5358
Author(s):  
MASSIMO PICA CIAMARRA ◽  
ANTONIO DE CANDIA ◽  
ANNALISA FIERRO ◽  
MARIO NICODEMI ◽  
ANTONIO CONIGLIO
Keyword(s):  
Statistical Mechanics ◽  
Granular Materials ◽  
Theoretical Framework ◽  
Numerical Results ◽  
Current Understanding ◽  
Natural Phenomena ◽  
Stable States ◽  
Granular Packings ◽  
Theoretical Foundations

Despite the large use of granular materials in the industry, and the large number of natural phenomena where granular materials are involved, a comprehensive theoretical framework of their physics is still missing. An important perspective was proposed almost 20 years ago by S.Edwards, which suggested the possibility of a statistical mechanics description of granular materials at rest in their mechanically stable states. This article focuses on the theoretical foundations and current understanding of a statistical mechanics approach of granular materials under gravity. Experimental and numerical results discussing such an approach and clarifying its limit of validity are described as well.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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