Wideband spectrum sensing technique based on Goodness-of-Fit testing

Scheers, Bart; Teguig, Djamal; Nir, Vincent Le

doi:10.1109/icmcis.2015.7158719

“…For the drone signal detection within two-stage detection and classification, we choose wideband GoF based blind spectrum sensing algorithm [26]. The GoF sensing can provide better performance compared to the conventional energy detection using fewer samples of the received signal, at low SNR conditions and in presence of non-Gaussian noise [27].…”

Section: A Signal Detectionmentioning

confidence: 99%

Combined RF-Based Drone Detection and Classification

Basak

¹

,

Rajendran

²

,

Pollin

³

et al. 2022

IEEE Trans. Cogn. Commun. Netw.

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Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats to security-sensitive places like airports and nuclear power plants. The existing drone localization and neutralization technologies work on the assumption that the drone has already been detected and classified. Although we have observed a tremendous advancement in the sensor industry in this decade, there is no robust drone detection and classification method proposed in the literature yet. This paper focuses on radio frequency (RF) based drone detection and classification using the frequency signature of the transmitted signal. We have created a novel drone RF dataset using commercial drones and presented a detailed comparison between a two-stage and combined detection and classification framework. The detection and classification performance of both frameworks are presented for a single-signal and simultaneous multi-signal scenario. With detailed analysis, we show that You Only Look Once (YOLO) framework provides better detection performance compared to the Goodness-of-Fit (GoF) spectrum sensing for a simultaneous multi-signal scenario and good classification performance comparable to Deep Residual Neural Network (DRNN) framework.

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“…For the drone signal detection within two-stage detection and classification, we choose wideband GoF based blind spectrum sensing algorithm [26]. The GoF sensing can provide better performance compared to the conventional energy detection using fewer samples of the received signal, at low SNR conditions and in presence of non-Gaussian noise [27].…”

Section: A Signal Detectionmentioning

confidence: 99%

“…Here, λ corresponds to the detection threshold. A detailed explanation of the AD GoF sensing, the derivation of A 2 n and the procedure to perform the hypothesis test on complex received signals from a Software Defined Radio (SDR) are provided in [26,27]. The value of λ is determined considering 5% False Alarm Rate (FAR).…”

Section: Combined Detection and Classificationmentioning

confidence: 99%

Combined RF-based drone detection and classification

Basak¹,

Rajendran²,

Pollin³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats to security-sensitive places like airports and nuclear power plants. The existing drone localization and neutralization technologies work on the assumption that the drone has already been detected and classified. Although we have observed a tremendous advancement in the sensor industry in this decade, there is no robust drone detection and classification method proposed in the literature yet. This paper focuses on radio frequency (RF) based drone detection and classification using the frequency signature of the transmitted signal. We have created a novel drone RF dataset using commercial drones and presented a detailed comparison between a two-stage and combined detection and classification framework. The detection and classification performance of both frameworks are presented for a single-signal and simultaneous multi-signal scenario. With detailed analysis, we show that You Only Look Once (YOLO) framework provides better detection performance compared to the Goodness-of-Fit (GoF) spectrum sensing for a simultaneous multi-signal scenario and good classification performance comparable to Deep Residual Neural Network (DRNN) framework.<br>

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“…In this section, motivated by its good characteristic mentioned in Section 5, GoF‐based sensing approach is used for a wide‐band (multi‐band) sensing. The wideband sensing method proposed in [36] is implemented on USRP B210 connected to a host personal computer via USB. Snapshot of the experimentation test‐bed is shown in Fig.…”

Section: Sdr Implementation Of Wideband Gof‐based Sensingmentioning

confidence: 99%