Software Instrumentation of an Unmanned Aerial Vehicle for HPEM Effects Detection

Lopes-Esteves, José; Cottais, Emmanuel; Kasmi, Chaouki

doi:10.23919/ursi-at-rasc.2018.8471395

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…In civilian use, UAVs can be used for disaster assessment (Al-Rawabdeh et al, 2016), environmental monitoring (Trasviña-Moreno et al, 2017;Zhou et al, 2015), etc. However, due to the low entry threshold and lack of effective control, UAVs also bring many safety problems (Birnbaum et al, 2016): (1) due to improper operation, poor signal and other reasons, UAV failure and fall can easily hurt people and damage objects; (2) the flight of UAVs may interfere with the flight of civil aircraft; (3) UAVs are difficult to be detected and intercepted, so they may be used by terrorists to threaten public safety (Lopes-Esteves et al, 2018). Therefore, the identification and detection of UAVs have become a difficult problem.…”

Section: Sound Signal Feature Extractionmentioning

confidence: 99%

A Study of Real-Time Recognition of Unmanned Aerial Vehicles in Outdoor Areas Based on a Random Forest Algorithm

Tian

2022

Aviation

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With the widespread use of unmanned aerial vehicles (UAVs) in life, the real-time recognition of UAVs has become an important issue. The authors of this paper mainly studied the application of the random forest (RF) algorithm in the outdoor real-time recognition of UAVs. Mel-Frequency Cepstral Coefficient (MFCC) features were extracted from sound signals firstly, and then the RF method was combined with weighted voting to obtain the improved random forest (IRF) method to identify UAV sounds and environmental sounds. An experimental analysis was conducted. The modeling time of the IRF method increased by 9.52% compared with the RF method, and the recognition rate of the IRF method decreased with the increase of the distance from the microphone; however, the recognition rate of the IRF method was always higher than that of the RF method, and the recognition rate of the IRF method for the mixed samples was always higher than 90%. When the distance was 10 m, the IRF method still had a recognition rate of 91.29%. The experimental results verify the effectiveness of the IRF method for the outdoor real-time recognition of UAVs and its practical application feasibility.

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Section: Sound Signal Feature Extractionmentioning

confidence: 99%

A Study of Real-Time Recognition of Unmanned Aerial Vehicles in Outdoor Areas Based on a Random Forest Algorithm

Tian

2022

Aviation

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Electromagnetic Watermarking: exploiting IEMI effects for forensic tracking of UAVs

Esteves¹

2019

2019 International Symposium on Electromagnetic Compatibility - EMC EUROPE

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Analysis of Soft-Faults induced by IEMI in Elementary Functions and Complex Electronics

Esteves¹,

Kasmi

Cottais³

2019

2019 URSI Asia-Pacific Radio Science Conference (AP-RASC)

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Software Instrumentation of an Unmanned Aerial Vehicle for HPEM Effects Detection

Cited by 3 publications

References 2 publications

A Study of Real-Time Recognition of Unmanned Aerial Vehicles in Outdoor Areas Based on a Random Forest Algorithm

A Study of Real-Time Recognition of Unmanned Aerial Vehicles in Outdoor Areas Based on a Random Forest Algorithm

Electromagnetic Watermarking: exploiting IEMI effects for forensic tracking of UAVs

Analysis of Soft-Faults induced by IEMI in Elementary Functions and Complex Electronics

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