Duct heights inferred from radar sea clutter using proper orthogonal bases

Fountoulakis, Vasileios; Earls, Christopher J.

doi:10.1002/2016rs005998

Cited by 12 publications

(9 citation statements)

References 49 publications

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“…The methods of sounding refractivity profiles-such as radiosondes, microwave refractometers, and evaporation duct sensors-have been developed [10][11][12][13][14]. However, these methods are direct sensing techniques and have lower sparse sampling rates.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

et al. 2018

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Abstract:The propagation of Global Positioning System (GPS) signals at low-elevation angles is significantly affected by a surface duct. In this paper, we present an improved algorithm known as NSSAGA, in which simulated annealing (SA) is combined with the non-dominated sorting genetic algorithm II (NSGA-II). Matched-field processing was used to remotely sense the refractivity structure by using the data of ground-based GPS phase delay and propagation loss from multiple antenna heights. The performance was checked by simulation data with and without noise. In comparison with NSGA-II, the new hybrid algorithm retrieved the refractivity structure more efficiently under various noise conditions. We then modified the objective function and found that matched-field processing is more effective than the conventional least-squares method for inferring the refractivity parameters. Comparing the inversion results and in situ sounding data suggests that the improved method presented herein can capture refractivity characteristics in realistic environments.

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Section: Introductionmentioning

confidence: 99%

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

et al. 2018

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“…This allows us to design an algorithm that seeks a refractivity profile associated with eigenvectors that best fit the data. The method presented in this paper is close in spirit to the idea presented in [3] and [16]. However, while they used a basis induced by the data, we use a basis induced by the forward model.…”

Section: Introductionmentioning

confidence: 97%

A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer

Gilles

Earls

Bindel

2019

IEEE Trans. Geosci. Remote Sensing

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Inferring electromagnetic propagation characteristics within the marine atmospheric boundary layer (MABL) from data in real time is crucial for modern maritime navigation and communications. The propagation of electromagnetic waves is well modeled by a partial differential equation (PDE): a Helmholtz equation. A natural way to solve the MABL characterization inverse problem is to minimize what is observed and what is predicted by the PDE. However, this optimization is difficult because it has many local minima. We propose an alternative solution that relies on the properties of the PDE but does not involve solving the full forward model. Ducted environments result in an EM field which can be decomposed into a few propagating, trapped modes. These modes are a subset of the solutions to a Sturm-Liouville eigenvalue problem. We design a new objective function that measures the distance from the observations to a subspace spanned by these eigenvectors. The resulting optimization problem is much easier than the one that arises in the standard approach, and we show how to solve the associated nonlinear eigenvalue problem efficiently, leading to a real-time method.

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“…Most inverse methods that have been applied to‐date involve use of sea clutter data to inversely determine various aspects of the refractivity (Douvenot et al, ; Fountoulakis & Earls, ; Gerstoft et al, , ; Karimian et al, ; Rogers et al, , ; Yardim et al, ; Zhao et al, ; Zhau & Huang, ). One of the difficulties in using sea clutter to perform refractivity inversions is that backscatter from the sea surface is not completely understood (Ward et al, ); thus, determining the proper reflection coefficients for the prediction of backscatter is challenging and likely introduces error into the inversion process for those approaches that utilize a backscatter model.…”

Section: Introductionmentioning

confidence: 99%

“…Here the observations are radar measurements, the unknown parameters are those that describe the atmospheric refractivity, and the nonlinear function is the differential equation governing radio frequency wave propagation. Often, the search space is too vast Most inverse methods that have been applied to-date involve use of sea clutter data to inversely determine various aspects of the refractivity (Douvenot et al, 2010;Fountoulakis & Earls, 2016;Gerstoft et al, 2003Gerstoft et al, , 2004Karimian et al, 2013;Rogers et al, 2000Rogers et al, , 2005Yardim et al, 2009;Zhao et al, 2017;Zhau & Huang, 2012). One of the difficulties in using sea clutter to perform refractivity inversions is that backscatter from the sea surface is not completely understood (Ward et al, 1990); thus, determining the proper reflection coefficients for the prediction of backscatter is challenging and likely introduces error into the inversion process for those approaches that utilize a backscatter model.…”

Section: Introductionmentioning

confidence: 99%

Rough Ocean Surface Effects on Evaporative Duct Atmospheric Refractivity Inversions Using Genetic Algorithms

Penton

Hackett

2018

Radio Science

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Radar performance is impacted by variations in atmospheric refractivity because it changes the direction of electromagnetic wave propagation. Because direct measurement and simulation of atmospheric refractivity at high resolution over large distances is challenging, inversion techniques have developed to address this knowledge gap in predicting refractivity. Here we use synthetic radar data to solve refractivity inversion problems, focusing on evaporation ducts. Unlike most prior “refractivity from clutter” studies, this study examines inversions based on point‐to‐point propagation rather than sea clutter. This approach removes complexities associated with uncertainties in surface reflection coefficients; however, the sea surface still plays a role in the forward scattering and reflection (multipath) of electromagnetic waves. Using synthetic data that permit quantitative evaluation of inverse solution accuracy and detailed knowledge regarding the sea state conditions used to produce the data, we evaluate the impact various representations of the sea surface have on the accuracy of refractivity inversions. These representations include neglect of the sea surface, use of the same phase‐resolved surface, and use of a statistically equivalent sea surface. Results show that neglect of the sea surface and use of a statistically equivalent sea surface significantly decreases the accuracy of inverse solutions, and this decrease primarily results from discrepancies in the multipath pattern that generate larger variations in the propagation than does the refractivity. We show that averaging propagation over several different phases of the sea surface aids in washing out the multipath pattern to increase sensitivity of the inversion to the features of the refractivity profile.

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Duct heights inferred from radar sea clutter using proper orthogonal bases

Cited by 12 publications

References 49 publications

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

Estimation of Surface Duct Using Ground-Based GPS Phase Delay and Propagation Loss

A Subspace Pursuit Method to Infer Refractivity in the Marine Atmospheric Boundary Layer

Rough Ocean Surface Effects on Evaporative Duct Atmospheric Refractivity Inversions Using Genetic Algorithms

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