UAVs for Medicine Delivery in a Smart City Using Fiducial Markers

Innocenti, Eros; Agostini, Giacomo; Giuliano, Romeo

doi:10.3390/info13100501

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…At the first stage, the use of a sliding buffer makes it possible to increase the number of bearing estimates from two to 5-7 with a serial connection to the input circuit of the receiver of the differential voltage PP and a high AE switching frequency (kHz) for the minimum possible duration of the radio signal of the UAV control channel μs. At the second stage, the degree of proximity of the received signal envelope to the reference one is determined (expression (7)). At the third stage, a one-time estimate of the bearing on the RES is formed according to the criterion of minimum mean square error (expression (9)).…”

Section: Discussionmentioning

confidence: 99%

“…After summary processing, the resulting signal is sent to a spectrum analyzer as shown in Figure 4. Estimation of the instantaneous spectrum of a signal can be realized using a discrete or fast Fourier transform (FFT) using statistical averaging (periodogram method) (2)- (7).…”

Section: Figure 2 Block Diagram Of a Two-channel Phase Direction Findermentioning

confidence: 99%

“…where 𝑓𝑙𝑜𝑜𝑟(⋅) is function of rounding to a smaller integer value; 𝑁 fft is FFT window size; 𝑠 is frequency sample number (FFT filter); i is FFT window number. Averaging the instantaneous spectrum in the 𝑠 -th FFT filter over the time of signal reception by one AE (𝑇 АЭ = 50) makes it possible to reduce its dispersion (7):…”

Section: Figure 2 Block Diagram Of a Two-channel Phase Direction Findermentioning

confidence: 99%

“…At the present stage of the evolution of unmanned aerial vehicles (UAVs), they are successfully integrated into various fields, including transportation [1]- [3], territory security [4]- [6], agriculture, and medicine [7]- [9]. In the military field, UAVs are used for reconnaissance, strike operations, cargo transportation, target designation of other weapons, and autonomous missions by programming [10].…”

Section: Introductionmentioning

confidence: 99%

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Drone direction estimation: phase method with two-channel direction finder

Kozhabayeva,

Yerzhan,

Boykachev

et al. 2024

IJECE

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This scientific article presents a block diagram of a two-channel radio direction finder that effectively uses the phase method to determine the direction of the signal source. The main attention is paid to the mathematical model of the formation of the cardioid radiation pattern of biconical antennas, which have unique directivity characteristics. These features significantly affect the accuracy and reliability of the bearing determination process. The developed algorithm aims to accurately determine the direction of motion of an unmanned aerial vehicle, especially in the context of a two-channel radio receiver and a five-element antenna system. This antenna system provides unique capabilities for increased resolution and directional accuracy. The article also touches on the issue of software implementation of the developed algorithm, which is aimed at increasing the number of generated bearing estimates in conditions of limited time for observing an unmanned aerial vehicle. Thus, the proposed method is of interest in the field of precision direction finding in the context of small unmanned vehicles.

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Section: Discussionmentioning

confidence: 99%

Section: Figure 2 Block Diagram Of a Two-channel Phase Direction Findermentioning

confidence: 99%

Section: Figure 2 Block Diagram Of a Two-channel Phase Direction Findermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drone direction estimation: phase method with two-channel direction finder

Kozhabayeva,

Yerzhan,

Boykachev

et al. 2024

IJECE

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“…In recent years, smart cities have gained huge popularity where they prompt innovative projects, research, and ideas for a “smart” city that is environmentally and socially sustainable [ 6 ]. Smart cities are using UAVs to participate in emergency situations [ 7 , 8 , 9 ], surveillance [ 10 , 11 ], and deliver goods/medicine [ 12 , 13 ]. They have varying degrees of autonomy, including total autonomy, but are usually controlled by a ground-based human controller.…”

Section: Introductionmentioning

confidence: 99%

UAV Detection Using Reinforcement Learning

AlKhonaini,

Sheltami,

Mahmoud

et al. 2024

Sensors

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Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.

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