A novel hybrid model for inversion problem of atmospheric refractivity estimation

Tepecik, Cemil; Navruz, İsa

doi:10.1016/j.aeue.2017.12.009

Cited by 18 publications

(9 citation statements)

References 22 publications

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“…Over the last couple decades, inversion methods of estimating refractivity from radar measurements have become increasingly prominent (Karimian et al., 2011). Inversion methods using measured sea clutter data (referred to as RFC approaches) have been examined extensively (Compaleo et al., 2018; Gerstoft et al., 2003; Karimian et al., 2013; Rogers et al., 2000, 2005; Tepecik & Navruz, 2018; Yardim et al., 2009; Zhao & Huang, 2012; Zhao et al., 2017), but few studies demonstrate inversion methods using point‐to‐point (PTP) radar wave propagation data. Of those studies, most have demonstrated the validity of inverse approaches using synthetic propagation loss (PL) data (Fountoulakis & Earls, 2016; Gersoft et al., 2000; Gingras et al., 1997; Matsko & Hackett, 2019; Penton & Hackett, 2018; Tabrikian & Krolik, 1999; Wagner et al., 2016; Zhang & Yang, 2018; Zhang et al., 2016; Zhao, 2012; Zhao et al., 2011).…”

Section: Introductionmentioning

confidence: 99%

Refractivity Inversions From Point‐To‐Point X‐Band Radar Propagation Measurements

et al. 2022

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Dynamic refractive environments within the marine atmospheric boundary layer (MABL) pose difficulties in the prediction of X‐band radar wave propagation due to natural phenomena such as evaporation ducts (ED). This study utilizes a unique data set collected during the Coupled Air‐Sea Processes and Electromagnetic Ducting Research (CASPER)‐East field campaign, including multiple refractivity estimation methods and twelve point‐to‐point (PTP) electromagnetic datasets, to assess the efficacy of PTP inversion techniques for remote sensing of atmospheric refractivity within the MABL. Comparison of refractivity between the inverse and other refractivity methods show reasonable evaporation duct height estimates by the inversion, and inverse‐based propagation predictions are also shown to be more accurate than propagation based on other refractivity prediction methods: numerical weather prediction, theory, and in situ atmospheric measurements. These results propose the effectiveness of a PTP metaheuristic radar inversion to remotely sense refractive environments from radar propagation measurements in stable and unstable atmospheric conditions.

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Section: Introductionmentioning

confidence: 99%

Refractivity Inversions From Point‐To‐Point X‐Band Radar Propagation Measurements

et al. 2022

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“…With the development of artificial intelligence, deep learning networks have been applied to atmospheric duct parameter inversion. Tepecik [4] proposed an atmospheric duct inversion method using a genetic algorithm and deep learning. Artificial Neural Networks make a pre-estimation and Genetic Algorithm solution that uses the result as a starting population for post-estimation.…”

Section: Introductionmentioning

confidence: 99%

Weight Loss Function for the Cooperative Inversion of Atmospheric Duct Parameters

Han

Wang³

et al. 2022

Atmosphere

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Low-altitude atmospheric ducts are abnormal atmospheric phenomena in the troposphere, impacting the operation of microwave or ultrashort wave radio systems. Therefore, the real-time acquisition of low-altitude atmospheric duct parameters is essential to ensure the successful operation of radio systems. Remote sensing methods based on deep learning are, presently, the most important tools to infer duct parameters. In a traditional deep learning loss function, different duct parameters adopt the same weight coefficient. This study establishes a weight loss function and proposes a method for determining the weight coefficient based on the extended Fourier amplitude sensitivity test method. Based on Global Navigation Satellite System (GNSS) occultation signals, the cooperative inversion model of atmospheric duct parameters is established. Test results show that our proposed loss function was feasible, effective, and yielded a higher inversion accuracy than the traditional loss function.

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“…This recursive process continues until the field exits the computational domain or a certain threshold is reached. The 2W-SSPE method was implemented in an open-source software, called PETOOL [15,16], which was then used in various studies [17][18][19][20]. Although terrain modeling with a staircase approximation provides reliable results in most situations, the accuracy of the 2W-SSPE method might degrade if there are curved/slanted surfaces over the terrain profile.…”

Section: Introductionmentioning

confidence: 99%

A hybrid numerical model for long-range electromagnetic wave propagation

ALTUN,

ÖZGÜN

2021

Turk J Elec Eng & Comp Sci

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A hybrid numerical model is presented for solving long range electromagnetic wave propagation problems involving objects on or above the ground surface by hybridizing the Two-Way Split-Step Parabolic Equation (2W-SSPE) method with the Method of Moments (MoM). The advantages of the proposed model are twofold: (i) It reduces the staircasing error in irregular terrain modeling, which usually occurs when the standard SSPE method is used alone. This is achieved by employing the MoM to more accurately obtain the scattered fields from slanted/curved surfaces.(ii) It enables the SSPE method to handle the problems involving objects above the Earth's surface, which cannot be easily modeled by the standard SSPE method due to difficulty in imposing boundary conditions. The accuracy of the hybrid method is numerically verified by comparing the numerical results with those of the 2W-SSPE and the GO+UTD (Geometric Optic + Uniform Theory of Diffraction) methods in some representative propagation problems.

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A novel hybrid model for inversion problem of atmospheric refractivity estimation

Cited by 18 publications

References 22 publications

Refractivity Inversions From Point‐To‐Point X‐Band Radar Propagation Measurements

Refractivity Inversions From Point‐To‐Point X‐Band Radar Propagation Measurements

Weight Loss Function for the Cooperative Inversion of Atmospheric Duct Parameters

A hybrid numerical model for long-range electromagnetic wave propagation

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