A duct mapping method using least squares support vector machines

Douvenot, Rémi; Fabbro, Vincent; Gerstoft, Peter; Bourlier, Christophe; Saillard, J.

doi:10.1029/2008rs003842

Cited by 58 publications

(59 citation statements)

References 32 publications

(50 reference statements)

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“…Afterward, the technique of refractivity from clutter (RFC) was developed substantially. A variety of intelligent algorithms have been used to estimate refractivity profiles, for example, the genetic algorithm (GA) [16,17], the Bayesian-Markov-chain Monte Carlo method [18], the support vector machine [19], and others [20][21][22][23]. However, these algorithms have some defects in practical applications [24,25].…”

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 phenomenon can be ascribed to prior distributions of the state vector and/or the measurement errors of the used observation data. However, the final goal of the refractivity estimation is not to give the exact refractivity profiles, but to propose potential structures that could be able to render an approximation of the real atmospheric condition to predict microwave propagation for assessing the performance of both communications and radar systems (Douvenot et al, 2008). Figure 5 shows the coverage diagrams (dB) of the modeled propagation loss computed by the split-step PE method using the measured refractivity and the estimated profiles.…”

Section: Real Data Resultsmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

2012

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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.

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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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A duct mapping method using least squares support vector machines

Cited by 58 publications

References 32 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

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

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

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