Empirical Path‐Loss Modeling and a RF Detection Scheme for Various Drones

Won, Hojeong; Choi, Hong Dae; Kim, Kyung-Seok

doi:10.1155/2018/6795931

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…The LD PL model is used in numerous applications, such as on-body PL modelling for ultra-wideband communications [15], drone aerial communications [16], mobile communication [17], [18], wireless sensor networks [13], [19], PL estimation in forested environments [20], and FWN [21], [22]. Many researchers have studied the existing PL models.…”

Section: State-of-the-art Of Log Distance Path Loss Modelmentioning

confidence: 99%

“…The maximum distance did not exceed 400 m, and the PL was compared with other models for many gains and directions. Jeong et al studied PL variations in the industrial, scientific and medical (ISM) bands for drone applications and they extended the LD model for aerial communications [16]. The horizontal distance range was between 10 and 70 m, and the altitude was between 1.5 and 101.5 m. Fernández et al divided the radio link into two intervals, and each one had its own propagation exponent [29].…”

Section: State-of-the-art Of Log Distance Path Loss Modelmentioning

confidence: 99%

“…In conclusion, even if many researchers have studied the PL models for FWN, the LD model was not always included [23], [24], [25]. The few researchers who considered this model did not do so for FWN applications [1], [17] and they used limited coverage ranges [16], [27], [28].…”

Section: State-of-the-art Of Log Distance Path Loss Modelmentioning

confidence: 99%

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Log Distance Path Loss Model: Application and Improvement for Sub 5 GHz Rural Fixed Wireless Networks

2022

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The study of radio signal propagation path loss (PL) is important for planning, designing and evaluating the performance of radio communication networks. However, the state-of-the-art in PL modelling for fixed wireless networks in rural environments is still ill-equipped for making accurate predictions. This paper explores the application of the log distance PL model to heterogeneous fixed wireless networks in harsh rural propagation conditions. This model is then extended and optimized to improve its accuracy. In particular, the dataset is classified according to many criteria, radio links are split into many intervals according to their distances, antenna heights and elevations are integrated into its formula and long-term extreme seasonal variations are considered. Our study uses a wide set of measurements from the fixed wireless networks of a wireless internet service provider in rural regions of Canada. The proposed modifications improve the accuracy by 7 to 15 dB in terms of the root mean squared error.

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Section: State-of-the-art Of Log Distance Path Loss Modelmentioning

confidence: 99%

Section: State-of-the-art Of Log Distance Path Loss Modelmentioning

confidence: 99%

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Log Distance Path Loss Model: Application and Improvement for Sub 5 GHz Rural Fixed Wireless Networks

2022

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“…Current studies based on radio communications affected by large scale free space propagation model has proposed several path loss method [21,42]. In this current work, a single carrier frequency of 3.55Ghz has been adopted.…”

Section: Path Lossmentioning

confidence: 99%

“…Where more focus has been put on distance dependency. The Close-in free space reference (CI) path loss models can be expressed as [42],…”

Section: Path Lossmentioning

confidence: 99%

Sensing spectrum sharing based massive MIMO radar for drone tracking and interception

Miantezila

Guo

2022

PLoS ONE

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Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious problem for the environment and public safety, given the fact that the dynamic movement of a drone’s emission signal and location tracking is different from existing positioning. This study proposes a safety zone characterized by the presence of N radars sensors with a goal to track and destabilized rogue drones attending to penetrate safety zones (stadium and school). Specifically, a new joint estimation based on a Gaussian filter has been introduced for spectrum sharing and detection awareness. The profit of this novel sensing method can be clearly seen when the two joint hidden states are taken into consideration. Therefore, the drone’s emission state is analyzed by estimating its movement jointly. Considering the drone’s unknown states and actual positioning, an algorithm is developed based on dynamic states space model. Where Bernoulli filter model is designed to estimate recursively the unknown stages of the drone and its changing location based on time. Meanwhile a power control acted from the radar to the targeted drones so that rogue drones are optimally tracked and destabilized over time. Furthermore, an expanding mechanism has been generated to accurately track the drone and enhance detection. A thoughtful result of the experimentation shows clearly that, even when the drone is moving, spectral detection can be performed accurately by chasing its positions. Its demonstrates at 90% of credibility that the original signal has a direct effect on the propagated signal. Therefore, the magnitude of the Doppler shift increases with frequency. And the clue of its positioning can be used for cognitive radio optimization.

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Measurement of Path Loss Characterization and Prediction Modeling for Swarm UAVs Air-to-Air Wireless Communication Systems

Duangsuwan¹,

District²

2021

jcm

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A challenge swarm unmanned aerial vehicles (swarm UAVs)-based wireless communication systems have been focused on channel modeling in various environments. In this paper, we present the characterized path loss air-to-air (A2A) channel modeling-based measurement and prediction model. The channel model was considered using A2A Two-Ray (A2AT-R) extended path loss modeling. The prediction model was considered using an artificial neural network (ANN) algorithm to train the measured dataset. To evaluate the measurement result, path loss models between the A2AT-R model and the prediction model are shown. We show that the prediction model using ANN is optimal to train the measured data for the A2A channel model. To discuss the result, the parametric prediction errors such as mean absolute error (MAE), root mean square error (RMSE), and R-square (R2), are performed.

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Empirical Path‐Loss Modeling and a RF Detection Scheme for Various Drones

Cited by 4 publications

References 15 publications

Log Distance Path Loss Model: Application and Improvement for Sub 5 GHz Rural Fixed Wireless Networks

Log Distance Path Loss Model: Application and Improvement for Sub 5 GHz Rural Fixed Wireless Networks

Sensing spectrum sharing based massive MIMO radar for drone tracking and interception

Measurement of Path Loss Characterization and Prediction Modeling for Swarm UAVs Air-to-Air Wireless Communication Systems

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