3-D Tomographic Reconstruction of Rain Field Using Microwave Signals From LEO Satellites: Principle and Simulation Results

Shen, Xi; Huang, Defeng; Song, Boming; Vincent, Claire Louise; Togneri, Roberto

doi:10.1109/tgrs.2019.2899391

Cited by 15 publications

(48 citation statements)

References 32 publications

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“…Moreover, five evaluation metrics are used to assess the performance of SVM classifier and they are defined as following equations [ (16) where TR denotes the number of rainy samples that are identified correctly, TD denotes the number of dry samples that are identified correctly, FR is the number of no-rain samples that are identified as rain, and FD is the number of rain samples that are identified as no-rain. In addition, OA indicates the accuracy that SVM classifier identify overall samples, Precision reflect the ability of classifier to distinguish the no-rain samples whose features are similar to that of rain, and Recall indicates the accuracy of identifying Fig.…”

Section: A Validation Of Svm Classifiermentioning

confidence: 99%

“…Barthes et al used the OELs from television satellite located at geostationary earth orbit (GEO) and successfully retrieved the rain intensity in Paris area [8]. In space, there are hundreds of GEO satellites operated by broadcast and telecommunication companies, and satellites emit stable microwave signal in C (4-8 GHz) and Ku (12)(13)(14)(15)(16)(17)(18) GHz) bands towards the earth. Theoretically speaking, three GEO satellites are capable of covering the whole globe expect polar regions, and with sufficient OELs it is possible to monitor the global precipitation.…”

mentioning

confidence: 99%

“…Moreover, to make the CSC commercially feasible, user ground terminals must be at a lower price. The relevant studies have shown the potential of LEO OEL system for precipitation monitoring [16,17]. Therefore, in the near future, using the densely commercial OEL networks can develop a high-resolution precipitation observation system all over the world which is part of the wireless environmental sensor networks (WESN) [18].…”

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confidence: 99%

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Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Xian

Liu

Yin

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, the oblique earth-space links (OELs) between satellite and earth station have been used for rainfall monitoring as a supplement to existing observation methods. Most present studies achieved the rainfall measurement by OELs based on the empirical method such as power-law (PL) model. In practice, two crucial issues need to be addressed: (1) identification of rain and no-rain periods; and (2) determination of attenuation baseline. To solve these problems, this paper adopts several machine learning algorithms based on the analysis of earth-space link signal characteristics. For the first issue, we choose the support vector machine (SVM) as a classifier and the adaptive synthetic sampling algorithm (ADSYN) is deployed to eliminate the effects caused by data imbalance. For the second issue, the long short-term (LSTM) neural network is selected for the determination of attenuation baseline since it has a good ability to solve time series problem. In terms of the rainfall inversion, we establish a new model by combining the back-propagation (BP) network and genetic algorithm (GA). The PL model is also used as a comparison. To validate the proposed method, we set up an earth-space link that receives the signal from AsiaSat5 in 12.32GHz. The results demonstrate that the two issues are successfully addressed and the inversion of precipitation is also carried out. Compared to disdrometer, the correlation and mean absolute error of GA-BP model are 0.83 and 1.30 mm/h, respectively, indicating a great potential to use densely OELs for global precipitation monitoring. Index Terms-rainfall monitoring, remote sensing, machine learning, earth-space link, Ku-band I. INTRODUCTION 1 CCURATE and real-time rainfall measurement plays an important role in many aspects of human life such as agricultural issues, water resource management and natural disaster warning. Existing rainfall detection method mainly comprises rain gauge, weather radar and weather satellite [1]. Based on the exploitation of existing radio spectrum sources, the opportunistic use of microwave links has become a new approach to detecting precipitation. Messer et al. firstly suggested the application of commercial wireless communication networks (CWCNs) to environmental monitoring [2]. In recent years, the use of horizontal microwave links (HMLs) has been developed rapidly in many fields such as path-average rain intensity inversion [3, 4], radar calibration [5] and regional rainfall monitoring [6].

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Section: A Validation Of Svm Classifiermentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Xian

Liu

Yin

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…the COSMIC satellite constellation) and can also be installed on flexible 'Spaceas-a-Service' platforms (e.g. Spire's Lemur satellites), due to its importance to weather forecasting; atmospheric variables beyond water vapor have been proposed to be monitored by the NASA and NSF funded Active Temperature, Ozone and Moisture Microwave Spectrometer (ATOMMS) program [15], [16] and the European Space Agency funded Normalized Differential Spectral Attenuation (NDSA) measurement technology [17]- [19]; recently, we have carried out research into 3D rainfall field retrieval with microwave signals from LEO satellites [20]- [23].…”

Section: Introductionmentioning

confidence: 99%

Passive Positioning of Flying Object With Microwave Signals From LEO Satellites: Concept and Preliminary Simulation Results

Huang

2021

IEEE Access

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Along with the deployment of low earth orbit (LEO) satellite constellations for global Internet services, Ku, Ka or V band microwave signals will be available from space anywhere on Earth. Utilizing the signals, a new approach to positioning flying objects passively is proposed, and a diffraction model to characterize the impact of the flying object on the intensity of the received signals is presented. A linear Minimum Mean Squared Error (MMSE) based estimator is then employed to position the flying object passively. Preliminary 2D simulation results are used to validate the effectiveness of the proposed method. INDEX TERMS Low earth orbit satellites, microwave propagation, passive microwave remote sensing.

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“…In [7], Shen et al have shown that vertical rain fields can be reconstructed from the received signal level (RSL) measurements of multiple receivers. The approach of using the estimated signal-to-noise ratio (SNR) at the ground receivers for LEO satellites to achieve 3-D tomographic rain field reconstruction was analyzed in detail in [8].…”

Section: Introductionmentioning

confidence: 99%

A Differential Approach for Rain Field Tomographic Reconstruction Using Microwave Signals from Leo Satellites

Shen

Huang

Vincent

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A differential approach is proposed for tomographic rain field reconstruction using the estimated signal-to-noise ratio of microwave signals from low earth orbit satellites at the ground receivers, with the unknown baseline values eliminated before using least squares to reconstruct the attenuation field. Simulations are done when the baseline is modelled by an autoregressive process and when the baseline is assumed fixed. Comparisons between the reconstruction results for the differential and non-differential approaches suggest that the differential approach performs better in both scenarios. For high correlation coefficient and low model noise in the autoregressive process, the differential approach surpasses the non-differential approach significantly.

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3-D Tomographic Reconstruction of Rain Field Using Microwave Signals From LEO Satellites: Principle and Simulation Results

Cited by 15 publications

References 32 publications

Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band

Passive Positioning of Flying Object With Microwave Signals From LEO Satellites: Concept and Preliminary Simulation Results

A Differential Approach for Rain Field Tomographic Reconstruction Using Microwave Signals from Leo Satellites

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