Study on evaporation duct estimation from point‐to‐point propagation measurements

Zhang, Qi; Yang, Kunde

doi:10.1049/iet-smt.2017.0342

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Genetic algorithms (GA), the chosen optimization method in this research, have been effective in deriving refractivity from sea surface clutter (Gerstoft et al., 2003; Rogers et al., 2000, 2005) and synthetic PTP propagation data (Fountoulakis & Earls, 2016; Matsko & Hackett, 2019; Penton & Hackett, 2018; Wagner et al., 2016; Zhang & Yang, 2018; Zhang et al., 2016). In addition to GAs, prior inversion studies have also used other optimization methods such as adjoint models (Zhao et al., 2017), Markov chain marching algorithms (Anderson et al., 2001), maximum likelihood estimation (Wagner et al., 2016), particle swarm optimization (Zhang & Yang, 2018), iterative local search (Wang et al., 2019), and simulated annealing (Xiao‐Feng et al., 2011) all of which could be feasible for this application as well. Nevertheless, GAs optimize a set of parameters through Darwin's theory of natural evolution, where individuals in a population influence future individuals through natural selection and genetic mutation (Goldberg, 1989; Johnson & Rahmat‐Samii, 1997; Penton & Hackett, 2018).…”

Section: Ptp Inversion Modelingmentioning

confidence: 99%

“…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). The PTP approach has a major advantage over RFC methods in that no sea surface scattering coefficient needs to be estimated, which is a major element of uncertainty in such approaches (Plant & Irisov, 2017).…”

Section: Introductionmentioning

confidence: 99%

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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: Ptp Inversion Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Pozderac et al [19] estimated the EDH values by comparing the propagation loss measured by an X-band beacon-receiver array system with that predicted by the variable terrain radio parabolic equation model. Zhang and Yang [20] used the signal strength sampled in the point-to-point propagation measurements to estimate the EDH and transmitting distance based on the repulsive particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Inversion for Inhomogeneous Surface Duct without a Base Layer Based on Ocean-Scattered Low-Elevation BDS Signals

Liu

Cao

et al. 2021

Remote Sensing

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The anomalous propagation conditions, particularly the tropospheric ducts, severely impact the regular operation and performance evaluation of radio devices in the atmospheric boundary layer. Therefore, it is necessary to provide the regional distribution of tropospheric ducts for utilizing or avoiding these abnormal propagation phenomena. As significant uncooperative signal sources, the global navigation satellite systems (GNSS) have been widely applied in the remote sensing of the ocean and atmosphere due to the greater convenience and lower cost. With the completed deployment of the BeiDou Navigation Satellite System (BDS) in 2020, an additional source can be chosen in the relevant studies. Taking the BDS as an example, since the scattered signals from the ocean surface at low satellite elevation angles can be effectively trapped by tropospheric ducts, we propose a method to invert for the regional distribution of tropospheric ducts using the received power of ocean-scattered signals in this paper. Firstly, the propagation model was built to calculate the received power, and a suite of simulations was made in various atmospheric environments. The results suggested that the received power is more sensitive to the surface duct without a base layer. Then, we made a preliminary estimation of the tropospheric ducts on the ocean nearby Qingdao utilizing the Weather Research and Forecasting (WRF) model as well as the echo data measured by a Doppler weather radar. Before the inversion, the actual satellite azimuth and elevation angles should be obtained to evaluate the bistatic scattering coefficients and the received powers of the selected satellite signals. Finally, we presented an inversion example using the proposed method. In absence of the actual measurements, the received powers pre-estimated at different SNRs served as the inputs of the inversion process and the estimated duct parameters were used to verify the validity of the proposed inversion method. For both the received power and modified refractivity profile, the fitness between the values pre-estimated using the estimated duct parameters and calculated by the inverted duct parameters gets better as the elevation angle decreases and the SNR increases. The variation of the fitness between the estimated and inverted values is slightly different for each duct parameter. Moreover, the calculation of inversion errors further explained the above behaviors, including the mean absolute error (MAE) and the root mean square error (RMSE). Despite some certain errors, the inversion results maintain the overall tendencies and most characteristics of the estimated values, thus proving the validity of the inversion method.

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“…Bistatic radar inversion approaches utilize radio frequency (RF) data from separated receivers and transmitters to invert for atmospheric refractivity parameters (Gerstoft et al, 2000;Gingras et al, 1997;Penton & Hackett, 2018;Pozderac et al, 2018;Tabrikian & Krolik, 1999;Wagner et al, 2016;Zhang & Yang, 2018;Zhao, 2012;Zhao et al, 2011). There are numerous factors that can impact the accuracy of an inversion using bistatic radar including forward scattering and multipath from the sea surface (Penton & Hackett, 2018); RF measurement quantity, location, and uncertainty (Zhao, 2012); lateral inhomogeneity of the atmosphere (Goldhirsh & Dockery, 1998); and atmospheric turbulence (Wagner et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Selected sampling density and locations for prior studies that utilize measured data (Pozderac et al, 2018) presumably result from primarily logistical and financial constraints. Numerous other numerical studies that utilize simulated data often use large amounts of data or arbitrarily select-simulated receiver array sizes (Penton & Hackett, 2018;Wagner et al, 2016;Zhang & Yang, 2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of Radar Data Sampling on the Accuracy of Atmospheric Refractivity Inversions Over Marine Surfaces

Matsko

Hackett

2019

Radio Science

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The turbulent nature of the marine atmospheric boundary layer and interactions across the air‐sea interface cause ever‐changing environmental conditions, including atmospheric properties that affect the index of refraction, or atmospheric refractivity. Variations in atmospheric refractivity lead to many types of anomalous propagation phenomena of electromagnetic (EM) signals; thus, improving performance of EM systems requires in situ knowledge of the refractivity. Inversion approaches to estimate refractivity rely on measured EM data; however, despite its importance, few studies have examined the influence of data density on the accuracy of refractivity inversions. This study applies a bistatic radar data inversion process to estimate atmospheric refractivity parameters in evaporative ducting conditions and examines the impacts of the sampling density of radar propagation loss data, and its source location, on accuracy of refractivity inversions. Genetic algorithms and a radar propagation model are used to perform the inversions. Numerical experiments examine various randomly distributed amounts of synthetic data from a 100‐m (altitude) by 60‐km (range) area. Three domains within this area are examined from which data were sourced. A data density of approximately 1% of the prediction domain yielded the smallest errors of refractivity parameters, and root mean square errors of refractivity and propagation loss. These error reductions are attributed to avoidance of nonunique solutions that likely impact lower data densities, supported by their classification as a misleading or difficult inverse problem. Generally, data sourced from long range result in lower refractivity and propagation loss root mean square errors compared to data sourced from other domains.

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Study on evaporation duct estimation from point‐to‐point propagation measurements

Cited by 10 publications

References 31 publications

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

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

Inversion for Inhomogeneous Surface Duct without a Base Layer Based on Ocean-Scattered Low-Elevation BDS Signals

Impact of Radar Data Sampling on the Accuracy of Atmospheric Refractivity Inversions Over Marine Surfaces

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