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

Zhao, Xiaofeng; Huang, Shuibo

doi:10.1175/jtech-d-13-00025.1

Cited by 14 publications

(10 citation statements)

References 42 publications

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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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“…For inverse problems, a physical model can be looked as an abstract system H which maps a vector of X onto a vector of Y, where X represents control variables and Y represents the needed predictions corresponding to observations [13][14][15][16][17][18] The adjoint of the PE propagation model has been widely used in atmospheric refractivity estimations [19][20][21] and ocean acoustic inversions [22][23][24], where the source information is assumed to be known and the environmental physical characteristics are estimated by matching the observed signals with predicted fields with a gradient-based minimization algorithm. For the source localization problem considered in this paper, the environmental conditions are assumed to be known.…”

Section: Adjoint Operatormentioning

confidence: 99%

Source Localization in the Duct Environment with the Adjoint of the PE Propagation Model

Zhao

2015

Atmosphere

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Owing to the absorbing, refracting and scattering effects of the propagation medium, electromagnetic (EM) energy will degrade with the increment of propagation range, and the maximum value exists at the point of the radiating source. Employing this phenomenon, this paper introduces a novel approach to detect the location of EM transmitters in an atmospheric duct environment. Different from previous matched-field processing (MFP) methods, the proposed method determines the source location through reconstructing the forward propagation field pattern by the backward adjoint integration of the parabolic equation (PE) propagation model. With this method, the repeated computations of PE used in the MFP methods are not needed. The performance of the method is evaluated via numerical simulations, where the influences of the measurement noise and the geometry of the receiver array on the localization results are considered.

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“…The vertical refractive index distribution of the atmosphere at each step is required when using the PE method to calculate the propagation loss in an uneven horizontal duct. The refractive index profile of the oceanic duct can be estimated using wave refraction technology (RFC) or global positioning system signals [21][22][23]. However, because of the short iteration step of the PE (depending on the frequency, the step range is between 200-900 m), the refractive index profile must still be adjusted through interpolation.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Decomposition Prediction of Propagation Loss in Oceanic Tropospheric Ducts

Dang

Cui

et al. 2021

Remote Sensing

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The oceanic tropospheric duct is a structure with an abnormal atmospheric refractive index. This structure severely affects the remote sensing detection capability of electromagnetic systems designed for an environment with normal atmospheric refraction. The propagation loss of electromagnetic waves in the oceanic duct is an important concept in oceanic duct research. Owing to the long-term stability and short-term irregular changes in marine environmental parameters, the propagation loss in oceanic ducts has nonstationary and multiscale time characteristics. In this paper, we propose a multiscale decomposition prediction method for predicting the propagation loss in oceanic tropospheric ducts. The prediction performance was verified by simulating propagation loss data with noise. Using different evaluation criteria, the experimental results indicated that the proposed method outperforms six other comparison methods. Under noisy conditions, ensemble empirical mode decomposition effectively disassembles the original propagation loss into a limited number of stable sequences with different scale characteristics. Accordingly, predictive modeling was conducted based on multiscale propagation loss characteristic sequences. Finally, we reconstructed the predicted result to obtain the predicted value of the propagation loss in the oceanic duct. Additionally, a genetic algorithm was used to improve the generalization ability of the proposed method while avoiding the nonlinear predictor from falling into a local optimum.

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

Cited by 14 publications

References 42 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

Source Localization in the Duct Environment with the Adjoint of the PE Propagation Model

Multiscale Decomposition Prediction of Propagation Loss in Oceanic Tropospheric Ducts

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